Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)
Alan Kuntz is an Assistant Professor at the University of Utah's Kahlert School of Computing (KSoC) and a core member of the Robotics Center. He leads the interdisciplinary Kuntz Research Lab, focusing on robotics and computational methods with medical applications, particularly in healthcare and surgery. His work spans robot motion planning, autonomous systems, and robot design optimization. Education: Ph.D. in Computer Science from the University of North Carolina at Chapel Hill, with research in the Computational Robotics Research Group. Previously a postdoctoral scholar at Vanderbilt University's Medical Engineering and Discovery Lab. Research interests include surgical robotics, continuum robots, needle steering, and medical device design. Recent projects include autonomous needle navigation, continuum lung staplers, and metamaterial-based robots. His team has published extensively on topics like kinematic modeling, uncertainty quantification, and medical intervention systems. Notable awards include the 2022 IEEE Access Best Video Award for his group's work, and mentoring over 15 students through the University of Utah's Undergraduate Research Opportunities Program. The Kuntz Lab actively collaborates on clinical applications, presenting at top conferences like IROS, Hamlyn Symposium, and ISMR. Labs/Teams: Directs the Kuntz Research Lab, known for its innovative medical robotics projects. The lab's work has been featured in Forbes and other media outlets for breakthroughs like in vivo needle steering demonstrations.
Sanjay Modgil is a Professor of Artificial Intelligence at King's College London's School of Informatics, specializing in argumentation theory, non-monotonic logic, and AI applications in medicine. He contributes to ethical AI research aligned with UN Sustainable Development Goals. Research Interests Argumentation Theory Non-monotonic Logic Normative Reasoning Agent Reasoning AI in Healthcare Human-AI Collaboration His recent publications focus on depth-bounded reasoning, ethical debates, and large language models. He leads EPSRC-funded projects like CONSULT and RESPECT, emphasizing responsible AI technologies and multimorbidity management systems.
Mostafa Milani is an Assistant Professor in the Department of Computer Science at Western University. His research focuses on data management, databases, and their applications in data cleaning, privacy, provenance, and fairness. Before joining Western, he held postdoctoral positions at the University of British Columbia and McMaster University, and earned his Ph.D. from Carleton University under Dr. Leopoldo Bertossi. Education: Ph.D. in Computer Science from Carleton University (supervised by Leopoldo Bertossi), Postdoctoral Fellowships at University of British Columbia and McMaster University. Research Interests: Data Quality, Privacy, Provenance, Fairness, Entity Matching, Query Optimization, and Database Systems. His work emphasizes ethical data practices and integrates machine learning for improved database interactions. He has contributed to projects like Building Trust in Data (privacy/fairness integration) and Unified Data Exploration (provenance and query recommendations). Courses taught include Databases I/II, Applied Logic, and Web Systems. Current advisees include 7 MSc and 1 PhD student. Former students have graduated across MSc and undergraduate programs. His research is supported by grants and collaborations, and he actively participates in program committees for top conferences like SIGMOD and VLDB.
Josie Clowney is an Associate Professor in the Department of Molecular, Cellular, and Developmental Biology at the University of Michigan, where she has held faculty position since 2017. Her research investigates the genomic algorithms that construct neural circuits during development, using Drosophila as a model to study how chemosensory systems drive both instinctual behaviors and learning. She teaches Bio 172 and an upper-level seminar on cellular diversity and scientific writing, and mentors graduate students through MCDB, CMB, NGP, and BIOINF PhD programs. Her educational background includes: Ph.D. in Biomedical Sciences (2012) from the University of California, San Francisco B.S. in Cellular and Molecular Biology (2005) from the University of Michigan, where she conducted research with Cunming Duan Clowney's research centers on understanding how definitive neuronal parameters are encoded in genomic information and translated into cellular architectures. Her lab hypothesizes that developmental algorithms for learning circuits versus instinctual circuits differ fundamentally in their genomic requirements, with chemosensory circuits serving as key models. Using fruit flies for their tractable brain organization, her work bridges computational principles and biological implementation to uncover universal brain organization rules. Analysis of her 15 most recent publications (2016-2025) reveals consistent focus on Drosophila mushroom body development, neural sexual differentiation, and spatial constraints in circuit formation. Key themes include non-deterministic mechanisms diversifying cell surface expression, chromatin dynamics in circadian regulation, and how input density tunes sensory responses. Her work integrates genomics, neuroanatomy, and behavior to model how compact genomic information generates complex neural architectures. No scientific awards were mentioned in the provided text. Dr. Clowney advises graduate students through multiple PhD programs at the University of Michigan, though specific student names and grant details are not provided in the source material. Her teaching includes foundational undergraduate coursework and advanced seminars emphasizing scientific writing. The active publication record spanning 2016-2025 indicates sustained research funding supporting her lab's investigations into neural circuit development. The Clowney Lab, housed in the Biological Sciences Building (4218 BSB), employs Drosophila genetics and neuroanatomical techniques to dissect developmental algorithms of brain wiring. Current projects explore how spatial constraints structure learning circuits, mechanisms of neural sexual differentiation, and the genomic encoding of circuit diversity. The lab collaborates within Michigan's neuroscience community through the Program in Biology and participates in interdisciplinary initiatives studying brain evolution and function.
