Aditya Parameswaran

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.

Biographical Sketch

Aditya Parameswaran is an Assistant Professor in Computer Science at the University of Illinois (UIUC). He is currently spending the year visiting MIT CSAIL and Microsoft Research NE, after completing his Ph.D. from Stanford University in Sept. 2013, 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 Computer Science at Stanford (2013), the SIGMOD Jim Gray dissertation award (2014), the Key Scientific Challenges Award from Yahoo! Research (2010), two best-of-conference citations (VLDB 2010 and KDD 2012), the Terry Groswith graduate fellowship at Stanford (2007), and the Gold Medal in Computer Science at IIT Bombay (2007).

Synergistic Activities

I am currently serving on or have served on the Program Committees of: VLDB 2013-14, WWW 2014, SIGMOD 2014-15, SOCC 2014, HCOMP 2014, ICDE 2014, and EDBT 2014.

Visual Analytics

Automatically recommending visualizations or visual summaries on very large volumes of data

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Approximate Analytics

Interactive querying of large datasets while sacrificing slightly on accuracy of query results

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Crowd-Powered Analytics

Using crowdsourcing to process and make sense of large volumes of data

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Information Extraction

Extracting information from the web, integrating it with existing information, and surfacing this information to users

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Recommendation Systems

Building scalable recommendation systems that take into account contextual information

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Recent Releases

Selected Projects


DataSift: A Crowd-Powered Search Engine

DataSift is a crowd-powered search engine that is useful for long or complex queries that traditional search engines have trouble with, or with queries that contain rich media, such as images or videos.


SeeDB: Automatic Visualization Recommendation

SeeDB automates the task of finding the right visualization for a query, significantly simplifying the laborious task of identifying appropriate visualizations.


Crowd Algorithms

Our work has developed a number of algorithms for gathering, processing, and understanding data obtained from humans (or crowds), while minimizing cost, latency, and error.


NeedleTail: A System for Browsing

NeedleTail is a system tuned towards instantly returning a small number (a "screenful") of query results very quickly on extremely large datasets.