Petros Drineas

Associate Professor
Computer Science Department
Purdue University

Contact Info

Email: drineas @ gmail.com
Physical address: 305 N University Street, West Lafayette, IN 47907, USA

Education

Ph.D./M.Phil./M.Sc. in Computer Science (May 2003), Computer Science Department, Yale University
BS/M.Sc. in Computer Engineering (July 1997), Computer Engineering and Informatics Department, University of Patras

Research interests

Theory: Randomization in Numerical Linear Algebra (RandNLA).
Applications: Data mining, in particular the analysis of population genetics data.
Click here for my Google Scholar page.

Teaching (at Purdue)

Fall 2017: CS59000-RND: Randomized Algorithms for Big Data Matrices
Spring 2017: CS182: Foundations of Computer Science
Fall 2016: CS59000-RND: Randomized Algorithms for Big Data Matrices

Recent news

Jun '17: Our paper on "Fast Monte-Carlo Algorithms for Matrices II: Computing a low-rank approximation to a matrix" was included in the list of classic papers for 2006 by Google Scholar.
Apr '17: Senior Program Committee member, Conference on Information and Knowledge Management (CIKM), Nov 6-10, 2017.
Mar '17: Our work on disproving Fallmerayer's hypothesis on the extinction of the medieval Peloponnesean Greeks has now appeared in the European Journal of Human Genetics. Articles discussing this discovery have appeared in multiple news venues (click here for a detailed article - in Greek - that appeared in the Greek newspaper To Vima).
Jan '17: Editorial Board Member, SIAM Journal on Scientific Computing (SISC).
Jan '17: Editorial Board Member, Applied and Computational Harmonic Analysis (ACHA).
Dec '16: Click here for a review of the CRC Handbook of Big Data (co-edited with Peter Bühlmann, Michael Kane, and Mark J. van der Laan) by Richard Samworth.
Nov '16: Following up on our April 2016 Workshop on Theoretical Foundations of Data Science (TFoDS), NSF announces a new program to support institutes for Theoretical Foundations of Data Science. Read Tracy Kimbrel's post at the Computing Community Consortium (CCC) explaining the TRIPODS program.
Oct '16: Our work on COGG (Correlation Optimization of Genetics and Geodemographics), a novel optimization method to model genetic relationships with social factors such as castes, languages, occupation, etc. and maximize their correlation with geography, was selected as a platform presentation at the 2016 Annual Meeting of the American Society of Human Genetics.
Sep '16: Our work on structural convergence results for low-rank approximations from Block Krylov spaces (joint with I. Ipsen, M. Magdon-Ismail, and E. Kontopoulou) is now available at ArXiv.
Jul '16: I taught a mini-course on RandNLA at the 26th Annual Summer Session of the Park City Math Institute (PCMI) and the Institute for Advanced Study (IAS). The overall topic of the summer school is Mathematics of Data and it ran from June 30 to July 20, 2016 in Midway, Utah.
Jun '16: Our review article (joint with M. W. Mahoney) on RandNLA (Randomized Numerical Linear Algebra) appeared in the June 2016 issue of the Communications of the ACM (CACM).
Jun '16: Sixth Workshop on "Algorithms for Modern Massive Datasets" (June 21-24, 2016, at the University of California Berkeley). Co-organizers: M. W. Mahoney and A. Shkolnik.
Apr'16: NSF-sponsored workshop (co-organizer: Xiaoming Huo) on "Theoretical Foundations of Data Science (TFoDS)" (April 28-30, 2016, at Hilton Arlington).
Mar '16: The CRC Handbook of Big Data that I co-edited with Peter Bühlmann, Michael Kane, and Mark J. van der Laan is now available!
Jan '16: Click here for the SIAM News article on the 2015 Gene Golub SIAM Summer School on the topic of Randomized Numerical Linear Algebra (RandNLA).

Student news

Nov '15: Congratulations to Srinivas Nambirajan for successfully defending his thesis.
May '15: Congratulations to Abhisek Kundu for successfully defending his thesis. Abhisek joined the Speech and Biomedical Analytics Group in Xerox Research Center India as a Research Scientist in January 2016.
Apr '15: Congratulations to Saurabh Paul for successfully defending his thesis. Saurabh will join the Risk Analytics team in PayPal as a Data Scientist in July 2015.
Apr '11: Congratulations to Christos Boutsidis for successfully defending his thesis. He was also awarded the 2011 Robert McNaughton Prize, given to an outstanding student in the computer science department. Christos joined the Mathematical Sciences Department in IBM T.J. Watson as a Research Staff Member.
Sep '10: Congratulations to Jamey Lewis for successfully defending his thesis!

Summer schools, (selected) tutorials, and talks

Oct '15: Click here for the slides from our invited mini-tutorial at the 2015 SIAM Conference on Applied Linear Algebra on "Randomization in Numerical Linear Algebra: Theory and Practice" (with I. Ipsen and M. W. Mahoney).
Jun '15: The 2015 Gene Golub SIAM Summer School (June 15 - 26, 2015) on the topic of Randomized Numerical Linear Algebra (RandNLA) was held at the ancient site of Delphi (Δελφοί), a UNESCO World Cultural Heritage Site, in Greece. Co-organizers: E. Gallopoulos, I. Ipsen, and M. W. Mahoney. Articles discussing the Summer School have appeared in news venues.

(in English) SIAM News
(in Greek) Kathimerini, University of Patras Press Release, University of Patras Magazine (pages 3-4).
Dec '14: Some thoughts on Linear Algebra that appeared in Kuldeep Singh's book.
Sep '13: Click here for slides from my talk at the workshop on "Succinct Data Representations and Applications" under the auspices of the Theoretical Foundations of Big Data Analysis program at the Simons Institute for the Theory of Computing.
Sep '13: Click here for slides and video from my tutorial on RandNLA at the Big Data Bootcamp week under the auspices of the Theoretical Foundations of Big Data Analysis program at the Simons Institute for the Theory of Computing.
Sep '12: Talk at the MIT CSAIL seminar: click here for the slides.
Sep '12: Tutorial at the opening workshop of SAMSI's "Massive Datasets" 2012-2013 program (September 9-12, 2012). Click here for the slides of my talk.
Jun '12: Keynote talk at the SIAM Conference on Applied Linear Algebra (June 18-22, 2012); click here for the slides.
May '12: Keynote talk at the "From Data to Knowledge" workshop (UC Berkeley, May 7-11, 2012); click here for the slides.
Apr '12: Talk at the CMU Computer Science Theory Lunch: click here for the slides.

Editorial boards and (selected) program committees

Jan '15: Editorial Board Member, SIAM Journal on Matrix Analysis and Applications (SIMAX).
Jul '14: Editorial Board Member, SIAM Journal on Scientific Computing, Special Issue for Software and Big Data.
Feb '14: Editorial Board Member, Information and Inference: A Journal of the IMA.
Jun '14: Fifth Workshop on "Algorithms for Modern Massive Datasets" (June 17-20, 2014, at the University of California Berkeley). Co-organizers: M. W. Mahoney, A. Shkolnik, R. Zadeh, and F. Perez.
Oct '13: Program Committee Member, 2014 ACM Symposium on Theory of Computing (May 31 - June 3, 2014).
May '13: Editorial Board Member, PLoS ONE.
Feb '13: Program Committee Member, 2014 ACM-SIAM Symposium on Discrete Algorithms (January 5-7, 2014).
Apr '13: Workshop on "Succinct Data Representations and Applications" (September 16-20, 2013, at University of California Berkeley).
Jan '13: Vice Chair, 2013 IEEE International Conference on Data Mining (December 8-11, 2013).
Oct '12: Workshop on "Randomized Numerical Linear Algebra (RandNLA): Theory and Practice" (October 20, 2012, Hyatt Regency, New Brunswick NJ, USA) held in conjunction with FOCS 2012. Co-organizers: H. Avron and C. Boutsidis. Read this blog post (written by Ludwig Schmidt and edited by Michael Mitzenmacher) for a concise summary of the workshop's talks.
Jul '12: Fourth Workshop on "Algorithms for Modern Massive Datasets" (July 10-13, 2012, at Stanford University). Co-organizers: G. Carlsson, A. Shkolnik, and M. W. Mahoney. Click here for the slides of my talk.
May '11: NSF-sponsored workshop on "Algorithms in the Field (A8F)" (May 16-18, 2011, at DIMACS).
Jun '10: Third Workshop on "Algorithms for Modern Massive Datasets" (June 15-18, 2010, at Stanford University). Co-organizers: G. Carlsson, L. H. Lim, and M. W. Mahoney.
Oct '09: Randomized Algorithms in Linear Algebra Minisymposia (parts I and II), under the auspices of the SIAM Conference on Applied Linear Algebra.

Other news

Jun '14: Our study on a maritime route of colonization of Europe, showing that island hopping was used by Near Eastern migrants to reach Southern Europe, has appeared in the the Proceedings of the National Academy of Sciences. Numerous news stories and blogs discuss our work:
(in English) Science, New Scientist, National Geographic, University of Washington HSNewsBeat, Popular Archaeology, Phys Org, Phys Org News, EurekAlert, Science Daily, Science Codex, Science World Report, Archeologie & Anthropology, Heritage Daily, Past Horizons, Archaeology, Science Newsline, Technology.org, Dienekes' Anthropology Blog.
(in Greek) Ethnos, To Vima, Ta Nea, Real.gr, News.gr, E-kriti.gr, Kool News, Sigma Live, Polis Press, Yahoo! Greece, Rodiaki, Go Thessaloniki, Nooz.
(in French) Le Figaro.
(in German) derStandard, Scinexx.
(in Italian) Le Scienze.
Sep '13: Harnessing the Petabyte: our new NSF IIS funded project investigates cloud computing and supercomputing to analyze Big Data; click here for details.
May '13: Our study on ancient mtDNA from the Minoans appeared in Nature Communications. Many news stories and blogs discuss our work (click here for a synopsis of the attention that our work generated, as reported by Nature Communications):
- Rensselaer's press release: "DNA Analysis Unearths Origins of Minoans"
- University of Washington press release:"DNA analysis unearths origins of Minoans, the first major European civilization"
- Nature News: "Minoan civilization was made in Europe"
- Dienekes Anthropology Blog: "mtDNA from Minoan Crete"
- BBC News: "DNA reveals origin of Greece's ancient Minoan culture"
- NBC News: "Mysterious Minoans really were Europeans, DNA finds"
- USA Today: "Europe's first civilization was home-grown"
- Scientific American: "Minoan civilization originated in Europe, not Egypt"
- Science World Report: "Europe's first advanced civilization was Minoan -- and it wasn't Egyptian"
- LiveScience: "Mysterious Minoans were European, DNA finds"
- Discovery Channel: "Mysterious Minoans were European, DNA finds"
- Discover Magazine: "Minoans, first advanced European civilization, originated from Europe, not Africa"
and, in Greek:
- Kathimerini
- Vima
- Proto Thema
- Nea Kriti
- in.gr
Feb '13: I will be a long-term participant of the "Theoretical Foundations of Big Data Analysis" program at the Simons Institute for the Theory of Computing at the University of California Berkeley in the fall of 2013.
May '11: Ravi Kannan won the 2011 Knuth prize! Congratulations Ravi!
Oct '10: I have joined the National Science Foundation (NSF) as a Program Director at the Computing and Communication Foundations (CCF) and the Information and Intelligent Systems (IIS) divisions in the Computer and Information Science and Engineering (CISE) directorate.
Oct '10: Our work (co-authored with A. Javed, J. Lewis, and P. Paschou) on Ancestry Informative Markers (AIMs) for Europeans (POPulation REference Sample - POPRES) and world-wide populations (Human Genome Diversity Panel - HGDP) has appeared in PLoS One and the Journal of Medical Genetics, respectively.
- Supplementary material and lists of AIMs for the European populations.
- Supplementary material and lists of AIMs for the HGDP populations.
Jan '10: Our paper on "Random Walks in Time-Graphs" (joint work with U. Acer and A. Abouzeid) won the Best Paper Award in MobiOpp 2010!
Jan '09: Our work on CUR Matrix Decompositions for Improved Data Analysis (with M. W. Mahoney) has appeared in the Proceedings of the National Academy of Sciences.

