Program

Dr. Liat Antwarg Friedman, with a Ph.D. in Information Systems Engineering, specializing in Explainable AI. A proven track record in leading research and applied machine learning projects—spanning fraud detection, NLP, and data quality improvement—I’ve worked with organizations such as eBay, Audi, Leumi and KBC Bank, both as a researcher and consultant. I bring a deep understanding of translating business needs into data-driven solutions.

Dr. Nataly Kuritz is an AI strategist, researcher, and innovation leader with a Ph.D. in Deep Learning for Quantum Chemistry. With over a decade of experience bridging academia and industry, she specializes in building trustworthy AI systems, designing explainable models, and guiding companies through GenAI transformation. Nataly is also a passionate educator and speaker, known for making complex AI concepts accessible and actionable.

Shir Chorev is the co-founder and CTO of Deepchecks, a startup for evaluation and continuous validation of AI-based applications. Previously, Shir worked at the Prime Minister’s Office and at Unit 8200, leading research in various Machine Learning and Cyber related problems. Shir has a B.Sc. in Physics from the Hebrew University, which she obtained as part of the Talpiot excellence program, and an M.Sc. in Electrical Engineering from Tel Aviv University. Shir was a featured honoree in the Forbes Europe 30 Under 30 list.

Serj Smorodinsky is a data science team lead with deep expertise in NLP, AI evaluation, and agentic workflows. He specializes in building intelligent systems combining Deep learning, LLMs, retrieval architectures, and interpretable rule-based models. Serj brings strong software engineering skills and a production-focused mindset, ensuring scalable, testable, and maintainable AI solutions. His approach bridges experimental research with real-world deployment, emphasizing reliability and continuous evaluation.

Dr. Tomer Gazit is a machine learning and AI researcher, manager, and consultant specializing in the medical domain. He helps organizations develop tailored data science solutions to clinical and operational challenges. He currently leads the Clinical Data Science team at Hello Heart, where he develops predictive models for heart-related diagnoses, integrating tabular data, time series and generative AI.

Previously, he led the Brain Informatics and Imaging Research Lab at Tel Aviv Sourasky Medical Center (Ichilov) where his work focused on time series and neuroimaging tools for neurological diagnosis and translational neuroscience.

Tomer holds a PhD in Neuroscience from Bar-Ilan University and a BSc in Mathematics and Psychology from the Hebrew University of Jerusalem.

Yuval is a Developer Advocate at AI21 Labs, deeply involved in advancing the frontiers of Natural Language Processing.

He holds an MSc in Computer Science from the Weizmann Institute of Science, underscoring his firm grounding in the field. Prior to his role at AI21 Labs, he served as a Chip Designer at Amazon, honing his ability to deliver efficient, impactful solutions.

Yuval’s professional passion lies in the realm of generative AI. He actively contributes to making this intricate field more comprehensible and accessible to diverse audiences. With an engaging style and in-depth expertise, he navigates the complexities of AI, bringing clarity and insight to both peers and newcomers in the field.

Segev Arbiv is a Data Scientist with expertise in the fields of Machine Learning and Computer Vision. He has a BSc. in Electrical Engineering from the Technion, and pursuing an MSc in these fields.

During the last few years, Segev is working at SimilarWeb in which he applies Deep Learning techniques and various statistical inference tools to overcome day to day challenges, such as NLP and estimations.

Prior to that, he was a researcher in the Image Processing\Computer Vision fields at AOL. Segev is passionate about everything related to numbers, lecturing and interacting with other passionate personas.

Rachel is a statistician in the IBD Epidemiology Research group at Shaare Zedek Hospital. She uses advanced methods in survival analysis and causal inference to gain a deeper knowledge about inflammatory bowel diseases (Crohn's disease and ulcerative colitis).

Rachel holds an MA Cum Laude in Statistics and Operations Research from the Hebrew University. In her thesis, she focused on statistical methodology for estimating hospital-acquired infections.

During her Master’s, she taught multiple courses ranging from basic probability and linear algebra to statistical inference and advanced probability, for which she received an award for outstanding TA.
Rachel has also worked for the Bank of Israel, doing statistical research and benchmarking for consumer goods across the retail industry.

Dr. Omri Allouche heads the Research department at Gong.io, helping sales organizations improve their performance by providing actionable, data-driven insights using machine learning. He also teaches Applied Data Science at Bar Ilan University.

Omri holds a Ph.D. in Computational Ecology from the Hebrew University (cum laude). He won several academic awards and scholarships, including the Clore fund, and his research papers had been cited over 2,000 times.

