Scholar
Raghavendra Addanki
Google Scholar ID: SUPaOhgAAAAJ
Adobe Research
Citations & Impact
Citations
280
H-index
11
i10-index
11
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Publications include 'ML-Tool-Bench: Tool-Augmented Planning for ML Tasks' and others, covering areas such as offline RL, semantic similarity experimentation, causality discovery in time series, and more.
Research Experience
- Research Scientist at Adobe Research (San Jose, California). Visiting student in the Causality program at the Simons Institute for the Theory of Computing, Berkeley, 2022. Worked on identifying new connections between combinatorial optimization and causal inference during his Ph.D.
Education
- Ph.D. in Computer Science from the College of Information and Computer Sciences at the University of Massachusetts Amherst, May 2022, co-advised by Prof. Andrew McGregor and Prof. Cameron Musco. Bachelors and Masters degrees (dual degree) in Computer Science from the Indian Institute of Technology Madras, 2016.
Background
- Interested in understanding and building foundations of applications of generative models by developing and borrowing ideas from theoretical computer science, causal inference, reinforcement learning, and statistics. Recently interested in a full stack approach: 1. Building agents for data science; 2. Building tools for agents; 3. Evaluation.
Miscellany
- Contact Email: raddanki AT adobe.com. Looking for Ph.D. students interested in working on full stack aspects of generative models for internships.
Co-authors