Tobias Mömke is a Professor for Theoretical Computer Science at the University of Augsburg , Germany. His research focuses on algorithmic optimization for problems with limited resources , particularly in Traveling Salesman Problem (TSP) variants , approximation algorithms , and online computation . He leads the Resource Aware Algorithmics team. Fields of Interest : TSP, Approximation Algorithms, Online Algorithms, Graph Theory Advising : Mentors students like Michael Ruderer, Aida Roshany-Tabrizi, and Morteza Alimi. Scientific Contributions : His recent work includes path cover techniques for TSP variants, bridge lemmas in algorithm design, and normalizing graphs for edge coloring. He has developed linear-time and polynomial-time solutions for scheduling and flow problems. Awards : DFG Heisenberg Grant (2020) , DFG Sachbeihilfe (2020) Email : moemke@informatik.uni-augsburg.de
Dr. Cheng Cheng is a Senior Research Officer at the Australian National University's School of Engineering. He holds a Bachelor of Engineering (Honours) and PhD from ANU. His research focuses on renewable energy integration, decarbonization pathways, energy system modeling, and GIS analysis. He specializes in optimizing off-river pumped hydro energy storage systems. Cheng leads multiple renewable energy projects funded by government and industry grants, and has developed methodologies for energy demand forecasting and least-cost optimization. His work bridges theoretical energy modeling with practical implementation through collaborative outreach activities. Education: B.Eng(Hons) & PhD (Australian National University) Research interests emphasize sustainable energy transitions, with particular attention to geospatial analysis for energy infrastructure planning. His recent projects include developing frameworks for integrating renewable generation with storage solutions. Over 150 publications span machine learning applications in energy systems, astrophysics, and bioinformatics. Awards include academic fellowships supporting his interdisciplinary work. Cheng collaborates with industry partners to translate research into policy and technology. He directs the ANU's Renewable Energy Integration Lab (RE100), advancing grid stability solutions through innovative storage technologies. His work has been recognized in special issues of journals focusing on robust machine learning applications.
Dr. Jonni Virtema is a Lecturer in Verification at the School of Computer Science, University of Sheffield, UK, where he has been employed since September 2021. He also serves as the Study Abroad/International Student Exchange Officer for the department. His research is centered within the Foundations of Computation research group, focusing on the theoretical aspects of computer science with particular emphasis on logic and verification. Dr. Virtema earned his MSc (2008) and PhD (2014) in mathematics from the University of Tampere, Finland. In 2020, he was awarded the title of Docent in Mathematical Logic from the University of Helsinki, Finland. Prior to joining the University of Sheffield, he held various research positions funded by prestigious organizations including the German Research Foundation (DFG), the Japan Society for the Promotion of Science (JSPS), the Research Foundation - Flanders (FWO), and the Academy of Finland (AKA). Dr. Virtema's research interests originate from finite model theory and logic in computer science, with specific focus areas including logics for dependence and independence, modal logics, logics for verification, logical foundations of neural networks, logical foundations of quantum information theory, computational complexity, and logics with team semantics. His work bridges theoretical computer science with practical applications in database theory, verification, and artificial intelligence. Recent publications demonstrate his expanding interest in the intersection of logic with neural networks and quantum information. Dr. Virtema has published extensively with 16 journal articles and 40 conference articles, accumulating 791 citations with an h-index of 18 according to Google Scholar. His recent work spans temporal logics, probabilistic team semantics, database repair mechanisms, and neural network theory, showing a clear trajectory toward interdisciplinary research connecting formal methods with emerging technologies. Program Committee Member for KR 2025, NeurIPS 2025, ICML 2025, ICLR 2025 Organizer of Dagstuhl Seminar on Logics for Dependence and Independence (2024) Local Chair for FoIKS 2024 in Sheffield Principal Investigator for DFG project LogQMC (2020-2024) Dr. Virtema maintains an active research presence through international collaborations and has delivered 44 conference talks and 21 seminar presentations worldwide. His work on graph neural networks and arithmetic circuits accepted to NeurIPS 2024 exemplifies his current research direction exploring the logical foundations of machine learning systems.
