Twenty-minute talks (including Q&A) by Zhenhua Liu, Paul Fodor, Scott Smolka and Dongyoon Lee:
2:30 pm - Zhenhua Liu - Systems, Algorithms, Learning, Energy
2:50 pm - Paul Fodor - Knowledge Authoring and Querying via Multi-Hop English Questions
Abstract: The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. We created the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring with very high accuracy that at present is unachievable via machine learning approaches. We show that KALM for Question Answering (QA) is capable of answering complex English questions and achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 300,000 questions. We contrast this with a published machine learning approach, which falls far short of this high mark.
Bio: Dr. Paul Fodor is an Associate Professor of Practice with the Department of Computer Science at Stony Brook University. His work on declarative rule languages and logic, used as a specification language and implementation framework for knowledge bases, was applied in areas ranging from natural language processing to complex event processing and semantic Web technologies. Through his research, Dr. Fodor has contributed to several large software projects: the IBM Watson natural language processing system for the Jeopardy! challenge with human champions, the OpenRuleBench suite of benchmarks for analyzing the performance and scalability of rule systems for the semantic Web, and the ETALIS and EP-SPARQL declarative complex event processing languages and stream reasoning systems. Dr. Fodor was awarded the 2019 SUNY Chancellor’s Award for Excellence in Teaching in recognition of the thousands of students whom he teaches and mentors each year in a range of classes focused on programming languages, database systems and discrete math.
3:10 pm - Scott Smolka - Machine Learning for Formal Modeling, Verification & Control of Autonomous Systems
Abstract: I will describe the research activities of the Formal Verification group (Professors Stoller, CR Ramakrishnan, Grosu and myself), especially as they pertain to the fundamental application of Machine Learning techniques to the Formal Modeling, Verification and Control of Autonomous Systems, including ground rovers, UAVs and drone swarms.
Bio: Scott A. Smolka is a SUNY Distinguished Professor of Computer Science at Stony Brook University. He is also a Fellow of the EATCS (European Association on Theoretical Computer Science). His research interests include model checking, runtime verification and the modeling and analysis of complex systems, including cyber-physical systems, cardiac tissue and other biological systems. He is the Lead PI for the multi-institutional NSF CPS Frontiers project on CyberCardia: Compositional, Approximate and Quantitative Reasoning for Medical Cyber-Physical Systems. He is perhaps best known for the algorithm he and Paris Kanellakis invented for deciding bisimulation. Smolka's
research has resulted in more than 200 publications, generating more than 9,300 citations, and an h-index of 50. He has also been PI/Co-PI on grants totaling more than $25M.
3:30 pm - Dongyoon Lee - Systems