PhD/MS Students

Publications

Available in ArXiv (under review or unpublished)

  1. Drineas, I. Ipsen, E. Kontopoulou, and M. Magdon-Ismail, Structural Convergenece Results for Low-Rank Approximations from Block Krylov Spaces, 2016.
  2. Boutsidis, P. Drineas, P. Kambadur, E. Kontopoulou, and A. Zouzias, A Qwanturank Algorithme for Approximating the Log Determinant of a Symmetric Positive Definite Matrix, 2016.
  3. Kundu, P. Drineas, and M. Magdon-Ismail, Recovering PCA from Hybrid-$(\ell_1,\ell_2)$ Sparse Sampling of Data Elements, 2016.
  4. Kundu and P. Drineas, A Note on Randomized Element-wise Matrix Sparsification, 2014.
  5. Boutsidis, M.W. Mahoney, and P. Drineas, An improved approximation algorithm for the column subset selection problem, 2013.
  6. Drineas and M.W. Mahoney, Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving, 2010.
2017
  1. Fountoulakis, A. Kundu, E. Kontopoulou, and P. Drineas, A Randomized Rounding Algorithm for Sparse PCA, ACM Transactions on Knowledge Discovery from Data (TKDD), 11(3), pp. 1-26, 2017.
  2. Stamatoyannopoulos, A. Bose, A. Teodosiadis, F. Tsetsos, A. Plantinga, N. Psatha, N. Zogas, E. Yannaki, P. Zalloua, K. K. Kidd, B. L. Browning, J. Stamatoyannopoulos, P. Paschou, and P. Drineas, Genetics of the Peloponnesean Populations and the Theory of Extinction of the Medieval Peloponnesean Greeks, European Journal of Human Genetics (EJHG), 25(5), pp. 637-645, 2017.
2016
  1. Iyer, H. Avron, G. Kollias, Y. Ineichen, C. Carothers, and P. Drineas, A Randomized Least Squares Solver for Terabyte-sized Dense Overdetermined Systems, Journal of Computational Science
  2. Iyer, C. Carothers, and P. Drineas, Randomized Sketching for Large-Scale Sparse Ridge Regression Problems, Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA16), held in conjunction with the 2016 International Conference on High Performance Computing, Networking, Storage and Analysis (SC16), 2016.
  3. J. Forde, A. S. Kanaan, J. Widomska, S. S. Padmanabhuni, E. Nespoli, J. Alexander, J. Rodriguez Arranz, S. Fan, R. Houssari, M. S. Nawaz, N. R. Zilhao, L. Pagliaroli, F. Rizzo, T. Aranyi, C. Barta, T. M. Boeckers, D. I. Boomsma, W. R. Buisman, J. K. Buitelaar, D. Cath, A. Dietrich, N. Driessen, P. Drineas, M. Dunlap, S. Gerasch, J. Glennon, B. Hengerer, O. A. van den Heuvel, C.e Jespersgaard, H. E. Moller, K. R. Müller-Vahl, T. Openneer, G. Poelmans, P. J. W. Pouwels, J. M. Scharf, H. Stefansson, Z. Tumer, D. Veltman, Y. D van der Werf, P. J. Hoekstra, A. Ludolph, and P. Paschou, TS-EUROTRAIN: A European-wide investigation and training network on the aetiology and pathophysiology of Gilles de la Tourette Syndrome, Frontiers in Neuroscience, 10, article 384, 2016.
  4. Tsetsos, S. S. Padmanabhuni, J. Alexander, I. Karagiannidis, M. Tsifintaris, A. Topaloudi, D. Mantzaris, M. Georgitsi, P. Drineas, and P. Paschou, Meta-analysis of Tourette Syndrome and Attention Deficit Hyperactivity Disorder provides support for a shared genetic basis, Frontiers in Neuroscience, 10, article 340, 2016.
  5. Clarkson, P. Drineas, M. Magdon-Ismail, M. W. Mahoney, X. Meng, and D. P. Woodruff, Faster Robust Linear Regression, SIAM Journal on Computing, 45(3), pp. 763-810, 2016.
  6. Paul, M. Magdon-Ismail, and P. Drineas, Feature Selection for Linear SVMs with Provable Guarantees, Pattern Recognition, 60, pp. 205-214, 2016.
  7. Mahoney and P. Drineas, RandNLA: Randomized Numerical Linear Algebra, Communications of the ACM (CACM), 59 (6), pp. 80-90, 2016.
  8. Alexander, O. Kalev, S. Mehrabian, L. Traykov, M. Raycheva, D. Kanakis, P. Drineas, M. I. Lutz, T. Ströbel, T. Penz, M. Schuster, C. Bock, I. Ferrer, P. Paschou, and G. G. Kovacs, Familial early-onset dementia with complex neuropathological phenotype and genomic background, Neurobiology of Aging, 42, pp. 199-204, 2016.
  9. Paul and P. Drineas, Feature Selection for Ridge Regression with Provable Guarantees, Neural Computation, MIT Press Journals, 28, pp. 716-742, 2016.
  10. W. Mahoney and P. Drineas, Structural Properties Underlying high-quality Randomized Numerical Linear Algebra algorithms, CRC Handbook on Big Data, pp. 137-154, 2016.
  11. Gallopoulos, P. Drineas, I. Ipsen, and M. W. Mahoney, RandNLA, Pythons, and the CUR for your Data problems, SIAM News, p. 7, February 2016.
2015
  1. Iyer, H. Avron, G. Kollias, Y. Ineichen, C. Carothers, and P. Drineas, A Scalable Randomized Least Squares Solver for Dense Overdetermined Systems, Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA15), held in conjunction with the 2015 International Conference on High Performance Computing, Networking, Storage and Analysis (SC15), 2015.
  2. Kundu, P. Drineas, and M. Magdon-Ismail, Approximating Sparse PCA from Incomplete Data, Proc. of Neural Information Processing Systems (NIPS), 2015.
  3. Paul, M. Magdon-Ismail, and P. Drineas, Column Selection via Adaptive Sampling, Proc. of Neural Information Processing Systems (NIPS), 2015.
  4. Nguyen, P. Drineas, and T. Tran, Tensor Sparsification via a Bound on the Spectral Norm of Random Tensors, Information and Inference: A Journal of the IMA, 4(3), pp. 195-229, 2015.
  5. Paul, M. Magdon-Ismail, and P. Drineas, Feature Selection for Linear SVM with Provable Guarantees, Proc. of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) and Journal of Machine Learning Research: Workshops and Conference Proceedings 38, pp. 735-743, 2015.
  6. Boutsidis, A. Zouzias, M. W. Mahoney, and P. Drineas, Randomized Dimensionality Reduction for K-means Clustering, IEEE Transactions on Information Theory, 62(2), pp. 1045-1062, 2015.
2014
  1. Paul and P. Drineas, Deterministic Feature Selection for Regularized Least Squares Classification, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), LNCS 8725, pp. 533-548, 2014.
  2. Saurabh, C. Boutsidis, M. Magdon-Ismail, and P. Drineas, Random Projections for Support Vector Machines, ACM Transactions on Knowledge Discovery from Data, 8(4):22, 2014.
  3. Paschou, P. Drineas, E. Yannaki, A. Razou, K. Kanaki, F. Tsetsos, S. Padmanabhuni, M. Michalodimitrakis, M. Renda, S. Pavlovic, A. Anagnostopoulos, J. Stamatoyannopoulos, K. K. Kidd, and G. Stamatoyannopoulos, Maritime route of colonization of Europe, Proceedings of the National Academy of Sciences, doi:10.1073/pnas.1320811111, 2014.
  4. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Column-Based Matrix Reconstruction, SIAM Journal on Computing, 43(2), pp. 687-717, 2014.
2013
  1. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Coresets for Least-Squares Regression, IEEE Transactions on Information Theory, 59(10), 6880 - 6892, 2013.
  2. R. Hughey, P. Paschou, P. Drineas, D. Mastropaolo, D. M. Lotakis, P. A. Navas, M. Michalodimitrakis, J. A. Stamatoyannopoulos, and G. Stamatoyannopoulos, A European Population in Minoan Bronze Age Crete, Nature Communications, (4)1861 doi:10.1038/ncomms2871, 2013.
  3. Paul, C. Boutsidis, M. Magdon-Ismail, and P. Drineas, Random Projections for Support Vector Machines, Proc. of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 498-506, 2013.
  4. Clarkson, P. Drineas, M. Magdon-Ismail, M. W. Mahoney, X. Meng, and D. P. Woodruff, The Fast Cauchy Transform and Faster Robust Linear Regression, Proc. of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2013.
2012
  1. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. Woodruff, Fast Approximation of Matrix Coherence and Statistical Leverage, Journal of Machine Learning Research, 13, pp. 3475-3506 , 2012.
  2. Stathias, G. Sotiris, I. Karagiannidis, G. Bourikas, G. Martinis, D. Papazoglou, A. Tavridou, N. Papanas, E. Maltezos, M. Theodoridis, V. Vargemezis, V. Manolopoulos, W. C. Speed, J. R. Kidd, K. K. Kidd, P. Drineas, and P. Paschou, Exploring genomic structure differences and similarities between the Greek and European HapMap populations: implications for association studies, Annals of Human Genetics, 76(6), pp. 472-483, 2012.
  3. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. Woodruff, Fast Approximation of Matrix Coherence and Statistical Leverage, International Conference on Machine Learning (ICML), 2012.
2011
  1. Kupp, H. Huang, P. Drineas, and Y. Makris, Improving Analog and RF Device Yield through Performance Calibration, IEEE Design and Test of Computers, 28(3), pp. 64-75, 2011.
  2. Kupp, H. Stratigopoulos, P. Drineas, and Y. Makris, On Proving the Efficiency of Alternative RF Tests, International Conference on Computer-Aided Design (ICCAD), pp. 762-767, 2011.
  3. Boutsidis, P. Drineas, and M. Magdon-Ismail, Sparse Features for PCA-Like Linear Regression, Proc. of Neural Information Processing Systems (NIPS), 2011.
  4. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Column-Based Matrix Reconstruction, Proc. of the 52nd IEEE Symposium on Foundations of Computer Science (FOCS), 2011.
  5. Javed, P. Drineas, M. W. Mahoney, P. Paschou, Efficient genome-wide selection of PCA-correlated tSNPs for genotype imputation, Annals of Human Genetics, 75(6), pp. 707-722, 2011.
  6. Lewis, Z. Abas, C. Dadousis, D. Lykidis, P. Paschou, and P. Drineas, Tracing Cattle Breeds With Principal Components Analysis Ancestry Informative SNPs, PLoS ONE, 6(4): e18007, 2011.
  7. Acer, P. Drineas, and A. Abouzeid, Connectivity in Time-Graphs, Pervasive and Mobile Computing, 7, pp. 160-171, 2011.
  8. G. Sgourakis, M. Merced-Serrano, C. Boutsidis, P. Drineas, Z. Du, C. Wang, and A. E. Garcia, Atomic-level characterization of the ensemble of the Aβ(1-42) monomer in water using unbiased molecular dynamics simulations and spectral algorithms, Journal of Molecular Biology, 405(2), pp.570-583, 2011.
  9. Drineas, M. W. Mahoney, S. Muthukrishnan, and T. Sarlos, Faster Least Squares Approximation, Numerische Mathematik, 117(2), pp. 217-249, 2011.
  10. Tsourakakis, P. Drineas, E. Michelakis, I. Koutis, and C. Faloutsos, Spectral Counting of Triangles via Element-Wise Sparsification and Triangle-Based Link Recommendation, Journal of Social Network Analysis and Mining (SNAM), 1(2), pp. 75-81, 2011.
  11. Drineas and A. Zouzias, A note on element-wise matrix sparsification via a matrix-valued Bernstein inequality, Information Processing Letters, 111, pp. 385-389, 2011.
2010
  1. Drineas and M. W. Mahoney, Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving, arXiv1005:3097, 2010.
  2. Paschou, J. Lewis, A. Javed, and P. Drineas, Ancestry Informative Markers for Fine-Scale Individual Assignment to Worldwide Populations, Journal of Medical Genetics, doi:10.1136/jmg.2010.078212, 2010.
  3. Drineas, J. Lewis, and P. Paschou, Inferring Geographic Coordinates of Origin for Europeans using Small Panels of Ancestry Informative Markers, PLoS ONE, 5(8):e11892, 2010.
H-G. D. Stratigopoulos, P. Drineas, M. Slamani, and Y. Makris, RF specification test compaction using learning machines, IEEE Transactions on VLSI Systems, 18(6), pp. 1002-1006, 2010.
  1. Acer, P. Drineas, and A. Abouzeid, Random walks in time-graphs, Proceedings of the Second International Workshop on Mobile Opportunistic Networking (MobiOpp), pp. 93-100, 2010.
  2. Boutsidis, A. Zouzias, and P. Drineas, Random Projections for k-means Clustering, Proc. of Neural Information Processing Systems (NIPS), 2010.
  3. Kupp, H. Huang, P. Drineas, and Y. Makris, Post-Production Performance Calibration in Analog/RF Devices, IEEE International Test Conference (ITC), 8.3.1-8.3.10, 2010.
