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), with affiliate appointments at the Institute for Genomic Biology and the Beckman Institute for Advanced Science and Technology. He spent a year as a PostDoc at MIT CSAIL 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 database systems, data mining, and human computation.
He has received the NSF CAREER Award (2017), the TCDE Early Career Award (2017), the Dean's Excellence in Research Award (2018) and the C. W. Gear Junior Faculty Award from the University of Illinois (2017), multiple "best" Doctoral Dissertation Awards (from SIGMOD, SIGKDD, and Stanford in 2014), an "Excellent" Lecturer award from Illinois (2016), a Google Faculty award (2015), the Key Scientific Challenges award from Yahoo!, five best-of-conference citations (VLDB 2010, KDD 2012, ICDE 2014, ICDE 2016, AISTATS 2017), a best demo honorable mention (SIGMOD 2017). He is an associate editor of SIGMOD Record, serves on the steering committee of the HILDA (Human-in-the-loop Data Analytics) Workshop, and has served on program committees of various database, data mining, web, systems, and crowdsourcing conferences. His research group is supported with funding from the NSF (CAREER, Medium, AITF, BigData), the NIH (2X), Adobe, the Siebel Energy Institute, and Google.
I am an Associate Editor for SIGMOD Record, focusing on vision articles. Please consider sending us your most controversial and/or interesting papers!
I served as a co-chair for the HILDA (Human-In-the-Loop Data Analytics) Workshop at SIGMOD 2017, and now serve on the steering committee. Website here. Please consider submitting a paper to further this nascent area at the intersection of databases, data mining, and visualization/HCI.
I've served on the program committees of VLDB, KDD, SIGMOD, WSDM, WWW, SOCC, HCOMP, ICDE, and EDBT, many of them multiple times.
I served as an Area Chair for SIGMOD 2017. I was the SIGMOD 2016 Undergraduate Research Chair. Our competition has concluded; we had 3X the number of submissions in 2016 compared to previous years.
Zenvisage is a tool for effortlessly visualizing insights from very large data sets. It automates finding the right visualization for a query, significantly simplifying the laborious task of identifying appropriate visualizations.
DataSpread is a tool that marries the best of databases and spreadsheets.
Project page: here
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. OrpheusDB is a component of DataHub focused on using a relational database for versioning.
Project page: here
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. Since 2014, our focus has been on optimizing open-ended crowdsourcing: an understudied and challenging class.
Project page: here