Robert Peharz is an Assistant Professor at Graz University of Technology, where he leads research at the Institute of Machine Learning and Neural Computation. His work focuses on probabilistic machine learning, with particular emphasis on tractable probabilistic models, causality, and neurosymbolic AI. Education and Career PhD from TU Graz (Austria) in 2015 Postdoc at Medical University of Graz Postdoc and Marie-Curie Individual Fellow at University of Cambridge (2017-2019) Assistant Professor at Eindhoven University of Technology (2019-2021) Current: Assistant Professor at Graz University of Technology Research Interests Peharz's research spans multiple areas of artificial intelligence with a focus on making probabilistic reasoning both theoretically sound and practically efficient. His work addresses fundamental challenges in tractable probabilistic inference and learning, probabilistic circuits as a unified framework for deep generative models, Bayesian causal inference, and neurosymbolic AI combining sub-symbolic and symbolic approaches. His research has applications in cybersecurity, healthcare, and energy systems. Research Projects VENTUS (2024-present): Physics-informed, probabilistic and causal machine learning for wind energy systems NEO DNA (2023-present): DNA-based data storage systems using computer vision and probabilistic ML VanillaFlow (2023-present): AI-guided development of novel vanillin-based molecules for redox flow batteries Bilateral AI : Cluster of Excellence focused on Broad AI combining sub-symbolic and symbolic AI approaches Awards and Recognition Finalist for TUG's Excellent Teaching Award (2023) for all 3 of his courses Marie-Curie Individual Fellow at University of Cambridge Academic Service Peharz is actively involved in the academic community through conference organization and reviewing: Area Chair: UAI (2022), ECML/PKDD (2022) Senior Committee Member: UAI (2021), IJCAI (2019, 2020) Reviewer for major conferences including ICML, NeurIPS, AAAI, IJCAI-ECAI Teaching and Mentorship Peharz supervises multiple PhD students working on diverse projects at the intersection of machine learning, causality, and neurosymbolic AI. His current advisees include Sepideh Adamiat, Irina Dobrianski, Johannes Exenberger, Giacomo Di Gobbi, Tim d'Hondt, Christian Toth, and Thomas Wedenig. Previous students include Alvaro Correia, Martin Trapp, and David Montalvan.
Simon Weber is a researcher affiliated with the ETH Zurich (Department of Computer Science). His work focuses on Unique Sink Orientations (USOs) , a combinatorial abstraction of optimization problems like Linear and Quadratic Programming. Simon's research spans three areas: (1) Structure of USOs and their links to Oriented Matroids; (2) Constructions of high-dimensional USOs to analyze algorithm complexity; and (3) Algorithmic improvements for sink-finding. He also explores topics in graph compression, neural networks, and ∃R-complete problems. Key Publications: PhD thesis on USO reductions, ∃R-completeness in neural training, and USO phase analysis. Scientific Contributions: Advances in USO complexity, FPT algorithms for MaxCut, and recognition of geometric hypergraphs and nerves of convex sets. He has supervised multiple theses at ETH Zurich, including topics on USO visualization, MaxCut algorithms, and necklace splitting. His teaching experience includes being a Head Assistant for courses like Geometry: Combinatorics & Algorithms and Topological Data Analysis . Simon's work has been recognized with Best Paper and Best Student Paper Finalist awards at SC19.
