people
members of the lab or group
555 your office number
123 your address street
Your City, State 12345
Hi, I am Pinaki! I am a third-year Ph.D. student in Computer Science at Purdue University, advised by Dr. Rajiv Khanna.
My research focuses on bridging Statistical foundations of Machine Learning with practical challenges. I study Data-centric AI, particularly ML-enabled Data Annotation, Data Quality Estimation, and Data Usage. I am also interested in Probabilistic Inference under MCMC Sampling. Lately, I’ve been exploring how these rigorous, theory-driven principles can be applied to LLMs to better understand and improve their behavior.
In Summer 2023, I completed my M.S. in Computer Science and Statistics at Purdue, where my thesis centered on studying Contextual Bandits under sub-region-based disagreement for Active Learning. Prior to that, I received my B.S. in Computer Science from Purdue in Spring 2021, specializing in Machine Learning, Theoretical Computer Science, and Databases & Information Systems, with minors in Mathematics and Statistics.
If you’re another researcher, outside Purdue, working in Machine Learning, Mathematical Statistics, or Healthcare+AI( a field I am deeply passionate about!), please feel free to email me. I welcome conversations about potential research collaborations and ways my expertise might be helpful. For current Purdue students interested in working with me, please email me to set up a one-off coffee chat if you’d like to discuss some research ideas.
555 your office number
123 your address street
Your City, State 12345
Hi, I am Pinaki! I am a third-year Ph.D. student in Computer Science at Purdue University, advised by Dr. Rajiv Khanna.
My research focuses on bridging Statistical foundations of Machine Learning with practical challenges. I study Data-centric AI, particularly ML-enabled Data Annotation, Data Quality Estimation, and Data Usage. I am also interested in Probabilistic Inference under MCMC Sampling. Lately, I’ve been exploring how these rigorous, theory-driven principles can be applied to LLMs to better understand and improve their behavior.
In Summer 2023, I completed my M.S. in Computer Science and Statistics at Purdue, where my thesis centered on studying Contextual Bandits under sub-region-based disagreement for Active Learning. Prior to that, I received my B.S. in Computer Science from Purdue in Spring 2021, specializing in Machine Learning, Theoretical Computer Science, and Databases & Information Systems, with minors in Mathematics and Statistics.
If you’re another researcher, outside Purdue, working in Machine Learning, Mathematical Statistics, or Healthcare+AI( a field I am deeply passionate about!), please feel free to email me. I welcome conversations about potential research collaborations and ways my expertise might be helpful. For current Purdue students interested in working with me, please email me to set up a one-off coffee chat if you’d like to discuss some research ideas.