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), the Key Scientific Challenges Award from Yahoo! Research (2010), three best-of-conference citations (VLDB 2010, KDD 2012 and ICDE 2014), the Terry Groswith graduate fellowship at Stanford (2007), and the Gold Medal in Computer Science at IIT Bombay (2007).
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.
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
Extracting information from the web, integrating it with existing information, and surfacing this information to users
Building scalable recommendation systems that take into account contextual information
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.