Faculty Profile

Aditya G Parameswaran

Computer Science
Aditya G Parameswaran
Aditya G Parameswaran
Assistant Professor
2114 Siebel Center for Comp Sci
201 N. Goodwin Ave.
Urbana Illinois 61801
(217) 244-1408

Primary Research Area

  • Database and Information Systems

Education

  • B.Tech in Computer Science and Engineering, IIT Bombay, 2007
  • Ph.D. in Computer Science, Stanford University, 2013

Biography

Aditya Parameswaran is an Assistant Professor in Computer Science at the University of Illinois (UIUC), with affiliate appointments at the Institute for Genomic Biology and the Beckman Interdisciplinary Institute for Advanced Science and Technology. He spent a year as a PostDoc at MIT following his PhD at Stanford University, before starting at Illinois in August 2014. He develops systems and algorithms for "human-in-the-loop" data analytics, synthesizing techniques from data mining, database systems, and human computation. He has received multiple dissertation awards (from SIGMOD, SIGKDD, and Stanford), an "Excellent" Lecturer award from Illinois, a Google Faculty award, the Key Scientific Challenges award from Yahoo!, four best-of-conference citations, and a Gold Medal from IIT Bombay. His research group is supported with funding from the Siebel Energy Institute, Toyota, Adobe, the NIH (2x), the NSF (3x), and Google.

Academic Positions

  •  Affiliated Faculty, Beckman Institute for Advanced Science and Technology
  • Affiliated Faculty, Institute for Genomic Biology
  • Tenure Track Assistant Professor, Computer Science

For more information

Other Professional Employment

  • Microsoft Research New England, Cambridge, MA. Consulting Researcher. September 2013￿January 2014.
  • Massachussetts Institute of Technology, Cambridge, MA. Postdoctoral Researcher. Computer Science and Artifical Intelligence Lab. September 2013￿August 2014.

Course Development

  • Development of "Human-in-the-loop Data Management": a graduate class covering the human aspects of data management. It considered the dual roles of humans in the data science space---both as humans analyzing the data (data analysts), and as humans processing the data (crowdsourced workers). This class has been taught twice, and I received an "Excellent Instructor" award for the class.

Short Courses

  • Development of "Crowdsourced Data Processing": a tutorial on the use of crowdsourcing for processing large volumes of data efficiently. This tutorial was conducted at HCOMP (The Human Computation Conference) in 2016.

Research Statement

My work centers on the design of interactive or human-in-the-loop data analytics systems by synthesizing techniques from multiple fields: databases, data mining, and human computation. I￿ve published over 60 papers in the top-tier venues of these fields with an h-index of 23. My research begins with a thorough exploration of the foundational principles, followed by the design of practical, scalable, and usable systems and algorithms.

Graduate Research Opportunities

I'm looking for Ph.D. students --- both students interested in building data analytics systems, as well as students interested in theoretical and algorithmic questions.

Research Interests

  • Data Management and Mining
  • Information Extraction and Integration
  • Crowdsourcing
  • Visual and Interactive Data Analytics

Books Authored or Co-Authored (Original Editions)

  • A. Marcus and A. Parameswaran, Crowdsourced Data Management: Industry and Academic Perspectives, Foundations and Trends in Databases Series, Vol. 6: No. 1-2, pp 1-161, Now Publishers, December 2015.

Selected Articles in Journals

  • Aditya Parameswaran, A. Das Sarma , and V. Venkataraman . Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Management. IEEE Data Engineering Bulletin, December 2016
  • M. Vartak , S. Huang , T. Siddiqui , S. Madden, and A. Parameswaran. Towards Visualization Recommendation Systems, SIGMOD Record, December 2016.
  • H. Garcia-Molina, M. Joglekar, A. Marcus, A. Parameswaran, and V. Verios. Challenges in Data Crowdsourcing. IEEE Transactions on Knowledge and Data Engineering (TKDE), January 2016
  • K. Bellare, S. Iyengar, Aditya Parameswaran, and V. Rastogi. Active Sampling for Entity Matching with Guarantees, ACM Transactions on Knowledge Discovery from Data, Volume 7(3), September 2013.
  • H. Park, R. Pang, Aditya Parameswaran, H. Garcia-Molina, N. Polyzotis, and J. Widom. An Overview of the Deco System: Data Model and Query Language; Query Processing and Optimization, SIGMOD Record, Volume 41, December 2012.
  • G. Koutrika, H. Garcia-Molina, and Aditya Parameswaran. Information Seeking: Convergence of Search, Recommendations, and Advertising, Communications of the ACM (CACM), November 2011.
  • Aditya Parameswaran, P. Venetis, and H. Garcia-Molina. Recommendation Systems with Complex Constraints: A Course Recommendation Perspective, ACM Transactions on Information Systems (TOIS), Volume 29(4), November 2011.
  • B. Berkovitz, F. Kaliszan, G. Koutrika, H. Liou, Aditya Parameswaran, P. Venetis, Z. Zadeh, and H. Garcia-Molina. Social Sites Research Through CourseRank, SIGMOD Record, Volume 38, December 2009.

