PURDUE CS E-NEWS January 2025

News from Purdue University's Department of Computer Science

Can Markets Balance Growth and Inequality? Researchers Explore the Trade-Off.

Prof. Simina Branzei, assistant professor of Computer Science at Purdue University. Photo supplied by Simina Branzei.

Simina Branzei’s research examines market dynamics using a mathematical model, revealing how growth and inequality emerge, influenced by human behaviors and market mechanisms, with implications for economic policy and digital economies.

Combating Food Insecurity with Award-Winning Research

Photo by Adobe/AI.

Purdue CS researchers won Best Student Paper at EAAMO 2024 for automating food redistribution. Their system optimizes fair food bank matching, improving efficiency. Already deployed in Indiana, it advances dynamic fair division and load balancing, with plans for expansion.

Purdue CS student honored at Academic All-Big Ten

Alex Frey, Purdue CS student athlete and Big Ten Distinguished Scholar

Four Purdue University College of Science students were honored at the Fall Sports Academic All-Big Ten, including Alex Frey, Purdue CS student. Frey has maintained a 4.0 GPA as he earns his master's degree in computer science. This year, 70 student athletes from Purdue University were honored.

First look: Purdue’s new Academic Success Building in Indianapolis

Academic Success Building rendering, Purdue University (Image provided)

The 248,000-square-foot building, set to be completed in May 2027, will be the centerpiece of Purdue’s expansion in the capital city.

Purdue names Indian American Ananth Grama Distinguished Professor of Computer Science

  • Purdue University Board of Trustees has appointed Ananth Grama as Distinguished Professor of Computer Science.

Cybersecurity regulations in 2025: Key insights from top industry experts

  • Several prominent cybersecurity frameworks, regulations, and government agencies recommend microsegmentation or network segmentation as critical security measures.

Researchers develop game-changing technology that could create 'digital forest[s]' of the future: 'Benefiting the health of our entire planet'

  • Ever stare at a drab city street and imagine how much better it would look lined with trees? So did a group of researchers who recently launched a sophisticated computer modeling tool that allows city planners to do exactly that.

Hey Google: Data Scientists Use AI to Plant New Forests in the Cities

  • The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.

How AI Is Revolutionizing Urban Forestry and Climate Resilience

  • Tree-D Fusion, an AI-driven tool developed by MIT and Purdue researchers, generates 3D models of urban trees to help city planners visualize future green spaces, address climate challenges, and enhance urban livability and sustainability.

Purdue Professor Talks Avoiding Charity Scams

  • Computer Science Professor Eugene Spafford says scammers will often send messages that appear to be legitimate, but are really just efforts to take your money.

How India’s EdTech Firms Are Empowering Learners on International Day of Education 2025

  • Simplilearn is a leader in upskilling professionals in digital skills like cloud computing, AI, and cybersecurity. Collaborating with institutions like Purdue University and IBM, it has trained over 3 million learners globally through hands-on certification programs and practical projects.

Purdue CS ranked #47 globally

Purdue CS has been ranked among the top 50 best computer science programs in the world by Times Higher Education.

Purdue CS continues to earn recognition as a global leader in computing research and education. In the latest Times Higher Education World University Rankings, Purdue CS is ranked #47 worldwide, underscoring the department’s excellence in research, faculty, and industry impact.

This ranking joins the accolades from csrankings.org, which has the program ranked at #13 in the United States and U.S. News and World Report, which ranks our undergraduate and graduate programs in the top 20 in the United States.

As the first degree-awarding program in the nation, Purdue CS is has a continued legacy of pioneering research, innovative curriculum, and preparing the next generation of computing leaders.

Professor and Head of the Department of Computer Science, Petros Drineas

Purdue Computes in action: Institute for Physical AI

The Institute of Physical AI will couple scientists from across Purdue who bring interdisciplinary thinking and problem-solving to solve issues at the intersection of AI and a variety of critical functions, such as more efficient pharmaceutical manufacturing, digital forestry and more efficient transportation." - Karen Plaut, Executive Vice President for Research

Leveraging Purdue’s signature strengths in materials science, engineering, microelectronics, computer science, agriculture and life sciences, the Institute for Physical AI (IPAI) is committed to solving the world’s toughest challenges.

