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Antonio OrvietoView profile

Lecturer

• Deep Learning
• Optimization Theory
• Neural Network Architecture Design
• +7 more

Antonio Orvieto is a Lecturer at the University of Tübingen, Principal Investigator at the ELLIS Institute Tübingen, and independent group leader at the Max Planck Institute for Intelligent Systems. He leads the Deep Models and Optimization research group and serves as faculty for the CLS, ELLIS, and IMPRS-IS PhD Programs. Orvieto holds a PhD from ETH Zürich and has conducted research at DeepMind London, Meta (FAIR) Seattle, MILA, and Inria Paris. Orvieto's research focuses on improving the efficiency and accessibility of deep learning technologies through theoretical advancements in optimization and architecture design. His work spans two main areas: understanding large-scale optimization dynamics and designing innovative neural network architectures capable of reasoning with complex sequential data. His research has significant implications across multiple domains including biology, neuroscience, natural language processing, and music generation. His approach combines rigorous theoretical analysis with practical applications to address fundamental challenges in deep learning. His recent publications reveal a strong emphasis on understanding optimization landscapes, developing efficient recurrent architectures, analyzing transformer behavior, and exploring the theoretical foundations of state-space models. The work shows a consistent pattern of bridging theoretical insights with practical implementations, particularly in handling sequential data and improving training efficiency. Schmidt Sciences AI2050 Early Career Fellow Orvieto actively mentors PhD students and researchers in his Deep Models and Optimization group, which includes PhD candidates working on various aspects of deep learning theory and applications. His research is supported through his positions at the University of Tübingen, ELLIS Institute, and Max Planck Institute for Intelligent Systems. He has collaborated with leading researchers across multiple institutions including ETH Zürich, DeepMind, Meta, MILA, and Inria. His research group focuses on investigating the interplay between optimizers and architectures in deep learning, with particular emphasis on developing new networks for long-range reasoning. The group strongly believes that deep learning will revolutionize science and technology, and they aim to provide theoretical foundations that will enable scientists and engineers with limited resources to leverage powerful deep learning solutions.

Sander BorstView profile

Researcher

• Online Algorithms
• Theoretical Computer Science
• Network Design
• +5 more

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.

Yoshua BengioView profile

Professor

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +5 more

Yoshua Bengio is a Full Professor at Université de Montréal's Department of Computer Science and Operations Research, and a leading global expert in deep learning. He is the founder and scientific advisor of Mila Quebec AI Institute, and holds a Canada CIFAR AI Chair. His work has revolutionized AI, earning him the 2018 Turing Award ("Nobel of computing") and multiple accolades including the Killam Prize and Officer of the Order of Canada. Education: PhD in Computer Science (McGill, 1991), postdocs at MIT (1991-1992) and AT&T Bell Labs (1992-1993). Research focuses on deep learning theory, generative models, and AI ethics. He co-leads the CIFAR Learning in Machines & Brains program and advises IVADO. Concerned about AI's societal impact, he co-authored the Montreal Declaration for Responsible AI and chairs global AI safety initiatives. Recent research spans AI safety frameworks, causal machine learning, and climate change applications. Over 200 students supervised, including PhDs and postdocs. Key awards include Royal Society Fellowships, IEEE Pioneer Award, and UN Scientific Advisory Board membership.

Sasikumar PunnekkatView profile

Professor

• Dependable Software Engineering
• Real-time Systems
• Safety-Critical Systems
• +5 more

Roles & Affiliations: Professor in Dependable Software Engineering at Mälardalen University (MDU), leading the Dependable Software Engineering research group. Former Head of Software Test & Reliability Engineering at Indian Space Research Organization (ISRO), Director of BITS Pilani KK Birla Goa Campus (Mar 2015–Aug 2016), and Visiting Researcher at Ericsson since 2022 (funded by SSF's strategic mobility program). Education: PhD in Computer Science from University of York, UK (1997), focusing on schedulability analysis of fault-tolerant systems (awarded Commonwealth Scholarship). Research Interests: Real-time systems, dependability, software engineering, safety-critical systems, cybersecurity, and industrial IoT. Key focus areas include fault-tolerant scheduling, safety-critical software design, and system-of-systems analysis. Funding & Projects: Led major EU projects (InSecTT, SUCCESS, DAIS, EUROWEB series) and national initiatives. Coordinated large EU educational collaborations with Western Balkans/Asia. Active in projects like FORA fog computing platform and TSN networks. Awards & Recognition: 6 Best Paper Awards, Senior Member of IEEE, and frequent contributor to conference program committees (ETFA, HASE, DATE, etc.). Grants & Labs: Recipient of SSF strategic mobility funding. Research group develops solutions for automotive systems, industrial automation, and safety-critical infrastructure.

