Sajjadur Rahman
Adobe Founders Tower, San Jose, CA 95113
I am a Senior Manager of Machine Learning at Adobe. I drive the Center for Excellence of AI Quality at Adobe Experience Platform. Prior to that, I was a Senior Research Scientist and the founding Research Manager of the Data-AI Symbiosis (DAIS) group at Megagon Labs where I led the development of data platforms for agentic systems over enterprise data. I received my PhD from CS@Illinois where I worked on interactive systems for big data exploration with Aditya Parameswaran.
My research synthesizes techniques from data management, AI, and HCI to build scalable, interactive, and reliable systems. My work 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 has been deployed in open-source data exploration systems (DataSpread and Lux) and enjoyed adoption in the industry to support creative design@B12. I served as Program Chair of several workshops (DAIS@ICDE'25 and MATCHING@ACL'23) and served on the program committee of SIGMOD, VLDB, EMNLP, ACL, and IEEE ISSRE.
News
- Received Distinguished Reviewer Award at SIGMOD 2025.
April 22, 2025
- Joined Adobe Inc. to lead AI Quaity and Safety initiatives at Adobe Experience Platform.
April 14, 2025
- Released Cypherbench, a large-scale benchmark for evaluating NL2Cypher tasks.
January 7, 2024
- Article on RAG in the Wild accepted at IEEE Data Engg. Bulletin.
December 22, 2024
- Workshop on Data-AI Systems accepted at ICDE'25.
September 15, 2024
- Paper on "Characterizing LLMs as Rationalizers" accepted at ACL'24 Findings.
May 15, 2024
- Paper on "Benchmarking Data Discovery in the Enterprise" accepted at GUIDE-AI@SIGMOD'24.
May 12, 2024
- Demo on "Human-LLM Collaborative Annotation System" accepted at EACL'24.
January 22, 2024
- Paper on "Human-LLM Collaborative Annotation Through Effective Verification" accepted at CHI'24.
January 18, 2024
We outline our approaches toward understanding and implementing a more effective agentic workflow in the wild. To achieve the goal, we draw on the cognitive science concepts of System 1 (fast, intuitive thinking) and System 2 (slow, deliberate, analytical thinking.).
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.
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.