2009
  1. Boutsidis, M.W. Mahoney, and P. Drineas, Unsupervised Feature Selection for the k-means Clustering Problem, Proc. of Neural Information Processing Systems (NIPS), 2009.
  2. Boutsidis and P. Drineas, Random projections for the nonnegative least-squares problem, Linear Algebra and its Applications, 431, pp. 760-771, 2009.
  3. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney, Sampling algorithms and coresets for lp regression, SIAM Journal on Computing, 38(5), pp. 2060-2078, 2009.
  4. W. Mahoney and P. Drineas, CUR matrix decompositions for improved data analysis, Proceedings of the National Academy of Sciences, 106(3), pp. 697-702, 2009.
  5. Tsourakakis, P. Drineas, E. Michelakis, I. Koutis, and C. Faloutsos, Spectral counting of triangles in power-law networks via element-wise sparsification, International Conference on Advances in Social Network Analysis and Mining (ASONAM), 2009.
  6. Boutsidis, M.W. Mahoney, and P. Drineas, An improved approximation algorithm for the column subset selection problem, Proc. of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 968-977, 2009.
2008
  1. W. Mahoney, M. Maggioni, and P. Drineas, Tensor-CUR decompositions for tensor-based data, SIAM Journal on Matrix Analysis and Applications, 30(2), pp. 957-987, 2008.
  2. Drineas, M.W. Mahoney, and S. Muthukrishnan, Relative-error CUR matrix decompositions, SIAM Journal on Matrix Analysis and Applications, 30(2), pp. 844-881, 2008.
  3. Paschou, P. Drineas, J. Lewis, C. Nievergelt, D. Nickerson, J. Smith, P. Ridker, D. Chasman, R. Krauss, and E. Ziv, Tracing sub-structure in the European American population with PCA-informative markers, PLoS Genetics, 4(7), pp. 1-13, 2008.
  4. Boutsidis, M.W. Mahoney, and P. Drineas, Unsupervised feature selection for Principal Components Analysis, Proc. of the 14th Annual ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 61-69, 2008.
  5. Kupp, P. Drineas, M. Slamani, and Y. Makris, Confidence estimation in non-RF to RF correlation-based specification test compaction, Proc. of the 13th European Test Symposium (ETS), pp. 35-40, 2008.
  6. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney, Sampling algorithms and coresets for lp regression, Proc. of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 932-941, 2008.
2007
  1. Paschou, E. Ziv, E. Burchard, S. Choudhry, W. Rodriguez-Cintron, M. W. Mahoney, and P. Drineas, PCA-correlated SNPs for structure identification in worldwide human populations, PLOS Genetics, 3(9), pp. 1672-1686, 2007.
  2. Paschou, M. W. Mahoney, A. Javed, J. Kidd, A. Pakstis, S. Gu, K. Kidd, and P. Drineas, Intra- and inter-population genotype reconstruction from tagging SNPs, Genome Research, 17(1), pp. 96-107, 2007.
  3. Drineas and M. W. Mahoney, A randomized algorithm for a tensor-based generalization of the SVD, Linear Algebra and its Applications, 420, pp. 553-571, 2007.
  4. Drineas, M. W. Mahoney, and R. Kannan, Sampling sub-problems of heterogeneous max-cut problems and approximation algorithms, Random Structures and Algorithms,32(3), pp. 307-333, 2007.
  5. Dasgupta, P. Drineas, B. Harb, V. Josifovski, and M. Mahoney, Feature selection methods for text classification, Proc. of the 13th Annual ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 230-239, 2007.
H-G. D. Stratigopoulos, P. Drineas, M. Slamani, and Y. Makris, Non-RF to RF test correlation using learning machines: a case study, Proc. of the 25th IEEE VLSITest Symposium (VTS), pp. 9-14, 2007.
2006
G.H. Golub, M.W. Mahoney, P. Drineas, and L.-H. Lim, MMDS 2006: bridging the gap between numerical linear algebra, theoretical computer science, and data applications, SIAM News, Oct 2006.
  1. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices I: approximating matrix multiplication, SIAM Journal on Computing, 36(1), pp. 132-157, 2006.
  2. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices II: computing a low rank approximation to a matrix, SIAM Journal on Computing, 36(1), pp. 158-183, 2006.
  3. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices III: computing a compressed approximate matrix decomposition, SIAM Journal on Computing, 36(1), pp. 184-206, 2006.
  4. Almukhaizim, P. Drineas, and Y. Makris, Entropy-driven parity tree selection for low-overhead concurrent error detection in finite state machines, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25(8), pp. 1547-1554, 2006.
  5. Drineas, M. W. Mahoney, and S. Muthukrishnan, Subspace sampling and relative error matrix approximation: column-based methods, Proc. of APPROX-RANDOM, pp. 316-326, 2006.
  6. Drineas, M. W. Mahoney, and S. Muthukrishnan, Subspace sampling and relative error matrix approximation: column-row-based methods, Proc. of the 14th Annual European Symposium on Algorithms (ESA), pp. 304-314, 2006.
  7. Drineas, M. W. Mahoney, and S. Muthukrishnan, Polynomial time algorithm for column-row based relative error low-rank matrix approximation, DIMACS Technical Report 2006-04, 2006.
  8. Drineas, A. Javed, M. Magdon-Ismail, G. Pandurangan, R. Virrankoski, and A. Savvides, Distance matrix reconstruction from incomplete distance information for sensor network localization, Proc. of the 3rd Annual IEEE Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 536-544, 2006.
  9. Drineas and M. W. Mahoney, Randomized algorithms for matrices and massive data sets, Proc. of the 32nd Annual Conference on Very Large Data Bases (VLDB), p. 1269, 2006.
  10. W. Mahoney, M. Maggioni, and P. Drineas, Tensor-CUR decompositions for tensor-based data, Proc. of the 12th Annual ACM Conference on Knowledge Discovery andData Mining (KDD), pp. 327-336, 2006.
  11. Drineas, M. W. Mahoney, and S. Muthukrishnan, Sampling algorithms for l2 regression and applications, Proc. of the 17th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1127-1136, 2006.
2005
  1. Drineas and M. W. Mahoney, On the Nystrom method for approximating a Gram matrix for improved kernel-based learning, Journal of Machine Learning Research 6, pp. 2153-2175, 2005.
  2. Almukhaizim, P. Drineas, and Y. Makris, Compaction-based concurrent error detection for digital circuits, Microelectronics Journal, 36(9), pp. 856-862, Elsevier, 2005.
  3. Freedman and P. Drineas, Energy minimization via graph cuts: settling what is possible, Proc. of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 939-946, 2005.
  4. Drineasand M. W. Mahoney, Approximating a Gram matrix for improved kernel-based learning, Proc. of the 18th Annual Symposium on Computational Learning Theory (COLT), pp. 323-337, 2005.
  5. Drineas, R. Kannan, and M. W. Mahoney, Sampling sub-problems of heterogeneous max-cut problems and approximation algorithms, Proc. of the 22nd Annual Symposium on Theoretical Aspects of Computer Science (STACS), Lecture Notes in ComputerScience 3404, pp. 57-68, 2005.
2004
  1. Drineas, R. Kannan, A. Frieze, S. Vempala, and V. Vinay, Clustering of large graphs via the singular value decomposition, Machine Learning (56), pp. 9-33, 2004.
  2. Akcoglu, P. Drineas, and M. Kao, Fast universalization of investment strategies, SIAM Journal on Computing 34(1), pp. 1-22, 2004.
  3. Drineas, M. Krishnamoorthy, D. Sofka, and B. Yener, Studying E-mail graphs for intelligence monitoring and analysis in the absence of semantic information, Proc. of the Symposium on Intelligence and Security Informatics, Lecture Notes in Computer Science 3073, pp. 297-306, 2004.
  4. Drineas, Pass efficient algorithms for approximating large matrices, Mathematisches Forschungsinstitut Oberwolfach (MFO) Workshop on Approximation Algorithms for NP-Hard Problems, Oberwolfach, 2004.
  5. Almukhaizim, P. Drineas, and Y. Makris, Cost-driven selection of parity trees, Proc. of the IEEE VLSI Test Symposium (VTS), pp. 319-324, 2004.
  6. Almukhaizim, P. Drineas, and Y. Makris, Concurrent error detection for combinational and sequential logic via output compaction, Proc. of the IEEE International Symposium on Quality Electronic Design (ISQED), pp. 459-464, 2004.
  7. Almukhaizim, P. Drineas, and Y. Makris, On concurrent error detection with bounded latency in FSMs, Proc. of the IEEE Design Automation and Test in Europe Conference(DATE), pp. 596-601, 2004.
2003
  1. Drineas and Y. Makris, SPaRe: selective partial replication for concurrent fault detection in FSMs, IEEE Transactions on Instrumentation and Measurement, 52(6), pp. 1729-1737, 2003.
  2. Drineas, E. Drinea, and P. Huggins, An experimental evaluation of a monte carlo algorithm for singular value decomposition, Y. Manolopoulos et. al. (Eds.): Revised Selected Papers from the 8th Panhellenic Conference on Informatics, Lecture Notes in Computer Science 2563, pp. 279-296, 2003.
  3. Drineas and R. Kannan, Pass efficient algorithms for approximating large matrices, Proc. of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 223-232, 2003.
  4. Almukhaizim, P. Drineas, and Y. Makris, On Compaction-based concurrent error detection, Proc. of the IEEE On-Line Test Symposium, pp. 157, 2003.
  5. Drineas and Y. Makris, Independent test sequence compaction through integer programming, Proc. of the IEEE International Conference on Computer Design (ICCD), pp. 380-386, 2003.
  6. Drineas and Y. Makris, Non-intrusive concurrent error detection in FSMs through State/Output compaction and monitoring via parity trees, Proc. of the Design Automation and Test in Europe Conference (DATE), pp. 1164-1165, 2003.
  7. Drineas and Y. Makris, SPaRe: selective partial replication for concurrent fault detection in FSMs, Proc. of the IEEE International Conference on VLSI Design, pp. 84-91, 2003.
  8. Drineas and Y. Makris, On the Compaction of Independent Test Sequences for Sequential Circuits, IEEE European Test Workshop (ETW), 2003.
  9. Drineas and Y. Makris, Concurrent fault detection in random combinational logic, Proc. of the IEEE International Symposium on Quality Electronic Design (ISQED), pp. 425-430, 2003.
2002
  1. Drineas, I. Kerenidis, and P. Raghavan, Competitive recommendation systems, Proc. of the 34th ACM Symposium on Theory of Computing (STOC), pp. 82-90, 2002.
  2. Akcoglu, P. Drineas, and M. Kao, Fast universalization of investment strategies with provably good relative returns, Proc. of the 29th International Colloquium on Automata, Languages and Programming (ICALP), pp. 888-900, 2002.
  3. Drineas and Y. Makris, Non-intrusive design of concurrently self-testable FSMs, Proc. of the IEEE Asian Test Symposium (ATS), pp. 33-38, 2002.
  4. Drineas and Y. Makris, Non-intrusive design of concurrently self-testable FSMs, IEEE North Atlantic Test Workshop (NATW), Montauk NY, USA, 2002.
2001
  1. Drinea, P. Drineas, and P. Huggins, A randomized singular value decomposition algorithm for image processing applications, Proc. of the 8th Panhellenic Conference on Informatics, pp. 278-288, 2001.
  2. Drineas and R. Kannan, Fast monte carlo algorithms for approximate matrix multiplication, Proc. of the 42nd IEEE Symposium on Foundations of Computer Science (FOCS), pp. 452-459, 2001.
1999
  1. Drineas, R. Kannan, A. Frieze, S. Vempala, and V. Vinay, Clustering in large graphs and matrices, Proc. of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 291-299, 1999.

