Angela Bonifati

Lyon 1 University and Liris CNRS (France)

Property Graphs to Graph Ecosystems: A Round Trip

Property graphs are a widespread data model for representing interconnected multi-labeled data enhanced with properties as key/value pairs. These highly expressive graphs are used in a wide range of domains, such as social and transportation networks, biological networks, finance, cybersecurity, logistics and planning, to name a few. Property graphs are currently used in a variety of graph databases showing a fragmented landscape in terms of query and schema language support, indexing mechanisms and query evaluation and optimization strategies.

Motivated by our community-wide vision on future graph processing systems, in this talk I will present the underpinnings of unified data and query model abstractions as well as the principles of graph ecosystems. Many current graph query engines only support very limited subsets of graph data manipulation and data definition primitives. It becomes crucial to address efficient query evaluation for complex graph queries, in both static and streaming environments. I will conclude my talk by pinpointing several research directions and open problems for graph ecosystems.

Speakers Bio: Angela Bonifati (PhD, 2002) is a Professor of Computer Science at Lyon 1 University and at the CNRS Liris research lab, where she leads the Database Group. She is also an Adjunct Professor at the University of Waterloo in Canada since 2020. Her current research interests are on the interplay between relational and graph-oriented data paradigms, particularly query processing, indexing, data integration and learning for both paradigms. She is involved in several grants at Lyon 1 University, including French, EU and industrial grants. She has also co-authored more than 150 publications in top venues of the data management field, and is the recipient of two Best Paper awards (ICDE22, VLDB22 runner up). She has co-authored two books (on Schema Matching and Mapping edited by Springer in 2011 and on Querying Graphs edited by Morgan & Claypool in 2018) and an invited paper in ACM Sigmod Record 2018 on Graph Queries. She was the Program Chair of ACM Sigmod 2022 and she is currently an Associate Editor for both Proceedings of VLDB and IEEE ICDE. She is an Associate Editor for several journals, including the VLDB Journal and ACM TODS. She is currently the President of the EDBT Executive Board and a member of the Sigmod Executive Committee.

Arun Kumar

University of California, San Diego

The New DBfication of ML/AI

The recent boom in ML/AI applications has brought into sharp focus the pressing need for tackling the concerns of scalability, usability, and manageability across the entire lifecycle of ML/AI applications. The ML/AI world has long studied the concerns of accuracy, automation, etc. from theoretical and algorithmic vantage points. But to truly democratize ML/AI, the vantage point of building and deploying practical systems is equally critical.

In this talk, I will make the case that it is high time to bridge the gap between the ML/AI world and a world that exemplifies successful democratization of data technology: databases. I will show how new bridges rooted in the principles, techniques, and tools of the database world are helping tackle the above pressing concerns and in turn, posing new research questions to the world of ML/AI. As case studies of such bridges, I will describe two lines of work from my group: query optimization for scalable deep learning systems and benchmarking data preparation in AutoML platforms. I will conclude with my thoughts on community mechanisms to foster more such bridges between research worlds and between research and practice.

Speaker’s Bio: Arun Kumar is an Associate Professor in Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego. His primary research interests are in data management and systems for ML/AI-based data analytics. Systems and ideas from his work have been released as part of the Apache MADlib open-source library and shipped as part of products from or used internally by many database, Web, and cloud companies. He is a recipient of three SIGMOD research paper awards, five distinguished reviewer/metareviewer awards from SIGMOD/VLDB, the IEEE TCDE Rising Star Award, and an NSF CAREER Award.

Anjana Susarla

Michigan State University

Is seeking information on social media harmful to your health: Using AI and ML to understand patient education needs on social media

The recent Congressional investigations of social media companies, growing media attention and public awareness of algorithmic harm caused by technology giants clearly indicate that their monetization strategies focused on engagement are not necessarily conducive to social good. Considering the vast global reach of social media platforms that are accessed by billions of users worldwide, and the potential for misleading medical information online, it is imperative to enhance user access and exposure to high quality, evidence-based information on social media platforms. In this study we provide an evidence-based conceptualization of engagement with algorithmic artifacts on social media to potentially improve health literacy of patients and the public. Our digital therapeutic solution is generalizable to other conditions, providing an automated mechanism to independently audit content moderation and credibility of health information online.

