New methods to understand and generate visual scenes with novel combinations and complex interactions.   

Gal Chechik is a Professor at the Gonda Brain research institute and a director of AI at NVIDIA. His research spans learning in brains and machines, including large-scale learning algorithms for machine perception, and analysis of representation and changes of mammalian brains.

Gal earned his PhD in 2004 from the Hebrew University developing machine learning methods to study neural coding. He then worked at Stanford CS department with D. Koller, studying computational principles regulating molecular pathways. In 2007 he joined Google research as a senior research scientist, developing large scale machine learning algorithm for machine perception. Since 2009, he heads the learning systems lab at the Gonda center of Bar Ilan university, and was appointed an associate professor in 2013. Gal is the author of ~80 refereed publications, including publications in Nature Biotechnology, Cell and PNAS. He was awarded a fullbright fellowship, a complexity scholarship and an Alon fellowship. In 2018 he joined NVIDIA as a director of AI, leading nvidia's research in Israel.

Niva Elkin-Koren is a Professor of Law at Tel Aviv University Faculty of Law, and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. She was the founding director of the Center for Cyber, Law and Policy (CCLP) and of the Haifa Center for Law & Technology (HCLT). During 2009-2012 she served as Dean of the Faculty of Law at the University of Haifa. Prof. Elkin-Koren has been a Visiting Professor of Law at Harvard University, Columbia Law School, UCLA, NYU, George Washington University and Villanova University School of Law. She is the Chair of the Scientific Advisory Council, of the Alexander von Humboldt Institute for Internet and Society in Berlin, a member of the Executive Committee of Association for the Advancement of Teaching and Research in Intellectual Property (ATRIP), and a board member of the MIPLC Scientific Advisory Board at the Munich IP Law Center, Max Planck Institute for Innovation and Competition. Prof. Elkin-Koren received her LL.B from Tel-Aviv University Faculty of Law in 1989, her LL.M from Harvard Law School in 1991, and her S.J.D from Stanford Law School in 1995. Her research is located at the intersection of law, computer science and data science. She has written extensively on content moderation by social media platforms, governance of AI, and governance by AI.

“Thanks for all the fish” ; “Happy bday grandma!” ; “Mercedes C-class Cabriolet” . Looks random, right? Well, maybe you know the old saying “one man’s trash is another woman’s treasure”. These texts, while very short, can be a virtual gold mine for many different business use-cases, some of which we tackle daily in our work. When we started working on unsupervised feature generation from very short texts, we started by looking into what’s already been done in the field, and to our surprise the answer was: not a lot. In this talk we’ll share some insights from our experience in dealing with short texts. We’ll start by defining what we mean by "short" in our unique case, why it’s interesting in various domains, where and why advanced out-of-the-box methods failed and finally, our tips for similar use-cases.

Gal is a senior data scientist at PayPal, working mostly on NLP applications and NLP variable generation. She currently heads up construction of an internal NLP infrastructure to feed a wide variety of models in diverse domains like fraud detection, marketing, and Credit risk. Gal holds a BSc in Electrical Engineering and Physics as well as an MSc in Electrical engineering, all from Tel Aviv University. Before PayPal, Gal worked as a guidance and control algorithms engineer for aerospace systems and later as a data scientist on cyber applications.

Deep learning models have gained widespread popularity for natural language processing (NLP) and have undergone a revolution in performance and capabilities. Nvidia talk will cover the latest tools that gives researchers access to optimized software for training cutting edge models like Bert and GPT on the most powerful GPU architecture A100 in large scale.

Ofir Zamir, Director of solutions architecture at Nvidia. leading a team of AI experts that help customers to utilize GPU computing for accelerating the entire Data Science pipeline from training to inference at any scale. Prior to Nvidia Ofir led system engineering at VMware for a decade and was specialized in software defined data center and cloud computing. Ofir holds B.SC in Communications Systems Engineering from Ben Gurion university.

For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge. Intel's Sales and Marketing Group (SMG) faces similar challenges while growing in new markets and domains and evolving its existing business. In today's complex technological and commercial landscape, there is need for intelligent automation supporting a fine-grained understanding of businesses in order to help SMG sift through millions of companies across many geographies and languages and identify relevant directions. We present a system developed in our company that mines millions of public business web pages, and extracts a faceted customer representation. We focus on two key customer aspects that are essential for finding relevant opportunities: industry segments (ranging from broad verticals such as healthcare, to more specific fields such as 'video analytics') and functional roles (e.g., 'manufacturer' or 'retail'). To address the challenge of labeled data collection, we enrich our data with external information gleaned from Wikipedia, and develop a semi-supervised multi-label, deep learning model that parses customer website texts and classifies them into their respective facets. Our system scans and indexes companies as part of a large-scale knowledge graph that currently holds tens of millions of connected entities with thousands being fetched, enriched and connected to the graph by the hour in real time, and also supports knowledge and insight discovery. In experiments conducted in our company, we are able to significantly boost the performance of sales personnel in the task of discovering new customers and commercial partnership opportunities.

Itay is an AI researcher at Intel. He studied human behavior with non-linear kernel methods for his PhD thesis at the Hebrew University. His earlier work at Intel focused on Natural Language Processing for Sales and Marketing team (SMG) and he is currently positioned as a tech R&D team lead. He teaches a university introductory Data Science course and co-authored an introductory AI programming book.

The latest methods in NLP involve pretraining a model on unlabeled text and then fine-tuning it on annotated input-output examples from the target task. While plain text is abundant, labeled examples must be collected by humans in a time-consuming and expensive process, which is often impractical to scale up via crowdsourcing due to the language (e.g. Hebrew) or domain (e.g. medicine). How can we train reasonable models with only a few annotated examples? In this talk, I present some first steps (and encouraging results!) towards training question answering models in a few-shot setting.

I am a senior lecturer at Tel Aviv University’s school of computer science and a research scientist at Facebook AI Research. I completed my BSc and MSc at Technion and my PhD at Bar-Ilan University, and did postdoctoral research at the University of Washington. Research: My research is in the intersection of natural language processing (NLP) and machine learning. I have worked on a variety of topics including semantics, self-supervised learning, machine translation, language modeling, dataset annotation, and explainability. I am particularly interested in creating NLP models that can generalize well.

https://www.cs.tau.ac.il/~levyomer/