Tutorials

Randomized Algorithms in Linear Algebra and Applications, Workshop on Algorithms for Modern Massive Datasets (MMDS), Jun 2010.
Information retrieval and data mining: a linear algebraic perspective, Institute for Pure and Applied Mathematics, UCLA, Sep 2007 (a podcast of the talk is also available).
Randomized algorithms for matrices and massive datasets, SIAM Conference on Data Mining (SDM), Apr 2006.
Randomized algorithms for matrices and massive datasets, ACM International Conference on Knowledge Discovery and Data Mining (KDD), Aug 2005.

Slides from various presentations

Randomized Algorithms in Linear Algebra and the Column Subset Selection Problem
Dimensionality reduction in the analysis of human genetics data
Identifying ancestry informative markers via the singular value decomposition
Randomized matrix algorithms and their applications

Teaching (since January 2003)

Resume / CV

Petros Drineas
Associate Professor
Computer Science Department
Purdue University

305 N. University Street
West Lafayette
IN 47907-2107
USA
Education Yale University, New Haven CT
Ph.D. in Computer Science (advisor: Ravi Kannan), May 2003.
Yale University, New Haven CT
M.Phil. in Computer Science, May 1999.
Yale University, New Haven CT
M.Sc. in Computer Science, May 1998.
University of Patras, Greece
BS and M.Sc. in Computer Engineering, (advisor: Athanasios Tsakalidis), Jun 1997.
Appoint- Associate Professor
ments Purdue University, Jul 2016 - now
Associate Professor
Rensselaer Polytechnic Institute, Jan 2009 - Jun 2016
Adjunct Faculty
Department of Mathematics, North Carolina State University, Nov 2012 - now
Long-term visitor
Simons Institute for the Theory of Computing, University of California, Berkeley, Fall 2013
Program Director
National Science Foundation, Information and Intelligent Systems (IIS) Division and Computing and Communication Foundations (CCF) Division, Oct 2010 - Nov 2011
Assistant Professor
Rensselaer Polytechnic Institute, Jan 2003 - Dec 2008
Visiting Assistant Professor
Institute of Pure & Applied Mathematics, University of California, Los Angeles, Sep 2007 -
Dec 2007
Visiting Research Scientist
Yahoo! Research, Jul 2006 - Sep 2006
Visiting Assistant Professor
Sandia National Laboratories, Aug 2005 - Dec 2005
Visiting Researcher
Microsoft Research Silicon Valley, Jul 2002
Summer Intern
Verity Inc., Silicon Valley CA, May 2001 - Aug 2001
Research Assistant
Yale University, Sep 1998 - Dec 2002
Teaching Assistant
Yale University, Sep 1998 - Dec 2002
Research Theory: The design and analysis of Randomized Numerical Linear Algebra algorithms
Interests (RandNLA).
Applications: Data mining, in particular the analysis of population genetics data, internet
data, and electronic circuit testing data.
Honors Near-Optimal Column-Based Matrix Reconstruction, which appeared in the 52nd IEEE
Symposium on Foundations of Computer Science (FOCS), was invited to the special issue of the SIAM Journal on Computing for the top papers from FOCS 2011.
European Molecular Biology Organization (EMBO) Fellowship, 2010.
Best paper award, MobiOpp 2010.
Senior Member, Association for Computing Machinery, 2009.
European Molecular Biology Organization (EMBO) Fellowship, 2009.
Mentoring Excellence Award, Rensselaer Polytechnic Institute, 2009.
Outstanding Early Research Award, School of Science, Rensselaer Polytechnic Institute,
2007.
NSF CAREER Award, 2006.

