Virtual Job Fair for New Stony Brook Graduates & Experienced Alumni Using a platform called Career Fair Plus, participants will be able to schedule 10-minute video meetings with participating employers of interest to them. Recent graduates and alumni can register and learn more about how the fair will be run by registering on Handshake.

Join us for presentations, a keynote speaker, a Q&A session, and a chance to network with faculty and industry professionals

Hosted by College of Engineering and Applied Sciences, Long Island Manufacturing Extension Partnership (LIMEP), and Center of Excellence Wireless and Information Technology (CEWIT)

Register here

Location: Stony Brook University, CEWIT Room 152

Face Editing with Machine Learning presented by Zhixin Shu

ABSTRACT: The face is the most informative feature of humans and has been a long-standing research topic in Computer Vision and Graphics. Images of faces are also ubiquitous in photography and social media, and people have devoted significant resources to capturing and editing face images. Face editing can be broadly viewed as the encoding, manipulation and the decoding of some representations for face images. The challenges are that we want to manipulate an image in a controllable way and generate results that are both desirable and as realistic as possible. This thesis explores different Machine Learning-based face-editing approaches. I discuss the role of machine learning for achieving desirable edits by learning both the physical aspects as well as the statistical manifold of human faces. In my work for eye-editing, I discuss the importance of understanding multiple physical elements of a face image, such as shape, illumination, pose, etc. In a deep-learning-based approach, I introduce image formation domain knowledge to the construction and training of a neural network. This network provides transparent access to the disentangled representations of the aforementioned physical properties. With this network, we can achieve various face editing tasks in forms of representation manipulation. After that, I introduce Deforming Autoencoders, a network that learns to disentangle shape and appearance in an unsupervised manner. This disentanglement is beneficial for the learning of some other factors of variations, such as illumination and facial expression. In an extension of Deforming Autoencoders, we incorporate non-rigid structure-from-motion to learn a 3D morphable model for faces that only requires an image set for training. At last, I describe an image-to-image network for 3D face reconstruction, which also utilizes structure-from-motion in deep learning. With real face images in training, this network not only reconstructs 3D faces more accurately than prior art but also has better generalization ability in real-life testing cases.
The talk will be exclusively on zoom https://stonybrook.zoom.us/j/7851507944 Speaker: Sooyeon Lee, Rochester Institute of Technology Title: Design and Evaluation of Accessible AI Technologies for Users with Disabilities Abstract: Over one billion people in the world live with some type of disability. Many of them experience barriers in accessing information or using technologies, which can limit social interactions in both physical and digital spaces. In my research, I focus on investigating and designing nonvisual interaction for the community of blind users and non-audio and non-speech interaction for the community of deaf and hard of hearing users. In this talk, I will first present my research investigating nonvisual interaction prototypes for supporting shopping activities for blind users, with an exploration of one-way instructional and two-way conversational interactions and with a variety of form factors and communication modalities through the use of human-computer interaction research methodologies. I will also discuss incorporation of AI technology and its impact on the nonvisual guidance experiences, and further meanings of independence and new ways for designing independence for people with visual impairments. This collaborative work included AI researchers, the community of the blind, and an industry research partner. Additionally, I will discuss my findings and further exciting research opportunities. Secondly, I will overview research projects investigating AI-based applications and tools that support deaf and hard of hearing people's equitable information access and societal participation. This work addresses engagement in online social media spaces, workplace communication, participation in gig work, and interaction with mainstream technology through American Sign Language (ASL) interaction. I will focus on a recent project on users' experiences with AI deep-fake face-transformation technologies to support anonymous participation of deaf and hard of hearing signers in online social media. Lastly, I will discuss my future research directions informed and inspired by this prior and current research. Bio: Sooyeon Lee is a postdoctoral research associate in the Golisano College of Computing and Information Sciences at Rochester Institute of Technology. She received her Ph.D., advised by Dr. John M. Carroll, in Information Sciences and Technology from the College of Information Sciences and Technology at The Pennsylvania State University, and she also conducted design research at Google and Uber. Her research is in the fields of Human-Computer Interaction and Human-AI Interaction with focus on accessibility. She designs, builds, and evaluates new systems and applications that address accessibility barriers. Her work investigates the diversity of users, explores and leverages emerging technologies, and adopts human-centered design and inclusive design approaches in an interdisciplinary research framework. She has multiple publications in top-tier human-computer interaction and computing accessibility journals and conferences, including ACM CHI, CSCW, ASSETS, and TACCESS, and she has received a Best Paper Award Nomination at ASSETS 2021. She has served on Associate Chair for the ACM CHI conference and will serve on Program Committee for ASSETS 2022.
Abstract: Large language models are prone to memorizing some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users. There is hope that model alignment---a standard training process that tunes a model to harmlessly follow user instructions---would mitigate the risk of extraction. However, we develop two novel attacks that undo a language model's alignment and recover thousands of training examples from popular proprietary aligned models such as OpenAI's ChatGPT. Our work highlights the limitations of existing safeguards to prevent training data leakage in production language models.

Speaker: Pegah Alipoormolabashi

Location: CS2311
Abstract: Gaussian Probability Path-based Generative Models (GPPGMs) generate data by reversing a stochastic process that progressively corrupts samples with Gaussian noise. Despite state-of-the-art results in 3D molecular generation, their deployment is hindered by the high cost of long generative trajectories, often requiring hundreds to thousands of steps during training and sampling. In this work, we propose a principled method, named GAGA, to improve generation efficiency without sacrificing training granularity or inference fidelity of GPPGMs. Our key insight is that different data modalities obtain sufficient Gaussianity at markedly different steps during the forward process. Based on this observation, we analytically identify a characteristic step at which molecular data attains sufficient Gaussianity, after which the trajectory can be replaced by a closed-form Gaussian approximation. Unlike existing accelerators that coarsen or reformulate trajectories, our approach preserves full-resolution learning dynamics while avoiding redundant transport through truncated distributional states. Experiments on 3D molecular generation benchmarks demonstrate that our GAGA achieves substantial improvement on both generation quality and computational efficiency.

Speaker: Jingxiang Qu

Location: New Computer Science 220

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.

We will demonstrate how to make a personalized AI partner that can do certain tasks for you. This step by step process can be used to develop your own companion.

Understand what is a Gemini Gems and how it can be personalized for your custom needs. We will show you how to capture the tasks that you would like to complete and give you higher quality responses that will ensure your communication and tasks are completed with ease.

Register here.