Publications

  • Suresh Venkatasubramanian, Philip Adler, Casey Falk, Sorelle A.Friedler, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith & Tionney Nix (2017). Auditing Black-box Models for Indirect Influence. Knowledge and Information Systems,. Vol. 54, 27. Published, 12/2017.
    https://link.springer.com/article/10.1007/s10115-0...
  • Suresh Venkatasubramanian, Danielle Ensign, Scott Neville & Arnab Paul (2017). The Complexity of Explaining Neural Networks Through (group) Invariants. In Algorithmic Learning Theory. 28th International Conference on Algorithmic Learning Theory. Vol. 76, 341-359. Published, 10/2017.
    http://proceedings.mlr.press/v76/ensign17a.html
  • A. Abdullah, S. Daruki, C. D. Roy, and S. Venkatasubramanian. Streaming veri cation of graph properties. In Algorithms and Computation: 27th International Symposium, ISAAC 2016, 2016. Published, 12/2016.
    https://arxiv.org/abs/1602.08162
  • P. Adler, C. Falk, S. A. Friedler, G. Rybeck, C. Scheidegger, B. Smith, and S. Venkata- subramanian. Auditing black-box models for indirect in uence. In Proc. IEEE International Conference on Data Mining, 2016. Published, 12/2016.
    https://arxiv.org/abs/1602.07043
  • J. Moeller, V. Srikumar, S. Swaminathan, S. Venkatasubramanian, and D. Webb. Continuous kernel learning. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, 2016. Published, 09/2016.
    http://svivek.com/research/publications/ckl-ecml20...
  • J. Moeller, S. Swaminathan, and S. Venkatasubramanian. A unifed view of localized kernel learning. In Proceedings of the SIAM conference on Data Mining, 2016. Published, 05/2016.
    https://arxiv.org/abs/1603.01374
  • Daruki, Samira, Justin Thaler, and Suresh Venkatasubramanian. "Streaming Verification in Data Analysis." Algorithms and Computation. Springer Berlin Heidelberg, 2015. 715-726. Published, 12/2015.
    http://link.springer.com/chapter/10.1007/978-3-662...
  • Feldman, Michael, et al. "Certifying and removing disparate impact." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. Published, 08/08/2015.
    http://arxiv.org/abs/1412.3756
  • Chakrabarti, Amit, et al. "Verifiable stream computation and arthur-merlin communication." Proceedings of the 30th Conference on Computational Complexity. Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2015. Published, 06/2015.
    http://eccc.hpi-web.de/report/2014/086/
  • Abdullah, Amirali, and Suresh Venkatasubramanian. "A directed isoperimetric inequality with application to Bregman near neighbor lower bounds." Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing. ACM, 2015. Published, 04/2015.
    http://arxiv.org/abs/1404.1191
  • A geometric algorithm for scalable multiple kernel learning. In 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014. Published, 04/16/2014.
    http://jmlr.org/proceedings/papers/v33/moeller14.p...
  • P. Chalermsook and S. Venkatasubramanian. Clustering with center constraints. In IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS), 2013. Published, 12/01/2013.
    http://www.cs.utah.edu/~suresh/web/2013/09/17/clus...
  • P. Raman and S. Venkatasubramanian. Power to the points: Validating data memberships in clusterings. In Proc. IEEE International Conference on Data Mining (ICDM), 2013. Published, 12/01/2013.
    http://www.cs.utah.edu/~suresh/web/2013/09/17/powe...
  • S. Venkatasubramanian. Moving heaven and earth: Distances between distributions. SIGACT News, 44(3):56–68, 2013. Published, 09/2013.
    http://www.cs.utah.edu/~suresh/web/2013/09/16/movi...
  • Y. Zhao, N. Patwari, J. Phillips, and S. Venkatasubramanian. Radio tomographic imaging and tracking of stationary and moving people via histogram difference. In 12th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN), 2013. Published, 04/2013.
    http://www.cs.utah.edu/~suresh/web/2012/07/18/radi...
  • Multiple target tracking with RF sensor networks. Maurizio Bocca, Ossi Kaltiokallio, Neal Patwari and Suresh Venkatasubramanian. IEEE Trans. Mobile Computing, PP(99). Published, 04/2013.
    http://www.cs.utah.edu/~suresh/web/2013/01/24/mult...
  • S. Venkatasubramanian. New developments in matrix factorization. SIGACT News, 44(1):70– 78, 2013. Published, 03/2013.
    http://www.cs.utah.edu/~suresh/web/2013/03/13/new-...
  • Fast Multiple Kernel Learning with Multiplicative Weight Updates. Published, 12/2012.
  • Sensor Network Localization for Moving Sensors. Published, 12/2012.
    http://www.cs.utah.edu/~suresh/web/2012/10/15/sens...