Articles in Conference Proceedings

  • Y. Gao , Aditya Parameswaran, and J. Peng. On the Interpretability of Conditional Probability Estimates in the Agnostic Setting. AISTATS ￿17: Conf on Artificial Intelligence and Statistics, Ft. Lauderdale, USA, 2017. Acceptance Rate: ∼30%.
  • L. Xu , S.Huang , S. Hui , A. Elmore, Aditya Parameswaran. OrpheusDB: A Light-weight Approach to Relational Dataset Versioning (Demo). SIGMOD ￿17: ACM SIGMOD Int￿l Conf. on Management of Data, Raleigh, USA, 2017
  • T. Siddiqui , J. Lee , A. Kim , E. Xue , X. Yu , S. Zou , L. Guo , C. Liu , C. Wang , K. Karahalios, and Aditya Parameswaran. Fast-forwarding to Desired Visualizations with Zenvisage. CIDR ￿17: Conf. on Innovative Data Management (CIDR), Chaminade, USA, 2017.
  • T. Siddiqui , X. Ren, Aditya Parameswaran, and Jiawei Han. FacetGist: Collective Extraction of Document Facets in Large Technical Corpora, CIKM ￿16: 25th Int￿l Conf. on Information and Knowledge Man- agement, Indianapolis, USA, 2016. Acceptance Rate: 23%.
  • M. Maddox, D. Goehring, A. Elmore, S. Madden, Aditya Parameswaran, and A. Deshpande. Decibel: The Relational Dataset Branching System. VLDB ￿16: 42nd Int￿l Conf on Very Large Data Bases, New Delhi, India, 2016. Acceptance Rate: ∼20%.
  • M. Vartak , S. Rahman , S. Madden, Aditya Parameswaran, and N. Polyzotis. SeeDB: Efficient Data- Driven Recommendations to Support Visual Analytics. VLDB ￿16: 42nd Int￿l Conf on Very Large Data Bases, New Delhi, India, 2016. Acceptance Rate: ∼20%.
  • Y. Gao and Aditya Parameswaran. Squish: Near-optimal Compression for Archival of Relational Datasets. KDD ￿16: 22nd ACM SIGKDD Int￿l Conf. on Knowledge Discovery and Data Mining, San Francisco, USA, 2016. Acceptance Rate: 6%.
  • A. Das Sarma , Aditya Parameswaran, and J. Widom. Towards Globally Optimal Crowdsourcing Quality Management. SIGMOD ￿16: ACM SIGMOD Int￿l Conf. on Management of Data, San Francisco, USA, 2016 Acceptance Rate: 19%.
  • M. Joglekar , H. Garcia-Molina, and Aditya Parameswaran. Interactive Data Exploration with Smart Drill-down. ICDE ￿16: 32nd Int￿l Conf on Data Engineering, Helsinki, Finland, 2016. Acceptance Rate: 25%. Invited to Special Issue of TKDE for ICDE 2016 Best Papers.
  • A. Das Sarma , A. Jain , A. Nandi, Aditya Parameswaran, and J. Widom. Surpassing Humans and Computers with JellyBean: Crowd-Vision-Hybrid Counting Algorithms. HCOMP ￿15: 3rd AAAI Int￿l Conf. on Human Computation and Crowdsourcing, San Diego, USA, 2015.
  • M. Vartak , S. Huang , T. Siddiqui , S. Madden, and A. Parameswaran. Towards Visualization Recommendation Systems, DSIA ￿15: Workshop on Interactive Analysis, Chicago, 2015.
  • S. Bhattacherjee, A. Chavan, S. Huang , A. Deshpande, and Aditya Parameswaran. Principles of Dataset Versioning: Exploring the Recreation/Storage Tradeoff . VLDB ￿15: 41st Int￿l Conf on Very Large Data Bases, Hawaii, USA, 2015. Acceptance Rate: 21%.
  • A. Kim , E. Blais, Aditya Parameswaran, P. Indyk, S. Madden, and R. Rubinfeld. Rapid Sampling for Visualizations with Ordering Guarantees. VLDB ￿15: 41st Int￿l Conf on Very Large Data Bases, Hawaii, USA, 2015. Acceptance Rate: 21%.
  • Y. Gao and Aditya Parameswaran. Finish Them!: Pricing Algorithms for Human Computation, VLDB ￿15: 41st Int￿l Conf on Very Large Data Bases, Hawaii, USA, 2015. Acceptance Rate: 21%.
  • M. Bendre , B. Sun , X. Zhou , D. Zhang , S. Lin , K. Chang, and Aditya Parameswaran. Data- Spread: Unifying Databases and Spreadsheets (Demo) VLDB ￿15: 41st Int￿l Conf on Very Large Data Bases, Hawaii, USA, 2015.
