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). He spent a year as a PostDoc at MIT CSAIL following his PhD at Stanford University (2013), before starting at Illinois in August 2014. He develops systems and algorithms for interactive or "human-in-the-loop" data analytics, synthesizing techniques from database systems, data mining, and human computation. Aditya received the NSF CAREER Award (2017), the IEEE TCDE Early Career Award (2017), the Dean's Award for Research Excellence (2018) and the C. W. Gear Junior Faculty Award from Illinois (2017), multiple "best" Doctoral Dissertation Awards (from SIGMOD, SIGKDD, and Stanford in 2014), an "Excellent" Instructor award from Illinois (2016, 2018), a Google Faculty award (2015) and a Focused Research award (2017), and five best-of-conference citations (from conferences like VLDB, KDD, and ICDE, 2010-17).

Academic Positions

  •  Affiliated Faculty, Beckman Institute for Advanced Science and Technology, June 2016--
  • Affiliated Faculty, Institute for Genomic Biology, June 2016--
  • Tenure Track Assistant Professor, Computer Science, August 2014--

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.

Teaching Statement

I have taught a variety of graduate and undergraduate classes during my time at the University of Illinois. is includes CS598 (Human-in-the-Loop Data Management)￿three times, CS511 (Advanced Data Management)￿ once, and CS411 (Database Systems)￿twice. I received a mention in the ￿List of Instructors Rated as Excellent by Students￿ for my CS598 class in Fall 2015 and Fall 2017, and I have managed to maintain a consistent performance with scores of around 4 or greater (out of 5) almost throughout, with an average of 4.35 as an instructor. Student projects from my CS598 class have been published as papers at venues like SIGMOD, VLDB, and KDD.

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 70 papers in the top-tier venues of these fields with an h-index of 25+. My research begins with a thorough exploration of the foundational principles, followed by the design of practical, scalable, and usable systems and algorithms.

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

  • Y. Gao , A. Parameswaran, J. Peng. On the interpretability of conditional probability estimates in the agnostic setting Electronic Journal of Statistics, Volume 11, Number 2, January 2018.
  • M. Joglekar , H. Garcia-Molina, and A. Parameswaran. Interactive Data Exploration with Smart Drill- down (Extended Version). IEEE Transactions on Knowledge and Data Engineering (TKDE), March 2017.
  • 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

  • S. Macke , Y. Zhang , S. Huang , A. Parameswaran. Adaptive Sampling for Rapidly Matching Histograms. VLDB￿18: 44th Int￿l Conf on Very Large Data Bases, Rio De Janeiro, Brazil, 2018.
  • Y. Gao , S. Huang , A. Parameswaran. Navigating the Data Lake with Datamaran: Automatically Extracting Structure from Log Datasets. SIGMOD ￿18: ACM SIGMOD Int￿l Conf. on Management of Data, Houston, USA, 2018. Acceptance Rate: ∼20%.
  • M.Bendre ,V.Venkataraman ,X.Zhou , K.Chang, A.Parameswaran.Towards a HolisticIntegration of Spreadsheets with Databases: A Scalable Storage Engine for Presentational Data Management. ICDE ￿18: 34th Int￿l Conf on Data Engineering, Paris, France, 2018.
  • S. Rahman , M. Aliakbarpour, H. Kong , E. Blais, K. Karahalios, A. Parameswaran, and R. Rubinfeld. I￿ve Seen Enough: Incrementally Improving Visualizations to Support Rapid Decision Making. VLDB ￿17: 43rd Int￿l Conf on Very Large Data Bases, Munich, Germany, 2017. Acceptance Rate: ∼20%.
  • S.Huang ,L.Xu ,J.Liu , A.Elmore, A.Parameswaran. OrpheusDB: Bolt-on Versioning for Relational Databases. VLDB ’17: 43rd Int’l Conf on Very Large Data Bases, Munich, Germany, 2017. Acceptance Rate: ∼20%.
  • A. Jain , A. Das Sarma , A. Parameswaran, and J. Widom. Understanding Workers, Developing Effective Tasks, and Enhancing Marketplace Dynamics: A Study of a Large Crowdsourcing Marketplace. VLDB ￿17: 43rd Int￿l Conf on Very Large Data Bases, Munich, Germany, 2017. Acceptance Rate: ∼20%.
  • T.Siddiqui , A.Kim , J.Lee , K.Karahalios, and A.Parameswaran. Effortless Visual Data Exploration with Zenvisage: An Expressive and Interactive Visual Analytics System. VLDB ￿17: 43rd Int￿l Conf on Very Large Data Bases, Munich, Germany, 2017. Acceptance Rate: ∼20%.
  • T. Rekatsinas, M. Joglekar , H. Garcia-Molina, A. Parameswaran, and C. Re. SLiMFast: Guaranteed Results for Data Fusion and Source Reliability SIGMOD ￿17: ACM SIGMOD Int￿l Conf. on Management of Data, Raleigh, USA, 2017 Acceptance Rate: ∼20%.
  • 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.