  • Efficient Protocols for Distributed Classification and Optimization. Published, 09/2012.
    http://www.cs.utah.edu/~suresh/web/2012/04/16/effi...
  • Approximate Bregman near neighbors in sublinear time: Beyond the triangle inequality. Published, 06/2012.
    http://www.cs.utah.edu/~suresh/web/2011/07/29/appr...
  • On minimizing budget and time in influence propagation over networks. Published, 03/2012.
    http://link.springer.com/article/10.1007%2Fs13278-...
  • Generating a diverse set of high-quality clusterings. In Proc. 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings (held in conjunction with ECML-PKDD 2011. Published, 09/2011.
    http://www.cs.utah.edu/~suresh/web/2011/07/29/gene...
  • Computing hulls and centerpoints in positive definite space. Algorithms and Data Structures Symposium (WADS). Published, 08/2011.
    http://www.cs.utah.edu/~suresh/web/2009/10/10/comp...
  • Comparing shapes and distributions using the kernel distance. 27th ACM Symposium on Computational Geometry. Published, 06/2011.
    http://www.cs.utah.edu/~suresh/web/2009/10/10/matc...
  • Active supervised domain adaptation. 14th International Conference on Artificial Intelligence and Statistics (AISTATS). Published, 04/2011.
    http://www.cs.utah.edu/~suresh/web/2011/07/29/acti...
  • Spatially-aware comparison and consensus for clusterings. SIAM International Conference on Data Mining. Published, 04/2011.
    http://www.cs.utah.edu/~suresh/web/2010/07/06/spat...
  • Online learning of tasks and their relationships. 14th International Conference on Artificial Intelligence and Statistics (AISTATS). Published, 04/2011.
    http://www.cs.utah.edu/~suresh/web/2010/05/26/acti...
  • Evaluating graph colorings on the gpu (poster). In 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming,. Published, 02/2011.
    http://www.cs.utah.edu/~suresh/web/2011/02/12/eval...
  • The Johnson-Lindenstrauss transform: An empirical study. Published, 01/2011.
    http://www.cs.utah.edu/~suresh/web/2010/10/05/the-...
  • A. Goyal, J. Jagarlamudi, H. Daume, and S. Venkatasubramanian. Sketch techniques for scaling distributional similarity to the web. In GEMS: Workshop on GEometrical Models of natural language Semantics (in conjunction with ACL 2010. Published, 09/2010.
  • A. Agarwal, J. Phillips, and S. Venkatasubramanian. Universal multidimensional scaling. In Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010. Published, 08/2010.
  • A. Goyal, J. Jagarlamudi, H. Daume, and S. Venkatasubramanian. Sketching techniques for large-scale nlp. In 6th Web as Corpus Workshop (in conjunction with NAACL-HLT, 2010. Published, 07/2010.
  • A. Saha, P. Rai, S. Venkatasubramanian, and H. Daume. Domain adaptation meets active learning. In Workshop on Active Learning For NLP (in conjunction with NAACL-HLT), 2010. Published, 07/2010.
  • H. Daume, P. Rai, A. Saha, and S. Venkatasubramanian. Active online multitask learning. In Budgeted Learning Workshop (in conjunction with ICML 2010),. Published, 07/2010.
  • A. Goyal, S. Venkatasubramanian, and H. Daume. Streaming for large scale NLP: Language modelling. In Proc. NAACL-HLT. Published, 2009.
    http://www.naaclhlt2009.org/
  • B. Amadi, M. Hadjieleftheriou, T. Seidl, D. Srivastava, and S. Venkatasubramanian. Type-based categorization of relational attributes. In Proc. 12th International Conference on Extending Database Technology (EDBT). Published, 2009.
    http://www.math.spbu.ru/edbticdt/
  • T. Dasu, S. Krishnan, D. Lin, S. Venkatasubramanian, and K. Yi. Change (detection) you can believe in: Finding distributional shifts in data. Published, 2009.
    http://ida09.liris.cnrs.fr/
  • P. Rai, H. Daume, and S. Venkatasubramanian. Streamed learning: One-pass SVMs. In Proc. 21st International Joint Conference on Arti cial Intelligence (IJCAI). Published, 2009.
    http://ijcai-09.org/
  • N. Koudas, A. Saha, D. Srivastava, and S. Venkatasubramanian. Metric functional dependencies. In 25th International Conference on Data Engineering (ICDE),. Published, 2009.
    http://i.cs.hku.hk/icde2009/
  • S. Krishnan and S. Venkatasubramanian. Approximate symmetry detection for 3D shapes with guaranteed error bounds. In IEEE Shape Modelling International (SMI). Published, 2009.
    http://cgcad.thss.tsinghua.edu.cn/SMI2009/home.htm
  • A. L. Buchsbaum, E. R. Gansner, C. M. Procopiuc, and S. Venkatasubramanian. Rectangular layouts and contact graphs. ACM Trans. Algorithms, 4(1):1-28, 2008. Published, 03/01/2008.