Speaker’s Bio: Anjana Susarla holds the Omura-Saxena Professorship in Responsible AI at the Broad College of Business. She earned an undergraduate degree in Mechanical Engineering from the Indian Institute of Technology, Chennai; a graduate degree in Business Administration from the Indian Institute of Management, Calcutta; and Ph.D. in Information Systems from the University of Texas at Austin. Her research interests include the economics of information systems, social media analytics and the economics of artificial intelligence. Her work has appeared in several academic journals and peer-reviewed conferences such as Academy of Management Conference, Conference on Knowledge Discovery and Data Mining, Conference on Neural Information Processing Systems (Neurips), Information Systems Research, International Conference in Information Systems, International Conference on Learning Representations (ICLR), Journal of Management Information Systems, Management Science and MIS Quarterly.

Anjana Susarla has been a recipient of the William S. Livingston Award for Outstanding Graduate Students at the University of Texas, a Steven Schrader Best Paper Finalist at the Academy of Management, the Association of Information Systems Best Publication Award and the Microsoft Prize by the International Network of Social Networks Analysis Sunbelt Conference. She has worked in consulting and led experiential projects with several companies. Her research has received grants and funding from several institutions including an R01 grant from the National Institute of Health.

Professor Susarla has given several invited talks at industry and academic events and has been a commentator on algorithmic bias and digital transformation. She has been interviewed in and had her op-eds and research quoted and published in several media outlets such as the Associated Press, Atlanta Journal Constitution, BBC, Business Insider, Business Standard, CBS, Channel News Asia, Chicago Tribune, The Conversation, The Daily Beast, Deutsche Welle, Economic Times, El Pais, Esquire, Fast Company, Fox News, Gizmodo, Haaretz, the Hindu, Houston Chronicle, Huffington Post, Michigan Public Radio, Marketplace Morning Report, The Mint, Nasdaq, National Public Radio, NBC, NDTV, Newsweek, Nieman Lab, Nikkei, OneZero, Outlook India, PBS, Pew Research, Philadelphia Inquirer, the Print, Quartz, Salon, Scientific American, Sirius XM, Slate, Snopes, UPI, Washington Post, the Week, Wired, World Economic Forum and Yahoo Finance. She has also been a speaker at various public forums.

Maria-Esther Vidal

TIB Leibniz and University of Hannover

Challenges of Knowledge Graphs and their Applications in Biomedicine

Data has drastically grown in the last decade and is expected to grow faster in the following years. Specifically, in the healthcare domain, a wide variety of methods, e.g., liquid biopsies, medical images, or genome sequencing, produce large volumes of data from where new biomarkers can be discovered. The outcomes of big data analysis correspond to building blocks for precise diagnostics and effective treatments. However, healthcare data may suffer from diverse complexity issues – volume, variety, and veracity– which demand novel techniques for data management and knowledge discovery to ensure accurate insights and conscientious decisions.

In this talk, we will discuss data integration and query processing methods for tackling the challenges imposed by the complexity issues of big data and their impact on analytics. In particular, knowledge graphs will be positioned as data structures enabling the integration of heterogeneous health data and merging data with ontologies describing their meaning. We will show the benefits of exploiting knowledge graphs to uncover patterns and associations among entities. Specifically, we will neuro-symbolic systems on top of knowledge graphs implemented with the aim of predicting the effectiveness of a cancer treatment and the effects of drug-drug interactions.