Science at Purdue University in Indianapolis

Essential understandings of science and lifelong critical thinking skills are developed at Purdue University in Indianapolis’s College of Science. Computational, math and data sciences form the foundation for innovation and thoroughly prepare graduates for careers in dynamic and rapidly changing environments.

Science Majors

Computer Science - Join a legacy of innovation in both advancing scientific research and creating industry applications by learning computing fundamentals.

Artificial Intelligence - Explore the link among cognitive psychology, neuroscience and artificial intelligence — and investigate AI ethics — for a holistic understanding of how to build and understand systems.

Data Science - Discover the intersection of computer science and statistics in a field that uses quantitative and analytical methods to help provide insights and form predictions based on big data.

Actuarial Science (Fall 2025) - Actuarial science combines business with mathematical and statistical tools to evaluate future risk and contingent events. 

Nikhil Anand Dhoka, a Purdue computer science and mathematics student in Indianapolis, is set up for success at the intersection of technology and finance. Learn more about his plan.
Sarah Papabathini, a Purdue AI student in Indianapolis, is making a difference in her community every day as a DREAM Alive mentor, an Indy 500 Festival princess, academic researcher and volunteer.

NeurIPS 2024

The Conference and Workshop on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference

The Space Complexity of Approximating Logistic Loss | Gregory Dexter, Petros Drineas, Rajiv Khanna

Information-theoretic Limits of Online Classification with Noisy Labels | Changlong Wu, Ananth Grama, Wojciech Szpankowski

Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases | Zian Su, Xiangzhe Xu, Ziyang Huang, Kaiyan Zhang, Xiangyu Zhang

When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search| Danning Xie (Purdue University), Zhuo Zhang (Purdue University), Nan Jiang (Purdue University), Xiangzhe Xu (Purdue University), Lin Tan (Purdue University), Xiangyu Zhang (Purdue University)

BiScope: AI-generated Text Detection by Checking Memorization of Preceding Tokens | Hanxi Guo, Siyuan Cheng, Xiaolong Jin, Zhuo Zhang, Kaiyuan Zhang, Guanhong Tao, Guangyu SHen, Xiangyu Zhang

LLMDFA: Analyzing Dataflow in Code with Large Language Models | Chengpeng Wang, Wuqi Zhang, Zian Su, Xiangzhe Xu, Xiaoheng Xie, Xiangyu Zhang

GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts | Shirley Wu, Kaidi Cao, Bruno Ribeiro, James Zou, Jure Leskovec

A Foundation Model for Zero-shot Logical Query Reasoning | Michael Galkin, Jincheng Zhou, Bruno Ribeiro, Jian Tang, Zhaocheng Zhu

DiGRAF: Diffeomorphic Graph-Adaptive Activation Function | Krishna Sri Ipsit Mantri, Xinzhi Wang, Carola-Bibiane Schonlieb, Bruno Ribeiro, Beatrice Bevilacqua, Moshe Eliasof

Dueling over Dessert, Mastering the Art of Repeated Cake Cutting | Simina Branzei, MohammadTaghi Hajiaghayi, Reed Phillips, Suho Shin, Kun Wang

A Theory of Optimistically Universal Online Learnability for General Concept Classes | Steve Hanneke, Hangao Wang

Universal Rates of Empirical Risk Minimization | Steve Hanneke, Mingyue Xu

Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs | Steve Hanneke, Idan Mahalel, Shay Moran

Multiclass Transductive Online Learning | Steve Hanneke, Vinod Raman, Amirreza Shaeiri, Unique Subedi

Learning from Snapshots of Discrete and Continuous Data Streams | Pramith Devulapalli, Steve Hanneke