Benoit ValironView profile

Researcher

• Formal Methods
• Logic and Programming Languages
• Quantum Computing
• +4 more

Benoit Valiron is a Researcher affiliated with the Formal Methods Laboratory, focusing on quantum computing, formal methods, and programming languages. His work integrates theoretical foundations with practical applications in quantum software development and formal verification. Research interests include semantics of quantum programming languages, reversible computing, and categorical logic. He explores quantum algorithms' operational and denotational semantics while developing tools like Qbricks for formal verification of quantum systems. Recent publications emphasize quantum circuit optimization, concurrency models in quantum systems, and applications of ZX-calculus. His work bridges theoretical advancements with real-world quantum technologies, addressing challenges in NISQ-era hardware and algorithm design. Key contributions include foundational papers on quantum control structures, time synchronization protocols for real-time systems, and vectorial λ-calculus extensions. His research often involves collaborations with institutions advancing quantum software ecosystems and formal methods in safety-critical systems.

Josh AlmanView profile

Assistant Professor

• Algorithm Design
• Complexity Theory
• Matrix Multiplication
• +4 more

Josh Alman is an Assistant Professor of Computer Science at Columbia University, affiliated with the Theory Group. His research focuses on algorithm design, complexity theory, and algebraic methods in computer science. Previously, he was a Rabin Postdoc in Theoretical Computer Science at Harvard University (2020–2023), and completed his PhD in Computer Science at MIT (2016–2019), advised by Ryan Williams and Virginia Vassilevska Williams. His work bridges foundational questions in algorithms and complexity, with notable contributions to matrix multiplication, lower bounds, and applications of algebraic tools. He advises PhD students at Columbia, including Hengjie Zhang, Kevin Yeo, and Hantao Yu. His notable awards include the Best Paper Award at SOSA 2023, the Machtey Award for Best Student Paper at FOCS 2019, and the Best Student Paper Award at CCC 2019. Alman’s research interests span theoretical computer science, with a focus on algebraic and combinatorial approaches to problems in algorithms, complexity, and machine learning. His recent work explores the limits of fast algorithms for fundamental tasks (e.g., matrix multiplication, kernel density estimation) and their implications for broader computational models. Education PhD in Computer Science, MIT (2019), advised by Ryan Williams and Virginia Vassilevska Williams M.S./B.S. in Computer Science, Stanford University (2014–2016) Key Research Themes Lower bounds for arithmetic circuits and matrix rigidity Algebraic methods for algorithm design Fine-grained complexity and its applications Machine learning theory and attention mechanisms

Thomas BourgeatView profile

Assistant Professor

• Computer Architecture
• Programming Languages
• Hardware Verification
• +3 more

Thomas Bourgeat is an Assistant Professor at EPFL, starting May 2023. He completed his PhD at MIT under the guidance of Arvind (CSG) and Adam Chlipala (PLV), focusing on the intersection of computer architecture and programming languages. PhD in Computer Science at MIT His research focuses on leveraging high-level hardware programming languages to design and verify hardware, particularly through theorem proving. He also explores security implications of microarchitectural features and domain-specific accelerators. Recent publications highlight trends in hardware security, domain-specific accelerators, and formal verification. His work includes projects like riscy-OOO and formal RISC-V specifications. Thomas actively recruits PhD students and postdocs. Prospective applicants can find details at EPFL's EDIC program .