Pending Articles

  • M.Bendre ,K.Mack ,S.Rahman ,T. Wattanawaroon , Y.Liu, Y.Lu, P.Yang, S.Zhou , X.Zhou , K. Chang, K. Karahalios, A. Parameswaran Towards Scalable, Navigable, and Expressive Spreadsheets: DataSpread to the Rescue! Technical Report (Under review at VLDB 2018).
  • T. Siddiqui , P. Luh , Z. Wang , K.Karahalios, A. Parameswaran ShapeSearch: Flexible Pattern-based Querying of Trend Line Visualizations Technical Report (Under review at VLDB 2018).
  • D. Xin , L. Ma , J. Liu , S. Macke , S. Song , A. Parameswaran, Helix: Accelerating Human-in-the-loop Machine Learning Technical Report (Under review at VLDB 2018).
  • T. Wattanawaroon , S. Macke , A. Parameswaran. Towards a eory of Data-Di : Optimal Synthesis of Succinct Data Modi cation Scripts. Technical Report (Under review at VLDB 2018). December 2017
  • D. Lee , J. Lee , T. Siddiqui , J. Kim , K. Karahalios, A. Parameswaran. Accelerating Scientific Data Exploration via Visual Query Systems (Under submission at TVCG 2018). Technical Report. September 2017

Invited Lectures

  • U Penn Distinguished Lecture: November 2017
  • U Waterloo Distinguished Lecture: October 2017
  • 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

Other Publications

  • G. Su Yilmaz, T. Wattanawaroon , L. Xu , A. Nigam , A. Elmore, A Parameswaran. DataDiff : User- Interpretable Data Transformation Summaries for Collaborative Data Analysis (Demo). SIGMOD ￿18: ACM SIGMOD Int￿l Conf. on Management of Data, Houston, USA, 2018.
  • L. Xu , S.Huang , S. Hui , A. Elmore, A. 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 Best Demo Honorable Mention.
  • M. Bendre , B. Sun , X. Zhou , D. Zhang , S. Lin , K. Chang, and A. 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 A. 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, A.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, A. Parameswaran. GeoHashViz: Interactive Analytics for Mapping Spatiotemporal Di usion of Twitter Hashtags (Poster). XSEDE ￿15, USA, 2015.
  • A. Das Sarma , A. 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.
  • A. 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, A. Parameswaran, and N. Polyzotis. SeeDB: Automatically Generating Query Visualizations (Demo), VLDB ￿14: 40th Int￿l Conf on Very Large Data Bases, Hangzhou, China, 2014.
  • A. Parameswaran, M. H. Teh, H. Garcia-Molina, and J. Widom. DataSift : A Crowd-Powered SearchToolkit (Demo), SIGMOD ￿14: ACM SIGMOD Int￿l Conf. on the Management of Data, Snowbird, USA, 2014.
  • H. Park, R. Pang, A. 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.
  • S. Guo, A. Parameswaran, and H. Garcia-Molina. So Who Won? Dynamic Max Discovery with the Crowd (Poster), CrowdConf ￿11: 2nd Crowdsourcing Conference, San Francisco, USA, 2011.

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

  • NIH BD2K Machine Learning Working Group, 2016￿
  • NIH BD2K Commons Working Group, 2015￿
  • 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

Presentations

  • Invited 3-hour Tutorial at the HCOMP (Human Computation and Crowdsourcing) Conference 2016, titled ￿Crowdsourced Data Management: Industry and Academic Perspectives￿, with Adam Marcus, 2016.

Other Scholarly Activities

  • Software Release: Populace: Software releases of optimized implementations of various crowdsourced data processing algorithms and systems. 2015. URL: http://populace-org.github.io
  • Software Release: Squish: A near-optimal structured data compression system. Squish identi es correlations between attributes to compress relational datasets both vertically as well as horizontally. 2016. URL: https://github.com/Preparation-Publication-BD2K/db compress
  • Software Release: Zenvisage: An ￿effortless￿ data visualization tool. Zenvisage can automatically identify and recommend visualizations that match desired user patterns. The user can specify at a high level what they are looking for either via interactions or via a query language (ZQL), and the system will perform the necessary computation to identify these visualizations. 2016. URL: http://zenvisage.github.io
  • Software Release: OrpheusDB: A relational dataset versioning system. OrpheusDB is built on top of standard relational databases, thus it inherits much of the same bene ts of relational databases, while also compactly storing, tracking, and recreating versions on demand, all very e ciently. 2016. URL: http://orpheus-db.github.io
  • Software Release: DataSpread: A Spreadsheet-Database Hybrid. DataSpread has a spreadsheet frontend, and a database backend. DataSpread inherits the exibility and ease-of-use of spreadsheets, as well as the scalability and power of databases, and scales to billions of cells seamlessly. 2016. URL: http://dataspread.github.io

Teaching Honors

  • Listed in "Instructors Rated as Excellent by their Students" (2017)
  • Listed in "Instructors Rated as Excellent by their Students" (2015)

Research Honors

  • Yahoo! Key Scientific Challenges Research Award (2010)
  •  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)
  • Best Paper Citation: Conference on Artificial Intelligence and Statistics for the paper "On the Interpretability of Conditional Probability Estimates in the Agnostic Setting" (2017)
  •  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)
  • Selected as an ACM Heidelberg Laureate (2013)
  • Google Faculty Research Award (2015)
  • NSF CAREER Award (2017)
  • IEEE TCDE (Technical Committee for Data Engineering) Early Career Award (2017)

Other Honors

  • Terry Groswith School of Engineering Fellowship, Stanford University (2007)
  • IIT Bombay Silver Medal and Jayati Deshmukh Gold Medal (2007)