  1. Tinsley Oden Visiting Faculty Fellowship, University of Texas at Austin, 2005.
ImpactPublications
Books
  1. P. Buhlmann, P. Drineas, M. Kane, and M. van der Laan, Handbook of Big Data,
Chapman and Hall/CRC Press, 2016.
Journal Publications (Accepted)
  1. G. Stamatoyannopoulos, A. Bose, A. Teodosiadis, F. Tsetsos, A. Plantinga, N. Psatha,
  2. Zogas, E. Yannaki, P. Zalloua, K. K. Kidd, B. L. Browning, J. Stamatoyannopoulos,
  3. Paschou, and Petros Drineas, Genetics of the Peloponnesean Populations and the
Theory of Extinction of the Medieval Peloponnesean Greeks, European Journal of
Human Genetics (EJHG), 25(5), pp. 637-645, 2017.
  1. K. Fountoulakis, A. Kundu, E. Kontopoulou, and P. Drineas, A Randomized Rounding
Algorithm for Sparse PCA, ACM Transactions on Knowledge Discovery from Data
(TKDD), 11(3), pp. 1-26, 2017.
  1. C. Iyer, H. Avron, G. Kollias, Y. Ineichen, C. Carothers, and P. Drineas, A Randomized
Least Squares Solver for Terabyte-sized Dense Overdetermined Systems, Journal of
Computational Science, Journal of Computational Science, http://dx.doi.org/10.1016/
j.jocs.2016.09.007, 2016.
  1. N. J. Forde, A. S. Kanaan, J. Widomska, S. S. Padmanabhuni, E. Nespoli, J. Alexander, J. Rodriguez Arranz, S. Fan, R. Houssari, M. S. Nawaz, N. R. Zilhao, L. Pagliaroli,
  2. Rizzo, T. Aranyi, C. Barta, T. M. Boeckers, D. I. Boomsma, W. R. Buisman, J.
  3. Buitelaar, D. Cath, A. Dietrich, N. Driessen, P. Drineas, M. Dunlap, S. Gerasch,
  4. Glennon, B. Hengerer, O. A. van den Heuvel, C.e Jespersgaard, H. E. Moller, K. R.
Mller-Vahl, T. Openneer, G. Poelmans, P. J. W. Pouwels, J. M. Scharf, H. Stefansson,
  1. Tumer, D. Veltman, Y. D van der Werf, P. J. Hoekstra, A. Ludolph, and P. Paschou,
TS-EUROTRAIN: A European-wide investigation and training network on the aetiology and pathophysiology of Gilles de la Tourette Syndrome, Frontiers in Neuroscience,
10, article 384, 2016.
  1. F. Tsetsos, S. S. Padmanabhuni, J. Alexander, I. Karagiannidis, M. Tsifintaris, A.
Topaloudi, D. Mantzaris, M. Georgitsi, P. Drineas, and P. Paschou, Meta-analysis of
Tourette Syndrome and Attention Deficit Hyperactivity Disorder provides support for
a shared genetic basis, Frontiers in Neuroscience, 10, article 340, 2016.
  1. K. Clarkson, P. Drineas, M. Magdon-Ismail, M. W. Mahoney, X. Meng, and D. P. Woodruff,
Faster Robust Linear Regression, SIAM Journal on Computing, 45(3), pp. 763-810,
2016.
  1. S. Paul, M. Magdon-Ismail, and P. Drineas, Feature Selection for Linear SVMs with
Provable Guarantees, Pattern Recognition, 60, pp. 205-214, 2016.
  1. M. W. Mahoney and P. Drineas, RandNLA: Randomized Numerical Linear Algebra,
Communications of the ACM (CACM), 59 (6), pp. 80-90, 2016.
  1. J. Alexander, O. Kalev, S. Mehrabian, L. Traykov, M. Raycheva, D. Kanakis, P. Drineas,
  2. I. Lutz, T. Strbel, T. Penz, M. Schuster, C. Bock, I. Ferrer, P. Paschou, and
  3. G. Kovacs, Familial early-onset dementia with complex neuropathological phenotype
and genomic background, Neurobiology of Aging, 42, pp. 199-204, 2016.
  1. S. Paul and P. Drineas, Feature Selection for Ridge Regression with Provable Guarantees, Neural Computation, MIT Press Journals, 28, pp. 716-742, 2016.
  2. M. W. Mahoney and P. Drineas, Structural Properties Underlying high-quality Randomized Numerical Linear Algebra algorithms, CRC Handbook on Big Data, pp. 137-154,
2016.
  1. N. Nguyen, P. Drineas, and T. Tran, Tensor sparsification via a bound on the spectral
norm of random tensors, Information and Inference: A Journal of the IMA, 4(3), pp.
195-229, 2015.
  1. C. Boutsidis, A. Zouzias, M. W. Mahoney, and P. Drineas, Randomized Dimensionality
Reduction for K-means Clustering, IEEE Transactions on Information Theory, 62(2),
pp. 1045-1062, 2015.
  1. P. Paschou, P. Drineas1
, E. Yannaki, A. Razou, K. Kanaki, F. Tsetsos, S. Padmanabhuni, M. Michalodimitrakis, M. Renda, S. Pavlovic, A. Anagnostopoulos, J. Stamatoyannopoulos, K. K. Kidd, and G. Stamatoyannopoulos, Maritime route of colonization of
Europe, Proceedings of the National Academy of Sciences, doi:10.1073/pnas.1320811111,
2014.
  1. P. Saurabh, C. Boutsidis, M. Magdon-Ismail, and P. Drineas, Random Projections
for Support Vector Machines, ACM Transactions on Knowledge Discovery from Data
(TKDD), 8(4):22, 2014.
  1. C. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Column-Based Matrix
Reconstruction, SIAM Journal on Computing, 43(2), pp. 687-717, 2014.
  1. C. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Coresets for LeastSquares Regression, IEEE Transactions on Information Theory, 59(10), 6880 - 6892,
2013.
  1. J. R. Hughey, P. Paschou, P. Drineas, D. Mastropaolo, D. M. Lotakis, P. A. Navas,
  2. Michalodimitrakis, J. A. Stamatoyannopoulos, and G. Stamatoyannopoulos, A European Population in Minoan Bronze Age Crete, Nature Communications, (4)1861
doi:10.1038/ncomms2871, 2013.
  1. P. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. Woodruff, Fast Approximation
of Matrix Coherence and Statistical Leverage, Journal of Machine Learning Research,
13, pp. 3475-3506, 2012.
1Equal contribution with the first author.
  1. V. Stathias, G. Sotiris, I. Karagiannidis, G. Bourikas, G. Martinis, G. Papazoglou,
  2. Tavridou, N. Papanas, E. Maltezos, M. Theodoridis, V. Vargemezis, V. Manolopoulos, W. C. Speed, J. R. Kidd, K. K. Kidd, P. Drineas, P. Paschou, Exploring genomic
structure differences and similarities between the Greek and European HapMap populations; implications for association studies, Annals of Human Genetics, 76(6), pp.
472-483, 2012.
  1. N. Kupp, H. Huang, P. Drineas, and Y. Makris, Improving Analog and RF Device
Yield through Performance Calibration, IEEE Design and Test of Computers, 28(3),
pp. 64-75, 2011.
  1. A. Javed, P. Drineas, M.W. Mahoney, and P. Paschou, Reconstructing the genome with
PCA-correlated tSNPs, Annals of Human Genetics, 75(6), pp. 707-722, 2011.
  1. J. Lewis, Z. Abas, C. Dadousis, D. Lykidis, P. Paschou, and P. Drineas, Tracing Cattle
Breeds With Principal Components Analysis Ancestry Informative SNPs, PLoS ONE,
6(4): e18007, 2011.
  1. U. Acer, P. Drineas, and A. Abouzeid, Connectivity in Time-Graphs, Pervasive and
Mobile Computing, 7, pp. 160–171, 2011.
  1. N. G. Sgourakis, M. Merced-Serrano, C. Boutsidis, P. Drineas, Z. Du, C. Wang, and
  2. E. Garcia, Atomic-level characterization of the ensemble of the Aβ(1−42) monomer
in water using unbiased molecular dynamics simulations and spectral algorithms, Journal of Molecular Biology, 405(2), pp.570-583, 2011.
  1. C. Tsourakakis, P. Drineas, E. Michelakis, I. Koutis, and C. Faloutsos, Spectral Counting of Triangles via Element-Wise Sparsification and Triangle-Based Link Recommendation, Journal of Social Network Analysis and Mining (SNAM), 1(2), pp. 75–81,
2011.
  1. P. Drineas, M. W. Mahoney, S. Muthukrishnan, and T. Sarlos, Faster least squares
approximation, Numerische Mathematik, 117(2), pp. 217–249, 2011.
  1. P. Drineas and A. Zouzias, A note on element-wise matrix sparsification via a matrixvalued Bernstein inequality, Information Processing Letters, 111, pp. 385-389, 2011.
  2. P. Paschou, J. Lewis, A. Javed, and P. Drineas, Ancestry Informative Markers for FineScale Individual Assignment to Worldwide Populations, Journal of Medical Genetics,
doi:10.1136/jmg.2010.078212, 2010.
  1. P. Drineas, J. Lewis, and P. Paschou, Inferring Geographic Coordinates of Origin
for Europeans using Small Panels of Ancestry Informative Markers, PLoS ONE 5(8):
e11892, 2010.
  1. H-G. D. Stratigopoulos, P. Drineas, M. Slamani, and Y. Makris, RF specification test
compaction using learning machines, IEEE Transactions on VLSI Systems, 18(6), pp.
1002–1006, 2010.
  1. N. Kupp, P. Drineas, M. Slamani, and Y. Makris, On Boosting the Accuracy of Non-RF
to RF Correlation-Based Specification Test Compaction, Journal of Electronic Testing
Theory and Applications, 25(6), pp. 309-321, 2009.
  1. C. Boutsidis and P. Drineas, Random projections for the nonnegative least-squares
problem, Linear Algebra and its Applications, 431, pp. 760–771, 2009.
  1. A. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney, Sampling algorithms and coresets for `p regression, SIAM Journal on Computing, 38(5), pp. 2060–
2078, 2009.
  1. M. W. Mahoney and P. Drineas, CUR matrix decompositions for improved data analysis, Proceedings of the National Academy of Sciences, 106(3), pp. 697–702, 2009.
  2. M. W. Mahoney, M. Maggioni, and P. Drineas, Tensor-CUR decompositions for tensorbased data, SIAM Journal on Matrix Analysis and Applications, 30(2), pp. 957–987,
2008.
  1. P. Drineas, M.W. Mahoney, and S. Muthukrishnan, Relative-error CUR matrix decompositions, SIAM Journal on Matrix Analysis and Applications, 30(2), pp. 844–881,
2008.
  1. P. Paschou, P. Drineas2
, J. Lewis, C. Nievergelt, D. Nickerson, J. Smith, P. Ridker,
  1. Chasman, R. Krauss, and E. Ziv, Tracing sub-structure in the European American
population with PCA-informative markers, PLoS Genetics, 4(7), pp. 1–13, 2008.
  1. P. Paschou, E. Ziv, E. Burchard, S. Choudhry, W. Rodriguez-Cintron, M. W. Mahoney,
and P. Drineas, PCA-correlated SNPs for structure identification in worldwide human
populations, PLOS Genetics, 3(9), pp. 1672-1686, 2007.
  1. P. Paschou, M. W. Mahoney, A. Javed, J. Kidd, A. Pakstis, S. Gu, K. Kidd, and
  2. Drineas, Intra- and inter-population genotype reconstruction from tagging SNPs,
Genome Research, 17(1), pp. 96-107, 2007.
  1. P. Drineas and M. W. Mahoney, A randomized algorithm for a tensor-based generalization of the SVD, Linear Algebra and its Applications, 420, pp. 553-571, 2007.
  2. P. Drineas, M. W. Mahoney, and R. Kannan, Sampling sub-problems of heterogeneous
max-cut problems and approximation algorithms, Random Structures and Algorithms,
32(3), pp. 307 – 333, 2007.
  1. P. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices
I: approximating matrix multiplication, SIAM Journal on Computing, 36(1), pp. 132-
157, 2006.
  1. P. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices
II: computing a low rank approximation to a matrix, SIAM Journal on Computing,
36(1), pp. 158-183, 2006.
  1. P. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices III: computing a compressed approximate matrix decomposition, SIAM Journal on
Computing, 36(1), pp. 184-206, 2006.
  1. S. Almukhaizim, P. Drineas, and Y. Makris, Entropy-driven parity tree selection for
low-overhead concurrent error detection in finite state machines, IEEE Transactions
on Computer-Aided Design of Integrated Circuits and Systems, 25(8), pp. 1547-1554,
2006.
  1. P. Drineas and M. W. Mahoney, On the Nystrom method for approximating a Gram
matrix for improved kernel-based learning, Journal of Machine Learning Research, 6,
pp. 2153-2175, 2005.
  1. S. Almukhaizim, P. Drineas, and Y. Makris, Compaction-based concurrent error detection for digital circuits, Microelectronics Journal, 36(9), pp. 856-862, Elsevier, 2005.
  2. P. Drineas, R. Kannan, A. Frieze, S. Vempala, and V. Vinay, Clustering of large graphs
via the singular value decomposition, Machine Learning (56), pp. 9-33, 2004.
  1. K. Akcoglu, P. Drineas, and M. Kao, Fast universalization of investment strategies,
SIAM Journal on Computing 34(1), pp. 1-22, 2004.
  1. P. Drineas and Y. Makris, SPaRe: selective partial replication for concurrent fault
detection in FSMs, IEEE Transactions on Instrumentation and Measurement, 52(6),
pp. 1729-1737, 2003.
  1. P. Drineas, E. Drinea, and P. Huggins, An experimental evaluation of a monte carlo
algorithm for singular value decomposition, Y. Manolopoulos et. al. (Eds.): Revised
Selected Papers from the 8th Panhellenic Conference on Informatics, Lecture Notes in
Computer Science 2563, pp. 279-296, 2003.
Journal Publications (submitted, available in ArXiv)
2Equal contribution with the first author.
  1. A. Kundu, P. Drineas, and M. Magdon-Ismail, Recovering PCA from Hybrid-(`1, `2)
Sparse Sampling of Data Elements, under review, 2016.
  1. P. Drineas, I. Ipsen, E. Kontopoulou, and M. Magdon-Ismail, Structural Convergenece
Results for Low-Rank Approximations from Block Krylov Spaces, under review, 2016.
  1. C. Boutsidis, P. Drineas, P. Kambadur, E. Kontopoulou, and A. Zouzias, A Randomized
Algorithm for Approximating the Log Determinant of a Symmetric Positive Definite
Matrix, under review, 2016.
Conference Publications
  1. C. Iyer, C. Carothers, and P. Drineas, Randomized Sketching for Large-Scale Sparse
Ridge Regression Problems, Workshop on Latest Advances in Scalable Algorithms for
Large-Scale Systems (ScalA16), held in conjunction with the 2016 International Conference on High Performance Computing, Networking, Storage and Analysis (SC16),
2016.
  1. C. Iyer, H. Avron, G. Kollias, Y. Ineichen, C. Carothers, and P. Drineas, A Scalable
Randomized Least Squares Solver for Dense Overdetermined Systems, Workshop on
Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA15), held in
conjunction with the 2015 International Conference on High Performance Computing,
Networking, Storage and Analysis (SC15), 2015.
  1. A. Kundu, P. Drineas, and M. Magdon-Ismail, Approximating Sparse PCA from Incomplete Data, Proc. of Neural Information Processing Systems (NIPS), 2015.
  2. S. Paul, M. Magdon-Ismail, and P. Drineas, Column Selection via Adaptive Sampling,
Proc. of Neural Information Processing Systems (NIPS), 2015.
  1. S. Paul, M. Magdon-Ismail, and P. Drineas, Feature Selection for Linear SVM with
Provable Guarantees, Proc. of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) and Journal of Machine Learning Research: Workshops and Conference Proceedings 38, pp. 735–743, 2015.