Daniel Lowd is a Professor in the Department of Computer Science at the University of Oregon . He studies adversarial machine learning , probabilistic graphical models , and statistical relational AI , with applications to security and data poisoning defense. His research has been funded by the DARPA Media Forensics (MediFor) program (2016), and he received tenure in 2017. He co-organizes the annual Workshop on Tractable Probabilistic Modeling , including at ICML and IJCAI. Key research areas: Probabilistic AI, Adversarial Learning, Markov Logic Major collaborations: Javid Ebrahimi, Jonathan Brophy, Zayd Hammoudeh Recent publications include work on robust regression (SaTML 2023), influence estimation (JMLR 2023), and neural score estimation (ICML/NeurIPS 2021-2023). He maintains the Libra Toolkit for probabilistic models. As an active academic, he advises PhD students like Shivvrat Arya (now at UTD), Jonathan Brophy , and Zayd Hammoudeh . He advocates for faculty unionization and open science, and is a unicyclist in his free time.
Antonio Vergari serves as a Reader (equivalent to Associate Professor) in the School of Informatics at the University of Edinburgh, affiliated with the Institute for Adaptive and Neural Computation (ANC). His research develops probabilistic machine learning systems that are provably reliable in real-world applications through the integration of complex reasoning, efficient inference, and neuro-symbolic learning paradigms. His primary research domains include Artificial Intelligence, Machine Learning, Probabilistic Modeling, and Neuro-symbolic AI, with specific expertise in probabilistic circuits, tensor networks, and constraint-satisfying architectures. Current investigations focus on unifying tensor factorization theory with circuit representations to overcome expressiveness limitations while maintaining computational tractability for complex reasoning tasks. Analysis of his publication trajectory reveals consistent advancement in reliable probabilistic modeling, particularly through circuit-based approaches that guarantee logical consistency in neural predictions. His work bridges theoretical foundations (e.g., expressiveness hierarchies) with practical implementations (e.g., Cirkit library), addressing critical gaps in benchmark complexity and scalable constraint satisfaction. Scientific recognition includes: ICLR 2024 Spotlight presentation (top 5% acceptance rate) NeurIPS 2023 Oral presentation (top 0.6% acceptance rate) NeurIPS 2021 Oral presentation (top 0.6% acceptance rate) Vergari leads the APRIL Lab, which actively contributes to open-source probabilistic modeling tools like Cirkit while organizing community initiatives such as the CoLoRAI workshop at AAAI-25. The lab maintains strong industry and academic collaborations focused on developing theoretically-grounded, deployable probabilistic systems.
Thomas Liebig is an Assistant Professor of Smart City Science at TU Dortmund University's Artificial Intelligence Unit and a Principal Investigator at the Lamarr Institute for Machine Learning and Artificial Intelligence. He also serves as a senior AI architect supporting Materna SE in integrating artificial intelligence into the public sector and industry, and previously held an Adjunct Professor position in Data Privacy and Ethics at the University of Nicosia. His research focuses on distributed data mining, reinforcement learning, multi-agent systems, privacy-preserving learning, and spatio-temporal modeling. Liebig has made significant contributions to graph neural networks, probabilistic modeling with sum-product networks, and traffic flow prediction systems. His work bridges theoretical machine learning with practical applications in smart cities, transportation, and healthcare. Liebig's recent publications demonstrate a strong trend toward privacy-preserving machine learning techniques, particularly differential privacy applied to distributed settings. His work on sum-product networks has advanced interpretable probabilistic modeling, while his research on graph neural networks has explored novel connections with classical algorithms like PageRank. His practical applications span transportation systems, healthcare, and smart city infrastructure. Liebig has supervised numerous graduate students working on topics including blockchain-based data analysis, traffic prediction systems, and privacy-preserving machine learning. His research has been supported by projects including SFB876 (Providing Information by Resource-Constrained Data Analysis) and collaborations with industry partners. He is actively involved in the academic community, having co-organized workshops such as Mining Urban Data at ICML and Computational Transportation Sciences. His work appears in top venues including IEEE International Conference on Knowledge Graph, ECML/PKDD, and IEEE Transactions on Intelligent Transportation Systems.
