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CS Colloquium (04/03): Oren Kurland (Technion)
by Sagie Benaim 04 Mar '24

04 Mar '24

Dear all, Next week, we have the pleasure of having Prof. Oren Kurland give a talk in the colloquium. The seminar will be held on Monday, March 4th at 14:00. Location: C220. The title, abstract and bio appear below. Looking forward to seeing you, Sagie and Liat *Title:* Competitive Search *Abstract:* The Web is a canonical example of a competitive search setting that includes document authors with ranking incentives: their goal is to promote their documents in rankings induced for queries. The incentives affect some of the corpus dynamics as the authors respond to rankings by applying strategic document manipulations. This reality has deep consequences that go well beyond the need to fight spam. We first show, using game theoretic analysis, that the probability ranking principle --- a fundamental principle underlying most retrieval methods ---- is not optimal in competitive retrieval settings; specifically, it leads to reduced topical diversity in the corpus. We then theoretically and empirically analyze the strategies of authors who strive to promote their documents in rankings. In addition, we present an approach for ranking-incentivized content manipulation. We then present novel retrieval methods that address strategic content manipulations. This is joint work with Moshe Tennenholtz, Fiana Raiber, Ran Ben Basat, Gregory Goren, Haya Nachimovsky, Nimrod Raifer and Ziv Vasilisky *Bio*: Prof. Oren Kurland is the Stephen and Sharon Seiden Chair at the Technion. He is a distinguished member of the ACM. Oren's research area is information retrieval. He was the founding chair of the steering committee of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR). He served as a program co-chair, area chair and senior program committee member for leading information retrieval conferences.

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CS Colloquium (26/02): Kobbi Nissim (Georgetown University)
by Sagie Benaim 26 Feb '24

26 Feb '24

Dear all, Next week, we have the pleasure of having Prof. Kobbi Nissim give a talk in the colloquium. The seminar will be held on Monday, February 26th, at 14:00. Location: C220. The title, abstract and bio appear below. Looking forward to seeing you, Sagie and Liat *Title:* A methodology for reconciling computer science and legal approaches to privacy *Abstract:* As law and computer science interact in critical ways within sociotechnical systems, recognition is growing of significant gaps between these disciplines that create potential risks for privacy and data protection. These gaps need to be bridged to ensure that computer systems are designed and implemented to correctly address applicable legal requirements and that interpretations of legal concepts accurately reflect the capabilities and limitations of technical systems. We will explore some of the gaps between the legal and computer science views of privacy and suggest directions towards mitigating them while respecting the values and principles of both disciplines. Partly based on work in progress with Micah Altman and Aloni Cohen *Bio*: Kobbi Nissim is a Professor of Computer Science at the Department of Computer Science, Georgetown University and an Affiliate Professor at Georgetown Law (currently on sabbatical in Google Tel-Aviv). Before Joining Georgetown, he was at the Department of Computer Science, Ben-Gurion University and a Senior Research Fellow at the Center for Research on Computation and Society, Harvard University. Nissim’s work from 2003 and 2004 with Dinur and Dwork initiated rigorous foundational research of privacy and in 2006 he introduced Differential Privacy with Dwork, McSherry and Smith. Nissim was awarded the Paris Kanellakis Theory and Practice Award in 2021, Caspar Bowden Outstanding Research in Privacy Enhancing Technologies (PETs) award in 2019, the Godel Prize In 2017, the IACR TCC Test of Time Award in 2016 and 2018, and the ACM PODS Alberto O. Mendelzon Test-of-Time Award in 2013. Kobbi Nissim studied at the Weizmann Institute of Science under the supervision of professors Uri Feige (towards an MSc degree) and Moni Naor (towards a PhD degree).

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CS Colloquium (19/02): Gal Chechik (Bar-Ilan University and Nvidia)
by Sagie Benaim 19 Feb '24

