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Team PurpCorn-PLAN

Team PurpCorn-PLAN

We are committed to developing advanced code models that prioritize safety, security, and trustworthiness in modern software development. By integrating interdisciplinary expertise, we strive to create AI systems that generate reliable code while proactively identifying and mitigating potential risks.

By University of Illinois at Urbana-Champaign
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Nirav Diwan - Team leader

Nirav Diwan is a Ph.D. student at University of Illinois Urbana-Champaign. His research focuses on Security and ML, particularly practical attacks on large language models (LLMs). He publishes in ACL, IEEE S&P, USENIX Security, and ICWSM. He actively collaborates with industry (e.g., LG AI Research, TCS Research).

Muntasir Wahed - Team leader

Muntasir Wahed is a Ph.D. student in Computer Science at the University of Illinois Urbana-Champaign. His research focuses on Robust ML, Multimodal ML, and Self-Supervised Learning. Alongside his academic work, he has extensive industry experience with three Research Internships at IBM Research Almaden Lab.

Jiawei Liu- Team leader

Jiawei Liu is a Ph.D. candidate at the University of Illinois Urbana-Champaign. His research focuses on assuring the software quality of machine learning systems and LLM-based code generation. He publishes at ASPLOS, FSE, OOPSLA, NeurIPS, and COLM, winning an ACM SIGSOFT Distinguished Paper Award and a Distinguished Artifact Award.

Hadjer Benkraouda

Hadjer Benkraouda is a PhD student at University of Illinois Urbana-Champaign. Her research interests include system security, binary analysis, and ML. Her projects explore the intersection of language processing and program analysis, aiming to automate analysis. She publishes at IEEE S&P, USENIX Security, ACM CHI, and CoNext.

Tianjiao Yu

Tianjiao (Joey) Yu is a Computer Science Ph.D at the University of Illinois Urbana-Champaign. His research aims to create AI models that understand and respond to natural language as effectively as humans, advancing interpretability and trust in multimodal and embodied AI systems.

Xiaona Zhou

Xiaona Zhou is a Ph.D. student in Computer Science at the University of Illinois Urbana-Champaign, with experience in cross-modal information retrieval and dataset selection through reinforcement learning. Besides her academic work, she is a year-round research intern at Sandia National Laboratories, focusing on anomaly detection across various scenarios.

Yinlin Deng

Yinlin Deng is a PhD student at the University of Illinois Urbana-Champaign working on LLM4Code, including automated testing, program synthesis, and program repair. Her research led to the automatic discovery of 350+ bugs in ML frameworks. She has actively collaborated with industry including Google DeepMind, Fujitsu Research, and Microsoft.

Yuxiang Wei

Yuxiang Wei is a PhD student at University of Illinois Urbana-Champaign working on building code intelligence. He leads the development of Magicoder and StarCoder2-Instruct, projects that have garnered over 405k downloads and 2.1k GitHub stars (techniques/data have been adopted by Meta, Google, IBM, and Snowflake).

Kiet Nguyen

Kiet Nguyen is a Ph.D. student in Computer Science at the University of Illinois Urbana-Champaign, specializing in multimodal deep learning with a focus on Large Vision-Language Models (LVLMs) for pixel-grounded reasoning. He has had extensive experience in designing, implementing, and training LVLMs on diverse text and multimodal datasets.

Zhe Wang

Zhe Wang is a first-year Masters student in Computer Science at the University of Illinois Urbana-Champaign working under Professor Lingming Zhang. His research focuses on AI for code. As part of the Magicoder project, he contributed to the OSS-INSTRUCT methodology which enables a 7B parameter model to outperform larger 16B parameter models in code generation tasks. Their work has been accepted by ICML 2024. Prior to UIUC, he graduated from Tsinghua University with a B.S. in Mathematics and Physics, where he worked on NLP-related projects.

Haoyu Zhai

Haoyu Zhai is a first-year Masters student in Computer Science at the University of Illinois Urbana-Champaign working under Professor Gang Wang. His research focuses on machine learning security, large language models, and computer vision. His past project examines the security of image anonymization techniques using machine learning models. He recently works on adversarial attacks against Code LLMs.

Gang Wang - Faculty advisor

Gang Wang is an Associate Professor of Computer Science at the University of Illinois Urbana-Champaign. His primary research topic is security and privacy. He is a recipient of the NSF CAREER Award, Amazon Research Award, Google Faculty Research Award, and Best Paper awards at IMWUT, ACM CCS, and SIGMETRICS.

Ismini Lourentzou - Faculty advisor

Ismini Lourentzou is an Assistant Professor in iSchool at University of Illinois Urbana-Champaign, where she leads the Perception and LANguage (PLAN) Lab. Her research focus is multimodal machine learning. Before iSchool, she was an Assistant Professor at Virginia Tech, where she received an Outstanding New Assistant Professor Award.

Lingming Zhang - Faculty advisor

Lingming Zhang is an Associate Professor of Computer Science at University of Illinois Urbana-Champaign. His research interests are Software Engineering, Programming Languages, and Machine Learning. He is a recipient of the ACM SIGSOFT Early Career Researcher Award, NSF CAREER Award, and research awards from Alibaba, Google, Kwai, Meta, and Sams.

About the Author
University of Illinois at Urbana-Champaign

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