CSE 600 Seminar Series | Fall 2025


Abstract: The first part of the presentation focuses on the fundamental role that failures play in the Ph.D. journey, highlighting how they offer invaluable learning experiences to build resilience, critical thinking, and adaptability. Instead of viewing failures as signs of inadequacy, they should be recognized as opportunities to learn, re-evaluate, and develop the persistence needed for success in a high-stakes research environment. In the second part of the presentation, we take a quick look at the evolution of distributed databases research at Stony Brook and then focus on different challenges associated with distributed transaction processing systems functioning in untrustworthy environments. Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by distributed transaction processing systems to establish consensus on the order of transactions. However, the proliferation of different BFT protocols has made it difficult to navigate the BFT landscape, let alone determine the protocol that best meets application needs. Moreover, as novel applications, modern hardware, and new cloud platforms arise, distributed transaction processing systems need to be designed with full-stack adaptivity in mind. This presentation discusses our vision for a reinforcement learning (RL)-based distributed transaction processing system that adjusts effectively in real time to dynamic fault scenarios and evolving workloads.

Bio: Mohammad Javad Amiri is an Assistant Professor in the Department of Computer Science at Stony Brook University. Before joining Stony Brook, he was a postdoctoral researcher in the Computer and Information Science Department at the University of Pennsylvania. He received his Ph.D. in Computer Science from the University of California, Santa Barbara. His research mainly lies at the intersection of data management and distributed systems, focusing on distributed transaction processing, consensus protocols, and blockchains.
Research challenges in using computer vision in robotics systems Abstract The past decade has seen a remarkable increase in the level of performance of computer vision techniques, including with the introduction of effective deep learning techniques. Much of this progress is in the form of rapidly increasing performance on standard, curated datasets. However, translating these results into operational vision systems for robotics applications remains a formidable challenge. This talk with explore some of the fundamental questions at the boundary between computer vision and robotics that need to be addressed. This includes introspection/self-awareness of performance, anytime algorithms for computer vision, multi-hypothesis generation, rapid learning and adaptation. The discussion will be illustrated by examples from autonomous air and ground robots.
Abstract: Recent work in NLP uses debates between multiple LLMs to arrive at a more accurate conclusion. Earlier chain-of-thought prompting also shows improvements in accuracy when the model is asked to provide step-by-step reasoning in its response. Many publications since have developed strategies to improve the reasoning of model output with the goal of generating a more accurate result. However, even when asked to provide problem solving steps, the content of the reasoning provided by models is not well studied for all tasks and sometimes contains errors or conflicting statements even when the final result is correct. In fact, when evaluated across reasoning tasks, evidence shows that LLMs are not learning how to reason but are instead mimicking relevant solutions from their training sets.
By studying and evaluating the argumentation that LLMs provide, we can determine factors that may benefit or hinder the model's ability to give a complete, cohesive, and thorough answer. While there are signs that LLMs pattern match, finding where, when, and why this fails is valuable, as there may be ways to help the model imitate solutions that are more relevant to the task it is attempting to solve. Determining when pattern matching is not enough could show an area of improvement for future generations of LLMs. This research may separately aid in work on human-(AI)agent and inter-agent interaction. Specifically, frameworks could be used to determine when and why other models or humans are convinced by LLM-generated responses and which argument methods cause other models to change their response. Our current research in systematic versus heuristic cues shows that large language models sometimes present systematic or heuristic reasoning patterns based on prompting. Future research aims to explore other methods of classifying argumentation.

Speaker: Kiera Gross

Joining link: https://meet.google.com/xae-ywpv-udo
The International Neuroethics Society (INS) Speaker Series on AI & Consciousness

Abstract: Colln Allen and I noted in our 2008 book Moral Machines: Teaching Robots Right From Wrong, that consciousness, a theory of mind, sociability, situational awareness and embodiment are all supra-rational (beyond reason) capabilities that contribute to making ethical decision Whether any of these can be fully instantiated in machines remains an open question. Nevertheless, moral decision making in the digital age will require an evolution in and refinement of specific skills for both humans and for AI. I call one of these evolutions in moral decision making capabilities tradeoff ethics and another a silent ethics. Aspects of this social, and not just technological evolution, will require research by neuroscientists.

Speaker Bio: Wendell Wallach has an international reputation as an expert on the ethics and governance of emerging technologies, particularly artificial intelligence and biotechnologies. He is also senior advisor to The Hastings Center and a scholar at the Yale University Interdisciplinary Center for Bioethics where he chaired Technology and Ethics studies for eleven years. Wallach's latest book, a primer on emerging technologies, is entitled, A Dangerous Master: How to keep technology from slipping beyond our control. He co-authored (with Colin Allen) Moral Machines: Teaching Robots Right From Wrong. Wallach has been referred to as, a Godfather of AI Ethics.