Research Statement

I'm interested in the social, legal and policy consequences of algorithmic decisiion making, and in designing technological solutions to make algorithms less biased and more fair. I'm also interested in many aspects of the theoretical foundations of learning and big data, including deep learning, and outsourced computations. In general, I'm interested in many aspects of unsupervised data mining (clustering) and algorithms for large data problems. 

Research Keywords

  • Machine Learning
  • Data Mining
  • Big data
  • Algorithms

Presentations

  • Computational Philosophy: Fairness in Automated Decision making. Keynote at 28th Interna- tional Symposium on Algorithms and Computation (ISAAC). Invited Talk/Keynote, Presented, 12/2017.
  • Sublinear verification in data analysis. Keynote at 27th Fall Workshop on Computational Geom- etry, Stony Brook University. Invited Talk/Keynote, Presented, 11/2017.
  • Computational Philosophy and Data Driven Science. Stony Brook University. Invited Talk/Keynote, Presented, 11/2017.
  • Ethical Auditing for Accountable Automated Decision-making. Oxford Internet Institute. Invited Talk/Keynote, Presented, 10/2017.
  • Panelist, United Nations Special Rapporteur on Privacy Meeting on Privacy, Personalization and AI. Invited Talk/Keynote, Presented, 09/2017.
  • Algorithmic fairness: From social good to mathematical framework. The Oringer Lecture, CalTech. Invited Talk/Keynote, Presented, 05/2017.
  • Algorithmic fairness: From social good to mathematical framework. Boise State University. Invited Talk/Keynote, Presented, 03/2017.
  • Algorithmic fairness: From social good to mathematical framework. Invited Talk/Keynote, Presented, 11/2016.
  • Algorithmic fairness: From social good to mathematical framework. Keynote address, 10th AAAI International Conference on the Web and Social Media (ICWSM), Cologne, Germany. Invited Talk/Keynote, Presented, 05/2016.
    http://www.icwsm.org/2016/program/speakers/
  • A mathematical theory of fairness. Automation, Prediction and Digital Inequalities. Department of Media and Communication, London School of Economics. Invited Talk/Keynote, Presented, 04/2016.
    http://blogs.lse.ac.uk/mediapolicyproject/2016/06/...
  • A Group-theoretic perspective on deep learning. Information Theory and Applications 2016, San Diego. Invited Talk/Keynote, Presented, 02/2016.
  • From Pigeons to Fano and Beyond - A Tutorial. Nexus of Information and Computation, February 2016. Other, Presented, 02/2016.
    http://csnexus.info/inequalitiestitles.html#Venkat...
  • Algorithmic Fairness. Microsoft Social Computing Symposium, New York. Invited Talk/Keynote, Presented, 01/2016.
    https://fuse.microsoft.com/scs/scs2016/tuesday-jan...
  • Challenges in Fairness-aware Data Mining. OSSMosis, Infosys, Pune, India. Invited Talk/Keynote, Presented, 08/2015.
  • Verifying outsourced computations in a few rounds. Invited Talk/Keynote, Presented, 06/2015.
    http://www.dis.uniroma1.it/~demetres/events/ads15/
  • Accountability in Data Mining. Invited Talk/Keynote, Presented, 12/2014.
    http://dcs.cse.iitk.ac.in/
  • Verifying outsourced computations in a few rounds. UC Irvine. Invited Talk/Keynote, Presented, 12/2014.
    http://www.cs.uci.edu/research/seminarseries/index...
  • Lower bounds for approximate near neighbor search with Bregman divergences. Workshop on Hashing, Copenhagen. Invited Talk/Keynote, Presented, 07/2014.
    http://www.diku.dk/summer-school-2014/workshop/
  • Lower bounds for approximate near neighbor search with Bregman divergences. Bertinoro Workshop on Sublinear Algorithms. Invited Talk/Keynote, Presented, 05/2014.
    http://www.wisdom.weizmann.ac.il/~robi/Bertinoro20...
  • Metaclustering. Haverford College. Invited Talk/Keynote, Presented, 04/2014.
    http://www.haverford.edu/calendar/details/255301
  • A Geometric Algorithm for Scalable Multiple Kernel Learning. Google, Inc. Invited Talk/Keynote, Presented, 04/2014.
  • The Shape of Information. Haverford College. Invited Talk/Keynote, Presented, 04/2014.
    http://www.haverford.edu/calendar/details/253842
  • Metaclustering. Yahoo!, Inc. Invited Talk/Keynote, Presented, 10/2013.
  • Power to the points: Validating data membership in clusterings. Workshop on Succinct Data Representations and Applications, Simons Institute of Theoretical Computer science. Invited Talk/Keynote, Presented, 09/2013.