19 Feb '24

Dear all, Next week, we have the pleasure of having Prof. Gal Chechik give a talk in the colloquium. The seminar will be held on Monday, February 19th, at 14:00. Location: C220. The title, abstract and bio appear below. Looking forward to seeing you, Sagie and Liat *Title:* Personalizing foundation models for visual generative AI *Abstract:* Machine learning now heavily relies on "foundation models", representations trained with massive data in a task-agnostic way, and then used for downstream tasks. But how can we make them most useful for our own tasks? I will discuss a series of studies addressing this problem in the context of vision-and-language models. These include textual-inversion techniques - where we teach new personalized visual concepts to the model that can be combined with known concepts, and ways to improve their editability and speed. I will also discuss generation of rare classes, personalized approaches to discriminative problems, and future directions in model personalization. *Bio*: Gal Chechik is a Professor at Bar-Ilan University and a senior director of AI research at NVIDIA. His current research focuses on learning for reasoning and perception. In 2018, Gal joined NVIDIA to found and head nvidia's research in Israel. Prior to that, Gal was a staff research scientist at Google Brain and Google research developing large-scale algorithms for machine perception, used by millions daily. Gal earned his PhD in 2004 from the Hebrew University, and completed his postdoctoral training at Stanford CS department. Gal authored ~130 refereed publications, ~50 patents, including publications in Nature Biotechnology, Cell and PNAS. His work won awards at ICML and NeurIPS. http://chechiklab.biu.ac.il , publications <https://scholar.google.com/citations?hl=en&user=Wk2gAZUAAAAJ>

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CS Colloquium (12/02): Paul Medvedev (Pennsylvania State University)
by Sagie Benaim 12 Feb '24

12 Feb '24

Dear all, Next week, we have the pleasure of having Prof. Paul Medvedev give a talk in the colloquium. The seminar will be held on Monday, February 12th, at 14:00. Location: C220. The title, abstract and bio appear below. Looking forward to seeing you, Sagie and Liat *Title:* Scalable methods for k-mer based biological sequence analysis *Abstract:* Scalable analysis of biological sequences often starts by breaking long strings into their constituent k-mers. A k-mer is simply a substring of a short fixed length k. Compact data structures and efficient algorithms for storing and analyzing k-mer datasets have therefore become one of the bottlenecks for biological discovery. In this talk, I will present several techniques we have developed to push the boundaries of what is possible with such datasets. I will present the spectrum-preserving string set representation (RECOMB 2020, best paper award) as well as space-efficient data structures for querying large sequence archives (RECOMB 2017). Time permitting, I will also present our work on the use of sketching algorithms to estimate sequence similarity from k-mer sets (RECOMB 2021 and ISMB 2022). *Bio*: Paul Medvedev is a Professor in the Department of Computer Science and Engineering and the Department of Biochemistry and Molecular Biology and the Director of the Center for Computational Biology and Bioinformatics at the Pennsylvania State University. His research focus is on developing computer science techniques for analysis of biological data and on answering fundamental biological questions using such methods. Prior to joining Penn State in 2012, he was a postdoc at the University of California, San Diego and a visiting scholar at the Oregon Health & Sciences University and the University of Bielefeld. He received his Ph.D. from the University of Toronto in 2010, his M.Sc. from the University of Southern Denmark in 2004, and his B.S. from the University of California, Los Angeles in 2002.

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CS Colloquium (05/02): Leo Joskowicz (Hebrew University)
by Sagie Benaim 05 Feb '24

05 Feb '24

Dear all, Next week, we have the pleasure of having Prof. Leo Joskowicz give a talk in the colloquium. The seminar will be held on Monday, February 5th, at 14:00. Location: C220. The title, abstract and bio appear below. Looking forward to seeing you, Sagie and Liat *Title:* Three is better than two and better than one: simultaneous deep learning for lesion changes analysis in medical images. *Abstract:* Monitoring cancer patients undergoing treatment requires the analysis by an expert clinician of radiological images acquired every few months. With improvements in treatments leading to longer patient lifespans and increased accessibility of scanners, longitudinal patient studies have increased in their number of CT and MRI scans, making their interpretation more challenging and time-consuming. While numerous AI and deep learning based methods have been developed for medical image analysis, they fail short of providing a viable solution for radiological follow-up. In this talk, we present a novel fully automatic end-to-end pipeline for the comprehensive detection and segmentation of cancer lesions and the analysis of their evolution over time. The key novelties are: 1) SimU-Net, a simultaneous multi-channel 3D deep learning model trained on pairs of registered scans of each patient that identifies the lesions and their changes based on the lesion and healthy tissue appearance differences; 2) a model-based bipartite graph lesions matching method for the analysis of lesion changes at the lesion level; 3) a method for longitudinal analysis of consecutive scans of a patient based on SimU-Net. We demonstrate the method with experimental studies on liver and lung metastases in CT scans and brain metastases in MRI scans that establish state-of the-art results. *Bio*: Leo Joskowicz has been a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel since 1995. He is the founder and director of the Computer-Aided Surgery and Medical Image Processing Laboratory (CASMIP Lab). Prof. Joskowicz is a Fellow of the IEEE, ASME, and MICCAI (Medical Image Processing and Computer Aided Intervention) Societies. He is the past President of the MICCAI Society, was the Secretary General of the International Society of Computer Aided Orthopaedic Surgery (CAOS International) and of the International Society for Computer Assisted Surgery (ISCAS). He is the recipient of the 2010 Maurice E. Muller Award for Excellence in Computer Assisted Surgery by CAOS International and the 2007 Kaye Innovation Award. He has published two books and over 300 technical works including conference and journal papers, book chapters, and editorials and has 14 issued patents. He is on the Editorial Boards of Medical Image Analysis, Int. J. of Computer Aided Surgery, and Computer Aided Surgery and has served on numerous related program committees.

