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AI Series – Part I: Introduction to Machine Learning

October 29, 2020 @ 1:00 pm - 2:00 pm

Join the ACEDS NY Metro Chapter for a panel discussion on machine learning.

Join data scientists from Brainspace, FRONTEO, NexLP and OpenText for an introduction to the technology that powers TAR and AI. We’ll give a non-technical overview of how the technology works and how that might inform your workflows.


Lilith Bat-Leah, Senior Director of Data Science, FRONTEO USA

Lilith Bat-Leah, CEDS, is a senior director of data science at FRONTEO, a publicly traded global technology and services company specializing in artificial intelligence, cross-border litigation, managed review, and consulting for the eDiscovery market. She has extensive experience managing, delivering and consulting on electronic discovery, including meet and confer, identification, preservation, collection, processing, review, analysis, and production of electronically stored information. She regularly participates in negotiations around electronic discovery and has provided expert testimony in both domestic and international court proceedings. Ms. Bat-Leah now specializes in the application of statistics, analytics, machine learning and data science within the context of electronic discovery. She writes and speaks on varied topics, including ESI protocols, statistical sampling, and technology assisted review. Ms. Bat-Leah currently serves on the EDRM Advisory Council and the ACEDS New York Chapter board. She is a member of Sedona Conference Working Groups 1 and 6, and was a founding board member of the ACEDS Chicago Chapter. Lilith graduated from Northwestern University magna cum laude and has been CEDS certified since 2015.


Dr. Jeremy Pickens, Principal Data Scientist, OpenText

Jeremy Pickens, PhD is one of the world’s leading information retrieval scientists and a pioneer in the field of collaborative exploratory search, a form of information seeking in which a group of people who share a common information need actively collaborate to achieve it. Dr. Pickens has seven patents and patents pending in the field of search and information retrieval. As Principal Data Scientist at OpenText, Dr. Pickens has spearheaded the development of Insight Predict. His ongoing research and development focuses on methods for continuous learning, and the variety of real world technology assisted review workflows that are only possible with this approach. Dr. Pickens earned his doctoral degree at the University of Massachusetts, Amherst, Center for Intelligent Information Retrieval. He conducted his post-doctoral work at King’s College, London. Before joining Catalyst Repository Systems and later OpenText, he spent five years as a research scientist at FX Palo Alto Lab, Inc. In addition to his OpenText responsibilities, he continues to organize research workshops and speak at scientific conferences around the world.

Dr. Irina Matveeva, Head of Machine Learning, NexLP

Dr. Irina Matveeva is the Head of Machine Learning at NexLP LLC and Adjunct Professor at the Illinois Institute of Technology. Her research interests include graph-based methods and semi-supervised methods for language applications. Her research focuses on latent semantic models of word and document representations and models of word similarity.
Dr. Matveeva received her Ph.D. from the University of Chicago. She co-chaired the TextGraphs workshops in 2012, 2011, 2008, and 2007, and is a reviewer for IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks, Journal for Information Retrieval and Journal of Artificial Intelligence Research.

Dr. David Lewis, Chief Data Scientist, Brainspace

Dr. Lewis brings 30 years of experience designing machine learning and language processing algorithms, dating back to his award-winning dissertation establishing text categorization as a new field of study at the intersection of information retrieval and machine learning. In research at the University of Chicago, Bell Labs, AT&T Labs, and David D. Lewis Consulting, Lewis pioneered many techniques that are now common in text analytics and beyond, including uncertainty sampling, stratified estimation of classification effectiveness, scaling of probabilistic classifier outputs, and high dimensional Bayesian logistic regression. For this work, Lewis was elected a Fellow of the American Association for the Advancement of Science in 2006. Since 2000, Lewis has served as an inventor and technology advisor for more than 50 companies and organizations, while continuing to pursue research in computer science. In 2005, Lewis created the first publicly available test collection for e-discovery research (the CDIP or “tobacco” collection), as well as co-founding, with Jason Baron and Doug Oard, the first open evaluation forum for e-discovery research (the TREC Legal Track). Lewis has also served as a consulting expert or expert witness in numerous patent and e-discovery disputes, including the landmark da Silva Moore, Kleen Products, Actos, and Rio Tinto cases. Lewis holds a BA in Mathematics and a BS in Computer Science from Michigan State University, and a PhD in Computer Science from the University of Massachusetts at Amherst.