REASONING WEB SCHOOL 2022
Co-located with "DECLARATIVE AI"
Berlin, Germany, September 26-30, 2022


Organizing and Program Chairs:

- Leopoldo Bertossi (Skema Business School, Montreal, Canada)

- Guohui Xiao (University of Bergen, Norway)

PROGRAM

<VIDEOS ON YOUTUBE>

September 27th:

(1) "Cross-Modal Knowledge Discovery, Inference, and Challenges"               <slides>      <video1> <video2>

Meng Wang (South East University, China) and Ningyu Zhang (Zhejiang University, China)


Zhang Ningyu, associate professor at Zhejiang University, his main research interests are knowledge graph, NLP, etc. He has published papers in top international academic conferences and journals such as NeurIPS/ICLR/WWW/KDD/AAAI/IJCAI/ACL with google h-index 20. Three paper has been selected as Paper Digest Most Influential Papers (WWW22.IJCAI21.KDD21).

Meng Wang is working as an assistant professor in the Knowledge Graph & AI Research Group, Southeast University, China. He obtained the doctoral degree from, Xi.an Jiaotong University and was a visiting scholar at the University of Queensland. He was awarded the CCF-Tencent Rhino-Bird Open Fund Award (46 young scientists in the world selected) and the CCF-Baidu Open Fund Award (23 young scientists in the world selected). His research area is in the knowledge graph and cross-modal data.

(2) "Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling"              <slides>      <video>

Stefan Pabst and Cassandra Hunt (RelationalAI Inc., USA)

Stefan Pabst: Stefan Pabst received his PhD in physics in 2012 from the University of Hamburg (Germany). In 2015 he received the ITAMP postdoctoral fellowship of the Harvard-Smithsonian Center for Astrophysics where he continued working on novel theoretical methods to simulate quantum dynamics in atoms and molecules.

In 2018 Stefan joined RelationalAI as a data scientist working on various client projects ranging from outlier detection and demand forecasting to schema mapping. After developing some of the first client applications on RelationalAI.s new knowledge graph system (RKGMS), Stefan moved in 2020 to R&D helping develop the product with a strong focus on benchmarking the expressiveness of RelationalAI's new modeling language Rel. Since 2021 he is leading the documentation and training team where his focus is on educating users about the RKGMS and Rel so users are able to use it to its fullest potential.

Cassandra Hunt: Cassandra Hunt joined RelationalAI in 2019 after completing a Miller Research Fellowship at UC Berkeley, pivoting away from condensed matter physics to tackle data and machine learning challenges for clients in a range of industries. She helped produce the first client application on RelationalAI.s knowledge graph management system (RKGMS), both as a developer and then as the interim project manager through the application go-live. In 2022 she began building a new team, dubbed Product Analysis, with the goal of pushing the platform to its limits, developing use cases, and competitive analysis.

September 28th:

(3) "Logic-Based Explainability in Machine Learning"               <slides>      <video>

Joao Marques-Silva (IRIT, CNRS, Toulouse, France)
Joao Marques-Silva is a CNRS Research Director (Directeur de Recherche), being affiliated with IRIT in Toulouse, France. He is also one of the Research Chairs of the Artificial and Natural Intelligence Toulouse Institute (ANITI). Before joining CNRS, IRIT and ANITI, Joao Marques-Silva was affiliated with the University of Lisbon in Portugal, the University College Dublin in Ireland, and the University of Southampton in the United Kingdom. Dr. Marques-Silva is a Fellow of the IEEE, and he was a recipient of the 2009 CAV Award for fundamental contributions to the development of high-performance Boolean satisfiability solvers.

(4) "AI Can Learn from Data. But Can It Learn to Reason?"              <slides>      <video>

Guy Van den Broeck (UCLA, USA)

Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His papers have been recognized with awards from key conferences such as AAAI, UAI, KR, OOPSLA, and ILP. Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.

September 29th:

(5) "From Statistical Relational to Neural Symbolic Artificial Intelligence"              <slides>      <video>

Giuseppe Marra (KU Leuven, Belgium)

Giuseppe Marra is an Assistant Professor at the department of Computer Science at KU Leuven in Belgium. His research interests include the integration of learning and reasoning, particularly focused on neural symbolic techniques, and geometrical deep learning, with a focus on knowledge graph embeddings and graph neural networks.

(6) "Statistical Relational Extensions of Answer Set Programming"              <slides>      <video>

Joohyung Lee (Arizona State University, USA & Samsung Research)

Joohyung Lee is an associate professor in the School of Computing and Augmented Intelligence at Arizona State University and the head of the AI Methods Team at Samsung Research. He has been working on knowledge representation, machine learning, neuro-symbolic AI, question answering, and logic programming, supported by the National Science Foundation, the Department of Defense, IARPA, Siemens, Bosch, and ETRI. He is a recipient of Outstanding Paper Honorable Mention Award from AAAI 2004.

September 30th:

(7) "Vadalog: Its Extensions and Business Applications"              <slides>      <video>

Emanuel Sallinger (TU Vienna & Oxford U.)

Emanuel Sallinger is Assistant Professor at TU Wien and Departmental Lecturer at Oxford University. He leads the Knowledge Graph Lab at TU Wien, the SIG Knowledge Graphs of the Center for AI and ML, and is a member of the Databases and AI group (DBAI). At Oxford University, he is lecturer of the courses .Knowledge Graphs. and .Database Design.. Before joining TU Wien, he directed the VADA (Value-Added Data Systems and Architecture) Laboratory at Oxford University for over six years. The Knowledge Graph Lab (formally .Scalable Reasoning in Knowledge Graphs.) is a WWTF Vienna Research Group, an ERC-sized funding program for creating long-term research groups. The VADA project was an EPSRC programme grant under Georg Gottlob, bringing together the universities of Oxford (directed by Emanuel Sallinger), Manchester (headed by Norman Paton), and Edinburgh (headed by Leonid Libkin).

(8) "Causal Inference in Data Analysis with Applications to Fairness and Explanations"              <slides>      <video>

Sudeepa Roy (Duke University, USA) and Babak Salimi (UC San Diego, USA)

Sudeepa Roy is an Associate Professor in Computer Science at Duke University. She works broadly in data management, with a focus on foundational aspects of big data analysis, including causality and explanations, data provenance, probabilistic databases, data repair, query optimization, and database theory. Prior to Duke, she did a postdoc at the University of Washington, and obtained her Ph.D. from the University of Pennsylvania. She is a recipient of the VLDB Early Career Research Contributions Award, an NSF CAREER Award, and a Google Ph.D. fellowship in structured data.

Babak Salimi is an assistant professor in HDSI at UC San Diego. Before joining UC San Diego, he was a postdoctoral research associate in the Department of Computer Science and Engineering, University of Washington where he worked with Prof. Dan Suciu and the database group. He received his Ph.D. from the School of Computer Science at Carleton University, advised by Prof. Leopoldo Bertossi. His research spans responsible data management and causal inference, including algorithmic fairness and transparency. He has made several contributions to the understanding of various aspects of responsible data management and analysis including explainability, fairness, reliability, and robustness. He is also very interested in data management and database theory. His research contributions have been recognized with a Postdoc Research Award at University of Washington, a Best Demonstration Paper Award at VLDB 2018, a Best Paper Award at SIGMOD 2019 and a Research highlight Award at SIGMOD 2020.