Aditya Parameswaran

I am an assistant professor at the University of California, Berkeley, with a joint appointment at the I School and EECS. I am part of the Data Systems & Foundations group and the Human-Computer Interaction group, and I am affiliated with the RISELab and the Berkeley Institute of Design.

My research interests are broadly in building tools for simplifying data analytics, i.e., empowering individuals and teams to leverage and make sense of their datasets more easily, efficiently, and effectively.

We are always looking for postdocs, PhD, MS, and UG students or research/development staff to join our efforts! If you are a postdoc or staff applicant, feel free to email me directly with your CV and qualifications. If you are an aspiring PhD student, please apply to the EECS or I School PhD programs. If you are an MS or UG student, feel free to fill out this form: it is rare that we will work with students outside UC Berkeley except in cases of unusually good fit.

Biographical Sketch

Aditya Parameswaran is an Assistant Professor in the School of Information (I School) and Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. Until June 2019, Aditya was an Assistant Professor in Computer Science at the University of Illinois, Urbana-Champaign. He spent a year as a PostDoc at MIT CSAIL following his PhD at Stanford University. He develops systems and algorithms for "human-in-the-loop" data analytics, synthesizing techniques from database systems, data mining, and human-computer interaction.

Click here for a longer bio.

Quick Project Links



  • December 6, 2019: The Morning Paper, a popular blog that covers academic papers, covered our paper on spreadsheet benchmarking. Thanks Adrian!
  • November 13, 2019: A nice article on the new Data Systems & Foundations group at UC Berkeley, from the Division of Data Science and Information. Excited for what lies ahead!
  • September 27, 2019: A generous article on the VLDB Early Career Award, from the School of Information. Here's the paper I had the privilege of writing as part of the award.
  • September 9. 2019: Double Doris Xin whammy! Helix was mentioned as a "project to know" in a blog post from Amplify Partners. Thanks Sarah! And Doris was selected as a Hiedelberg Laureate. Congratulations!
  • August 26, 2019: Grateful to receive the VLDB Early Career Research Contributions Award for "developing tools for large-scale data exploration, targeting non-programmers." The award is given for research impact through a specific technical contribution of high significance since completing the Ph.D. (for up to 8 years post Ph.D.)
  • August 22, 2019: Please send us your most compelling SIGMOD workshop ideas for SIGMOD2020! Deadline September 27. Here's the call for proposals.
  • July 1, 2019: Berkeley logo *BIG NEWS!* I moved to UC Berkeley, with a faculty appointment at the School of Information and the EECS Department; you can find articles about my appointment in EECS and the I School. Berkeley has been making some exciting moves in the broad data and information space, including a new Division of Data Science and Information, a very popular Masters in Information and Data Science program, and a new and increasingly popular data science major. Looking forward to being a part of this journey, and to moving back to the Bay Area! Illinois has been a wonderful home for the past 5-odd years with terrific students, a collegial research environment, and Midwestern charm (plus baby goats!); I'm going to miss my colleagues, the university, and the town terribly.
  • June 25, 2019: Our new paper describing our design study with Zenvisage was accepted at VAST'19 at VIS. Congrats Doris Lee!
  • June 20, 2019: Our vision paper on the data management and HCI challenges underlying AutoML was published in the IEEE Data Engg Bulletin.
  • June 20, 2019: Silu Huang defended her thesis on versioning-meets-databases. Congrats Silu!
  • May 1, 2019: Our proposal received a runner-up mention for the Facebook Probability and Programming Research Award.
  • April 11, 2019: Congratulations to the DataSpread team for a best demo award at ICDE 2019! The best demo awards went to two out of 24 demonstrations.
  • March 20, 2019: Silu Huang accepted her offer to be a researcher at the DMX group at Microsoft Research! I interned in the DMX group back in 2010 and it's a fantastic place to work.
  • March 15, 2019: DataSpread's asynch computation framework was accepted at SIGMOD'19. Congrats to Mangesh, Tana et al.!
  • March 1, 2019: Yihan Gao defended his thesis on data compression and data extraction, titled "Extracting and Utilizing Hidden Structures in Large Datasets". Yihan will return to his undergraduate alma mater to be an assistant professor at Tsinghua University. Congratulations Yihan!
  • February 10, 2019: Our demo paper on DataSpread's navigation, formula, and relational capabilities was accepted at ICDE'19. Congrats Mangesh, Tana, Sajjadur, and gang!
  • February 1, 2019: Mangesh Bendre is leaving the group to start as a research scientist at Visa Research. Congratulations Mangesh! Mangesh's thesis on DataSpread is up at this link.
        Click here for more news.

Synergistic Activities

I am a Co-Chair of Workshops for SIGMOD 2020. Please send us your exciting and engaging community-building ideas! Interdisciplinary/novel workshop ideas encouraged. Here is the call for proposals.

I serve on the steering committees of HILDA (Human-in-the-loop Data Analytics) at SIGMOD and DSIA (Data Systems for Interactive Analysis) at VIS. Lots of excitement around this nascent area at the intersection of databases, data mining, and visualization/HCI - join us!

This cycle, I am serving as an Area/Associate Chair for HCOMP 2020, VLDB 2020, and SIGMOD 2020, as a Program Committee member for VLDB Demo 2019 and HILDA 2019 (phew!) I've served on the program committees of VLDB, KDD, SIGMOD, WSDM, WWW, SOCC, HCOMP, ICDE, and EDBT, many of them multiple times.

Past: I stepped down as Associate Editor for SIGMOD Record after nearly half a decade. I co-organized HILDA 2017. I was the SIGMOD 2016 Undergraduate Research Chair.

Recent Releases

Medium Blog

Selected Projects


Zenvisage: A visualization recommendation system

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.

Project page here. Try it live here!


Helix: An Accelerated Human-in-the-loop Machine Learning System

Helix accelerates the iterative development of machine learning pipelines with a human developer "in the loop" via intelligent caching and reuse.

Project page here.


DataSpread: A Spreadsheet-Database Hybrid

DataSpread is a tool that marries the best of databases and spreadsheets.

Project page: here


Orpheus: Relational Dataset Version Management at Scale

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


Populace: A Suite of Crowd-Powered 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. Since 2014, our focus has been on optimizing open-ended crowdsourcing: an understudied and challenging class.

Project page: here