Prof Partha P Chakrabarti works in the area of Artificial Intelligence & Machine Learning with applications to various practical domains. He works extensively in several areas of AI and ML covering search and heuristics, reasoning and deduction, learning and cognition. He particularly contributed in topics related to low footprint (memory, time), explainable (reasoned), dependable methods (fault tolerance, resilient to adversaries) and machine vis-à-vis human problem solving approaches combining Cognitive Science and AI. He has applied his work to CAD for VLSI, especially in areas related to high level synthesis, formal verification and dependability. Currently he is working on combining search and reasoning with data driven methods to produce trustworthy, interpretable AI solutions. Areas of recent application interest include electronic design automation, education, science-based engineered systems and the domain of justice. He has developed several such tools for industry and government. Public level tools include the National Digital Library of India. He is working on an AI Platform for the Indian Judiciary.
Professor for Machine Learning
Centre for Cognitive Interaction Technologies
Barbara Hammer is a full Professor for Machine Learning at the CITEC Cluster at Bielefeld University, Germany. She received her Ph.D. in Computer Science in 1999 and her venia legendi (permission to teach) in 2003, both from the University of Osnabrueck, Germany, where she was head of an independent research group on the topic 'Learning with Neural Methods on Structured Data'. In 2004, she accepted an offer for a professorship at Clausthal University of Technology, Germany, before moving to Bielefeld in 2010. Barbara's research interests cover theory and algorithms in machine learning and neural networks and their application for technical systems and the life sciences, including explainability, learning with drift, nonlinear dimensionality reduction, recursive models, and learning with non-standard data. Barbara has been chairing the IEEE CIS Technical Committee on Data Mining and Big Data Analytics, the IEEE CIS Technical Committee on Neural Networks, and the IEEE CIS Distinguished Lecturer Committee. She has been elected as member of the IEEE CIS Administrative Committee and the INNS Board. She is an associate editor of the IEEE Computational Intelligence Magazine, the IEEE TNNLS, and IEEE TPAMI. Currently, large parts of her work focus on explainable machine learning for spatial-temporal data in her role as a PI of the ERC Synergy Grant Water-Futures. Here, the particular challenge is how to device explainable machine learning technologies which can deal with spatio-temporal data as occur in the short-term control and long-term optimization of drinking water supply systems.
Assistant Professor and TBO Group Faculty Fellow Department of Computer Science and Engineering IIT Delhi
My research interests fall under the broad theme of Computing and Society, spanning the research areas of Social Computing, Information Retrieval, Legal Analytics, Computational Journalism and Fairness in Algorithmic Decision Making. The problems I study usually have two distinct aspects: an algorithmic aspect and a societal aspect. A common thread in my research lies in collecting and analyzing large scale data from online systems to understand whether there is any cause for concern, and then develop mechanisms to eliminate or reduce the impact. My research agenda is inherently interdisciplinary, and therefore, the tools and theories that I often use in my works come from machine learning, network science, media studies, computational social choice, social welfare and behavioral economics.
A major thrust area of my research over last several years has been on two-sided online platforms. Major online platforms today (such as Amazon, Netflix, Spotify, Uber, LinkedIn or AirBnB) can be thought of as two-sided markets, with multiple stakeholders: (i) producers/providers of goods and services (e.g., artists on Spotify, drivers on Uber, hosts on Airbnb), (ii) customers who pay for them, and (iii) the platform which sits at the center of the ecosystem, mediating transactions between producers and customers, and essentially controlling the information access through search, recommendation or matching services. I have been working on two-sided platforms, aiming to achieve different goals for different stakeholders. While some of my works are focused on improving the efficiency of the platforms, in others, I have attempted to identify and address the concerns of bias and unfairness for both producers and customers. One key aspect of any two-sided platform is that both customers and producers have preferences, and the platform needs to take into account those preferences while developing search, recommendation or matching algorithms. My focus has been on considering these preferences in a fair manner, and this is a marked departure from the majority of FairML literature where the focus is mostly on fair classification or regression tasks. Please check my website at https://www.cse.iitd.ac.in/~abhijnan to know more about the research agenda.
Research Group Leader
Max Planck Institute for Software Systems
Daniel is a research group leader at the Max Planck Institute for Software Systems in Kaiserslautern. His research interests broadly lie in the intersection of machine learning and formal methods. Daniel is especially interested in combining inductive techniques from machine learning and symbolic techniques from logic. The overall research goals of his group are to build automated tools for designing, constructing, and analyzing intelligent systems. This includes topics such as verification of machine learning-enabled systems, formal explainability of artificial intelligence, learning-based synthesis of hardware and software, and specification learning/recommendation.
Abstract of Proposed Discussion :
Proving the reliability and safety of AI-enabled systems has recently received a lot of attention. However, this topic is commonly approached from either the machine learning community or the formal methods community, with very little interaction. It would be interesting to discuss how these two research approaches can come together.