  • M. Joglekar , H. Garcia-Molina, and Aditya Parameswaran. Smart Drill-down: A New Data Exploration Operator (Demo). VLDB ￿15: 41st Int￿l Conf on Very Large Data Bases, Hawaii, USA, 2015.
  • A. Bhardwaj, A. Deshpande, A. Elmore, D. Karger, S. Madden, Aditya Parameswaran, H. Subramanyam, E. Wu, and R. Zhang. Collaborative Data Analytics with Datahub (Demo). VLDB ￿15: 41st Int￿l Conf on Very Large Data Bases, Hawaii, USA, 2015.
  • S. Koltani, S. Wang, Aditya Parameswaran. GeoHashViz: Interactive Analytics for Mapping Spatiotemporal Diffusion of Twitter Hashtags (Poster). XSEDE ￿15, USA, 2015.
  • H. Zhuang , Aditya Parameswaran, D. Roth, and J. Han. Debiasing Crowdsourced Batches, KDD ￿15: 21st ACM SIGKDD Int￿l Conf. on Knowledge Discovery and Data Mining, Sydney, Australia, 2015.Acceptance Rate: 20%.
  • A. Chavan, S. Huang , A. Deshpande, A. Elmore, S. Madden, and Aditya Parameswaran. Towards a Unified Query Language for Provenance and Versioning, TAPP￿15: 7th Int￿l Conf. on Theory and Practice of Provenance, Edinburgh, Scotland, 2015.
  • M. Joglekar , H. Garcia-Molina, Aditya Parameswaran, and C. Re. Exploiting Correlations for Expensive Predicate Evaluation, SIGMOD ￿15: ACM SIGMOD Int￿l Conf. on Management of Data, Melbourne, Australia, 2015 Acceptance Rate: 26%.
  • M. Joglekar , H. Garcia-Molina, and Aditya Parameswaran. Comprehensive and Reliable Crowd Assessment Algorithms, ICDE ￿15: 31st Int￿l Conf on Data Engineering, Seoul, Korea, 2015. Acceptance Rate: 25%.
  • A.Bhardwaj, S. Bhattacherjee, A. Chavan, A. Deshpande, A. Elmore, S. Madden, and Aditya Parameswaran. DataHub: Collaborative Data Science & Dataset Version Management at Scale, CIDR ￿15: Conf. on Innovative Data Management (CIDR), Asilomar, USA, 2015.
  • A. Das Sarma, Aditya Parameswaran, and J. Widom. Optimal Worker Quality and Answer Estimates in Crowd-Powered Filtering and Rating (Short Paper). HCOMP ￿14: 2nd Int￿ Conf. on Human Computation and Crowdsourcing, Pittsburgh, USA, 2014.
  • Aditya Parameswaran, N. Polyzotis, and H. Garcia-Molina. SeeDB: Visualizing Database Queries Efficiently (Vision), VLDB ￿14: 40th Int￿l Conf on Very Large Data Bases, Hangzhou, China, 2014.
  • M. Vartak , S. Madden, Aditya Parameswaran, and N. Polyzotis. SeeDB: Automatically Generating Query Visualizations (Demo), VLDB ￿14: 40th Int￿l Conf on Very Large Data Bases, Hangzhou, China, 2014.
  • Aditya Parameswaran, M. H. Teh, H. Garcia-Molina, and J. Widom. DataSift : A Crowd-Powered Search Toolkit (Demo), SIGMOD ￿14: ACM SIGMOD Int￿l Conf. on the Management of Data, Snowbird, USA, 2014.
  • Aditya Parameswaran, S. Boyd, H. Garcia-Molina, A. Gupta, N. Polyzotis, and J. Widom. Optimal Crowd-Powered Rating and Filtering Algorithms, VLDB ￿14: 40th Int￿l Conf on Very Large Data Bases, Hangzhou, China, 2014. Acceptance Rate: ∼20%.
  • A. Das Sarma, Aditya Parameswaran, H. Garcia-Molina, and A. Halevy. Crowd-Powered Find Algorithms, ICDE ￿14: 30th Int￿l Conf on Data Engineering, Chicago, USA, April 2014. Acceptance Rate: 20%. Invited to Special Issue of TKDE for ICDE 2014 Best Papers.
  • Aditya Parameswaran, M. H. Teh, H. Garcia-Molina, and J. Widom. DataSift: An Expressive and Accurate Crowd-Powered Search Toolkit, HCOMP ￿13: 1st AAAI Int￿l Conf. on Human Computation and Crowdsourcing, Palm Springs, USA, 2013. Acceptance Rate: 30%.
  • Aditya Parameswaran, R. Kaushik, and A. Arasu. Efficient Parsing-based Search over Databases,CIKM ￿13: 22nd Int￿l Conf on Information and Knowledge Management, Burlingame, USA, 2013. Acceptance Rate: 16.8%.
  • M. Joglekar, H. Garcia-Molina, and Aditya Parameswaran. Evaluating the Crowd with Confidence, KDD ￿13: 19th ACM SIGKDD Int￿l Conf. on Knowledge Discovery and Data Mining, Chicago, USA, 2013. Acceptance Rate: 17%.