  1. S. Paul and P. Drineas, Deterministic Feature Selection for Regularized Least Squares
Classification, European Conference on Machine Learning and Principles and Practice
of Knowledge Discovery in Databases (ECML-PKDD), LNCS 8725, pp. 533-548, 2014.
  1. S. Paul, C. Boutsidis, M. Magdon-Ismail, and P. Drineas, Random Projections and
Support Vector Machines, Proc. of the 16th International Conference on Artificial
Intelligence and Statistics (AISTATS), 2013.
  1. K. L. Clarkson, P. Drineas, M. Magdon-Ismail, M. W. Mahoney, X. Meng, and D. P. Woodruff,
The Fast Cauchy Transform and Faster Robust Linear Regression, Proc. of the 24th
Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2013.
  1. P. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. Woodruff, Fast Approximation
of Matrix Coherence and Statistical Leverage, Proc. of the International Conference
on Machine Learning (ICML), 2012.
  1. C. Boutsidis, P. Drineas, and M. Magdon-Ismail, Sparse Features for PCA-like Linear
Regression, Proc. of Neural Information Processing Systems (NIPS), 2011.
  1. C. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Column-Based Matrix
Reconstruction, Proc. of the 52nd IEEE Symposium on Foundations of Computer
Science (FOCS), 2011.
  1. N. Kupp, H. Stratigopoulos, P. Drineas, and Y. Makris, On Proving the Efficiency of
Alternative RF Tests, International Conference on Computer-Aided Design (ICCAD),
2011.
  1. C. Boutsidis, A. Zouzias, and P. Drineas, Random Projections for k-means Clustering,
Proc. of Neural Information Processing Systems (NIPS), 2010.
  1. N. Kupp, H. Huang, P. Drineas, and Y. Makris, Post-Production Performance Calibration in Analog/RF Devices, IEEE International Test Conference (ITC), 8.3.1-8.3.10,
2010.
  1. U. Acer, P. Drineas, and A. Abouzeid, Random walks in time-graphs, Proceedings of
the Second International Workshop on Mobile Opportunistic Networking (MobiOpp),
pp. 93–100, 2010.
  1. C. Boutsidis, M. W. Mahoney, and P. Drineas, Unsupervised Feature Selection for the
k-means Clustering Problem, Proc. of Neural Information Processing Systems (NIPS),
2009.
  1. C. Tsourakakis, P. Drineas, E. Michelakis, I. Koutis, and C. Faloutsos, Spectral Counting of Triangles in Power-Law Networks via the Element-wise Sparsification, Proc.
of the International Conference on Advances in Social Network Analysis and Mining
(ASONAM), pp. 66–72, 2009.
  1. C. Boutsidis, M.W. Mahoney, and P. Drineas, An improved approximation algorithm for
the column subset selection problem, Proc. of the 20th Annual ACM-SIAM Symposium
on Discrete Algorithms (SODA), pp. 968–977, 2009.
  1. C. Boutsidis, M.W. Mahoney, and P. Drineas, Unsupervised feature selection for Principal Components Analysis, Proc. of the 14th Annual ACM Conference on Knowledge
Discovery and Data Mining (KDD), pp. 61–69, 2008.
  1. N. Kupp, P. Drineas, M. Slamani, and Y. Makris, Confidence Estimation in Non-RF
to RF Correlation-Based Specification Test Compaction, Proc. of the 13th European
Test Symposium (ETS), pp. 35–40, 2008.
  1. A. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney, Sampling algorithms and coresets for `p regression, Proc. of the 19th Annual ACM-SIAM Symposium
on Discrete Algorithms (SODA), pp. 932–941, 2008.
  1. A. Dasgupta, P. Drineas, B. Harb, V. Josifovski, and M. Mahoney, Feature selection
methods for text classification, Proc. of the 13th Annual ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 230–239, 2007.
  1. H-G. D. Stratigopoulos, P. Drineas, M. Slamani, and Y. Makris, Non-RF to RF test
correlation using learning machines: a case study, Proc. of the 25th IEEE VLSI Test
Symposium (VTS), pp. 9–14, 2007.
  1. P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Subspace sampling and relative
error matrix approximation: column-based methods, Proc. of APPROX-RANDOM,
pp. 316-326, 2006.
  1. P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Subspace sampling and relative
error matrix approximation: column-row-based methods, Proc. of the 14th Annual
European Symposium on Algorithms (ESA), pp. 304-314, 2006.
  1. P. Drineas, A. Javed, M. Magdon-Ismail, G. Pandurangan, R. Virrankoski, and A. Savvides, Distance matrix reconstruction from incomplete distance information for sensor
network localization, Proc. of the 3rd Annual IEEE Conference on Sensor, Mesh and
Ad Hoc Communications and Networks (SECON), pp. 536-544, 2006.
  1. P. Drineas and M. W. Mahoney, Randomized algorithms for matrices and massive data
sets, Proc. of the 32nd Annual Conference on Very Large Data Bases (VLDB), p.
1269, 2006.
  1. M. W. Mahoney, M. Maggioni, and P. Drineas, Tensor-CUR decompositions for tensorbased data, Proc. of the 12th Annual ACM Conference on Knowledge Discovery and
Data Mining (KDD), pp. 327-336, 2006.
  1. P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Sampling algorithms for `2 regression and applications, Proc. of the 17th Annual ACM-SIAM Symposium on Discrete
Algorithms (SODA), pp. 1127-1136, 2006.
  1. D. Freedman and P. Drineas, Energy minimization via graph cuts: settling what is
possible, Proc. of the IEEE International Conference on Computer Vision and Pattern
Recognition (CVPR), pp. 939-946, 2005.
  1. P. Drineas and M. W. Mahoney, Approximating a Gram matrix for improved kernelbased learning, Proc. of the 18th Annual Symposium on Computational Learning
Theory (COLT), pp. 323-337, 2005.
  1. P. Drineas, R. Kannan, and M. W. Mahoney, Sampling sub-problems of heterogeneous
max-cut problems and approximation algorithms, Proc. of the 22nd Annual Symposium
on Theoretical Aspects of Computer Science (STACS), Lecture Notes in Computer
Science 3404, pp. 57-68, 2005.
  1. P. Drineas, M. Krishnamoorthy, D. Sofka, and B. Yener, Studying E-mail graphs for
intelligence monitoring and analysis in the absence of semantic information, Proc. of
the Symposium on Intelligence and Security Informatics, Lecture Notes in Computer
Science 3073, pp. 297-306, 2004.
  1. S. Almukhaizim, P. Drineas, and Y. Makris, Cost-driven selection of parity trees, Proc.
of the IEEE VLSI Test Symposium (VTS), pp. 319-324, 2004.
  1. S. Almukhaizim, P. Drineas, and Y. Makris, Concurrent error detection for combinational and sequential logic via output compaction, Proc. of the IEEE International
Symposium on Quality Electronic Design (ISQED), pp. 459-464, 2004.
  1. S. Almukhaizim, P. Drineas, and Y. Makris, On concurrent error detection with bounded
latency in FSMs, Proc. of the IEEE Design Automation and Test in Europe Conference
(DATE), pp. 596-601, 2004.
  1. P. Drineas and R. Kannan, Pass Efficient Algorithms for Approximating Large Matrices, Proc. of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA),
pp. 223-232, 2003.
  1. S. Almukhaizim, P. Drineas, and Y. Makris, On Compaction-based concurrent error
detection, On compaction-based concurrent error detection, Proc. of the IEEE OnLine Test Symposium, pp. 157-161, 2003.
  1. P. Drineas and Y. Makris, On the compaction of independent test sequences for sequential circuits, Proc. of the IEEE International Conference on Computer Design (ICCD),
pp. 380-386, 2003.
  1. P. Drineas and Y. Makris, Non-intrusive concurrent error detection in FSMs through
State/Output compaction and monitoring via parity trees, Proc. of the Design Automation and Test in Europe Conference (DATE), pp. 1164-1165, 2003.
  1. P. Drineas and Y. Makris, SPaRe: selective partial replication for concurrent fault
detection in FSMs, Proc. of the IEEE International Conference on VLSI Design, pp.
84-91, 2003.
  1. P. Drineas and Y. Makris, On the Compaction of Independent Test Sequences for Sequential Circuits, IEEE European Test Workshop (ETS), Maastricht, Netherlands,
2003.
  1. P. Drineas and Y. Makris, Concurrent fault detection in random combinational logic,
Proc. of the IEEE International Symposium on Quality Electronic Design (ISQED),
pp. 425-430, 2003.
  1. P. Drineas, I. Kerenidis, and P. Raghavan, Competitive recommendation systems, Proc.
of the 34th ACM Symposium on Theory of Computing (STOC), pp. 82-90, 2002.
  1. K. Akcoglu, P. Drineas, and M. Kao, Fast universalization of investment strategies
with provably good relative returns, Proc. of the 29th International Colloquium on
Automata, Languages and Programming (ICALP), pp. 888-900, 2002.
  1. P. Drineas and Y. Makris, Non-intrusive design of concurrently self-testable FSMs,
Proc. of the IEEE Asian Test Symposium (ATS), pp. 33-38, 2002.
  1. P. Drineas and Y. Makris, Non-intrusive design of concurrently self-testable FSMs,
IEEE North Atlantic Test Workshop (NATW), Montauk NY, USA, 2002.
  1. E. Drinea, P. Drineas, and P. Huggins, A randomized singular value decomposition
algorithm for image processing applications, Proc. of the 8th Panhellenic Conference
on Informatics, pp. 278-288, 2001.
  1. P. Drineas and R. Kannan, Fast monte carlo algorithms for approximate matrix multiplication, Proc. of the 42nd IEEE Symposium on Foundations of Computer Science
(FOCS), pp. 452-459, 2001.
  1. P. Drineas, R. Kannan, A. Frieze, S. Vempala, and V. Vinay, Clustering in large graphs
and matrices, Proc. of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 291-299, 1999.
Abstracts & Technical Reports
  1. A. Bose, D. E. Platt, L. Parida, P. Paschou, P. Drineas, Genetic Variation reveals
migrations into the Indian subcontinent and its influence on the Indian society, Annual
Meeting of the American Society of Human Genetics, 2016. Selected for platform
presentation.
  1. A. Plantinga, F. Tsetsos, P. Paschou, P. Drineas, B. Browning, and G. Stamatoyannopoulos, Identity by descent analysis reveals fine-scale population structure in Crete,
Annual Meeting of the American Society of Human Genetics, 2015.
  1. P. Paschou, I. Karagiannidis, A. Tsirigoti, A. Stampoliou, V. Papadopoulou, V. G. Manolopoulos, J. R. Kidd, K. K. Kidd, and P. Drineas, Evaluation of the HapMap dataset as
reference for the Greek population, Annual Meeting of the American Society of Human
Genetics, 2010.
  1. J. Lewis, Z. Abas, C. Dadousis, D. Lykidis, P. Paschou, and P. Drineas, Tracing The
Origin Of Cattle Breeds With PCA-based Ancestry Informative SNPs, World Congress
on Genetics Applied to Livestock Production, 2010. Selected for platform presentation.
  1. P. Paschou, J. Lewis, and P. Drineas, Accurate inference of individual ancestry geographic coordinates within Europe using small panels of genetic markers, Annual Meeting of the American Society of Human Genetics, 2009.
  2. P. Paschou, J. Lewis, A. Javed, and P. Drineas, Using principal components analysis to
identify candidate genes for natural selection, Annual Meeting of the American Society
of Human Genetics, 2008.
  1. P. Paschou, E. Ziv, E. G. Burchard, M. W. Mahoney, and P. Drineas, PCA-correlated
SNPs for structure identification in worldwide human populations, Annual Meeting of
the American Society of Human Genetics, 2007.
  1. P. Paschou, M. W. Mahoney, A. Javed, J. R. Kidd, A. J. Pakstis, S. Gu, K. K. Kidd,
and P. Drineas, Intra- and inter-population genotype reconstruction from tagging SNPs,
Annual Meeting of the American Society of Human Genetics, 2006. Selected for
platform presentation.
  1. P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Polynomial time algorithm for
column-row based relative error low-rank matrix approximation, DIMACS Technical
Report 2006-04, 2006.
  1. P. Drineas, Pass efficient algorithms for approximating large matrices, Mathematisches
Forschungsinstitut Oberwolfach (MFO) Workshop on Approximation Algorithms for
NP-Hard Problems, Oberwolfach, Germany, 2004.
News
Articles 1. E. Gallopoulos, P. Drineas, I. Ipsen, and M. W. Mahoney, RandNLA, Pythons, and the
CUR for your Data problems, SIAM News, p. 7, February 2016.
  1. A brief interview including my thoughts on Linear Algebra appeared in Kuldeep Singh’s
book “Linear Algebra: Step by Step.” A link to the interview is available at http:
//drineas.org.
  1. Numerous news articles have covered my June 2014 paper in the Proceedings of the
National Academy of Sciences on a “Maritime Route Of Colonization of Europe” (including National Geographic, Science, Science Daily, etc.). A partial list of links to the
relevant articles is available at http://drineas.org.
  1. Harnessing the Petabyte: Data Science Research Center at Rensselaer Polytechnic
Institute Explores Cloud Computing and Supercomputing To Analyze Big Data by
Mary L. Martialay, RPI Press Release, Sep 2013.
(Available at http://news.rpi.edu/content/2013/09/06/harnessingpetabyte-data-science-research-center-explores
-cloud-computing-and?destination=node/40180)
  1. DNA Analysis Unearths Origins of Minoans, the First Major European Civilization by
Mary L. Martialay, RPI Press Release, May 2013.
(Available at http://news.rpi.edu/luwakkey/3181)
  1. Study Helps Pinpoint Genetic Variations in European Americans (by Gabrielle DeMarco), RPI Press Release, Aug 2008.
(Available at http://news.rpi.edu/update.do?artcenterkey=2479)
  1. Computer Program Reveals Anyone’s Ancestry (by Gabrielle DeMarco), featured at
Yahoo! News and LiveScience, Apr 2008.
(Available at http://www.livescience.com/health/080404-bts-drineas.html)
  1. Tracing Your Ancestry: Computer Program Accurately Analyzes Anonymous DNA
Samples, featured at ScienceDaily, Sep 2007.
(Available at http://www.sciencedaily.com/releases/2007/09/070921071744.htm)
  1. DNA Markers and Computer Science Methodology Can be Used to Trace Individual
Ancestry, featured at Scitizen, Sep 2007.
(Available at http://scitizen.com/stories/Biotechnology/2007/10/DNA-Markers
-and-Computer-Science-Methodology-Can-be-Used-to-Trace-Individual-Ancestry/)