Milos Nikolic is a Lecturer in Database Systems at the School of Informatics, University of Edinburgh. He is a member of the Database Group and the Laboratory for Foundations of Computer Science. Prior to joining Edinburgh, he was a Departmental Lecturer in the Department of Computer Science at the University of Oxford. He holds a PhD in Computer Science from EPFL. Research Interests: Milos Nikolic's research focuses on databases and large-scale data management, with emphasis on incremental computation, in-database learning, stream processing, and query compilation. His work explores how complex analytical queries—such as SQL, linear algebra, and machine learning tasks—can be efficiently maintained and evaluated in dynamic and distributed environments. He develops novel techniques in query optimization and compilation to enable real-time analytics over evolving data. His recent publications demonstrate a strong trend in dynamic query evaluation, particularly around conjunctive and hierarchical queries under updates, incremental view maintenance (e.g., F-IVM), and scalable processing of nested and biomedical data. His work often leverages theoretical foundations to achieve worst-case optimal performance, grounded in conjectures like the Online Matrix-Vector Multiplication (OMv) hypothesis. Scientific Awards: Best Paper Award, ICDT 2019 Advising and Grants: Milos is actively seeking PhD students to work on data management topics. While no formal list of advisees is provided, he collaborates extensively with researchers such as Dan Olteanu, Ahmet Kara, and Haozhe Zhang. His projects, including Adaptive Query Processing , Incremental Maintenance of Complex Analytics , and Declarative Data Pipelines (industry-supported), suggest ongoing grant and industry collaborations. Labs and Teams: He is a key member of the Database Group and the Laboratory for Foundations of Computer Science at the University of Edinburgh, contributing to cutting-edge research in foundational and applied database systems.
Simon Lacoste-Julien is an Associate Professor at Université de Montréal, affiliated with the Department of Computer Science and Operations Research (DIRO). He also serves as the Associate Scientific Director of Mila – Quebec Institute of Artificial Intelligence and holds the position of Vice President Lab Director at Samsung SAIT AI Lab Montreal (SAIL). His research focuses on machine learning, optimization, and their applications in areas like deep learning, generative models, causality, and computer vision. Lacoste-Julien has held academic positions at INRIA in Paris and has a PhD from UC Berkeley, with postdoctoral work at the University of Cambridge. He teaches advanced graduate courses on probabilistic graphical models and structured prediction. His work includes contributions to optimization algorithms (e.g., Frank-Wolfe methods), causal discovery, and generative models. Lacoste-Julien has supervised numerous students and postdocs, and his awards include being a CIFAR Fellow and Canada CIFAR AI Chair. His research spans theoretical foundations and practical applications, with a strong emphasis on scalable and efficient machine learning techniques.
Matthias Paul Lanzinger is an Assistant Professor at the Technische Universität Wien's Faculty of Informatics, Department of Database and Artificial Intelligence. His research focuses on algorithms, graph neural networks, hypergraph decomposition techniques, parameterized complexity, and computational logic. He leads projects like 'DeConquer' (Vienna Science Fund) and 'HyperTrac', exploring efficient query processing and hypergraph-based algorithms. Research interests include theoretical computer science, database systems, and applying logical frameworks to solve complex computational problems. Recent work emphasizes hypertree decompositions, fuzzy Datalog, and graph motif analysis via the Weisfeiler-Leman test. He co-edited the 2024 Datalog-2.0 workshop proceedings and has supervised students on topics like column-store performance and graph query languages. His publications span venues like ACM Transactions on Database Systems, ICLR, and IJCAI, highlighting contributions to algorithmic efficiency, database theory, and logical reasoning systems. Active in academic service, he teaches courses on database systems, scientific research, and advanced topics in informatics.
Andrei Bulatov is a Professor of Computing Science at Simon Fraser University (SFU), affiliated with the School of Computing Science within the Faculty of Applied Sciences. His research focuses on computational complexity, constraint satisfaction problems (CSP), combinatorics, and universal algebra. Bulatov earned his Ph.D. and M.Sc. in Mathematics from Ural State University, Russia, in 1995 and 1991, respectively. His work bridges theoretical computer science and algebra, with notable contributions to the complexity classification of CSPs. He has received a Best Paper Award at FOCS 2002 for his dichotomy theorem on three-element set constraints. Bulatov’s research also explores counting CSPs, algorithms for satisfiability, and applications of algebraic methods in discrete mathematics. He is part of the Algorithms & Theory Group and the Computational Logic Laboratory at SFU. Bulatov’s publications span journals like Journal of Computer and System Sciences, Theoretical Computer Science, and SIAM Journal on Computing, addressing topics from graph theory to randomized algorithms. His academic service includes roles in professional organizations and editorial work. Bulatov’s teaching includes courses such as Discrete Mathematics (MACM 101) and Directed Reading (CMPT 894).
