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.