  1. G.H. Golub, M.W. Mahoney, P. Drineas, and L.-H. Lim, MMDS 2006: bridging the gap
between numerical linear algebra, theoretical computer science, and data applications,
SIAM News, Oct 2006.
Teaching
(The last column reflects the Summary Evaluation Adjusted Score from the IDEA report.
The maximum score is five.)
Date Number Title Enrol. IDEA
2003 Spring CSCI-1200 Computer Science II 140 3.8
2003 Fall CSCI-6962 Randomized Algorithms 12 4.3
2004 Spring CSCI-1200 Computer Science II 206 4.0
2004 Spring CSCI-4961 Network Flows & Linear Programming 18 4.1
2005 Spring CSCI-2400 Models of Computations 99 4.0
2006 Spring CSCI-2400 Models of Computations 63 4.1
2006 Spring CSCI-6962 Randomized Algorithms 15 4.6
2007 Spring CSCI-2400 Models of Computations 69 4.1
2007 Spring CSCI-6962 Randomized Algorithms 11 4.4
2008 Spring CSCI-2400 Models of Computations 102 4.4
2008 Spring CSCI-6962 Randomized Algorithms 6 5
2009 Spring CSCI-2400 Models of Computations 78 4.4
2009 Spring CSCI-6962 Randomized Algorithms 8 4.9
2010 Spring CSCI-2400 Models of Computations 79 4.5
2010 Spring CSCI-6962 Randomized Algorithms 10 5
2012 Spring CSCI-2400 Models of Computations 77 4.4
2012 Spring CSCI-6962 Randomized Algorithms 12 5
2013 Spring CSCI-2200 Foundations of Computer Science 95 4.3
2013 Spring CSCI-2400 Models of Computations 54 4.1
2013 Spring CSCI-6962 Randomized Algorithms 14 4.6
2015 Spring CSCI-4966/6967 Foundations of Data Science 23 4.7
2016 Spring CSCI-2200 Foundations of Computer Science 198 4.4
2016 Spring CSCI-6962 Randomized Algorithms 31 4.9
2016 Fall CS-59000 Randomized Algorithms in NLA 11 4.9
Graduated Ph.D. StudentsPhD
Students Asif Javed, graduated December 2008
Utku Gunay Acer, graduated August 2009
Jamey Lewis, graduated September 2010
Christos Boutsidis, graduated May 2011 (awarded the 2011 Robert McNaughton Prize)
Saurabh Paul, graduated May 2015
Abhisek Kundu, graduated Dec 2015
Nivas Nambirajan, graduated Dec 2015
Current Ph.D. Students
Chander Iyer, joined Sep 2012
Aritra Bose, joined Sep 2014
Eugenia Kontopoulou, joined Sep 2015
Agniva Chowdhury, joined Jan 2017
Ph.D. Committee Memberships
Kevin Cheng (advisor: Ravi Kannan, Yale University)
John Holodnak (advisor: Ilse Ipsen, North Carolina State University)
Thomas Wentworth (advisor: Ilse Ipsen, North Carolina State University)
Chris Stuetzle (advisor: Barbara Cutler)
Nabhendra Bisnik (advisor: Alhussein Abouzeid)
Evrim Acar (advisor: Bulent Yener)
Utku Gunay Acer (advisor: Alhussein Abouzeid)
Victor Boyarshinov (advisor: Malik Magdon-Ismail)
Bugra Caskurlu (advisor: Malik Magdon-Ismail)
Ali Civril (advisor: Malik Magdon-Ismail)
Karlton Sequeira (advisor: Mohammed Zaki)
Tutorials (Keynote talks and tutorials.)
& Keynotes 1. Summer School (lecturer): RandNLA: Randomization in Numerical Linear Algebra, Institute for Advanced Study (IAS) and the Park City Mathematics Institute
(PCMI) Graduate Summer School 2016: The Mathematics of Data, Midway, Utah,
Jul 2016.
  1. Invited Tutorial: RandNLA: Randomization in Numerical Linear Algebra, SIAM
Conference on Applied Linear Algebra, Atlanta, Georgia, Oct 2015.
  1. Summer School (organizer and lecturer): RandNLA: Randomization in Numerical Linear Algebra, Gene Golub SIAM Summer School (G2S3), Delphi, Greece, Jun
2015.
  1. Keynote Speaker: RandNLA: Randomization in Numerical Linear Algebra, SIAM
Workshop on Combinatorial Scientific Computing (CSC), Jul 2014.
  1. Tutorial: Past, Present and Future of Randomized Numerical Linear Algebra , Big
Data Bootcamp, Simons Institute for the Theory of Computing, University of California, Berkeley, Sep 2013.
  1. Tutorial: Mining Massive Datasets: a (Randomized) Linear Algebraic Approach,
Opening workshop of the Statistical and Applied Mathematical Sciences Institute (SAMSI)
“Massive Datasets” 2012-2013 program, Sep 2012.
  1. Keynote Speaker: Randomized Algorithms in Linear Algebra, SIAM Conference on
Applied Linear Algebra, Valencia, Spain, Jun 2012.
  1. Keynote Speaker: Randomized Algorithms in Data Mining: a Linear Algebraic
Approach, From Data to Knowledge Workshop, University of California, Berkeley,
May 2012.
  1. Keynote Speaker: Randomized Algorithms for Low-Rank Approximations and Data
Applications, Workshop on Low-rank Methods for Large-scale Machine Learning, help
in conjunction with the Neural Information Processing Systems Conference, 2010.
  1. Tutorial: Randomized Algorithms in Linear Algebra and Applications, Workshop on
Algorithms for Modern Massive Datasets, Stanford University, Jun 2010.
  1. Keynote Speaker: Randomized algorithms for the least-squares approximation problem, Midwest Theory day, University of Michigan, Ann Arbor, May 2008.
  2. Tutorial: Information retrieval and data mining: a linear algebraic perspective, Mathematics of Knowledge and Search Engines, Institute for Pure and Applied Mathematics,
University of California Los Angeles, Sep 2007.
  1. Tutorial: Randomized algorithms for matrices and massive datasets, VLDB, Sep 2006.
  2. Tutorial: Randomized algorithms for matrices and massive datasets, SIAM Conference on Data Mining (SDM), Apr 2006.
  3. Tutorial: Randomized algorithms for matrices and massive datasets, ACM International Conference on Knowledge Discovery and Data Mining (KDD), Aug 2005.
Invited (Invited presentations only; contributed conference presentations are not included.)
Talks 16. Dimensionality reduction in the analysis of human genetics data, Math-Bio Seminar,
University of Pennsylvania, Oct 2016.
  1. Leverage Scores, Statistics Colloquium, University of Chicago, Oct 2016.
  2. An approximation algorithm for Sparse PCA, Theory Seminar, Purdue University, Sep
2016.
  1. RandNLA: Randomization in Numerical Linear Algebra, Computer Science Colloquium, Washington University St. Louis, Sep 2016.
  2. RandNLA: Randomization in Numerical Linear Algebra, Machine Learning Day,
Google Research, Mar 2016.
  1. Leverage Scores in Data Analysis, Mathematical Institute, Oxford University, May
2015.
  1. RandNLA: Randomization in Numerical Linear Algebra, Workshop on Optimization
and Matrix Methods in Big Data, Fields Institute, Feb 2015.
  1. RandNLA: Randomization in Numerical Linear Algebra, Computer Science Colloquium, Purdue University, Nov 2014.
  2. Identifying Influential Entries in a Matrix, Yahoo Labs New York, Nov 2014.
  3. RandNLA: Randomization in Numerical Linear Algebra, Computational Math Colloquium, University of Waterloo, Nov 2014.
  4. Identifying Influential Entries in a Matrix, Householder Symposium XIX, Spa, Belgium,
Jun 2014.
  1. Leverage scores, School of Mathematics, University of Edinburgh, Apr 2014.
  2. RandNLA: Randomization in Numerical Linear Algebra, Computer Science Department, University of Edinburgh, Apr 2014.
  3. Leverage Scores in Data Analysis, International Computer Science Institute (ICSI),
University of California Berkeley, Feb 2014.
  1. RandNLA: Randomization in Numerical Linear Algebra, SKYTREE, The Machine
Learning Company, Feb 2014.
  1. Leverage Scores in Data Analysis, Neyman Seminar, Department of Statistics, University of California Berkeley, Nov 2013.
  2. RandNLA: Randomization in Numerical Linear Algebra, Workshop on Succinct Data
Representations and Applications, Simons Institute for the Theory of Computing, University of California, Berkeley, Sep 2013.
  1. RandNLA: Randomization in Numerical Linear Algebra, Simons Foundation Workshop on Computer Science Issues in Big Data, Apr 2013.
  2. RandNLA: Randomization in Numerical Linear Algebra, Workshop celebrating Ravi
Kannan’s 60th birthday, Carnegie-Mellon University, May 2013.
  1. Randomized Algorithms in Numerical Linear Algebra, NSF workshop on Big Data:
From Signal Processing to Systems Engineering, Mar 2013.
  1. RandNLA: Randomization in Numerical Linear Algebra, DARPA Workshop on Big
Data and Large-Scale Analytics, Mar 2013.
  1. Leverage Scores, Numerical Analysis Seminar, North Carolina State University, Nov
2012.
  1. Randomized Algorithms in Linear Algebra and Applications in Data Analysis, Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT, Sep 2012.
  2. Leverage Scores, Scientific Computing and Numerics (SCAN) Seminar, Cornell University, Sep 2012.
  3. Randomized Algorithms in Linear Algebra and the Column Subset Selection Problem,
Theory Seminar, Carnegie Mellon University, Apr 2012.
  1. Dimensionality reduction in the analysis of human genetics data, Bioinformatics Seminar, MIT, Apr 2011.
  2. Randomized matrix algorithms, Theory Colloquium, University of Maryland College
Park, Feb 2011.
  1. Randomized matrix algorithms and their applications, Special Session on Random Matrix Theory and Applications in 2010 Spring Western Sectional Meeting of the American Mathematical Society, Apr 2010.
  2. Sampling algorithms for `2 regression, Theory Seminar, University of Toronto, Nov
2009.
  1. Randomized Algorithms in Linear Algebra, SIAM Conference on Applied Linear Algebra, Oct 2009.
  2. Dimensionality Reduction in the Analysis of Human Genetics Data, DIMACS Workshop on Algorithmics in Human Population-Genomics, Apr 2009.
  3. Randomized Algorithms for Matrix Computations and Applications to Data Mining,
Numerical Analysis and Scientific Computing Seminar, Courant Institute of Mathematical Sciences, New York University, Apr 2009.
  1. Approximating a tensor as a sum of rank-one components, NSF Workshop on Future
Directions in Tensor-Based Computation and Modeling, Feb 2009.
  1. Randomized Algorithms for Matrix Computations and Applications to Data Mining,
RPI Brown Bag Lunch Lecture Series, Feb 2009.
  1. Randomized Algorithms for Linear Algebraic Computations and Applications to Network Analysis, Workshop on New Mathematical Frontiers in Network Multi-Resolution
Analysis, Institute for Pure and Applied Mathematics, University of California Los Angeles, Nov 2008.
  1. The Column Subset Selection Problem: Theory and Applications, Computer Science
Department, University of Pennsylvania, Nov 2008.
  1. Randomized Algorithms for Matrix Computations and Applications to Data Mining,
IBM T.J. Watson Research Center, Sep 2008.
  1. The Column Subset Selection Problem, Householder Symposium XVII, Zeuthen, Germany, Jun 2008.
  2. Randomized Algorithms for Matrix Computations and Applications to Data Mining,
Colloquium, Computer Science Department, Johns Hopkins University, May 2008.
  1. Randomized Algorithms for Matrix Computations and Applications to Data Mining,
Colloquium, Computer Science Department, Northeastern University, Feb 2008.
  1. Identifying ancestry informative markers via Principal Components Analysis, Workshop on Search and Knowledge Building for Biological Datasets, Institute for Pure and
Applied Mathematics, University of California Los Angeles, Nov 2007.
  1. Sampling algorithms for `2 regression and the column subset selection problem, Applied
Mathematics Seminar, University of California Davis, Nov 2007.
  1. Deterministic and randomized algorithms for column subset selection, NumAn2007
Conference in Numerical Analysis, Kalamata, Greece, Sep 2007.
  1. From the singular value decomposition of matrices to CUR-type decompositions, Colloquium, Max Planck Institute for Informatics, Aug 2007.
  2. Fast randomized algorithms for least squares approximations, Theory colloquium, Max
Planck Institute for Informatics, Aug 2007.
  1. Fast randomized algorithms for least squares approximations, International Congress
on Industrial and Applied Mathematics, ETH Zurich, Jul 2007.
  1. From the singular value decomposition of matrices to CUR-type decompositions: algorithms and applications, Colloquium, Computer Science Department, Dartmouth University, Apr 2007.
  2. Sampling algorithms and coresets for `2 regression and applications, Princeton Theory
Lunch, Mar 2007.
  1. From the singular value decomposition of matrices to CUR-type decompositions, New
England Complex Systems Institute, Dec 2006.
  1. From the singular value decomposition of matrices to CUR-type decompositions, General Electric Research Division, Niskayuna, Nov 2006.
  2. Subspace sampling and relative error matrix approximation, Workshop on Algorithms
for Modern Massive Datasets, Stanford University, Jun 2006.
  1. Subspace sampling: coresets for `2 regression problems, Bertinoro workshop on spaceconscious algorithms, Jun 2006.
  2. From the singular value decomposition of matrices to CUR-type decompositions: algorithms and applications, Bioinformatics Colloquium, Rensselaer Polytechnic Institute,
Apr 2006.
  1. Approximating a matrix with submatrices: algorithms and applications, Theory Colloquium, Computer Science Department, Yale University, Apr 2006.
  2. A relative-error CUR decomposition for matrices and its data applications, Theory
Colloquium, Computer Science Department, University of Pennsylvania, Mar 2006.
  1. A relative-error CUR decomposition for matrices and its data applications, Theory
Colloquium, Computer Science Department, Columbia University, Feb 2006.
  1. CUR matrix decompositions for improved data analysis, Yahoo! Research, Oct 2005.
  2. Randomized algorithms for matrices and applications, Sandia National Laboratories,
Aug 2005.
  1. Sampling algorithms for `2 regression and applications, Dagstuhl Seminar on Sublinear
Algorithms, Jul 2005.
  1. Randomized algorithms for matrices and applications, IBM Research, Almaden, May
2005.
  1. Monte-carlo algorithms for matrices and massive datasets, Theory Colloquium, Computer Science Department, Stanford University, May 2005.
  2. The CUR matrix decomposition and its applications to algorithm design and massive data sets, Colloquium, Computer Science Department, Rutgers University and
