IBM Neuro-Symbolic AI Workshop 2022     


IBM Neuro-Symbolic AI Workshop 2022 - Speakers

Alex Alexander Gray
IBM Research 

Alexander Gray serves as VP of Foundations of AI at IBM, and currently leads IBM’s Neuro-Symbolic AI Theme (distinct from the work in the MIT-IBM Lab). He received AB degrees in Applied Mathematics and Computer Science from UC Berkeley and a PhD in Computer Science from Carnegie Mellon University. BeforeIBM he worked at NASA, served as a tenured Associate Professor at the Georgia Institute of Technology, and co-founded and sold an AI startup in Silicon Valley. His work on machine learning, statistics, and algorithms for massive datasets, predating the movement of "big data" in industry, has been honored with a number of research awards including the NSF CAREER Award, multiple best paper awards, selection as a National Academy of Sciences Kavli Scholar, and service as a member of the 2010 National Academy of Sciences Committee on the Analysis of Massive Data. His current interests generally revolve around the injection of non-mainstream ideas into ML/AI to attempt to break through long-standing bottlenecks of the field.
Ron Ron Fagin
IBM Research 

Ronald Fagin is an IBM Fellow at IBM Research – Almaden. Fagin received his B.A. in mathematics from Dartmouth College and his Ph.D. in mathematics from the UC Berkeley. He is a Fellow of ACM and AAAS (American Association for the Advancement of Science), and a Life Fellow of IEEE. He has co-authored four papers that won Best Paper Awards and three papers that won Test-of-time Awards, all in major conferences. One of his papers won the Gödel Prize, the top prize for a paper in theoretical computer science. His work on data exchange won the ACM SIGLOG Alonzo Church Award for Outstanding Contributions to Logic and Computation. He was named Docteur Honoris Causa by the University of Paris, and Laurea Honoris Causa by the University of Calabria (the highest honor of the Italian university system) . He won the IEEE Technical Achievement Award (now called the Edward J. McCluskey Technical Achievement Award) , IEEE W. Wallace McDowell Award (the highest award of the IEEE Computer Society), and ACM SIGMOD Edgar F. Codd Innovations Award (a lifetime achievement award in databases). He was elected to the U.S. National Academy of Sciences, the US National Academy of Engineering and the American Academy of Arts and Sciences.
Antonio Antonio di Nola
Università degli Studi di Salerno 

Currently Honorary Professor of Department of Mathematics of University of Salerno. He was Full Professor of Mathematical Logic and Director of the Department of Mathematics of the University of Salerno. Since the nineties he has been a leading proponent of the study of algebraic models of Lukasiewicz logic (MV-algebras), the most important among the many-valued logics. His contribution to the study of MV-algebras, witnessed by the seventeen citations of his works in the fundamental monograph "Algebraic foundations of many-valued reasoning", includes: a functional representation theorem for all MV-algebras (aka Di Nola's Representation Theorem); the discovery of categorical equivalences between categories of MV-algebras and categories of groups, rings, and semi-rings, profitably used in the literature of MV-algebras, the discovery of an equational axiomatization of all varieties of MV-algebras, and a normal form theorem for Lukasiewicz logic. He introduced the class of Riesz MV-algebras, opening the study connecting Many valued logic and Riesz Spaces. Today is actively committed to apply ideas from algebraic geometry in the MV-algebra and Riesz MV-algebras and to the study of generalizations of probability which admit infinitesimal values. He is author/coauthor of more 200 scientific works, published on international journals of logic, algebra and computer science. He was coordinator of many international projects. He is coauthor of the monograph: "Fuzzy Relation Equations and Their Applications to Knowledge Engineering" (A. Di Nola, W. Pedrycz, S. Sessa, E. Sanchez - Kluwer Acad. Publ.), “The Mathematics of FuzzySystems””, A. Di Nola, A.G.S. Ventre (Eds.), Verlag TUV. Rheinland, Koln 1986, and  co-editor of the volume "Lectures on Soft Computing and Fuzzy Logic", Springer. He is Editor in Chief of the international journal "Soft Computing", Springer-Verlag, and Associate Editor of the following journals: International Journal of Computers, Communications and Control, Fuzzy Sets and Systems, Mathematica Slovaca, Fuzzy Optimization and Decision Making. He has been Associate Editor of the Journal of Mathematical Analysis and Applications. He has been Invited Speaker of many international conferences and received the IFSA Fellowship.
Luis Luis Lamb
State of Rio Grande do Sul, Brazil 

