444 Castro St, Mountain View, CA 94041
I am a Senior 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 key research areas include data management for data science and human-centered design. I build human-centered data systems to address usability and scalability challenges in data science workflows. I approach these challenges from a human-centric view where I study existing work practices to inform design guidelines for instrumentation in data management systems and interactive tools for data science. My research has been published in premier conferences in Databases (SIGMOD and VLDB), HCI (CHI and CSCW), and NLP (EMNLP and NAACL.) I collaborated on research projects that were recognized with the best demo award (at ICDE) and featured in popular tech blogs. My research also helped identify performance bottlenecks of popular spreadsheet systems such as Microsoft Excel, enabled scalable data exploration in open-source tools (e.g., DataSpread), and enjoyed adoption in the industry (e.g., a planning tool for creative design at the startup B12.) I have served on the program committee of SIGMOD and IEEE ISSRE and as a reviewer at CHI, VLDB, and EMNLP.
- Organized the MATCHING Workshop@ACL'23.
July 13, 2023
- Demo on "Interactive Text Exploration Widgets" accepted at WWW'23.
March 6, 2023
- Paper on "Redesigning Interactive Widgets in Computational Notebooks" accepted at CHI'23 as a Late-Breaking Work.
February 25, 2023
- Delivered a talk at UCLIC research seminar.
February 15, 2023
- Proposed Workshop on Matching from Structured and Unstrcutured Data accepted at ACL 2023.
December 18, 2022
- Participated in a panel at the HCAI@NeurIPS workshop on Critical Commentary of HCAI Research.
December 09, 2022
- Our position paper on designing human-centered AI systems was accepted at HCAI@NeurIPS.
October 19, 2022
We observed that data science workers follow an iterative task model consisting of information foraging and sensemaking loops across all the phases of an information extraction workflow. We found several limitations in both loops stemming from a lack of adherence to existing cognitive engineering principles.
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.
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.