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Team Purpl3Pwn3rs

Our team integrates expertise across software engineering, human-computer interaction, and machine learning, with deep specialization in ML. This unique combination enables us to advance post-training and inference-time control, driving innovation in secure, adaptive AI systems that enhance code generation while ensuring safety and reliability.

By Carnegie Mellon University
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Atharva Naik - Team leader

Atharva Naik is a first-year PhD student at the Language Technologies Institute at Carnegie Mellon University. He works on natural language processing techniques to create LLM agents that can collaborate with humans in software engineering while facilitating on-the-job learning. He has published work on automated code translation, generation, and review.

Alex Xie

Alex Xie is a master's student in LTI at CMU, where he also completed his undergrad in CS. His research interests broadly revolve around large language models for code, including human-AI collaboration for code, evaluation benchmarks for code generation models, and LLM decoding methods.

Abhinav Rao

Abhinav is a Master's student at LTI, CMU. He studied his undergrad at BITS Pilani in 2022 and worked at Microsoft for a year. His research is on Responsible AI, dealing with AI Ethics and Safety, studying moral and cultural biases, and exploring the jailbreaking space of language models.

Anmol Agarwal

Anmol Agarwal is an MS student at LTI, CMU. Before CMU, he was a Research Fellow at Microsoft Research India in the AI4Code group and completed his undergrad in CS at IIIT-Hyderabad in 2023. His research interests include machine learning and natural language processing, with a recent focus on ML4Code and alignment.

Shubham Gandhi

Shubham Gandhi is a Master's student at LTI, CMU, having previously worked at Microsoft Research and TCS Research after graduating from BITS Pilani in 2023. His research explores advancing Large Language Models and Agents beyond vanilla code generation, including non-functional requirements, code onboarding, and improving low-resource code translation and repair.

Carolyn Rose - Faculty advisor

Dr. Carolyn Rosé is a Professor at Carnegie Mellon University. Her research focuses on interactive and explainable Sociotechnical Artificial Intelligence, investigating novel representations and architectures using a problem-driven approach motivated by error analysis, with a current emphasis on abstraction and decomposition, which are arguably two great challenges for LLMs.

Michael Hilton - Faculty advisor

Dr. Michael Hilton is an Associate Teaching Professor in the Software and Societal Systems Department in the School of Computer Science at Carnegie Mellon University. He holds a PhD from Oregon State University. He has won several awards for his teaching, including the 2021 Spira Excellence in Teaching Award, and the 2020-21 Wimmer Faculty Fellowship. He publishes in top Software Engineering venues, where his research is focused on flaky tests, Software Quality, and other CI/CD topics.

About the Author
Carnegie Mellon University

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