Luis C. Lamb is a Full Professor and Secretary of Innovation, Science and Technology of the State of Rio Grande do Sul, Brazil. He was formerly Vice President for Research (2016-2018) and Dean of the Institute of Informatics (2011-2016) at the Federal University of Rio Grande do Sul (UFRGS), Brazil. He holds both the Ph.D. in Computer Science from Imperial College London (2000) and the Diploma of the Imperial College, MSc by research (1995) and BSc in Computer Science (1992) from UFRGS, Brazil. His research interest includes neural-symbolic computing, the integration of learning and reasoning, and ethics in AI. He co-authored two research monographs: Neural-Symbolic Cognitive Reasoning, with Garcez and Gabbay (Springer, 2009) and Compiled Labelled Deductive Systems, with Broda, Gabbay, and Russo (IoP, 2004). His research has led to publications at flagship journals, AI and neural computation conferences. He was co-organizer of two Dagstuhl Seminars on Neuro-symbolic AI: the Dagstuhl Seminar 14381: Neural-Symbolic Learning and Reasoning (2014) and Dagstuhl Seminar 17192: Human-Like Neural-Symbolic Computing (2017) and several workshops on neural-symbolic learning and reasoning at AAAI and IJCAI.
Leslie Leslie Kaelbling
Massachusetts Institute of Technology (MIT) 

Leslie is a Professor in EECS at MIT. She has an undergraduate degree in Philosophy and a PhD in Computer Science from Stanford, and was previously on the faculty at Brown University. She was the founder of the Journal of Machine Learning Research. Her goal is to make robots that are as smart as you are.
Deborah Deborah L. McGuinness
Rensselaer Polytechnic Institute (RPI) 

Deborah McGuinness is the Tetherless World Senior Constellation Chair and Professor of Computer, Cognitive, and Web Sciences at RPI. She is also the founding director of the RPI Web Science Research Center. Deborah has been recognized as a fellow of the American Association for the Advancement of Science (AAAS) and as the recipient of the Robert Engelmore award from AAAI for his numerous contributions to the Semantic Web, knowledge representation, and reasoning and for bridging Artificial Intelligence (AI) and eScience. Deborah currently leads a number of large diverse data intensive resource efforts and her team is creating next generation ontology-enabled research infrastructure for work in large interdisciplinary settings.
Sheila Sheila McIlraith
University of Toronto 

Sheila McIlraith is a Professor in the Department of Computer Science, University of Toronto, a Canada CIFAR AI Chair (Vector Institute), and an Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Prior to joining the University of Toronto, Prof. McIlraith spent six years as a Research Scientist at Stanford University, and one year at Xerox PARC. McIlraith's research is in the area of knowledge representation and sequential decision making, broadly construed, with a focus on human-compatible AI. She also has a particular interest in the ethics of AI and the impact of AI on society. McIlraith is a Fellow of the Association for Computing Machinery (ACM), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Associate Editor for the Journal of AI Research (JAIR), and a past Associate Editor of the Journal of Artificial Intelligence. McIlraith is past program Co-Chair of the 32nd AAAI Conference on Artificial Intelligence (AAAI), the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR2012), and the International Semantic Web Conference (ISWC2004). Her work on semantic web services has had notable impact. In 2011 she and her co-authors were honoured with the SWSA 10-year Award, recognizing the highest impact paper from the International Semantic Web Conference, 10 years prior.
Maarten Maarten Sap
Allen Institute and CMU 

Maarten is a Young investigator at the Allen Institute for AI (AI2) and a leading researcher in ATOMIC, the most complete common sense, rule-based knowledge base to date. His work on ATOMIC, Mosaic and Comet has been amply cited and recognized since 2014. His research focuses on making NLP systems socially intelligent, and understanding social inequality and bias in language. He has presented his work in top-tier NLP and AI conferences, receiving a best short paper nomination at ACL 2019 and a best paper award at the WeCNLP 2020 summit. His research has been covered in the New York Times, Forbes, Fortune, and Vox. Additionally, he and his team won the inaugural 2017 Amazon Alexa Prize, a social chatbot competition. Maarten will be joining CMU as an assistant professor in the Fall of 2022.
Henry Henry Kautz
University of Rochester 

Henry Kautz is a Professor in the Department of Computer Science and was the founding director of the Goergen Institute for Data Science at the University of Rochester, and is currently serving as Division Director for Information & Intelligent Systems (IIS) at the National Science Foundation where he leads the National AI Research Institutes program. His interdisciplinary research includes practical algorithms for solving worst-case intractable problems in logical and probabilistic reasoning; models for inferring human behavior from sensor data; pervasive healthcare applications of AI; and social media analytics. In 2018 he received the ACM-AAAI Allen Newell Award for career contributions that have breadth within computer science and that bridge computer science and other disciplines.
Gary Gary Marcus
New York University 

GARY MARCUS is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. He is the author of five books, including The Algebraic Mind, Kluge, The Birth of the Mind, and The New York Times best seller Guitar Zero, as well as editor of The Future of the Brain and The Norton Psychology Reader.

