AI Innovation Institute

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.

AI/ML Working Group Seminar

Time/Date: 12:00 PM ET, Tuesday, March 1st, 2022

Seminar Speaker: Yen-Chi (Sam) Chen, CSI, Brookhaven National Laboratory

Title: When reinforcement learning meets quantum computing

Abstract: Recently, reinforcement learning (RL) has demonstrated
various applications with superhuman performance such as mastering the
game of Go.  Meanwhile, the development of quantum computing hardware
shed light on building practical quantum applications to tackle
previously unsolved problems. What will happen if we combine these two
fascinating techniques? In this talk, I will present the recent
progress in quantum RL as well as using classical RL to help certain
tasks in quantum computing.



Host: Meifeng Lin, Computational Science Initiative

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talk at 4:00 in 214

• CEWIT's 6th annual hackathon sponsored by Major League Hacking, Hack@CEWIT2022, is taking place virtually on February 18-20, 2022. This year's theme is Hacking Into the Metaverse and will focus on NFT's, Blockchain, Crypto, and the Metaverse. To find out more about the event, mentoring, sponsoring, or to register, visit:

• https://www.cewit.org/programs/events/hack.php

I will be holding an informal 2-week short optimization course, to try
to cover a few important proofs in the field. The goal will be depth
over breadth, with focus on:

 - convergence proofs for gradient descent and stochastic gradient descent
 - energy functions and continuous time optimization
 - estimate sequences and Nesterov acceleration

and, time permitting, additional topics like variance reduction,
quasi-Newton methods, and Frank-Wolfe methods. If we go super fast, we
can spend a few days at the end brainstorming interesting research
project ideas.

Details: NCS 220 6:15pm-7:45pm, Monday-Friday, Feb 7-Feb 18.

In person only, since I plan to use the whiteboard (but may be recorded)

More details will be uploaded here (notes, specific schedule):
https://sites.google.com/view/optimization-short-course/home

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Abstract Over the last decade, artificial neural networks have undergone a revolution, catalyzed by better
supervised learning algorithms. However, in stark contrast to young animals (including humans), training such
networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead
mainly on unsupervised learning. The reason is that most animal behavior is not the result of clever learning
algorithms--supervised or unsupervised-- but is encoded in the genome. Specifically, animals are born with
highly structured brain connectivity, which enables them to learn very rapidly. Because the wiring diagram is
far too complex to be specified explicitly in the genome, it must be compressed through a genomic
bottleneck. I will describe results showing how the genomic bottleneck algorithm can lead to dramatic
compression of networks and better generalization, particularly for rapid transfer in supervised and
reinforcement learning.

Anthony Zador is professor of neuroscience at CSHL.

The next AI Institute seminar speaker will be Chao Chen of Biomedical Informatics, on Monday November 29 at noon via zoom:

https://stonybrook.zoom.us/j/96233844681?pwd=aVVsUnIzMWJDMHRqVXcrQU5HMjFVQT09

He will be talking on the Detection of Trojan Attacks to Deep Neural Networks - A Topological Perspective, with his abstract and bio below.


Abstract: Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through Trojaned training samples, i.e., samples with special trigger injected and labels altered. To identify a Trojaned model at deployment is challenging, due to limited access to the training data. We propose to identify Trojaned neural networks using methods from topological data analysis. In particular, we propose to (1) inspect high-order topological features of the neuron interactions and (2) reverse engineer the injected triggers using a topological loss. These approaches take different angles and reveal insights into the behavior of neural networks when their strong memorialization power is exploited maliciously. The work has been accepted to NeurIPS'21. I will also briefly mention other research directions from my group, including incorporating topological information into deep image analysis, topology-inspired graph neural networks, and robust training of neural networks with label noise. These works have been published in ICLR, ICML, NeurIPS, ECCV, ICCV and AAAI in recent years.
Bio: Dr. Chao Chen is an assistant professor of Biomedical Informatics at Stony Brook University. His research interests span topological data analysis (TDA), machine learning and biomedical image analysis. He develops principled learning methods inspired by the theory from TDA, such as persistent homology and discrete Morse theory. These methods address problems in biomedical image analysis, robust machine learning, and graph neural networks from a unique topological view. His research results have been published in major machine learning, computer vision, and medical image analysis conferences. He is serving as an area chair for MICCAI, AAAI, CVPR and NeurIPS.

When: Thu: 10/28/2021, 10 am
Where: NCS Room 220, or
Zoom: https://stonybrook.zoom.us/j/97978463739?pwd=aVJFVERQa25jYjJrOFZEcWVuSzJLdz09

Deep Surface MeshesPascal FuaEPFLGeometric Deep Learning has recently made striking progress with the advent of Deep Implicit Fields (SDFs). They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable 3D surface parameterization that is not limited in resolution. Unfortunately, they have not yet reached their full potential for applications that require an explicit surface representation in terms of vertices and facets because converting the SDF to such a 3D mesh representation requires a marching-cube algorithm, whose output cannot be easily differentiated with respect to the SDF parameters. In this talk, I will discuss our approach to overcoming this limitation and implementing convolutional neural nets that output complex 3D surface meshes while remaining fully-differentiable and end-to-end trainable. I will also present applications to single view reconstruction, physically-driven Shape optimization, and bio-medical image segmentation.

Bio:

Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996 where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. He has (co)authored over 300 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. He often serves as program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies. 

Topic: AI Seminar: Stanley Bak
Time: Monday Nov 1, 2021 12:00 PM Eastern Time (US and Canada)

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Abstract: The field of formal verification has traditionally looked at proving properties about finite state machines or software programs. The surge in deep learning has been accompanied by a surge of progress in trying to apply mathematical and algorithmic techniques to prove things about the function being computed by a neural network.

This talk formalizes the neural network verification problem and describes technical methods for neural network verification based on reachability analysis. Improvements to analysis efficiency will be given, as well as research directions for further exploration. We also include an objective comparison performed this last summer trying to evaluate the best existing verification methods in terms of speed and network size. The competition was performed on common hardware and involved the participation of twelve international teams (the tool authors) on a common set of benchmarks. 

Biography: Stanley Bak is an assistant professor in the Department of Computer Science at Stony Brook University investigating the verification of autonomy, cyber-physical systems, and neural networks. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems.
Stanley Bak received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He helped run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performed teaching at Georgetown University before joining Stony Brook University as an assistant professor in Fall 2020.