DIMACS, Nov 2004.
  1. A Novel matrix decomposition with applications to algorithm design and massive data
sets, Theory Colloquium, Computer Science Department, University of Michigan at
Ann Arbor, Sep 2004.
  1. Fast monte-carlo algorithms for common matrix operations, Colloquium, Computer
Science Department, Purdue University, Sep 2004.
  1. Randomized algorithms for matrix operations, Colloquium, Computer Engineering and
Informatics Department, University of Patras, Jun 2004.
  1. Pass-efficient algorithms for approximating large matrices, Mathematisches Forschungsinstitut Oberwolfach (MFO) Workshop on Approximation Algorithms for NP-Hard Problems, Jun 2004.
  2. Computing sketches of matrices efficiently and privacy preserving data mining, DIMACS Workshop on Privacy Preserving Data Mining, Mar 2004.
  3. Randomized algorithms for matrix operations, Colloquium, Department of Mathematics, Rensselaer Polytechnic Institute, Feb 2004.
  4. Pass efficient algorithms for matrix operations and max-2-CSP problems, NEC Research, Princeton, Jul 2003.
  5. Pass efficient algorithms for matrix approximations, Colloquium, Department of Computer Science, Brown University, Mar 2002.
  6. Pass efficient algorithms for matrix approximations, Colloquium, Department of Computer Science, Rensselaer Polytechnic Institute, Feb 2002.
  7. Pass efficient algorithms for matrix approximations, Theory Colloquium, Department
of Engineering and Applied Sciences, Harvard University, Feb 2002.
  1. Randomized algorithms for approximate matrix multiplication and singular value decomposition, Theory Colloquium, Department of Computer Science, Brown University,
Dec 2001.
  1. Fast monte carlo algorithms for matrix multiplication, DIMACS Workshop on Sublinear Algorithms, Sep 2000.
  2. A fast monte carlo singular value decomposition algorithm, Theory Colloquium, Department of Computer Science, Yale University, Apr 1999.
Grants
  1. (PI: Huo, co-PI Drineas)“Workshop on Theoretical Foundations of Data Science
(TFoDS)”, National Science Foundation (NSF), $99,997, 2016.
  1. (PI: Ipsen, co-PI Drineas)“RandNLA: Randomization in Numerical Linear Algebra”, National Science Foundation (NSF), $25,000, 2015.
  2. (co-PIs: Drineas, Gallopoulos, Ipsen, Mahoney)“RandNLA: Randomization in
Numerical Linear Algebra”, Society for Industrial and Applied Mathematics (SIAM),
$85,000, 2015.
  1. (PI Mahoney, co-PI Drineas), “BIGDATA:F:DKA:Collaborative Research: Randomized Numerical Linear Algebra (RandNLA) for multi-linear and non-linear data”,
National Science Foundation (NSF), $800,000, 2014 – 2017.
  1. (PI Drineas), “III: Small: Fast and Efficient Algorithms for Matrix Decompositions
and Applications to Human Genetics”, National Science Foundation (NSF), $329,455,
2013 – 2016.
  1. (PI Drineas, co-PIs Carothers, Garcia, Yener, and Zaki), “III: Medium: Mining petabytes of data using cloud computing and a massively parallel cyberinstrument”,
National Science Foundation (NSF), $1,000,000, 2013 – 2017.
  1. (PI Drineas), “Intergovernmental Mobility Assignment”, National Science Foundation (NSF), $225,000, 2010-2011.
  2. (PI Drineas, co-PI Saunders), “Randomized Algorithms in Linear Algebra and
Numerical Evaluations on Massive Datasets”, National Science Foundation (NSF),
$450,000, 2010 – 2013.
  1. (PI Drineas), “Fast and Efficient Randomized Algorithms for Solving Laplacian Systems of Linear Equations and Sparse Least Squares Problems”, National Science Foundation (NSF), $323,000, 2010 – 2013.
  2. (PI Drineas), European Molecular Biology Organization (EMBO) short term fellowship, $12,000, Jun - Aug 2010.
  3. (PI Makris, co-PI Drineas), “Collaborative Research: Correlation Mining and its
Applications in Test Cost Reduction, Yield Enhancement, and Performance Calibration
in Analog/RF Circuits”, National Science Foundation (NSF), $450,000, 2009 – 2012.
  1. (PI Drineas), European Molecular Biology Organization (EMBO) short term fellowship, $12,000, Jun - Aug 2009.
  2. (PI Drineas), “Extracting PCA-correlated SNPs from the Human Genome Diversity
Panel data”, National Science Foundation (NSF), $30,844, 2009 – 2011.
  1. (PI Isler, co-PI Drineas, co-PI Trinkle), “Research/Education Infrastructure
Based on Modular Miniature Robot Teams”, National Science Foundation (NSF),
$350,000, 2007 – 2010.
  1. (PI Makris, co-PI Drineas), “Statistical Analysis of Parametric Measurements and
its Applications in Analog/RF Test”, Semiconductor Research Corporation (SRC),
$150,000, 2007 – 2010.
  1. (PI Drineas, co-PI Abouzeid), “NeTS-NBD: Towards a Disconnection-Tolerant,
Opportunistic Internet”, National Science Foundation (NSF), $460,000, 2006 – 2009.
  1. (PI Drineas),Yahoo! Research Gift, $18,000, 2006.
  2. (PI Drineas), “Research Experience for Undergraduates (REU) Supplement: Implementing Algorithms for tSNP selection in MatLab”, National Science Foundation
(NSF), $12,000, 2006 – 2011.
  1. (PI Drineas), “CAREER: A Framework for Mining Multimode, Non-Homogeneous
Tensor Data Sets With Linear and Non-Linear Degrees of Freedom”, National Science
Foundation (NSF), $400,000, 2006 – 2011.
  1. (PI Golub, co-PIs Drineas, Mahoney, and Lim), “Workshop on Algorithms for
Modern Massive Datasets”, National Science Foundation (NSF), $15,000, 2005 – 2006.
Boards
  1. Member, Gene Golub SIAM Summer School Committee, Jan 2018 – now.
  2. Editorial Board Member, SIAM Journal on Scientific Computing, Jan 2017 – now.
  3. Editorial Board Member, Applied and Computational Harmonic Analysis, Jan 2017 –
now.
  1. Editor, Handbook of Big Data, CRC Press, Taylor & Francis Group, 2016.
  2. Editorial Board Member, SIAM Journal on Scientific Computing, Special Issue for
Software and Big Data, 2015.
  1. Editorial Board Member, SIAM Journal on Matrix Analysis and Applications, Jan
2015 – now.
  1. Editorial Board Member, Information and Inference: A Journal of the IMA, Feb 2014
– now.
  1. Editorial Board Member, PLoS ONE, May 2013 – now.
  2. Member, Committee for the Advancement of Theoretical Computer Science (CATCS),
Sep 2012 – Jun 2016.
Committee
Service 1. Senior Program Committee Member, ACM Conference on Information and Knowledge
Management, Nov 2017.
  1. Program Committee Member, ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, Aug 2017.
  1. Program Committee Member, 16th IEEE International Workshop on High Performance Computational Biology, held in conjunction with IEEE IPDPS, May 2017.
  2. Invited Reviewer, Neural Information Processing Systems Conference, Dec 2016.
  3. Program Committee Member, ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, Aug 2016.
  1. Organizing Committee Member, 5th Workshop on Algorithms for Modern Massive
Datasets (MMDS), Jun 2016.
  1. Chair, Workshop on Theoretical Foundations of Data Science (TFoDS), Apr 2016.
  2. Organizing Committee Member, Gene Golub SIAM Summer School (G2S3), Jun 2015.
  3. Program Committee Member, IEEE International Conference on Data Mining (ICDM),
Dec 2015.
  1. Program Committee Member, International Conference on Parallel Processing (ICPP),
Sep 2015.
  1. Organizing Committee Member, SIAM Conference on Applied Linear Algebra (ALA),
Oct 2015.
  1. Organizing Committee Member, Workshop on Optimization and Matrix Methods in
Big Data, Fields Institute, Feb 2015.
  1. Program Committee Member, IEEE International Conference on Data Mining (ICDM),
Dec 2013.
  1. Program Committee Member, International Conference on Parallel Processing (ICPP),
Sep 2014.
  1. Organizing Committee Member, 5th Workshop on Algorithms for Modern Massive
Datasets (MMDS), Jun 2014.
  1. Program Committee Member, ACM Symposium on Theory of Computing (STOC),
Jun 2014.
  1. NSF DMS Review Panel, 2014.
  2. NSF CCF Review Panel, 2014.
  3. Program Committee Member, ACM-SIAM Symposium on Discrete Algorithms (SODA),
Jan 2014.
  1. Invited Reviewer, Neural Information Processing Systems Conference, Dec 2013.
  2. Chair, Workshop on Succinct Data Representations and Applications, Simons Institute
for the Theory of Computing, University of California, Berkeley, Sep 2013.
  1. NSF IIS Review Panel, 2013.
  2. Vice Chair, IEEE International Conference on Data Mining (ICDM), Dec 2013.
  3. Program Committee Member, ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, Aug 2013.
  1. NSF CCF Review Panel, 2012.
  2. NSF IIS Review Panel, 2012.
  3. Program Committee Member, International Conference on Pattern Recognition Applications and Methods, Feb 2013.
  4. Organizing Committee Member, Randomized Numerical Linear Algebra: Theory and
Practice, one-day workshop held in conjunction with the 53rd Annual IEEE Symposium
on Foundations of Computer Science (FOCS), Oct 2012.
  1. Invited Reviewer, Neural Information Processing Systems Conference, Dec 2012.
  2. Program Committee Member, ACM International Conference on Information and
Knowledge Management, Nov 2012.
  1. Program Committee Member, International Conference on Data Technologies and Applications, Jul 2012.
  2. Organizing Committee Member, Workshop on Algorithms for Modern Massive Datasets
(MMDS) IV, Jul 2012.
  1. Program Committee Member, Workshop on Large-scale Data Mining: Theory and Applications, to be held in conjunction with the ACM SIGKDD Conference on Knowledge
Discovery and Data Mining, Jul 2011.
  1. Program Committee Member, International Conference on Pattern Recognition Applications and Methods, Feb 2012.
  2. Program Committee Member, ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, Aug 2011.
  1. Invited Reviewer, Neural Information Processing Systems Conference, Dec 2010.
  2. Program Committee Member, Workshop on Large-scale Data Mining: Theory and Applications, to be held in conjunction with the ACM SIGKDD Conference on Knowledge
Discovery and Data Mining, Jul 2010.
  1. Organizing Committee Member, Workshop on Algorithms for Modern Massive Datasets
(MMDS) III, Jun 2010.
  1. Program Committee Member, ACM Transactions on Knowledge Discovery from Data:
Special Issue on Large-Scale Data Mining: Theory and Applications, Mar 2010.
  1. Program Committee Member, Workshop on Feature Selection in Data Mining, to be
held in conjunction with the Pacific-Asia Conference on Knowledge Discovery and Data
Mining, Jun 2010.
  1. Program Committee Member, 21st Annual Symposium on Combinatorial Pattern Matching, Jun 2010.
  2. Program Committee Member, Pacific-Asia Conference on Knowledge Discovery and
Data Mining, Jun 2010.
  1. NSF IIS Review Panel, 2009.
  2. Program Committee Member, ICDM Workshop on Large-scale Data Mining: Theory
and Application, Dec 2009.
  1. Invited Reviewer, Neural Information Processing Systems Conference, Dec 2009.
  2. Co-organizer (with I. Ipsen), Randomized Algorithms in Linear Algebra, minisymposium in the SIAM Conference on Applied Linear Algebra, Oct 2009.
  3. Program Committee Member, ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, Aug 2009.
  1. Program Committee Member, International Conference on Artificial Intelligence and
Statistics, Apr 2009.
  1. Program Committee Member, Pacific-Asia Conference on Knowledge Discovery and
Data Mining, Apr 2009.
  1. NSF CDI Review Panel, 2009.
  2. Invited Reviewer, Neural Information Processing Systems Conference, Dec 2008.
  3. Co-organizer (with S. Das and M. Zaki), RPI Computer Science Day: Data Mining
and Machine Learning, Sep 2008.
  1. Technical Program Committee Member, NumAn 2008 Conference in Numerical Analysis, Sep 2008.
  2. Co-chair, Data-Centric Computing Group for the Visions for Theoretical Computer
Science Workshop, University of Washington in Seattle, May 2008.
  1. Technical Program Committee Member, Workshop on Data Mining Using Matrices
and Tensors, held in conjunction with ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, Aug 2008.
  1. Organizing Committee Member, Workshop on Algorithms for Modern Massive Datasets
(MMDS) II, Jun 2008.
  1. Organizing Committee Member, Workshop on Data Mining for Biomedical Informatics, held in conjunction with the SIAM Conference on Data Mining, Apr 2008.
  2. Technical Program Committee Member, SIAM Conference on Data Mining, Apr 2008.
  3. Technical Program Committee Member, NumAn 2007 Conference in Numerical Analysis, Sep 2007.
  4. Technical Program Committee Member, ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, Aug 2007.
  1. Organizing Committee Member, Workshop on Data Mining for Biomedical Informatics, held in conjunction with the SIAM Conference on Data Mining, Apr 2007.
  2. Co-organizer (with D. Freedman), RPI Computer Science Day: Aspects of Geometric
Computing, Oct 2006.
  1. NSF CCF review panel, 2006.
  2. Organizing Committee Member, Workshop on Algorithms for Modern Massive Datasets
(MMDS), Jun 2006.
  1. Program Committee Member, International Workshop on architectures, models and
infrastructures to generate semantics in Peer to Peer and Hypermedia Systems, in
conjunction with the 17th ACM Conference on Hypertext and Hypermedia, 2006.
  1. Technical Program Committee Member, ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, Aug 2006.
  1. Technical Program Committee Member, ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, Aug 2005.
  1. Technical Program Committee Member, Workshop on Peer to Peer and Service-Oriented
Hypermedia: Techniques and Systems, ACM Hypertext 2005.
  1. Technical Program Committee Member, NSF-RPI Workshop on Pervasive Computing,
Apr 2004.
Biographical Year of birth: 1975
Data Country of birth: Greece
Citizenship: USA, Greece
Updated May 6, 2017.

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