  • Power to the points: Validating data membership in clusterings. Workshop on Succinct Data Representations and Applications, Simons Institute of Theoretical Computer science. Invited Talk/Keynote, Presented, 09/2013.
  • Metaclustering. Amazon, Inc. Invited Talk/Keynote, Presented, 04/2013.
  • Reverse line stabbing in galleries. Dagstuhl Seminar on Computational Geometry, Dagstuhl, Germany. Invited Talk/Keynote, Presented, 03/2013.
  • Approximate Bregman Near Neighbors. Max Planck Institute, Saarbrucken, Germany. Invited Talk/Keynote, Presented, 03/2013.
  • A Geometric Interpretation of Multiplicative-Weight-Updates for (Multiple) Kernel Learning. Information Theory And Applications Workshop, San Diego. Invited Talk/Keynote, Presented, 02/2013.
  • Clustering and Metaclustering. Summer School on Massive Data Mining. IT University, Copenhagen. Other, Presented, 08/2012.
  • GPU Algorithms. Summer School on Algorithms for Modern Parallel and Distributed Models. Aarhus University. Other, Presented, 08/2012.
  • Protocols for Distributed Learning. Workshop on Streaming Algorithms. Dortmund, Germany. Invited Talk/Keynote, Presented, 07/2012.
  • Protocols for Distributed Learning. From Data to Knowledge: Machine Learning with real- time and streaming applications, Berkeley, CA. Invited Talk/Keynote, Presented, 05/2012.
  • Protocols for Distributed Learning, NII Shonan Meeting on Large-scale Distributed Computa- tion, Japan. Invited Talk/Keynote, Presented, 01/2012.
  • The Geometry of Distributions. Computational Geometry and Learning Summer School, Paris, France. Other, Presented, 06/2011.
  • Dimensionality Reduction for Distributions: The Good and the Bad. Yahoo! Research. Invited Talk/Keynote, Presented, 05/2011.
  • Comparing Shapes using the Kernel Distance. Dagstuhl Workshop on Computational Geometry, Germany. Invited Talk/Keynote, Presented, 03/2011.
  • Dimensionality Reduction for Distributions: The Good and the Bad. CoE Lecture @ Montana State University. Invited Talk/Keynote, Presented, 02/2011.
  • Dimensionality Reduction for Distributions: The Good and the Bad. At ITA workshop on Information Theory and Applications, San Diego, CA. Invited Talk/Keynote, Presented, 02/2011.
  • Shapely Measures, Measures on Shapes, ... and one distance to rule them all. University of Illinois, Urbana-Champaign. Invited Talk/Keynote, Presented, 09/2010.
  • TUTORIAL: New Developments in the theory of clustering. 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010. Other, Presented, 07/2010.
  • TUTORIAL: Information Theory for Data Management. ACM Symposium on the Management of Data (SIGMOD) 2010. Other, Presented, 06/2010.
  • A Uni fed Algorithmic Framework for Multidimensional Scaling. The Ohio State University. Invited Talk/Keynote, Presented, 04/2010.
  • The Geometry of Clusterings, Hard and Soft. SIAM Data Mining Workshop on Clustering: Theory and Applications. Invited Talk/Keynote, Presented, 04/2010.
  • Non-standard Geometries and Data Analysis. Texas Tech University. Invited Talk/Keynote, Presented, 01/2010.
  • NSF Workshop on Electronic Design Automation: Past, Present and Future. Invited Talk/Keynote, Presented, 07/12/2009.
    http://cadlab.cs.ucla.edu/nsf09/
  • Non-standard Geometries and Data Analysis. Emerging Trends in Visual Computing (ETVC'08). Invited Talk/Keynote, Presented, 11/20/2008.
  • Robust Shape Matching on Manifolds. Fall Workshop on Computational Geometry. Invited Talk/Keynote, Presented, 11/01/2008.
  • Information-Theoretic Approaches For Taming Large Data. Harvey Mudd College. Invited Talk/Keynote, Presented, 03/01/2008.
  • Information-Theoretic Approaches For Taming Large Data. Biomedical Informatics Department, U. Utah. Invited Talk/Keynote, Presented, 02/01/2008.
  • P. T. Fletcher, S. Venkatasubramanian, and S. Joshi. Robust statistics on Riemannian manifolds via the geometric median. In Proc. Conference on Vision and Pattern Recognition (CVPR). Conference Paper, Other, 2008.
  • B. T. Dai, N. Koudas, D. Srivastava, A. K. H. Tung, and S. Venkatasubramanian. Validating multi-column schema matchings by type. In 24th International Conference on Data Engineering (ICDE), pages 120-129. Conference Paper, Other, 2008.

