Current Students

Sean Mcleish <

Sean Mcleish

Language models, algorithmic reasoning

Khalid Saifullah <

Khalid Saifullah

Large language models, multilingual models

Neel Jain <

Neel Jain

Large language models, data-efficient finetuning

John Kirchenbauer <

John Kirchenbauer

Large language models, AI safety

Yuxin Wen <

Yuxin Wen

Computer vision, AI safety

Monte Hoover <

Monte Hoover

Learning from tabular and event data, large language models

Jie Li <

Jie Li

AI security

Hong-Min Chu <

Hong-Min Chu

Active learning, dataset composition

Kezhi Kong <

Kezhi Kong

Graph neural networks, computer vision

Pedro Sandoval-Segura <

Pedro Sandoval-Segura

Dataset security and attribution

Vasu Singla <

Vasu Singla

Computer vision, dataset attribution, multi-modal models

Arpit Bansal <

Arpit Bansal

Computer vision, logical reasoning

Hamid Kazemi <

Hamid Kazemi

Computer vision, visualization

Gowthami Somepalli <

Gowthami Somepalli

Multi-modal foundation models, computer vision

Former lab members

Renkun Ni <

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 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 Saha

Aniruddha was a postdoc studying robust computer vision.

Manli Shu <

Manli Shu

Manli’s thesis was on multi-modal machine learning, with an emphasis on computer vision. She is currently at SalesForce Research.

Jonas <

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 <

Liam Fowl

Robust machine learning, meta-learning

Micah Goldblum <

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 <

Amin Ghiasi

Amin’s experience is a blend of theoretical computer science, and machine learning. Currently at Apple.

Zeyad <

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 Reich

Steven was a postdoc at UMD working on algorithmic fairness.

Chen <

Chen Zhu

Chen’s research focused on large language models. He is currently a research scientist at Nvidia.

Ping <

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 <

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 <

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 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 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 <

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 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 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.