Current Students
Sean Mcleish
Language models, algorithmic reasoning
Khalid Saifullah
Large language models, multilingual models
Neel Jain
Large language models, data-efficient finetuning
John Kirchenbauer
Large language models, AI safety
Yuxin Wen
Computer vision, AI safety
Monte Hoover
Learning from tabular and event data, large language models
Jie Li
AI security
Hong-Min Chu
Active learning, dataset composition
Abhimanyu Hans
Large language models, memorization and privacy
Kaiyu Yue
Vision-language models
Kaiyu Yue
Vision-language models
Alex Hanson
Vision-language models
Mayuka Jayawardhana
Tabular data, conputer vision
Pedro Sandoval-Segura
Dataset security and attribution
Vasu Singla
Computer vision, dataset attribution, multi-modal models
Current Postdocs
Zikui Cai
Computer vision, robotics
Ashwinee Panda
Large language model, privacy
Former lab members
Arpit Bansal
Computer vision, multi-modal models. Currently at Meta.
Gowthami Somepalli
Compiuter vision, multi-modal models. Currently at Adobe.
Hamid Kazemi
Computer vision, visualization. Currently at Apple.
Kezhi Kong
Graph neural networks, computer vision. Currently at Nvidia.
Renkun Ni
Renkun studied meta-learning and efficient machine learning for computer vision. He is currently a research scientist at Capital One.
Avi Schwarzschild
Avi was a Ph.D. student in the Applied Math and Scientific Computation program at the University of Maryland. His thesis focuses on dataset security and logical reasoning. Currently a post-doc at CMU.
Aniruddha Saha
Aniruddha was a postdoc studying robust computer vision.
Manli Shu
Manli’s thesis was on multi-modal machine learning, with an emphasis on computer vision. She is currently at SalesForce Research.
Jonas Geiping
Jonas was a postdoctoral researcher at UMD with diverse experience in applied math, image processing, and large language models. He is currently a faculty member at ELLIS Institute & MPI-IS.
Liam Fowl
Robust machine learning, meta-learning
Micah Goldblum
Micah was PhD student in mathematics, studying meta-learning and robust computer vision. He is currently a post-doc at NYU with Andrew Wilson and Yann leCun.
Amin Ghiasi
Amin’s experience is a blend of theoretical computer science, and machine learning. Currently at Apple.
Zeyad Emam
Zeyad’s primary advisor was Wojciech Czaja. He has a background in harmonic analysis and image processing. His thesis work was on improved segmentation methods for electron microscopy (work sponsored by the NIH). Currently at Apple.
Steven Reich
Steven was a postdoc at UMD working on algorithmic fairness.
Chen Zhu
Chen’s research focused on large language models. He is currently a research scientist at Nvidia.
Ping-Yeh Chiang
Ping-Yeh studied certifiable and provably robust methods for computer vision. His later work branched into multi-modal models and LLMs. He is currently a research scientist at Tesla.
Ronny Huang
Ronny received his PhD in Physics at MIT in the Optics and Quantum Electronics Group under Professors Franz Kärtner and Erich Ippen at MIT. In addition to his expertise in optics and imaging, Ronny has done a lot of work in machine learning and computer vision. His recent research is focused on poisoning attacks and defenses for deep neural networks.
Parsa Saadatpanah
Parsa is a machine Learning researcher working on recommendation systems, content Discovery, and how usage data can be leveraged to create data-driven solutions. His recent work has focused on security concerns arising in content management and copyright control systems.
Ali Shafahi
Ali works on a range of topics related to data security for neural networks and other machine learning systems. He is particularly interested in adversarial machine learning, including evasion and poisoning attacks for deep classifiers. Before joining the group, Ali complete a PhD in Civil Engineering under the supervision of Ali Haghani, and he is an expert in operations research and transportation systems.
Zheng Xu
Zheng completed his PhD in 2019, after which he joined Google. His thesis was on optimization and machine learning. His focus was on automated and distributing optimization routines for model fitting and data science.
Soham De
Soham completed his PhD in 2018, and joined Google DeepMind in London. Soham’s thesis work included topics in machine learning and game theory. In machine learning, he worked on optimization methods, distributed algorithms, and online learning. Soham was co-advised by Dana Nau, and also collaborated with Michele Gelfand for his work on game theoretic models of human behavior and cultural bias.
Hao Li
Hao completed his PhD in 2018, and joined Amazon Research. Hao’s research interests lie at the intersection of machine learning and systems. Specifically, he is interested in designing efficient and scalable machine learning algorithms for high-performance and resource-constrained systems. Hao was co-advised by Hanan Samet.
Sohil Shah
Sohil completed his PhD in 2017, and joined Intel Research. Sohil focused on solving difficult computer vision problems using large-scale optimization, deep learning, and graphical models.