  • N. Dalvi, Aditya Parameswaran, and V. Rastogi. Minimizing Uncertainty in Pipelines, NIPS ￿12: 25th Int￿l Conf on Neural Information Processing Systems, Tahoe, Nevada, USA, 2012.
  • Aditya Parameswaran, H. Park, H. Garcia-Molina, N. Polyzotis, and J. Widom. Deco: Declarative Crowdsourcing, CIKM ￿12: 21st Int￿l Conf on Information and Knowledge Management, Maui, USA, 2012. Acceptance Rate: 13.4%.
  • H. Park, R. Pang, Aditya Parameswaran, H. Garcia-Molina, N. Polyzotis, and J. Widom. Deco: A System for Declarative Crowdsourcing (Demo), VLDB ￿12: 38th Int￿l Conf on Very Large Data Bases, Istanbul, Turkey, 2012.
  • K. Bellare, S. Iyengar, Aditya Parameswaran, and V. Rastogi. Active Sampling for Entity Matching, KDD ￿12: 18th ACM SIGKDD Int￿l Conf. on Knowledge Discovery and Data Mining, Beijing, China, 2012. Acceptance Rate: 18%. Invited to Special Issue of TKDD for KDD 2012 Best Papers.
  • S. Guo, Aditya Parameswaran, and H. Garcia-Molina. So Who Won? Dynamic Max Discovery with the Crowd, SIGMOD ￿12: ACM SIGMOD Int￿l Conf. on the Management of Data, Scottsdale, USA, 2012. Acceptance Rate: 17%.
  • Aditya Parameswaran, H. Garcia-Molina, H. Park, N. Polyzotis, A. Ramesh, and J. Widom. Crowd-Screen: Algorithms for Filtering Data with Humans SIGMOD ￿12: ACM SIGMOD Int￿l Conf. on the Management of Data, Scottsdale, USA, 2012. Acceptance Rate: 17%.
  • F. Afrati, A. Das Sarma, D. Menestrina, Aditya Parameswaran, and J. D. Ullman. Fuzzy joins using MapReduce, ICDE ￿12: 28th Int￿l Conf. on Data Engineering, Washington DC, USA, 2012. Acceptance Rate: 24%
  • Aditya Parameswaran, N. Dalvi, H. Garcia-Molina, and R. Rastogi. Optimal Schemes for Robust Web Extraction, VLDB ￿11: 37th Int￿l Conf. on Very Large Data Bases, Seattle, USA, 2011. Acceptance Rate: 18.1%.
  • Aditya Parameswaran, A. Das Sarma, H. Garcia-Molina, N. Polyzotis, and J. Widom. Human-assisted Graph Search: It￿s Okay to Ask Questions, VLDB ￿11: 37th Int￿l Conf. on Very Large Data Bases, Seattle, USA, 2011. Acceptance Rate: 18.1%.
  • Aditya Parameswaran and N. Polyzotis. Answering Queries using Databases, Humans, and Algorithms, CIDR ￿11: Conf. on Innovative Data Management (CIDR), Asilomar, USA, 2011.
  • Aditya Parameswaran, H. Garcia-Molina, and J. D. Ullman. Evaluating, Combining, and Generalizing Recommendations with Prerequisites, CIKM ￿10: 19th Int￿l Conf. on Information and Knowledge Management, Toronto, Canada, 2010. Acceptance Rate: 13%.
  • Aditya Parameswaran, H. Garcia-Molina, and A. Rajaraman. Towards the Web of Concepts: Extracting Concepts from Large Datasets, VLDB ￿10: 36th Int￿l Conf. on Very Large Data Bases, Singapore, 2010. Acceptance Rate: 18.4%. Invited to Special Issue of VLDB Journal for VLDB 2010 Best Papers.
  • Aditya Parameswaran, G. Koutrika, B. Berkovitz, and H. Garcia-Molina. Recsplorer: Recommendation Algorithms Based on Precedence Mining, SIGMOD ￿10: ACM SIGMOD Int￿l Conf. on the Management of Data, Indianapolis, USA, 2010. Acceptance Rate: 21%.
  • A. Das Sarma, Aditya Parameswaran, H. Garcia-Molina, and J. Widom. Synthesizing View Definitions from Data, ICDT ￿10: 13th Int￿l Conf. on Database eory, Lausanne, Switzerland, 2010. Acceptance Rate: 36%.
  • Aditya Parameswaran and H. Garcia-Molina. Recommendations with Prerequisites RecSys ￿09: 3rd ACM Conf. on Recommender Systems, New York, USA, 2009. Acceptance Rate: 43%.
  • E. Sadikov, Aditya Parameswaran, and P. Venetis. Blogs as Predictors of Movie Success, ICWSM ￿09: AAAI Conf. on Weblogs and Social Media, San Jose, USA, 2009.