Dr André V. Ribeiro Amaral is a Lecturer in Statistical Learning at the School of Mathematical Sciences, University of Southampton. His research focuses on spatio-temporal statistical methods for environmental and public health applications, including Bayesian computation and machine learning for infectious disease modeling. PhD in Statistics from KAUST (2023) MSc in Statistics from UFMG (2020) Previous Research Associate at Imperial College London (2024–2025) Key research themes include: Computationally tractable spatio-temporal models Bayesian inference for complex datasets Machine learning integration in statistical frameworks Applications to epidemiology and environmental science His methodological work spans data fusion, preferential sampling correction, and disease spread dynamics, with recent publications on dengue fever nowcasting and Antarctic krill abundance modeling. Teaching: Co-teaching Statistical Modelling II (MATH3091)
Roles and Affiliations: Associate Professor of Statistics at Bocconi University's Department of Decision Sciences. Research Affiliate at Bocconi Institute for Data Science and Analytics (BIDSA), DONDENA Centre, and Laboratory for Coronavirus Crisis Research. Former Post-Doctoral Fellow at University of Padova and Visiting Scholar at Duke University. Education: Ph.D. (2016) and M.Sc. (2012) in Statistics from University of Padova. Research Interests: Bayesian methodology, network science, categorical data, latent variable models, criminology, and demography. Focus on statistical models for complex data, including criminal networks and cause-of-death dependencies. Grants and Awards: ERC Starting Grant (NEMESIS, 2024–2029), PRIN-MUR Grant (CARONTE), COPSS Emerging Leader Award, Leonardo da Vinci Medal, Mitchell Prize (twice), Laplace Prize. Editor for Biometrika , Journal of Computational and Graphical Statistics , and Journal of Multivariate Analysis . Teaching and Service: Courses on Machine Learning, Data Science, and Statistical Methods. Organized Stats under the Stars hackathons, Bocconi Data Science Challenge platform. Served on committees for ISBA, ASA, and others. Labs/Teams: Leads NEMESIS and CARONTE projects. Collaborates with interdisciplinary teams in quantitative criminology and demography.
Jan Niclas Dreier is a PostDoc Researcher at TU Wien's Department of Algorithms and Complexity. His research focuses on theoretical computer science, particularly algorithmic meta-theorems, structural graph theory, and computational complexity. He is affiliated with projects such as REVEAL-AI and SLIM. His work addresses topics like monadic stability, SAT solving, and model checking on sparse graph classes. Research Interests : Dreier's primary areas include algorithm design, parameterized complexity, and logic in computer science. His contributions span model checking for graph classes, backdoor analysis in SAT solving, and combinatorial properties of monadic dependence. Recent work explores the interplay between structural graph theory and algorithmic efficiency, with applications to finite model theory and pseudorandom models. Teaching : He teaches courses such as Algorithmic Meta-Theorems , Algorithmics , and Bachelor Thesis in Computer Science . His courses emphasize theoretical foundations and practical algorithmic techniques. Projects : Active involvement in the REVEAL-AI project (2020–2024) and SLIM (2019–2024), focusing on algorithmic advancements in AI and structural graph analysis.
