About Me
I am a Research Scientist 2 at Adobe Research, working on data-driven multimodal understanding and generation, advanced planning in Multimodal Large Language Models (MLLMs), and intelligent agents with dynamic planning capabilities. My research focuses on developing solutions that learn efficiently from data with applications in multimodal understanding and generation.
Before joining Adobe Research, I completed my Ph.D. in Computer Science at the University of Massachusetts, Amherst, where I was advised by Prof. Michael Zink in the Multimedia & Networks Lab. My research focus during my Ph.D. was on optimizing multimedia experiences in live video streaming.
I am passionate about building intelligent systems that enhance human productivity in creativity.
In my free time, I explore creativity through capturing photos, videos, and paintings, stay expressive with music and dance, and keep my brain entertained with strategy games.
Career Trajectory
Research Scientist 2
Adobe Research
Jul 2025 - Present
Focusing on data-driven intelligence and intelligent agents in creative content workflows. Working on optimizing and improving creative workflows for Adobe customers and users through AI-powered systems.
Research Scientist 1
Adobe Research
Mar 2023 - Jul 2025
Worked on data-driven intelligence and intelligent agents in creative content workflows, focusing on optimizing creative workflows for Adobe customers and users.
Ph.D. in Computer Science
University of Massachusetts, Amherst
Sep 2016 - Feb 2023 | Advised by Prof. Michael Zink
Completed Ph.D. in the Multimedia & Networks Lab with a research focus on optimizing user QoE in live video streaming via named-data networking and intelligent caching. Built domain expertise in network protocols, video streaming optimization, and multimedia systems.
Masters
University of Massachusetts, Amherst
Sep 2014 - Aug 2016
Graduate studies in Computer Science covering fundamentals of computer science systems & network, Machine learning and theory.
Research Interests
🎥 Short Video Understanding
Understanding editing patterns, engagement factors, and narrative structures in short-form videos, applied to content creation workflows
📊 Data-Driven Multimodal Understanding
Training models to understand multimodal data and using insights to optimize content creation workflows
🤖 Dynamic Planning in Agents/MLLMs
Developing data-driven agents with advanced planning capabilities for content creation workflows