Abstract: Trustworthy AI deployment in high-stakes domains requires systems that are fair, private, robust, and controllable as they scale. Yet these demands are often pursued through ad-hoc approaches, lacking a systematic understanding of the inherent trade-offs between competing objectives. We add fairness regularizers and hope bias decreases. We train on massive datasets and hope the model learns the underlying logic of how concepts combine, rather than memorizing statistical shortcuts. We encrypt data and hope the resulting computational overhead remains manageable. But hope isnot a science.
In this talk, I argue that what trustworthy AI lacks is not better heuristics but a deeper science of what these properties fundamentally cost and what is achievable. Before we can fix a system, we must map the terrain: what trade-offs are unavoidable, what regions of performance areunreachable, and how far current methods fall from what is actually achievable. My research builds this map across fairness, privacy, robustness, and controllability, following a common methodology: diagnose where models fail, characterize the fundamental limits any method must obey, and design systems that approach those limits. I will present this framework, its extension to scientific applications where we replace statistical constraints with physical laws to ensure AI systems remain grounded in reality, and a vision for scaling these principles to the rapidly expanding ecosystem of composed and interacting AI systems.


Bio: Dr. Vishnu Boddeti is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University, where he leads the Human Analysis Lab (HAL). His research develops mathematical frameworks for trustworthy AI, spanning fairness, privacy, robustness, and physics-informed learning, with an emphasis on characterizing fundamental limits and building systems that achieve them. His work has been supported by NSF, NIST, DARPA, ONR, Ford, and others, and recognized with a Meta Research Award (2021). His research has been featured on the cover of Nature, recognized as an Editor's Highlight in Nature Communications, and received multiple best paper awards, including the 2024 IEEE-CCF Cloud Computing Best Paper Award and the TMLR Outstanding Certification Finalist (2023). He serves as Senior Area Editor for IEEE Transactions on Information Forensics and Security and completed his PhD in ECE from Carnegie Mellon University in 2012.

Location: NCS 120

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Learning Generalizable Program and Architecture Representations for Performance Modeling

Abstract: Performance modeling is an essential tool in many areas of computer science and engineering. However, existing performance modeling approaches have limitations, such as high computational cost, narrow flexibility, or restricted accuracy/generality. To address these limitations, this talk introduces PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional and independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling-related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general and efficient than previous approaches. This talk will also introduce how PerfVec's design principles can benefit broader research areas.

Biography: Lingda Li is a computer scientist at Brookhaven National Laboratory. He is generally interested in computer architecture and programming model research, with focus on simulation/modeling, memory systems, and machine learning. Before joining BNL, he worked at the Department of Computer Science of Rutgers University as a postdoc to carry out GPGPU research. He obtained a PhD in computer architecture from the Microprocessor Research and Development Center at Peking University.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605837856?pwd=kYqJs4bVBt4E0cMCWR6GXH3wxzOoiw.1

Meeting ID: 160 583 7856
Passcode: 161580

Academic Integrity and CELT invite faculty to an open discussion on managing the challenges of classroom technology and AI, handling misconduct suspicions and reports, and accessing resources like syllabus templates and recommended policy language. Our goal is to promote transparency about policies and procedures and empower faculty with easily accessible resources and strategies.

Register here to join.

Abstract: Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes per-pixel offsets to the input RGB and output normal along with a learned reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven benchmarks and three backbone architectures (diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23-26% on synthetic data and 6-16% on real data. These results show that guiding learned RGB-based normal estimators with polarization cues at test time refines normals on challenging surfaces without retraining.

Speaker: Irene Kim

Location: NCS 115
Abstract: Robot control has evolved from optimization-based controllers---precise but task-specific---through deep reinforcement learning's learned policies, to Vision-Language-Action (VLA) models that leverage pretrained vision-language backbones for language-conditioned manipulation across diverse tasks.
Despite their promise, VLAs exhibit a critical limitation: they function primarily as trajectory learners rather than skill learners. Recent evaluations reveal that VLAs often fail when faced with even minor variations in object initialization or environmental conditions, suggesting they memorize specific trajectories rather than acquiring generalizable manipulation skills. Attempts to address this through 3D spatial representations have shown limited success, indicating that the missing component may be more fundamental than geometric understanding alone.
This work argues that World Models (WMs)---internal representations that predict future states given actions---constitute the missing piece for robust VLA systems. We present one completed contribution and two ongoing investigations.
We developed a dual-layer world model for human-robot interaction that anticipates both physical scene evolution and latent human preferences for assistive tasks. Building on these foundations, we present ongoing work probing VLA internal representations to verify implicit world model existence, and propose a WM-VLA integration approach operating in the native visual domain through embedding prediction and image decoding.
Together, these contributions and investigations establish a foundation for WM-VLA systems, pointing toward robust, generalizable robot policies.
Speaker: Jason Qin
Location: NCS 220

The AI Community will be hosting our very first Datathon💡📊

Ready to turn data into groundbreaking insights? 🧠

Compete in our Datathon, where you'll analyze real-world data 📈 and share innovate solutions in these tracks:

🏫 Student Life

🌱 Environment & Sustainability

💉 Health & Wellness

💰 Finance & Economics

Whether you're a data pro or just starting out, this is your chance to network, learn, and win exciting prizes! 🏆🎉 Bring your creativity 🧩 collaborate with fellow students 🧑‍🤝‍🧑 and gain hands-on experience showcasing your analytical skills 💻

Submissions will be judged by professors 🧑‍🏫 so take this chance to impress them!

There will be free food ☕ and games 🎲 to fuel your brain and imagination! Don't miss out--register now and unleash the power of data! 🔥✨

Registration Form: https://forms.gle/6XYMfmhyAByzFpxz5

Time: Friday (4/4) 10:30am - 5pm ⏰

Location: Bauman Center 📍

As generative AI (GenAI) continues to reshape the educational landscape, educators must critically examine its implications for course design. How can we adapt our courses to ensure meaningful learning in a post-GenAI world? How can we harness its potential while mitigating risks to student learning? This seminar explores the evolving role of GenAI in higher education, emphasizing learner-centered teaching practices--such as backward design, transparency, and active learning--as essential strategies for navigating both the opportunities and challenges posed by GenAI. We will examine how GenAI disrupts traditional models of teaching and assessment, highlighting course design choices that intentionally promote deep learning and critical thinking in this new era.

Speaker Bio: Dr. Lourdes Alemán is an Associate Director at MIT's Teaching and Learning Lab (TLL). She earned her Ph.D. in Biology from MIT, studying RNA interference (RNAi) with Professor Phil Sharp. She later completed a postdoc in curriculum innovation with Professor Graham Walker's HHMI MIT Education Group. As a postdoc and research scientist, she helped develop software tools for teaching experimental design and data analysis, including collaborations with the MIT-Haiti Initiative. Before joining TLL, she worked at MIT's Open Learning, supporting MIT faculty in blended and online education. At TLL, Lourdes trains graduate students and postdocs in college-level teaching, advises faculty on classroom innovation, and previously designed and taught a hands-on biology module on novel antibiotic discovery for first-year students. She has served on university committees focused on mentoring and advising. Drawing from her experiences as a Cuban immigrant student, she developed MIT's first curriculum on growth mindset and co-founded Flipping Failure, a campus-wide initiative for students to share their stories of academic challenges and the strategies they have used to overcome them.