Research Groups

  • Dillon Lee, Graduate Student. School of Computing. 08/01/2017 - present.
  • Chitradeep Dutta Roy, Graduate Student. School of Computing. 08/01/2017 - present.
  • Jie (Claire) Zhang, Undergraduate Student. School of Computing. 05/01/2017 - 12/01/2018.
  • Sonam Choudhary, Graduate Student. School of Computing. 05/01/2017 - 05/01/2018.
  • Kaveri Gupta, Graduate Student. School of Computing. 01/01/2017 - 05/01/2018.
  • Danielle Ensign, Undergraduate Student. School of Computing. 01/01/2016 - 12/01/2017.
  • Mohsen Abbasi, Graduate Student. School of Computing. 09/01/2015 - present.
  • Ashkan Bashardoust, Graduate Student. School of Computing. 09/01/2015 - present.
  • Scott Neville, Undergraduate Student. School of Computing. 01/01/2015 - 12/01/2017.
  • Sarathkrishna Swaminathan, Graduate Student. School of Computing. 01/01/2015 - 12/31/2016.
  • Dustin Webb, Graduate Student. School of Computing. 01/01/2015 - present.
  • Jeff Phillips, Postdoc. School of Computing. 09/2009 - 08/2011.
  • Qiushi Wang, Visiting Student. School of Computing. 01/2010 - 09/2011.
  • Avishek Saha, Graduate Student. School of Computing. 09/2007 - 05/2012.
  • Swetha Machanavajhala, Graduate Student. School of Computing. 09/2011 - 05/2013.
  • Parasaran Raman, Graduate Student. School of Computing. 09/2009 - 10/2013.
  • John Moeller, Graduate Student. School of Computing. 09/2009 - 12/2015. http://www.cs.utah.edu/~moeller/.
  • Amirali Abdullah, Graduate Student. School of Computing. 09/2010 - 09/2015. http://www.cs.utah.edu/~amirali/.
  • Samira Daruki, Graduate Student. School of Computing. 08/2011 - 10/01/2017. https://sites.google.com/site/samiradaruki/home.
  • Nitin Yadav, Graduate Student. School of Computing. 01/2014 - 05/2015. http://www.cs.utah.edu/~nityadav/.
  • Dustin Webb, Graduate Student. School of Computing. 03/2015 - present. http://www.cs.utah.edu/~dustin/.

Geographical Regions of Interest

  • India

Software Titles

  • Auditing Black Box Models. Software to accompany the paper: https://arxiv.org/abs/1602.07043. https://github.com/algofairness/BlackBoxAuditing. Release Date: 12/2016.