He has published extensively in fields ranging from human and animal behavior to neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence, often in leading journals such as Science and Nature, and is perhaps the youngest Professor Emeritus at NYU. His newest book, co-authored with Ernest Davis, Rebooting AI: Building Machines We Can Trust aims to shake up the field of artificial intelligence

Don Dan Roth
University of Pennsylvania & Amazon AWS AI 

Dan Roth is the Eduardo D. Glandt Distinguished Professor at the Department of Computer and Information Science, University of Pennsylvania, lead of NLP Science at Amazon AWS AI, and a Fellow of the AAAS, the ACM, AAAI, and the ACL. In 2017 Roth was awarded the John McCarthy Award, the highest award the AI community gives to mid-career AI researchers. Roth was recognized “for major conceptual and theoretical advances in the modeling of natural language understanding, machine learning, and reasoning.” Roth has published broadly in machine learning, natural language processing, knowledge representation and reasoning, and learning theory, and has developed advanced machine learning based tools for natural language applications that are being used widely. Roth was the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR) and a program chair of AAAI, ACL, and CoNLL. Roth has been involved in several startups; most recently he was a co-founder and chief scientist of NexLP, a startup that leverages the latest advances in Natural Language Processing (NLP), Cognitive Analytics, and Machine Learning in the legal and compliance domains. NexLP was acquired by Reveal in 2020. Prof. Roth received his B.A Summa cum laude in Mathematics from the Technion, Israel, and his Ph.D. in Computer Science from Harvard University in 1995.
Andrew Andrew McCallum
University of Massachusetts Amherst 

Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He has published over 300 papers in many areas of artificial intelligence, including natural language processing, machine learning, data mining and reinforcement learning; his work has received over 70,000 citations. He received his PhD from University of Rochester in 1995 with Dana Ballard and a postdoctoral fellowship from Carnegie Mellon University with Tom Mitchell and Sebastian Thrun. Afterward he worked in an industrial research lab, where he spearheaded the creation of CORA, an early research paper search engine that used machine learning for spidering, extraction, classification and citation analysis. In the early 2000's he was Vice President of Research and Development at at WhizBang Labs, a 170-person start-up company that used machine learning for information extraction from the Web. He was named a AAAI Fellow in 2009, and an ACM Fellow in 2017. He is the recipient of two NSF ITR awards, the UMass Chancellor’s Award for Outstanding Accomplishments in Research and Creative Activity, the UMass Lilly Teaching Fellowship, and research awards from IBM, Microsoft, Facebook, and Google. He was the Program Co-chair for the International Conference on Machine Learning (ICML) 2008, its General Chair in 2012, and from 2013 to 2017 was the President of the International Machine Learning Society. He is also a member of the editorial board of the Journal of Machine Learning Research. He has given tutorials or invited talks at NIPS, KDD, EMNLP, ISWC, and elsewhere. He organized the first workshop on Automated Knowledge Base Construction in 2009, and is the instigator and General Chair of the first international conference on Automated Knowledge Base Construction in 2019. He is also the creator of, which is being used for peer review management and/or reviewer assignment by ICLR, UAI, COLT, ICML, CVPR, and ECCV. For the past twenty years, McCallum has been active in research on statistical machine learning applied to text, especially information extraction, entity resolution, information integration, structured prediction, clustering, finite state models, semi-supervised learning, and social network analysis. McCallum’s web page is
Stephen Stephen H. Muggleton
Imperial College London 

Stephen Muggleton (SM) is Professor of Machine Learning in the Department of Computing at Imperial College London and is internationally recognised as the founder of the field of Inductive Logic Programming. SM’s career has concentrated on the development of theory, implementations, and applications of Machine Learning, particularly in the field of Inductive Logic Programming (ILP) and Probabilistic ILP (PILP). Over the last decade he has collaborated with biological colleagues, such as Prof Mike Sternberg, on applications of Machine Learning to Biological prediction tasks. He is a Fellow of the Royal Academy of Engineering, a Fellow of the Royal Society of Biology and a Fellow of the AAAI.
Jacob Jacob Andreas
Massachusetts Institute of Technology 