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In Conversation series - PANEL DISCUSSION
by Sagie Benaim 02 Feb '24

02 Feb '24

Dear all, The conversation series below may be of interest to you. Best, Sagie and Liat ****************************************** Dear All, For the next In Conversation series , we will be hosting a panel discussion. The panel discussion will take place at The Federmann Center for the Study of Rationality <https://ratio.huji.ac.il/>, Elath Hall (top floor of the Feldman Building, on the Safra Campus) on Sunday February 11 at 14:00. *Title:* AI+Science+Economy *Abstract:* How will recent advances in artificial intelligence (AI) transform science, tech, and the global economy? Will the coming years see a rapid acceleration of AI-driven scientific discovery? Will generative AI make all too many jobs obsolete, putting millions of people out of work? Will markets function better in the hands of AI, or are we headed for a series of catastrophic failures? Who will be the new elites in this rapidly evolving world? Moderator: Prof. Aviv Zohar Panelists: Dr. Tom Hope, Prof. Alon Eizenberg, Dr. Itamar Caspi Google Calendar: https://calendar.google.com/calendar/u/0?cid=NGQyMDg1MjAwYjI1MWQ0YTg2NjNjNz… Follow us on LinkedIn : https://www.linkedin.com/school/federmann-center-of-rationality/?viewAsMemb… <https://www.linkedin.com/school/federmann-center-of-rationality/?viewAsMemb…>

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CS Colloquium (29/01): Gal Vardi (TTI-Chicago and Hebrew University)
by Sagie Benaim 29 Jan '24

29 Jan '24

Dear all, Next week, we have the pleasure of having Dr Gal Vardi give a talk in the colloquium. The seminar will be held on Monday, January 29th, at 14:00. Location: C220. The title, abstract and bio appear below. Looking forward to seeing you, Sagie and Liat *Title:* On Implicit Bias and Benign Overfitting in Neural Networks *Abstract:* When training large neural networks, there are typically many solutions that perfectly fit the training data. Nevertheless, gradient-based methods often have a tendency to reach those which generalize well, namely, perform well also on test data. Thus, the training algorithm seems to be implicitly biased towards certain networks, which exhibit good generalization performance. Understanding this “implicit bias” has been a subject of extensive research recently. Moreover, in contradiction to conventional wisdom in machine learning theory, trained networks often generalize well even when perfectly fitting noisy training data (i.e., data with label noise), a phenomenon called “benign overfitting”. In this talk, I will discuss the above phenomena. In the first part of the talk, I will discuss the implicit bias and its implications. I will show how the implicit bias can lead to good generalization performance, but can also have negative implications in the context of susceptibility to adversarial examples and privacy attacks. In the second part of the talk, I will explore benign overfitting and the settings in which it occurs in neural networks. *Bio*: Gal is a postdoctoral researcher at TTI-Chicago and the Hebrew University, hosted by Nati Srebro and Amit Daniely as part of the NSF/Simons Collaboration on the Theoretical Foundations of Deep Learning. Prior to that, he was a postdoc at the Weizmann Institute, hosted by Ohad Shamir, and a PhD student at the Hebrew University, advised by Orna Kupferman. His research focuses on theoretical machine learning, with an emphasis on deep-learning theory.