Improved Sample Complexity for Multiclass PAC Learning | Steve Hanneke, Shay Moran, Qian Zhang

Universal Rates for Active Learning | Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas

LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing | Xionan Nie, Liu Qibin, Fancheng Fu, Shanhan Zhu, Xupeng Miao, Xiaoyang Li, Yang Zhang, Shouda Liu, Bin Cui

ReMI: A Dataset for Reasoning with Multiple Images | Mehran Kazemi, Nishanth Dikkala, Ankit Anand, Petar Devic, Ishita Dasgupta, Fangyu Liu, Bahare Fatemi, Pranjal Awasthi, Sreenivas Gollapudi, Dee Guo, Ahmed Qureshi

Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs | Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Animashree Anandkumar

Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention | Haomeng Zhang, Chiao-An Yang, Raymond A. Yeh

Offline Multitask Representation Learning for Reinforcement Learning | Haque Ishfaq, Thanh Nguyen-Tang, Sangtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup

A Theoretical Perspective for Speculative Decoding Algorithm | Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang

Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary | Zhuoyan Li, Ming Yin

Transfer Q-star : Principled Decoding for LLM Alignment | Souradip Chakraborty, Soumya Suvra Ghosal, Ming Yin, Dinesh Manocha, Mengdi Wang, Amrit Singh Bedi, Furong Huang

NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation | Momin Haider, Ming Yin, Menglei Zhang, Arpit Gupta, Jing Zhu, Yu-Xiang Wang

Fast Best-of-N Decoding via Speculative Rejection | Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao qui, Mong Yin, Mengdi Wang, Peter Bartlett, Andrea Zanette

Gradient-based Discrete Sampling with Automatic Cyclical Scheduling | Patrick Pynadath, Riddihiman Bhattacharya, Arun Hariharan, Ruqi Zhang

Stronger Than You Think: Benchmarking Weak Supervision on Realistic Tasks | Tianyi Zhang, Linrong Cai, Jeffrey Li, Nocholas Roberts, Neel Guha, Frederic Sala

NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention | Tianyi Zhang, Jonah Yi, Bowen Yao, Zhaozhuo Xu, Anshumali Shrivastava

KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization | Tianyi Zhang, Jonah Yi, Zhaozhuo Xu, Anshumali Shrivastava

POPL 2024

The annual Symposium on Principles of Programming Languages is a forum for the discussion of all aspects of programming languages and programming systems. Both theoretical and experimental papers are welcome on topics ranging from formal frameworks to experience reports. We seek submissions that make principled, enduring contributions to the theory, design, understanding, implementation, or application of programming languages.

From Traces to Program Incorrectness: A Type-Theoretic Approach Yongwei Yuan, Zhe Zhou, Julia Belyakova, Benjamin Delaware, Suresh Jagannathan

Derivative-Guided Symbolic Execution | Yongwei Yuan, Zhe Zhou, Julia Belyakova, Suresh Jagannathan

Towards Automated Verification of LLM-Synthesized C Programs | Prasita Mukherjee, Benjamin Delaware

PURDUE CS | BY THE NUMBERS

UNDERGRADUATE PROGRAM

AN ERA OF GROWTH

In the field of computer science, there is a sustained and significant increase in demand for our academic programs. We are thrilled to announce that, once again, we have surpassed our previous records for freshman admission applications, with the total exceeding 11,000. At the start of fall classes, 1,001 freshman students joined our previous classes across two campuses for more than 3,500 undergraduates.

This year, freshman women students represent 22% of the undergraduate population and women are 23% among all undergraduate students.

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GRADUATE PROGRAM

Our graduate population has exploded with 568 MS and PhD students for the 2024-25 academic year. This represents a 23% increase in growth from the previous year.

Purdue Computer Science graduate students work in any of the 14 research areas in the department.

Purdue Computer Science offers the traditional PhD and master's degree programs in addition to a professional master's degree in information security.

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