Dr. Sander Borst is a postdoctoral researcher in the Algorithms and Complexity group at the Max Planck Institute for Informatics in Saarbrücken, Germany. Previously, he completed his Ph.D. in the Networks & Optimization group at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, advised by Daniel Dadush. He holds bachelor’s degrees in Mathematics and Computer Science and a master’s degree in Mathematics from Delft University of Technology . Current Role: Postdoctoral Researcher at Max Planck Institute for Informatics. Education: B.Sc. and M.Sc. in Mathematics and Computer Science from Delft University of Technology. Sander's research focuses on the design and analysis of online algorithms , particularly in online network design , hypergraph matching , and graph exploration . His work bridges theoretical computer science with practical applications in optimization and algorithmic randomness. Recent projects include developing algorithms for explorable heap selection , constraint propagation in MIP solvers, and analyzing integrality gaps in integer programming under random data assumptions. The trends in his publications (2020–2025) span online optimization , randomized algorithms , hypergraph theory , and integer programming . Key venues include SODA, IPCO, ITCS, and Algorithmica, with recurring themes in network design , combinatorial optimization , and theoretical guarantees for algorithmic solutions. His technical expertise extends to software development and programming languages , ensuring practical implementation of theoretical models. He has collaborated with researchers such as Daniel Dadush, Neil Olver, and Ambros Gleixner.
Leslie Griffith is the Nancy Lurie Marks Professor of Neuroscience and Director of the Volen National Center for Complex Systems Research at Brandeis University's Department of Biology. Her research program focuses on understanding the biochemical and cellular basis of behavior using Drosophila melanogaster as a model organism. Griffith's lab employs genetic, imaging, electrophysiological, and behavioral approaches to investigate complex neural processes. Her primary research interests include sleep regulation, learning and memory mechanisms, circadian rhythms, and neural circuit function. Griffith has made significant contributions to understanding how sleep architecture affects memory consolidation, the role of microRNAs in regulating sleep and behavior, and the neural circuits underlying associative learning in fruit flies. Her work often bridges molecular mechanisms with organism-level behavior, providing insights into fundamental neuroscience principles. Analysis of Griffith's recent publications reveals a consistent focus on the relationship between sleep and memory, with particular attention to how sleep structure (rather than just total sleep amount) influences cognitive function. Her research has demonstrated that food availability modulates the sleep-memory relationship and that specific neural circuits involving serotonin and dopamine play critical roles in these processes. The use of Drosophila as a genetically tractable model system has allowed her lab to identify specific genes, neurons, and circuits involved in these complex behaviors. As Director of the Volen National Center for Complex Systems Research, Griffith oversees interdisciplinary research initiatives that bring together scientists from neuroscience, physics, computer science, and other fields to study complex biological systems. Her leadership in this role demonstrates her commitment to fostering collaborative, cross-disciplinary approaches to understanding neural function.
Daniel Kuhn is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he holds the Chair of Risk Analytics and Optimization in the College of Management of Technology. His research focuses on developing computational methods for data-driven decision-making under uncertainty, with applications in engineered systems, machine learning, and finance. Previously, he held positions at Imperial College London and Stanford University. Education includes: PhD in Economics, University of St. Gallen MSc in Theoretical Physics, ETH Zurich Research interests span data-driven optimization , stochastic programming , and robust decision-making frameworks . His work develops computationally tractable methods for uncertainty quantification in complex systems, bridging operations research with statistical learning. Current investigations focus on distributionally robust optimization using Wasserstein metrics and applications in energy markets and fair machine learning. Publication analysis reveals three primary trends: 1) Fundamental advances in distributionally robust optimization theory, 2) Machine learning applications with uncertainty guarantees, and 3) Energy system optimization under regulatory constraints. His methodological work consistently emphasizes computational tractability and practical applicability. As lab director of the Risk Analytics and Optimization group, he leads research on: Stochastic control systems Data-driven decision frameworks Robust machine learning Current PhD students include researchers working on federated learning fairness, optimal power flow, and reinforcement learning theory. Past graduates have made significant contributions to Wasserstein distributionally robust optimization and vehicle-to-grid frequency regulation.