Jacob Andreas is the X Consortium Assistant Professor at MIT. His research aims to build intelligent systems that can communicate effectively using language and learn from human guidance. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. As a researcher at Microsoft Semantic Machines, he founded the language generation team and helped develop core pieces of the technology that powers conversational interaction in Microsoft Outlook. He has been the recipient of Samsung's AI Researcher of the Year award, MIT's Kolokotrones teaching award, and paper awards at NAACL and ICML.
Josh Joshua Tenenbaum
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology 

Joshua Tenenbaum is a professor of computational cognitive science in MIT’s Department of Brain and Cognitive Sciences and a scientific director with the MIT Quest for Intelligence. He is also an investigator at the Center for Brains, Minds and Machines and the Computer Science and Artificial Intelligence Laboratory. Tenenbaum’s research straddles cognitive science and artificial intelligence, where his goals are to reverse engineer human intelligence and to build machines that behave in human-like ways and have greater use to society. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, Tenenbaum tries to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating “style” and “content” in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences and predicting the future. Tenenbaum is a MacArthur Fellow and has received the National Academy of Sciences’ Troland Research Award. He earned a BA from Yale University, and a PhD in brain and cognitive sciences from MIT.
James James R. Kozloski
IBM Research 

James Kozloski joined IBM Research in May, 2001, where he has worked in the Computational Biology Center at the T.J. Watson Labs in Yorktown Heights, NY.

He leads and manages IBM's department of Computational Neuroscience and Multiscale Brain Modeling, which collaborates with researchers worldwide modeling brain function from synaptic plasticity in neural circuits to whole neural tissues, using heterogeneous supercomputing platforms. He proposed a closed-loop model of resting state brain function in a 2016 hypothesis and theory paper in Frontiers in Neuroanatomy. He devised a novel, ultrascalable Computational Neuroscience methodology and implemented it as the Neural Tissue Simulator, a high performance computing tool for detailed modeling of large scale neural tissue anatomy and physiology. The solution was published in 2011 in Frontiers in Neuroinformatics. He also helped formulate a Theory of Loop-Regulating Plasticity, published in 2010 in Frontiers in Neural Circuits. From 2004-2007, he collaborated on the Blue Brain Project, and his solution to the problem of calculating the connectome of simulated tissues was featured on the January 2008 cover of the IBM Journal of Research and Development.

James joined IBM after completing a 2 1/2 year postdoctoral fellowship at Columbia University, where he helped discover stereotyped positions of local synaptic targets in neocortex, published in Science in 2001. He received his PhD. in Neuroscience and Biomedical Graduate Studies, from the University of Pennsylvania in 1999, where his thesis described the functional neuroanatomy of a vertebrate fish auditory system and was featured on the cover of the Journal of Neuroscience. He received a B.A. from the University of Virginia in 1992, having double majored in English and Biology. From 1992-1993, prior to graduate school, he worked in the Laboratory of Human Genetics in New York City, studying the rare genetic disorder Bloom Syndrome and contributing to the discovery of the gene responsible for it.

James has coauthored more than 30 papers and 250 issued patents in areas of neuroscience, neurotechnology, and computer science. In 2010, he was named an IBM Master Inventor, and in 2017 he was inducted into IBM's Academy of Technology. In 2003, he invented recording neuronal signals to DNA using errors in oligonucleotide synthesis. In 2015, he invented an information-based exchange network based on a model of neocortical, thalamic, and basal ganglia interaction.

Timothy Timothy Hospedales
University of Edinburgh 

Timothy Hospedales is a Professor within IPAB in the School of Informatics at the University of Edinburgh, where he heads the Machine Intelligence Research group; Principal Scientist at Samsung AI Research Centre, Cambridge; Turing Fellow of the Alan Turing Institute. Previously he was Reader ('16-'20) at Edinburgh and Senior Lecturer/Lecturer ('12-16) at QMUL within the Risk and Information Management (RIM) group and Centre for Intelligent Sensing, where he founded the Applied Machine Learning Lab. He recieved his PhD in Neuroinformatics from Edinburgh in 2008, working with Sethu Vijayakumar in the Statistical Machine Learning and Motor Control group, and his BA in Computer Science from the University of Cambridge in 2002. His research focuses on data-efficient and robust machine learning using techniques such as meta-learning and lifelong transfer-learning, in both probabilistic and deep learning contexts. He has also worked in variety application areas including computer vision, vision and language, reinforcement learning for robot control, finance and beyond.