I am an assistant professor of Computer Science at the University of Illinois (UIUC) . My research interests are broadly in simplifying and improving data analytics, i.e., helping users make better use of their data.
My work involves building real data analytics systems with principled foundations, designing algorithms (with formal guarantees) for the systems, as well as mining data obtained from such systems.
Aditya Parameswaran is an Assistant Professor in Computer Science at the University of Illinois (UIUC). He spent the 2013-14 year visiting MIT CSAIL and Microsoft Research New England, after completing his Ph.D. from Stanford University, advised by Prof. Hector Garcia-Molina. He is broadly interested in data analytics, with research results in human computation, visual analytics, information extraction and integration, and recommender systems.
Aditya is a recipient of the Arthur Samuel award for the best dissertation in CS at Stanford (2014), the SIGMOD Jim Gray dissertation award (2014), the SIGKDD dissertation award runner up (2014), a Google Faculty Research Award (2015), an "Excellent Instructor" award from Illinois (2016), the Key Scientific Challenges Award from Yahoo! Research (2010), four best-of-conference citations (VLDB 2010, KDD 2012, ICDE 2014, ICDE 2016), the Terry Groswith graduate fellowship at Stanford (2007), and the Gold Medal in Computer Science at IIT Bombay (2007). His research group is supported with funding from by the Siebel Energy Institute, the NIH, the NSF, and Google.
I am serving as an Area Chair for SIGMOD 2017.
I am currently serving on or have served on the Program Committees of: VLDB 2013-14-15, KDD 2015, SIGMOD 2014-15, WSDM 2015, WWW 2014, SOCC 2014, HCOMP 2014, ICDE 2014, and EDBT 2014.
I am an Associate Editor for SIGMOD Record. Please consider sending us your most controversial and/or interesting papers!
I am the SIGMOD 2016 Undergraduate Research Chair. Our competition has concluded; we had 3X the number of submissions this year compared to previous years.
Automatically recommending visualizations or visual summaries on very large volumes of data
Interactive querying of large datasets, keeping track of versions, while possibly sacrificing slightly on accuracy of query results
Using crowdsourcing to process and make sense of large volumes of data
DataHub (or "GitHub for Data") is a system that enables collaborative data science by keeping track of large numbers of versions and their dependencies compactly, and allowing users to progressively clean, integrate and visualize their datasets.