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.
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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Le Hou Dissertation Defense: Deep Learning for Digital Histopathology across Multiple Scales

ABSTRACT: Histopathology is the study of tissue changes caused by diseases such as cancer. It plays a crucial role in disease diagnosis, survival analysis and development of new treatments. Using computer vision techniques, I focus on multiple tasks for automated analysis in digital histopathology images, which are challenging because histopathology images are heterogeneous and complex, due to the large variation of hundreds of cancer types in gigapixel resolution. In this thesis, I show how histopathology image analysis tasks can be viewed in three scales: Whole Slide Image (WSI)-level, patch-level and cellular-level, and present my contributions in each resolution level.

BIO: WSI-level analysis such as classifying WSIs into cancer types is challenging, because conventional classification methods such as off-the-shelf deep learning models cannot be applied directly on gigapixel WSIs due to computational limitations. I contribute a patch-based deep learning method that classifies gigapixel WSIs into cancer types and subtypes with close-to-human performance. This method is useful for computer-aided diagnosis. At patch-level, I contribute a novel method for histopathology image patch classification. On the task of identifying Tumor Infiltrating Lymphocyte (TIL) regions, the prediction result of this method correlates to the survival rate of patients. At cellular-level, I contribute novel methods for nucleus classification and roundness regression, which are interpretable features for histopathology studies. With this method, I generated a large-scale dataset of segmented nuclei, in WSIs from a large publicly available digital histopathology image dataset, to help advance histopathology research.

George Em Karniadakis received his SM and PhD from Massachusetts Institute of Technology. He was appointed lecturer in the Department of Mechanical Engineering at MIT in 1987 and subsequently he joined the Center for Turbulence Research at Stanford/Nasa Ames. He joined Princeton University as assistant professor in the Department of Mechanical and Aerospace Engineering and as associate faculty in the program of applied and computational mathematics. He was a visiting professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as associate professor of applied mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continues to be a visiting professor and senior lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS fellow (2018), fellow of the Society for Industrial and Applied Mathematics (2010), fellow of the American Physical Society (2004), fellow of the American Society of Mechanical Engineers (2003) and associate fellow of the American Institute of Aeronautics and Astronautics (2006). He received the Alexander von Humboldt award in 2017, the Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) from the US Association in Computational Mechanics. His h-index is 103, and he has been cited over 52,000 times.


Abstract:
Karniadakis will present a new approach to develop a data-driven, learning-based framework for predicting outcomes of physical and biological systems, governed by PDEs, and for discovering hidden physics from noisy data. He will introduce a deep learning approach based on neural networks (NNs) and generative adversarial networks (GANs). He will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. Unlike other approaches that rely on big data, here we learn from small data by exploiting the information provided by the physical conservation laws, which are used to obtain informative priors or regularize the neural networks. He will demonstrate the power of PINNs for several inverse problems in fluid mechanics, solid mechanics and biomedicine including wake flows, shock tube problems, material characterization, brain aneurysms, etc., where traditional methods fail due to lack of boundary and initial conditions or material properties. He will also present a new NN, DeepM&Mnet, which uses DeepOnets as building blocks for multiphysics problems, and he will demonstrate its unique capability in a 7-field hypersonics application.  

To register and for more information, click here.  

AI is everywhere -- and so are the privacy concerns that come with it. At its core, the most common forms of AI we use today are online digital services -- and thus inherit the usual privacy risks of any internet-based tool. However, AI also introduces a set of unique and evolving risks. We'll take a closer look at one of the newest developments in this area: indirect prompt injection -- a technique that can trick AI tools into revealing or extracting private information. You'll learn how this emerging form of AI manipulation works, why it matters, and how to protect yourself -- as well as how similar techniques are being used in academic contexts to manipulate systems and even mislead researchers.

Register for this Zoom workshop.