Noa is a Data Scientist, currently working as Director of Data Science at Fido. Formerly worked as a researcher at NASA, at Diagnostic Robotics, Amazon, Elbit and IAI. Her main topics of specialisation and focus are NLP, healthcare and space.

Noa is an AI leader, social entrepreneur, lecturer and space enthusiast.

Volunteering as CTO at Tod’aers - combining AI and space research for sustainable technological developments.

Previously worked as Machine Learning Team Lead at Diagnostic Robotics, at Amazon, NASA, Elbit Systems and the Israeli Aerospace Industry.

Computer Science Master's degree, Bar-Ilan University (Magna Cum Laude), NLP thesis advised by Prof. Yoav Goldberg.

Electrical Engineering Bachelor's degree, Technion (Summa Cum Laude).

Niv Haim is a computer vision and machine learning researcher at the Weizmann Institute of Science, where he earned his PhD under the guidance of Prof. Michal Irani.

Niv's research focuses on developing algorithms and models to analyze and interpret visual data, with a particular interest in understanding memorization in neural networks and the intersection of theory and practice in deep learning. His works were published in top-tier machine learning conferences, including NeurIPS, ICML, ICCV, and ECCV.

Prior to his PhD studies, Niv received an MSc in theoretical astrophysics under the guidance of Prof. Boaz Katz and worked with Prof. Yaron Lipman on Geometric Deep Learning. Niv holds a BSc in computer science and physics from the Technion, where he was a member of the Lapidim excellence program.

Lior has years of experience and works closely with companies to improve their product and operations using data science.

He co-founded Braincast to help product managers to make impactful decisions by explaining causality in meaningful events.

Lior holds BSc and MSc in software and information engineering department from Ben Gurion University, where he researched the appliance of deep learning, embedding, and adversarial learning in the fraud and cybersecurity domains.

Kosta holds a B.Sc and M.Sc. in Industrial Engineering and Management from Ben Gurion Universirty (Summa Cum Laude both). He won several academic awards and scholarships, and has published multiple research papers in academic journals, including best paper of 2018 in IEEE Transactions on Semiconductor Manufacturing. Kosta is also a Certified Scrum Master and Project Management Professional (PMP).

In his role as Product Analytics Lead at Waze, Kosta uses data to improve every aspect of Waze - one of most popular navigation apps in the world.

Before joining Waze, Kosta worked as a Data Scientist at Intel and as a Lecturer at Ben Gurion University.

Karin Brisker is a data scientist at Microsoft, with a background in NLP and machine learning. She began her journey developing AI-driven solutions for the healthcare domain, and now focuses on cybersecurity, building data-driven tools to enhance organizational protection. Karin holds an M.Sc. from the NLP Lab at Bar-Ilan University.

Inbar has just finished her PhD at the Hebrew University of Jerusalem, her research is focused on image processing and machine learning.

Inbar received her Ms.c in computer science from the Hebrew University and her Bs.c in applied mathematics from Bar-Ilan University, both graduated cum laude. She published several papers in computer vision top-tier conferences.

During her PhD studies, she was titled best teacher assistant.

Guy Shtar holds a Ph.D in Machine learning as well as an M.Sc. degree in Software and Information Systems Engineering from the Ben-Gurion University of the Negev in 2016.

His research interests include machine learning and deep learning in the medical and cybersecurity fields, as well as geographical information systems.

Dr. Daniel Nevo is a Senior Lecturer at the Department of Statistics and Operations Research at Tel Aviv University since 2018. Dr. Nevo’s research focuses on causal inference in widespread domains, and specifically on developing and implementing causal inference methods for real-life problems.

Dr. Daniel Nevo is a Senior Lecturer at the Department of Statistics and Operations Research at Tel Aviv University since 2018. Before that he was a postdoctoral fellow at Harvard Departments of Biostatistics and Epidemiology (2016-2018). Daniel received his BA (Statistics and Economics, 2009) and MA (Statistics, 2011), both Magna Cum Laude, from the Hebrew University of Jerusalem. He received his PhD in Statistics from the Hebrew University of Jerusalem (2016).

Daniel’s research focuses on causal inference in widespread domains, and specifically on developing and implementing causal inference methods for real-life problems. As of 2022, he published 35 peer-reviewed papers.

Daniel has been collaborating with clinicians, epidemiologists, economists, and computer scientists in academia and health organizations to reach conclusions about causal effects from rich datasets.