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CS Colloquium (24/01): Yaniv David (Colombia University)
by Sagie Benaim 24 Jan '24

24 Jan '24

Dear all, Next week, we have the pleasure of having Dr Yaniv David give a talk in the colloquium. The seminar will be held on Wednesday, January 24th at 14:00. Location: C220. The title, abstract and bio appear below. Looking forward to seeing you, Sagie and Liat *Title:* Challenges and Opportunities In Securing Software Supply Chains. *Abstract:* Racing to be first to market and deploy new features, developers rely on many external libraries to underpin their software. Each library uses more libraries, creating vast networks of dependencies that the developers know little about and have no control over, forming a knowledge gap that quickly turns into technical debt. Repaying this debt is difficult, as analyzing or examining all libraries is infeasible, and worse, the debt keeps growing due to frequent library updates. Attackers move quickly to collect on this debt by reverse-engineering security updates into 1-day attacks or injecting malicious code into libraries and data used by the applications. In this talk, I will present the systems I built to tackle these challenges: (1) detecting vulnerable libraries in firmware by comparing multiple significant code segments aligned via re-optimizing and normalizing; (2) streamlining software dependency updates via a production-ready hybrid static-dynamic approach for studying the risks of the update before applying it; (3) detecting rogue updates via trust-domain-based tracking for data-flows between different packages in JavaScript code; and (4) hardening applications against data deserialization attacks via a novel type inference technique we call Static Duck Typing, which is based on object behaviors and usage. *Bio*: Yaniv is a postdoc at Columbia University working with Junfeng Yang. His research focuses on improving the reliability and safety of software. He is broadly interested in securing systems and program analysis. He received his PhD from the Technion in 2020, where he was advised by Eran Yahav.

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CS Colloquium (22/01): Tomer Galanti (MIT) - In Zoom
by Sagie Benaim 22 Jan '24

22 Jan '24

Dear all, Next week, we have the pleasure of having Dr. Tomer Galanti give a talk in the colloquium. The seminar will be held on Monday, January 22nd at 14:00. The seminar will be given virtually in Zoom using the following details: https://huji.zoom.us/j/84520950608?pwd=N1VJVHdsRDVLS3crK2R6UlpwNHR5UT09 Meeting ID: 845 2095 0608 Passcode: 393721 The title, abstract, and bio appear below. Looking forward to seeing you all, Sagie and Liat *Title:* Fundamental Problems in AI: Transferability, Compressibility and Generalization *Abstract:* In this talk, we delve into several fundamental questions in deep learning. We start by addressing the question, "What are good representations of data?" Recent studies have shown that the representations learned by a single classifier over multiple classes can be easily adapted to new classes with very few samples. We offer a compelling explanation for this behavior by drawing a relationship between transferability and an emergent property known as neural collapse. Additionally, we explore why certain architectures, such as convolutional networks, outperform fully-connected networks, providing theoretical support for how their inherent sparsity aids learning with fewer samples. Lastly, I present recent findings on how training hyperparameters implicitly control the ranks of weight matrices, consequently affecting the model's compressibility and the dimensionality of the learned features. Additionally, I will describe how this research integrates into a broader research program where I aim to develop realistic models of contemporary learning settings to guide practices in deep learning and artificial intelligence. Utilizing both theory and experiments, I study fundamental questions in the field of deep learning, including why certain architectural choices improve performance or convergence rates, when transfer learning and self-supervised learning work, and what kinds of data representations are learned with Stochastic Gradient Descent. *Bio*: Tomer Galanti is a Postdoctoral Associate at the Center for Brains, Minds, and Machines at MIT, where he focuses on the theoretical and algorithmic aspects of deep learning. He received his Ph.D. in Computer Science from Tel Aviv University and served as a Research Scientist Intern at Google DeepMind's Foundations team during his doctoral studies. He has published numerous papers in top-tier conferences and journals, including NeurIPS, ICML, ICLR, and JMLR. His work, titled "On the Modularity of Hypernetworks," was awarded an oral presentation at NeurIPS 2020.

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Speaker series
by Sagie Benaim 15 Jan '24

15 Jan '24

Hi All, Please refer to the following information about a speaker series that may be of interest to some of you. Best, Sagie *********************************** At Georgia tech this semester, we in the department of computer science are hosting a Turing Award winner Speaker series where we will have 5 Turing award winners and many other well-known computer scientists and academic leaders giving presentations and discussing with us students their experiences through their careers via Microsoft teams video . We will have many other top computer science departments joining us including the departments, department chairs, and vice chancellors of Tel Aviv University, MIT, Yale, Columbia, UCLA, Northwestern, Vanderbilt, and Bowdoin. This is the website for our events: https://www.turing.rsvp/

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