Pre-register here (required): https://umaryland.zoom.us/meeting/register/sPpiR_drR4-9JYDhI2NhJg
Abstract: As computing and society become increasingly inseparable, we confront a fundamental design challenge: creating AI systems where human-machine interactions authentically embody our diverse values while thoughtfully evolving our social relationships. The recursive nature of these interactions--where human behavior shapes technology design and technological affordances influence human behavior--presents both profound risks and transformative opportunities as we reimagine our collective digital future. What interaction patterns emerge when algorithmic systems become active participants in societal decision-making? How can we design human-AI collaboration that ensures algorithmic systems align with diverse community values while serving the public interest? Through Public Interest AI, we explore a Pluralistic Design Language that creates interaction models for value-sensitive algorithmic ecosystems, strengthening AI-society alignment in both technology design and policy development. Through collaborative interaction with communities, we create systems that augment human capabilities while embedding ethical principles into the sociotechnical design of AI itself--ultimately redefining possibilities at the intersection of technology, policy, and society. This talk will examine the challenges of designing meaningful human-AI systems within social contexts through real-world applications that combine value-sensitive interaction design, human-inspired computing, and societal development to create technologies that advance our shared commitment to the public good.

Bio: Neil Gaikwad is an Assistant Professor of Data Science and Computer Science at UNC Chapel Hill. Additionally, he serves on the Faculty Advisory Council of the UNC Parr Center for Ethics and is a Fellow at the MIT Dalai Lama Center for Ethics and Transformative Values. Neil holds a Ph.D. in Society-Centered AI from MIT and is an alumnus of Carnegie Mellon University's School of Computer Science. Neil's scholarship, published in prominent AI and HCI conferences, has been recognized with several prestigious honors, including the Facebook Research Fellowship, UIST Best Paper Honorable Mention, MIT Engineering Fellowship, Human Rights & Technology Fellowship, Graduate Teaching Award, and the Karl Taylor Compton Prize, MIT's highest student honor. He has been recognized as a Rising Star by both Stanford University and the University of Chicago. Translating research into real-world impact, Neil is a dedicated educator and mentor who has taught over 500 students throughout his career. He has guided more than 30 students to publish influential papers on AI fairness, secure prestigious fellowships, and contribute to shaping AI policy through public interest research. Neil is also the founder of the AI Policy Global Initiative, which has successfully brought together academia, industry, government, and communities to address critical challenges in AI governance and develop collaborative approaches to responsible AI.

Location: Old Computer Science, room 1310
How to Do Spectral Learning at Scale for Science and Engineering

Abstract: Spectral decompositions such as singular value decompositions (SVDs) and eigenvalue decompositions (EVDs) are central tools across a vast swath of scientific computing and machine learning, with abundant engineering applications. Yet many modern methods for learning such decompositions in high dimensions struggle with instability, bias, and poor scalability, even when approximation power is not the limiting factor. I argue that these difficulties are not intrinsic to spectral problems, but instead arise from a shared reliance on Rayleigh-quotient-based constrained optimization, which forces explicit orthogonality handling through penalties, normalization, or whitening.
To address these challenges, I present a reformulation based on unconstrained variational objectives that implicitly encode spectral structure, eliminating the need for orthogonalization and ad-hoc regularization. This perspective leads to a conceptually simpler and scalable parametric framework for learning ordered spectral representations via nested optimization. The resulting framework is well matched to diverse settings in science and engineering. As examples, I demonstrate its effectiveness on eigenvalue problems for linear PDEs such as the Schrödinger equation, spectral (Koopman) analysis of nonlinear dynamical systems such as molecular dynamics, and structured representation learning with deep neural nets. Collectively, these examples illustrate how abandoning Rayleigh-quotient-based formulations resolves long-standing optimization pathologies across domains.

Bio: Jongha (Jon) Ryu is a postdoctoral associate at MIT EECS. He received his Ph.D. in Electrical and Computer Engineering from UC San Diego. His research develops statistical and mathematical foundations for scientific machine learning, with a focus on scalable spectral methods, efficient generative modeling, and reliable uncertainty quantification for scientific and engineering systems.

Location: NCS 120
Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a bootcamp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, and other generative AI platforms can support you in crafting learning objectives, writing exam questions, composing rubrics, and designing course content such as lesson plans, in-class activities, instructional videos, and more.

Register here.