Join us at the Center for Excellence in Learning and Teaching (CELT) for an interactive Zoom workshop on Generative AI designed for faculty and staff interested in enhancing teaching and assessment practices, increasing student engagement, and navigating the rapidly evolving landscape of AI tools. Participants will be introduced to common AI tools, explore potential instructional uses, and discuss key considerations such as academic integrity, transparency, and equity.
Register now: https://stonybrook.zoom.us/meeting/register/6js1eP64T1ys8tyU57EJ7Q#/registration

The Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop.

This workshop will focus on topics including big data analytics and machine learning with Spark, as well as deep learning.

This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event. SBU's Institute for Advanced Computational Science (IACS) is one of those satellite sites!

Location: IACS Conference Room #2

Interested applicants must first have an ACCESS ID. If you don't have the ID, please visit this page to create one: ACCESS USER REGISTRATION.


Once you have an ACCESS ID, please login (see top right here) then register here.

Abstract:

Many real world complex problems are multi-step reasoning tasks. These range from analytic tasks such as answering questions to automation tasks where agents complete tasks on behalf of users.. Evaluation, datasets, and models for such tasks can be unreliable for multiple reasons. (i) Datasets often have annotation artifacts and biases, allowing models to take reasoning shortcuts. Such shortcuts can allow models to make effective guesses -- or, in a sense, cheat -- to achieve high performance without any multi-step reasoning. This issue is further exacerbated for complex tasks because as the number of the required reasoning steps increases, so do the avenues for bypassing those steps. (ii) Models trained on such dataset/s learn to solve the task by taking reasoning shortcuts instead of proper multi-step reasoning. As a result, these models are not robust (reliable) when evaluated in an out-of-distribution evaluation setting. (iii) Lastly, recent works have shown that language models can solve complex multi-step tasks by producing a step-by-step explanation without any training. However, these methods often hallucinate factually incorrect (i.e., unreliable) explanations when posed with knowledge-intensive tasks.

I address these challenges by carefully characterizing the requirements of robust multi-step reasoning and designing reliable evaluation datasets and training methods that necessitate thorough multi-step reasoning. In DiRe, I first formalize and introduce Disconnected Reasoning, i.e., reasoning that allows models to arrive at the correct answer by bypassing necessary reasoning steps, and use this formalization to measure how much multi-step reasoning a model does on a dataset. In MuSiQue, I built a multi-step reasoning dataset for QA from scratch that avoids cheatability via disconnected reasoning, providing a more reliable evaluation. In TeaBReaC, I developed a synthetically generated multi-step QA pretraining dataset designed to force models to avoid disconnected reasoning and learn reliable multi-step reasoning. In IRCoT, I address the reliability of model-generated multi-step reasoning chains by interleaving models' step-by-step reasoning with a step-by-step retrieval from an external corpus, resulting in more factually correct reasoning. Finally, in AppWorld, I built a multi-step reasoning dataset that requires highly interactive problem-solving in an environment carefully designed to ensure models need thorough reasoning to succeed.
Speaker: Harsh Trivedi

Location: NCS 220 or Zoom

https://stonybrook.zoom.us/j/99096379762?pwd=zYCJZQVxRuZd9BboscO4nlodCwsKBr.1
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
Abstract: Modern technologies enable enhanced integrity and privacy guarantees not just for data, but also for computation. This is perhaps most emphatically demonstrated by the steady rise of zero-knowledge proofs, which are short certificates that attest to the correctness of computations (e.g., an age verification check) without revealing any secret inputs (e.g., the birth date on a digital ID). This subtly powerful technology enables anonymous credentials, privacy-preserving machine learning, anonymous blockchains, and much more--making the question of efficient zero-knowledge proofs fundamental to modern secure systems. Echoing Moore's law for computing, zero-knowledge proofs have improved on this front by ten orders of magnitude in the last two decades. In this talk, I will discuss our work on overcoming a key bottleneck that has emerged in this development: memory efficiency.

Speaker: Abhiram Kothapalli is a postdoctoral scholar at the University of California, Berkeley, hosted by Sanjam Garg. He is a recent graduate of Carnegie Mellon University, where he earned his Ph.D. in Computer Science, advised by Bryan Parno. Previously, he was at the University of Illinois at Urbana-Champaign, where he earned his B.S. in Computer Science and B.S. in Mathematics. Kothapalli's research develops cryptographic techniques aimed at scaling expressive privacy and integrity guarantees across the internet.

Location: NCS 120
Discover how U.S. Census Bureau Tools can help you find free data for your research projects, community, and more. See how to access the latest American Community Survey and 2020 Census data for various geographies including New York City and Long Island at data.census.gov. Learn about Community Resilience Estimates and how to navigate My Community Explorer; an interactive map-based tool which highlights demographic and socioeconomic data that measure inequality. This session will involve live demonstrations and hands-on exercises for participants. Registrants will receive the Zoom link one day prior to the event.

Please Register for SBU Libraries' AI Club: Exploring Census Data here.