Guy Bresler is Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, with affiliations in LIDS, Theory of Computation, Center for Statistics, FODSI, and IDSS. He researches the interface of information theory, statistics, theoretical computer science, and probability, developing mathematical models to understand computational tractability of high-dimensional inference. His research characterizes relationships between combinatorial structure and computational tractability in inference problems, with applications in statistical physics, random graphs, and learning theory. Recent work establishes fundamental limits in statistical estimation and develops efficient algorithms for high-dimensional problems. Publications demonstrate consistent focus on computational-statistical tradeoffs across random graphs, Markov chains, neural networks, and graphical models. Techniques involve information-theoretic analysis, reduction methods, and phase transition characterization. He advises doctoral students Kiril Bangachev, Alina Harbuzova, Chenghao Guo, and Brice Huang. Previously advised researchers include Enric Boix-Adserà, Dheeraj Nagaraj, and Mina Karzand. He co-organized the Simons Institute program on computational aspects of high-dimensional inference.
Prashant Doshi is a Professor of Computer Science at the University of Georgia's School of Computing. He leads the THINC Lab , focusing on AI and Robotics, particularly multiagent decision-making frameworks like the Interactive POMDP (I-POMDP) . His work bridges game theory and decision theory, addressing challenges in uncertain, multiagent environments. Notable contributions include applications of I-POMDPs in counter-terrorism, defense simulations, and human behavior modeling. Doshi has published over 150 papers and holds editorial roles at Springer's Journal of AAMAS . Research interests span inverse reinforcement learning (IRL) , robotic learning from observation, SLAM in occluded settings, and semantic Web technologies. Awards include UGA’s Creative Research Medal (2011) and NSF CAREER Award (2009). He has been recognized three times with the CS Department’s Outstanding Faculty Research Award (2009, 2012, 2018). Key Projects: Open Human-Robot Collaboration Systems, I-POMDP framework development, cybersecurity intent recognition. Grants: NSF CAREER Award, Collaborative Research grants on multiagent systems. Labs: THINC Lab (focusing on human-robot interaction and decision-making). Recent work explores open multiagent systems , where agents dynamically join/leave, and occluded IRL for robots learning from partial observations. His research has practical applications in disaster response robots, healthcare resource allocation, and autonomous vehicle decision-making.
Antoine Amarilli is an Associate Professor in Computer Science at Télécom Paris, part of Institut Polytechnique de Paris. Since September 2024, he has been on leave to work as an advanced researcher in the LINKS team at Inria Lille. His research spans theoretical computer science, focusing on query evaluation, probabilistic data, knowledge compilation, and computational logic. Member of DIG and LINKS research teams Part of LTCI (Information Processing and Communication Laboratory) Education: PhD in Computer Science (Télécom ParisTech, 2016) Habilitation (HDR) in Computer Science (Institut Polytechnique de Paris, 2023) Research Interests: He investigates efficient query evaluation on uncertain data, dynamic data maintenance, knowledge compilation techniques, and the interplay between circuits, automata, and logical languages. His work intersects database theory, graph theory, and computational complexity. Publications Trends: Recent articles address tractability of queries on probabilistic graphs, complexity classifications for MSO and regular path queries, and knowledge compilation frameworks for enumeration algorithms. Key subfields include probabilistic reasoning, graph constraints, and circuit-based optimizations. Awards: Best Paper at ICDT 2020 Best Paper at ICALP 2021 (Track B) Télécom Paris PhD Prize (2017) Beth Dissertation Award (2017) Teaching & Service: He taught competitive programming, algorithms, and data management at Télécom Paris and represented the institution on the MPRI master’s committee. He co-organized Highlights of Logic, Games, and Automata (2022) and served as a program committee member for ICDT, ICALP, and AAAI. Research Activism: Co-founded TCS4F (Theoretical Computer Scientists for Future) and No Free View? No Review! initiatives to address climate impact and open-access advocacy in academia.
