The University at Albany will host a national gathering of professionals and academics that will focus on the transformative potential of AI while addressing the ethical, technical and institutional challenges posed by AI in education.

The Symposium will feature dynamic keynotes, hands-on workshops and engaging conversations with other participants and subject matter experts.

Topics

  • Teaching, Learning and Workforce Development
  • Research, Creative Arts, and Practice
  • Ethics, Governance and Academic Administration

For event information and registration, visit Events@Albany.
Join us for the New York State Innovation Summit on October 28-29, 2024 in Syracuse, NY. This multi-day is event for NYS organizations that want to showcase and discover new and emerging technologies that support innovation and drive business growth. The event serves as an opportunity to foster collaboration; introduce industry to experts that can assist growth, strengthen our statewide innovation ecosystem and showcase promising early stage companies. Whether you're a startup, an economic developer, or an established manufacturer, the NYS Innovation Summit is for you. The 2024 New York State Innovation Summit will showcase companies and researchers at the forefront of emerging technologies and new advancements in production capabilities. This event celebrates New York State leadership in technology-led economic growth with experts in biotechnology, new materials, energy innovation, and artificial intelligence that will explore current technology convergence opportunities, ways to accelerate commercialization, and issues of manufacturing sustainability.
Title: Sustainable NLP

Time: Friday 4/29, 2:40 PM

Location: NCS 120

Abstract:


Natural language processing (NLP) technology has supercharged many real-world applications ranging from intelligent personal assistants (like Alexa, Siri, and Google Assistant) to commercial search engines such as Google and Bing. But current NLP applications use extremely large neural models, making them (i) expensive to deploy on servers, requiring large amounts of compute resources and power, and (ii) impossible to run on mobile devices, making on-device, privacy-preserving applications impractical.

In the first part of the talk, I will describe systems optimizations we have developed that significantly reduce the compute and memory requirement of NLP models. The optimizations we developed can be applied broadly and results in over 10x reduction in latency when deployed on mobile devices. In the second part of the talk, I will describe our recent work on predicting energy consumption of NLP models. Existing energy prediction approaches are not accurate, making it difficult for developers and practitioners to reason about their models in terms of power. We use a multi-level regression approach that produces highly accurate and interpretable energy predictions.



Bio:
Aruna Balasubramanian is an Associate Professor at Stony Brook University. She received her Ph.D from the University of Massachusetts Amherst, where her dissertation won the UMass outstanding dissertation award and was the SIGCOMM dissertation award runner up. She works in the area of networked systems. Her current work consists of two threads: (1) significantly improving Quality of Experience of Internet applications, and (2) improving the usability, accessibility, and privacy of mobile systems. She is the recipient of the SIGMobile Rockstar award, a Ubicomp best paper award, a Computing Innovation Fellowship, a VMWare Early Career award, several Google research awards, an
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.

We invite faculty to deliver a 10-minute presentation during our afternoon session at the CELT Symposium on April 11, 2025. Showcase how you use emerging technology (i.e. AI, VR, etc.) to support diverse student populations and enhance learning experiences. Share your innovative strategies and inspire others!

CELT Symposium Theme: A New Era of Inclusivity and Innovation in Higher Education

https://t.e2ma.net/click/5w0gph/5wwlu4oe/9v63j6
Abstract: Human gaze behavior is a fundamental cue for understanding social intent, human-machine interaction, and cognitive processes. This thesis addresses the challenges of gaze target estimation (GTE), also known as gaze following, by developing a holistic understanding of gaze in complex environments.

The first part of this work improves GTE performance by introducing Patch-level Distribution Prediction (PDP). Unlike traditional models that rely on strict pixel-wise regression, PDP models gaze as a distribution over patches, which better accounts for annotation variance and bridges the gap between target location and in/out-of-frame prediction. To address the laborious nature of data labeling, the second part presents GCDR, the first semi-supervised method for gaze following. By prompting large Visual Question Answering (VQA) models to generate initial Grad-CAM heatmaps and refining them with a diffusion model, this method achieves high performance with significantly fewer human annotations. The third part expands the applicability of GTE to multi-camera environments. By introducing the Multi-View Gaze Target (MVGT) dataset, along with two novel frameworks for integrating information between multiple views and predicting the gaze target across views, we explore a new direction that overcomes single-view limitations such as face occlusion and out-of-view targets.

Building on these foundations, the final part of this thesis proposes a new direction toward semantic social gaze understanding using next-generation multimodal Large Language Models (LLMs). Rather than focusing solely on geometric gaze target localization, we aim to enrich gaze prediction with semantic and relational interpretation in complex social scenes. To this end, we will leverage existing gaze following datasets to derive social gaze supervision, including mutual gaze and shared attention, and obtain aligned language descriptions of scene-level gaze behaviors. This proposed work will enable the model to not only locate gaze targets but also predict structured social gaze relations among individuals, meanwhile generating a concise natural-language summary describing the dominant gaze interactions. By integrating spatial gaze estimation, social relation reasoning, and language-based scene understanding within a unified multimodal model, this work takes an important step toward a holistic understanding of human gaze behavior in real-world environments.

Speaker: Qiaomu Miao
Abstract: Large language models (LLMs) may exhibit unintended or undesirable behaviors. Recent works have concentrated on aligning LLMs to mitigate harmful outputs. Despite these efforts, some anomalies indicate that even a well-conducted alignment process can be easily circumvented, whether intentionally or accidentally. Does alignment fine-tuning yield have robust effects on models, or are its impacts merely superficial? In this work, we make the first exploration of this phenomenon from both theoretical and empirical perspectives. Empirically, we demonstrate the elasticity of post-alignment models, i.e., the tendency to revert to the behavior distribution formed during the pre-training phase upon further fine-tuning. Leveraging compression theory, we formally deduce that fine-tuning disproportionately undermines alignment relative to pre-training, potentially by orders of magnitude. We validate the presence of elasticity through experiments on models of varying types and scales. Specifically, we find that model performance declines rapidly before reverting to the pre-training distribution, after which the rate of decline drops significantly. Furthermore, we further reveal that elasticity positively correlates with the increased model size and the expansion of pre-training data. Our findings underscore the need to address the inherent elasticity of LLMs to mitigate their resistance to alignment.

Speaker: Huajian Zhang

Location: CS2311

Designed for faculty, staff, presidents, provosts, academic leaders, student affairs professionals, IT specialists, librarians, researchers, administrators, institutional decision-makers, and other higher education stakeholders, the conference highlights practical strategies institutions can implement now while exploring longer-term governance, policy, and ethical considerations. Participants will leave with concrete tools, cross-institutional insights, and collaborative connections that support mission-aligned AI innovation.

Hosted by: AAC&U

Location: Atlanta, GA and Virtual

Register here.