Senior Scientist & Head Deep Learning & AI TCS Research & Innovation New Delhi
Dr. Lovekesh Vig heads the Deep Learning and Artificial Intelligence Research Area at TCS Research where he serves as a senior scientist. After obtaining his PhD from Vanderbilt University in 2007 in robotics and AI, Dr. Vig worked at Bloomberg R&D, New York for a year before joining JNU as a faculty at the School of Computational and Integrative Sciences in 2009. While performing research and teaching duties at JNU, Dr. Vig also consulted for InfoEdge and TCS Research before joining TCS full time in 2015 where he has been overseeing the research and adoption of deep learning across TCS. Dr. Vig has several publications in leading AI journals and conferences and has built assets that are delivering value across industry verticals like Insurance, Banking, Manufacturing, Retail, Energy, Utilities, Media and Advertising. His team at TCS Research works on a broad spectrum of AI problems that span Neuro-Symbolic Learning, Meta-Learning, Resource constrained machine learning, Active Learning, Program Synthesis, Optimization, Time Series Analytics, Natural Language Processing and Vision.
Institute of Informatics
Technical University of Munich
Research areas: verification and synthesis, probabilistic model checking, automata theory, temporal logics, continuous-time stochastic processes, games, verification and learning
Interaction of machine learning and verification: (1) use of machine learning in verification to improve scalability of the methods and explainability of the results, e.g. controllers; (2) verification of machine-learnt systems, in particular neural networks, e.g. for autonomous driving, using abstraction, runtime monitoring, robustness analysis
Department of Computer Science & Engineering, Indian Institute of Technology Kharagpur
Prof. Pallab Dasgupta works on AI and Formal Methods and their application on industrial verification problems. He has made fundamental contributions in temporal logic, model checking, and symbolic reasoning. He has applied his research on the verification of integrated circuits, embedded real-time control systems, and intelligent autonomous systems. He leads the research group on Formal Methods and Trusted AI at IIT Kharagpur and the Synopsys CAD Labs. His collaboration with the Technical University of Munich on the verification of ADAS features led to the setting up of the Indo-German Collaborative Research Centre on Intelligent Transportation Systems. His work has been published in more than 200 research papers, and brought into industrial practice by leading companies including Intel, Synopsys, Texas Instruments, General Motors, and SRC. He was conferred the IBM Faculty award, the Qualcomm Faculty Award, and the Techno-Mentor award of the India Semiconductor Association. Hindustan Aeronautics Ltd. (HAL) contracted his expertise to formally prove the safety of India’s first avionic Real Time Operating System. He delivered a tool to Indian Railways for checking the safety of signalling logic and interlocking systems. His current line of research on Trusted AI focusses on integrating provable artefacts such as rules, logic, languages, with machine learning techniques such as deep reinforcement learning. He has ongoing collaborations for using such integrators on autonomous driving systems.
Prof. Dasgupta served as the Associate Dean and then the Dean of Sponsored Research, IIT Kharagpur between 2013-2019. He is a Fellow of the Indian National Academy of Engineering and a Fellow of the Indian Academy of Sciences. He plays the classical instrument, sitar, and currently leads the Academy of Classical and Folk Arts at IIT Kharagpur.
CISPA Helmholtz Center for Information Security Saarbrücken
Mario Fritz is faculty member at the CISPA Helmholtz Center for Information Security, honorary professor at the Saarland University, and a fellow of the European Laboratory of Learning and Intelligent Systems (ELLIS). His work is centered around Trustworthy Information Processing with a focus on the intersection of AI & Machine Learning with Security & Privacy.
On the one hand, his most recent work is advancing the state of the art in Deep Learning, Deep Generative Models, uncertainty modelling, trajectory prediction, semantic segmentation/detection/classification, federated learning with applications to computer vision, NLP, graphics, HCI. On the other hand, his work is focused on trustworthy AI approaches that achieve privacy-preserving learning, interpretability, fairness, attacks and defenses for AI, adversarial robustness, DeepFake detection, applications of AI to cybersecurity.
He is currently involved in several initiative and projects related to trustworthy AI in health, as a leading scientist of the Helmholtz Medical Security, Privacy, and AI Research Center (HMSP), coordinator and PI of a project on Trustworthy Federated Data Analytics Project (TFDA), coordinator and PI of a project on Protecting Genetic Data with Synthetic Cohorts from Deep Generative Models (PRO-GENE-GEN), and partner PI in The German Human Genome-Phenome Archive (GHGA) and member of the working group of German ministry of education and research (BMBF) “Utilization of digital data for AI developments and other data-driven research in health research” (“Nutzbarmachung digitaler Daten für KI-Entwicklungen in der Gesundheitsforschung”).
Department of Computer Science and Engineering
Prof. Sandeep K. Shukla is an IEEE fellow, and ACM Distinguished Scientist. He is currently a professor of Computer Science and Engineering department at IIT Kanpur which he headed during 2017-2020. He was the editor in chief of the ACM Transactions on Embedded Computing Systems during 2013-2020. He is currently associate editors of ACM Transactions on Cyber Physical Systems, and Journal of the British Blockchain Association. In the past he has served as associate editors of IEEE Transactions on Computers, IEEE Transactions on Industrial Informatics, IEEE Design and Test, and IEEE Embedded Systems Letters. Before joining IIT Kanpur in 2015, he was a professor at Virginia Tech, USA. He served as ACM Distinguished Speaker, and IEEE Computer Society Distinguished Visitor in the past. He has authored over 200 peer reviewed journal and conference papers, and authored/edited 10 books. He was awarded the Presidential Early Career Award in Science and Engineering (PECASE) in 2004, The Bessel Award by Humboldt Foundation in 2009, a Distinguished Alumnus Award by SUNY Albany in 2007, a Ramanujan Fellowship in 2015. His major research interest is Cyber Security of Critical Infrastructures, Cyber Security of IT/OT systems, and Applications of Blockchain Technology in Security and Privacy.