Sajjadur Rahman
Sajjadur Rahman
444 Castro St, Mountain View, CA 94041

I am a research scientist at Megagon Labs. I received my PhD from CS@Illinois where I worked with Aditya Parameswaran. Before that, I was a lecturer at Department of CSE, BUET.

My research interest lies in interactive data exploration. My work synthesizes techniques from data management, visualization, and human-computer interaction to develop interactive systems that make information space intuitive and interpretable for users to explore. I collborated on research projects that were recognized with best demo award (at ICDE), featured in popular tech-blogs, and adopted in industry.


  • Exciting news: Our paper on exisitng info extraction practices, their limitations, and improvement opportunities for related HITL tools is accepted to CHI 2022.
    November 15, 2021
  • I am serving as panelist on the "interactive querying and visualization of large data" round-table at VLDB 2021.
    August 17, 2021
  • Our first prototype of an integrated system for visual interactive text analysis has been accepted at DASH@NAACL 2021.
    May 15, 2021
  • Our work on developing a general-purpose spreadsheet exploration system, NOAH, has been accepted at VLDB2021.
    January 16, 2021

Selected Projects

Towards Visual Interactive Text Analysis

Towards Visual Interactive Text Analysis

Leam is an interactive tool for text data analysis that, backed by a visual text algebra, combines the strengths of spreadsheets, computational notebooks, and visualizations libraries. The visual text algebra supports a number of text analysis and visualization operations.

A Benchmark for Spreadsheet Systems


sheetperf is the first ever spreadsheet benchmark that evaluates the performance of popular spreadsheet systems like Micrsoft Excel, LibreOffice Calc, and Google Sheets. sheetperf also identifies a number of optimization opportunities for these systems.

Progressive Visualization with Incvisage


IncVisage is a progressive visualization tool that reveals “salient” features of a visualization quickly while minimizing error, enabling rapid and error-free decision making. The approach is orders of magnitude faster than the traditional visualization systems.


Synergistic Activities

August, 2021 VLDB 2021 (Panelist)
July, 2021 SIGMOD 2022 (PC)
January, 2021 BigVIS 2021 (PC)
October, 2020 SIGMOD 2021 (PC)
September, 2018 IIT 2018 (PC)