Speaker’s Bio: Maria-Esther Vidal is a professor at the Institute of Data Science at the Leibniz University of Hannover (LUH). She leads the Scientific Data Management group at the TIB-Leibniz Information Center of Science and Technology and the Data Science Institute at LUH. She is also a member f the L3S Research Center at LUH. Furthermore, she has received the Stifterverband Science Award on Responsible Research in Germany 2020, and the Leibniz Best Minds: Programme for Women Professors has partially supported her research since 2021. Her interests include Big data and knowledge management, knowledge representation, and the semantic web. She has published more than 180 peer-reviewed papers in Semantic Web, Databases, Bioinformatics, and Artificial Intelligence. She has co-authored one monograph and co-edited books and journal special issues. She has been part of various editorial boards, general chair, co-chair, senior member, and reviewer of several scientific events and journals. She is leading data management tasks in national and international projects (e.g., EU H2020 CLARIFY, IASIS, and BigMedilytics) and is a PI of MSCA-ETN projects (e.g., WDAqua and NoBIAS). She has been a visiting professor at universities (e.g., Univ. Maryland, UPM Madrid, UPC, KIT Karlsruhe, and Univ. Nantes).

Sriram Rajamani

Microsoft Research India

AI Assisted Programming

We present a vision for a futuristic programming environment, where ML models and logical rules co-exist and evolve over time. We substantiate our vision using three case studies: (1) safe code generation using large language models, (2) heterogeneous data extraction, and (2) regulatory compliance for online advertising. Using these case studies, we hypothesize what such a futuristic programming system can do to balance productivity with safety, security and reliability, present new research results, and point to directions for future work.

Speaker’s Bio: Sriram Rajamani is Distinguished Scientist and Managing Director of Microsoft Research India. Sriram is an ACM fellow, INAE fellow, and winner of the Computer Aided Verification Award. His work has impacted both academic and industrial practice in programming languages, systems, security, and formal verification. He is currently working on reimagining programming by combining machine learning with program analysis and synthesis. Sriram did his PhD in Computer Science at UC Berkeley.

Prof. Mausam

Founding Head, Yardi School of Artificial Intelligence, IIT Delhi

Neural Models with Symbolic Representations for Perceptuo-Reasoning Tasks

Abstract: The field of artificial intelligence (AI) has seen several proposals for representing a task. Two prominent ones are (1) neural AI which represents it in a brain-like computation network and (2) symbolic AI which uses explicit symbolic theories over a logic-based system. While deep neural models have revolutionized perceptual AI in modern times, symbolic representations have continued to remain the model of choice for pure reasoning problems. An emerging body of work called Neuro-Symbolic AI attempts at combining both representations for varied benefits. In this talk, I will discuss the importance of Neuro-Symbolic AI, and describe several recent research threads in my group, with applications ranging from natural language processing, probabilistic decision-making, and constraint satisfaction.

Bio: Mausam is the founding head of Yardi School of Artificial Intelligence, along with being a Professor of Computer Science at IIT Delhi. He is also an affiliate professor at University of Washington, Seattle. With a twenty year research experience in artificial intelligence, he has, over time, contributed to many research areas such as large scale information extraction over the Web, AI approaches for optimizing crowdsourced workflows, and probabilistic planning algorithms. More recently, his research is exploring neuro-symbolic machine learning, computer vision for radiology, NLP for robotics, multilingual NLP, and several threads in intelligent information systems that include information extraction, knowledge base completion, question answering, summarization and dialogue systems. He has over 100 archival papers to his credit, along with a book, two best paper awards, and one test of time award. Mausam was awarded the AAAI Senior Member status in 2015 for his long-term participation in AAAI and distinction in the field of artificial intelligence. He has had the privilege of being a program chair for two top conferences, AAAI 2021, and ICAPS 2017. He was ranked the 56th most influential NLP scholar and 64th most influential AI scholar by ArnetMiner AI2000 Ranking in 2021. He received his PhD from University of Washington in 2007 and a B.Tech. from IIT Delhi in 2001.