Invited Lectures

  • IBM TJ Watson Invited Lecture: December 2016
  • Northwestern EECS Distinguished Lecture: November 2016
  • Midwest BigData Workshop: October 2016
  • U. Illinois BigData Day: October 2016
  • BIRS-CMO Workshop on Theory of Crowds and Networks: August 2016
  • SIGKDD EI (Enterprise Intelligence) Workshop Keynote: August 2016
  • National Center for Supercomputing Applications: August 2016
  • University of Texas-Austin: November 2015
  • l University of Michigan: October 2015
  • l NIH Center Meeting: August 2015
  • Google Mountain View Invited Talk: September 2014
  • SIGKDD Dissertation Award Talk: August 2014
  • SIGKDD IDEA (Interactive Data Exploration and Analysis) Workshop Invited Keynote: August 2014
  • SIGMOD Jim Gray Award Keynote: June 2014
  • UMass Amherst Invited Talk: April 2014
  • MIT Data Analytics Workshop: April 2014
  • INFORMS Conference Invited Talk: October 2013

Magazine Articles

  • MIT Tech Review on Where Siri Has Trouble Hearing, a Crowd of Humans Could Help, March 2013.
  • WP.PL, Tom￿s Hardware, Mulfin on Datasift: An Internet Search Engine better than Google, December 2013.
  • New Scientist on Find the Ungoogleable with a Crowdsourced Search Engine, December 2013.

Conferences Organized or Chaired

  • Area Chair, SIGMOD 2017
  • Chair, SIGMOD Undergraduate Research Competition, 2016. Via increased advertising and word of mouth, achieved an increase of 300% in the number of submissions.
  • Chair, Human-in-the-loop Data Analytics Workshop (HILDA) at SIGMOD, 2017.
  • Associate Chair, Workshops and Tutorials, 1st HCOMP (Human Computation Conference) 2013

Teaching Honors

  • Listed in "Instructors Rated as Excellent by their Students" (2015)

Research Honors

  •  Best Paper Citation: Conference on Very Large Data Bases for the paper "Towards the Web of Concepts: Extracting Concepts from Large Datasets" ( 2010)
  •  Best Paper Citation: International Conference on Knowledge Discovery from Databases and Data Mining for the paper "Active Sampling for Entity Matching" ( 2012)
  •  Best Paper Citation: International Conference on Data Engineering for the paper "Crowd-Powered Find Algorithms" ( 2014)
  • ￿Best Paper Citation: International Conference on Data Engineering for the paper "Interactive Data Exploration with Smart Drill-Down" (￿2016)
  •  SIGKDD (Special Interest Group for Knowledge Discovery from Data and Data Mining) Best Dissertation Award Runner up ( 2013)
  •  SIGMOD (Special Interest Group for the Management of Data) Jim Gray Best Dissertation Award ( 2013)
  •  Stanford University's Arthur Samuel Best Dissertation Award in Computer Science ( 2013)
  • Yahoo! Key Scientific Challenges Research Award (2010)
  • Selected as an ACM Heidelberg Laureate (2013)
  • Google Faculty Research Award (2015)
  • NSF CAREER Award (2017)