Richard G. Baraniuk, Randall Balestriero, and  Ahmed Imtiaz Humayun explore the mathematical underpinnings of deep learning in their latest article published in the Notices of the American Mathematical Society (April 2025). The paper highlights a decade of progress connecting deep networks to function approximation via affine splines, shedding light on how these models tessellate input space and revealing geometric insights into their behavior. This perspective offers a promising framework for analyzing and enhancing deep network performance through a more principled mathematical lens.

Richard Baraniuk, C. Sidney Burrus Professor of Electrical and Computer Engineering at Rice University and founder of OpenStax and SafeInsights, has been awarded the 2025 IEEE Jack S. Kilby Signal Processing Medal. The honor recognizes his pioneering contributions to multiscale and sparse signal processing, advancing the field significantly. Presented annually since 1995, the medal celebrates excellence in signal processing and will be formally awarded at the IEEE VIC Summit & Honors Gala in Tokyo in April 2025.

The New York Times highlighted recent research from Richard G. Baraniuk’s group on the risks of training generative AI models with AI-generated data, a phenomenon explored in their paper Self-Consuming Generative Models Go MAD. The team, including Sina Alemohammad and others, warns that when models repeatedly train on their own synthetic outputs—without enough fresh real data—they enter a decline known as Model Autophagy Disorder (MAD), leading to reduced precision and diversity. This feedback loop, now attracting widespread attention, raises urgent concerns about the future robustness and reliability of generative AI systems.

Rama Chellappa was awarded the Edwin H Land Medal by Optica (formerly Optical Society of America) in their 2024 Awards announcement, honoring his seminal work in image/video processing and computer vision. His contributions have led to breakthroughs in various technologies, improving countless lives worldwide and marking him as a pioneer in his field.

The University of Maryland team, led by Tom Goldstein, received a notable grant from the DOE's INCITE program, awarding them 750,000 node-hours on advanced supercomputers. This substantial computational time is a boon for their AI research, specifically aimed at studying and scaling foundation models in AI, including innovative vision and language models. This allocation not only significantly enhances their research capabilities but also democratizes access to state-of-the-art computational resources. It marks a crucial step in advancing AI technology, fostering greater innovation, and broadening the scope of AI research accessibility.

Rama Chellappa received the prestigious Distinguished Researcher Award from the IEEE Computer Society's Pattern Analysis and Machine Intelligence Technical Committee. This accolade recognizes Chellappa’s extensive contributions to the fields of computer vision, pattern recognition, and machine learning, showcasing the profound effect of his research on technologies such as biometrics and smart cars.

Professor Stanley Osher achieved a monumental milestone, surpassing 150,000 citations on Google Scholar, reflecting the vast impact of his work across various disciplines. This achievement not only highlights his contributions to mathematics and computational science but also underscores the global recognition of his research and its influence on generations of scientists and researchers.

Ryan Tibshirani was awarded the 2023 COPSS Presidents’ Award, a testament to his significant contributions to nonparametric estimation, high-dimensional inference, and distribution-free inference. His work has deeply influenced the statistical and machine learning communities, demonstrating the broad applications of his research in both theoretical and practical domains. The COPSS Presidents' Award, which is often referred to as the "Nobel Prize of Statistics" is one of the highest honors in the field of statistics.

Tom Goldstein was celebrated with the Outstanding Paper Award at ICML for his innovative work on watermarking large language models (LLMs). His research presents a novel method for embedding signals in generated text, invisible to humans but detectable algorithmically, addressing the challenges in the authenticity and origin of AI-generated content.

Moshe Vardi was elected as a Foreign Member of the Royal Society, recognizing his pivotal role in using logic as a foundational framework for modeling computational systems. This honor acknowledges Vardi's lasting impact on fields such as automatic verification and database theory, underscoring his standing as a leading figure in computer science.