Karthekeyan Chandrasekaran is an Associate Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign (UIUC), where he has been since 2014. He holds an affiliate position in the Department of Computer Science. His academic career includes a Visiting Fellowship at ICERM (Spring 2023) and Eötvös Loránd University (Budapest, Fall 2022). He earned a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology Madras (2007) and a Ph.D. in Algorithms, Combinatorics, and Optimization from Georgia Institute of Technology (2012). Before joining UIUC, he was a Simons Postdoctoral Research Fellow at Harvard University (2012–2014). His research focuses on Probabilistic Methods and Analysis , Algorithms , Mathematical Programming , and Combinatorial Optimization . He explores theoretical foundations of optimization, graph theory, and hypergraphs, with applications in algorithm design and complexity analysis. Recent work emphasizes hypergraph partitioning, submodular functions, and approximation algorithms. Chandrasekaran has received the Sharp Outstanding Teaching Award in Industrial Engineering (2018) and the College of Computing Dissertation Prize (2012) . His teaching includes courses such as Deterministic Models in Optimization and Combinatorial Optimization , reflecting his expertise in operations research and algorithmic theory. His research output spans over 50 publications, addressing cutting-edge topics like hypergraph connectivity augmentation, feedback vertex set problems, and strongly polynomial algorithms. He actively contributes to theoretical computer science and combinatorial optimization, with a focus on bridging mathematical rigor and practical algorithmic solutions.
V. Arvind is a Professor in the Theoretical Computer Science faculty at the Institute of Mathematical Sciences (IMSc) , Chennai. His research is centered on computational complexity theory, with a focus on structural complexity, randomized and algebraic computation, and quantum information and computation. He explores the deep connections between theoretical computer science and mathematics. Institution: Institute of Mathematical Sciences (IMSc), Chennai School: Theoretical Computer Science Academic Rank: Professor Arvind's research interests include computational complexity, structural complexity theory, algebraic computation, derandomization, and quantum computing. He is particularly interested in the interplay between mathematical structures and computation. His work often bridges theoretical computer science with algebra, combinatorics, and logic. His recent publications, primarily expository articles in the EATCS Bulletin’s Computational Complexity Column, cover a wide range of topics such as robust oracle machines, the Alon-Roichman theorem, noncommutative arithmetic circuits, graph isomorphism, and quantum computation. These works reflect trends in foundational complexity theory, algebraic methods in computation, and the exploration of quantum models. The articles emphasize structural insights, lower bounds, and connections to mathematical disciplines. Professional Service and Editorial Roles: Associate Editor, ACM Transactions on Computation Theory Editor, EATCS Computational Complexity Column (since June 2011) Editorial Board Member, International Journal of Computer Mathematics (2009–2013) Co-organizer, ICM Satellite Conference on Algebraic and Probabilistic Aspects of Combinatorics and Computing Program Committee Member for WALCOM 2014, STACS 2012, COCOON 2009, FSTTCS (multiple years, including chair roles), CCC 2006, INDOCRYPT (2002, 2005), and others Teaching: Arvind has taught advanced courses including Computational Complexity, Algorithms, Algebra and Computation, and Discrete Mathematics, often based on foundational texts and notes from leading experts. Lecture notes from his courses have been compiled by students and collaborators. Collaborations: He has an extensive list of co-authors, including prominent researchers such as Manindra Agrawal, Eric Allender, Johannes Köbler, Meena Mahajan, Jacobo Torán, and Ramprasad Saptharishi, indicating strong collaborative research networks in complexity theory and algorithms.
Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.
Professor Jun Hong holds the chair in Artificial Intelligence at the University of the West of England within the School of Computing and Creative Technologies. He is a key member of the Computer Science Research Centre (CCRC) and has led research initiatives in data science, graph mining, and intelligent autonomous systems. Research focus areas: Data extraction, integration, and linkage; graph mining; social network analysis; AI planning; BDI-based multi-agent systems; privacy preservation Major grants: Administrative Data Research Centre (ESRC, £4.9M), PACES (EPSRC, £623K), DEVELOP (EU Horizon 2020, £113K), and multiple Innovate UK projects His work spans from foundational research in probabilistic BDI agents and multi-agent planning to applied projects in social media analysis, insurance fraud detection, and healthcare communication systems. Recent publications emphasize graph neural networks, social influence prediction, and uncertainty management in autonomous systems. Supervised 12+ PhD/MSc students Acted as PC/OC Chair for IDEAS 2017 , BNCOD 2006 , and AICS 2006 Peer reviewer for top UK councils and international journals
