AI Innovation Institute

AI Seminar: Computational Pathology: Deep Learning, Classification and
Predicting the Future  - Joel Saltz

Abstract:  Pathologists have been looking at tissue through microscopes since the 1800s.  During each pathologist's career,  he or she views slides having  roughly 1,000,000,000,000 cells. Deep learning methods are rapidly being developed to assimilate the huge amount of information walked inside of tissue images and to use this information to predict outcomes and responses to treatments.

Stony Brook is a leader in this type of multi-disciplinary work. I will provide an overview of Stony Brook computational Pathology efforts and articulate how these have the potential to create biomedical advances as well as to drive development of new computer science. 


Bio: Dr. Joel Saltz is a leader in research on advanced information technologies for large scale data science and biomedical/scientific research. He has developed innovative pathology informatics methods, including: the first published whole slide virtual microscope system; pioneering pathology computer-aided diagnosis techniques; and methods for decomposing pathology images into features and linking those features to cancer omics, response to treatment and outcome. He has broken new ground in big data through development of the filter-stream based DataCutter system, the map-reduce style Active Data Repository and the inspector-executor runtime compiler framework. He has also been an active contributor in clinical informatics, having developed
predictive models for hospital readmissions, point of care laboratory testing quality assurance systems, decision support systems for electrophoresis interpretation and graphical user interfaces to support clinical data warehouse queries. Dr. Saltz has been a pioneer in establishing the field of biomedical informatics; he founded and built two highly successful departments of biomedical informatics, one at Ohio State University and one at Emory University. In 2013, he came to Stony Brook as Vice President for Clinical Informatics and Founding Department Chair of Biomedical Informatics - to create a living laboratory for biomedical informatics and to create a third unique biomedical informatics department dually housed in the School of Medicine and the College of Engineering. Dr. Saltz is trained both as a computer scientist and as a physician through the MSTP program at Duke University. He has deep experience in computer science, having served on the computer science faculties at Yale University and the University of Maryland. He completed his residency in clinical
pathology at Johns Hopkins University and he is a practicing, board-certified clinical pathologist. 

Abstract: Self-supervised representation learning (SRL) has emerged as a pivotal advancement in machine learning, offering high-quality data representations without the need for labeled datasets. While SRL has demonstrated enhanced adversarial robustness compared to supervised learning, its resilience against other attack types, particularly backdoor attacks, remains an open question. Recent studies have revealed potential vulnerabilities in SRL, underscoring the necessity for a comprehensive security analysis. However, existing research often extrapolates attacks from supervised learning paradigms, neglecting the unique challenges and opportunities inherent to self-supervised mechanisms.

This thesis proposal aims to address three critical objectives in the realm of self-supervised learning: (1) exploring novel attack vectors, (2) implementing and evaluating practical attacks, and (3) developing robust countermeasures. We focus on two key SRL paradigms: Contrastive Learning and Diffusion Models. For Contrastive Learning, we synthesize existing security vulnerabilities and introduce innovative attack vectors, such as CTRL, to uncover distinctive risks. We conduct a comparative analysis of contrastive and supervised learning approaches in their defense against these threats, exploring potential safeguards and highlighting the limitations of current protective measures in self-supervised contexts. Regarding Diffusion Models, we demonstrate inherent vulnerabilities in their application to adversarial purification.

Our research aims to illuminate the unique challenges posed by emerging attack vectors in self-supervised learning, fostering technical advancements to address underlying security risks in real-world applications. By contributing to the development of more resilient and secure self-supervised representation learning systems, we seek to enhance their reliability and trustworthiness in practical scenarios. This comprehensive examination of SRL's security landscape will provide valuable insights for the broader machine-learning community and pave the way for more robust AI systems.

Join here.

The Center of Excellence in Wireless and Information Technology (CEWIT) will host the 16th International Conference on Emerging Technologies for a Smarter World (CEWIT2020) virtually on November 5, 2020. The conference will center on the four major fields which are penetrating our business and personal lives: Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For more info visit: https://www.cewit.org/.

Zoom Like a Pro! Unlock Whiteboard, Polls, AI Companion, and more to supercharge student participation. This hands-on workshop explores innovative ways to use Zoom's built-in tools to enhance active learning activities in your classes. Learn how to utilize the Whiteboard feature to make collaborative work more engaging, use Polling and Quizzes for instant feedback, AI Companion for summary, and Breakout Sessions for group activities. Register here: https://stonybrook.zoom.us/meeting/register/tJckf--rpj4pGdRV0ItgTW8Lk7gn_RuykByO#/registration

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.



Location: Colorado Convention Center

Abstract:
Coarse grained (CG) models alleviate the drawbacks of all-atom simulations. The latter still pose challenges because they are computationally expensive and give access to limited spatiotemporal scales, despite the use of modern high-performance computing clusters. CG models ignore some of the atomistic degrees of freedom, leading to fewer interatomic interactions, hence less computing time. Introducing such models emphasizes the need to properly manage these multiple scales, by carefully deriving potentials and reconstructing conformations from their CG representations, usually with the help of Machine Learning. Following a bottom-up and force matching approach, we train a Physics-Informed Neural Network to extract the CG force field parameters from all-atom simulation data. We verify our approach by applying it to fibrin monomers to study multiple-fibrin polymerization in solution at the microsecond scale, after modifying the force field to incorporate further non-bonded interactions, not present in the training data. Access to these scales will allow us to study the effects of some of the molecules' components. Furthermore, we modify recent solutions in data-driven protein backmapping. Taking advantage of the developments in graph neural networks and variational inference, we introduce an intermediate step in the all-atom reconstruction of a molecule given its CG configuration, in an attempt to more accurately de-coarsen structures whose atom-to-CG-beads ratio is very high. The combined effect of our new forward and inverse coarse graining methodology will enable the in silico study of many phenomena that are highly dynamic and intrinsically multiscale.

Bio:
Georgios Kementzidis is a third year PhD student in the Department of Applied Mathematics and Statistics at Stony Brook University. His advisor is Dr. Yuefan Deng. His research interests lie at the intersection of Computational Science, molecular dynamics (MD) simulations, and Machine Learning (ML) applications to Computational Biophysics. He is particularly interested in coarse-graining and multi-scale simulations.

*Note: this seminar will be held in-person (food provided on a first-come, first serve basis) and online*

Join Zoom Meeting https://stonybrook.zoom.us/j/99510099036?pwd=EyowuLBGvUVLZDBlG6F6chkMICFOZ7.1
Meeting ID: 995 1009 9036
Passcode: 132419

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.