• Artificial Intelligence
• Game Theory
• Stochastic Games
• +4 more

James R. Glenn is a Senior Lecturer in the Department of Computer Science at Yale University's Faculty of Arts and Sciences. He teaches multiple computer science courses including systems programming, algorithms, data structures, and computational intelligence for games. His research focuses on stochastic games , particularly on computing optimal strategies and using artificial intelligence techniques to create near-optimal strategies when optimal solutions are infeasible. He also investigates mathematical problems related to '3-free sets' and methods for constructing large sets. His work bridges theoretical computer science with practical applications in game theory and algorithm design. Dr. Glenn teaches courses including CPSC 223 (Data Structures), CPSC 323 (Systems Programming), CPSC 365 (Algorithms), and CPSC 474/574 (Computational Intelligence for Games). His teaching spans foundational computer science concepts to advanced topics in game AI. Current research focuses on stochastic game strategies Expertise in algorithms and computational intelligence Extensive teaching experience across various CS topics His publications reflect his interests in game theory, algorithms, and educational tools, with work spanning from theoretical mathematics to practical software implementations. Dr. Glenn maintains an active teaching schedule with regular office hours in Dunham Lab 429 at Yale University.

Maurice ter BeekView profile

Researcher

• Formal Methods
• Software Engineering
• Railway Systems
• +5 more

Maurice ter Beek is a Senior Researcher at the Formal Methods and Tools (FMT) Group of the Institute of Information Science and Technologies (ISTI) , part of the Italian National Research Council (CNR) in Pisa, Italy. He leads the FMT Lab since 2019 and coordinates critical projects like ADVENTURE (railway systems evaluation) and T-LADIES (typeful language adaptation). He serves as Editor-in-Chief of Formal Aspects of Computing and holds editorial roles in journals like Science of Computer Programming . His career spans academic leadership in international conferences (chair of ETAPS 2026 , RSSRail 2025 , etc.) and extensive collaborations in formal methods, railway systems, and software product lines. Research Interests: His work focuses on formal methods applied to industrial critical systems, particularly railways, with expertise in variability modeling, attack-defense trees, contract automata, and team automata. Publications address quantitative analysis of reconfigurable systems model checking for railway safety empirical studies on formal methods security risk modeling collaborative software engineering Scientific Awards: ERCIM Fellowship (2003, 2002) SegraVis Fellowship (2005) Best Paper at SPLC 2019 Best Paper at ICIW 2007 Socrates Grant (EU, 2000-2001) Additional Roles: He is a faculty member of the joint PhD program in Smart Computing (Florence, Pisa, Siena), member of ISTI’s Institute Council, and coordinates CNR’s involvement in the PNRR National Sustainable Mobility Centre . His global engagement includes over 50 conference organizing roles and editorial board memberships.

Stefan LeueView profile

Professor

• Formal Methods
• Software and Systems Engineering
• Model Checking
• +7 more

Stefan Leue is a Professor for Software and Systems Engineering at the Department of Computer and Information Science at the University of Konstanz since 2004. He serves as a member of the Extended Directorate of the Centre for Human | Data | Society (elected in 2022) and is part of the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour since 2018. His academic background includes a doctorate from the University of Bern (1995) and prior professorial positions at the University of Freiburg (2000-2004) and University of Waterloo (1995-2000). Leue's research focuses on formal methods in software engineering, particularly in the design and analysis of complex systems. His expertise spans embedded software systems, verification of AI-based software, automotive software engineering, causality analysis, and safety-critical systems. He leads several research projects including Neural Network Repair, Causality in Systems (QuantUM and CausCheck), SCADNet, TarTar, and DiRePro. His research group has produced significant work in model checking, directed search algorithms, real-time systems verification, and probabilistic system analysis. Current research trends emphasize the application of formal methods to AI safety, particularly in neural network verification and repair, as well as extending formal techniques to collective behavior modeling and safety-critical automotive systems. Steering Committee Member, SPIN Symposium on Model Checking of Software since 2007 Member, Cluster of Excellence Centre for the Advanced Study of Collective Behaviour since 2018 IEEE Computer Society member ACM member Gesellschaft für Informatik (German Informatics Society) Professor Leue has supervised numerous PhD and Master's students, many of whom now hold prominent positions in industry and academia. His research group maintains active collaborations with industry partners in automotive and safety-critical systems domains. Current work focuses on bridging formal methods with machine learning to address safety challenges in autonomous systems.

Gizem ÇaylakView profile

Researcher

• Probabilistic Programming Languages
• Clinical Data Analysis
• Machine Learning
• +2 more

Gizem Çaylak is a doctoral researcher at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Software and Computer Systems. She is supervised by David Broman (principal) and Magnus Boman (co-supervisor). B.S. in Computer Engineering, Bilkent University (2012-2017) M.S. in Computer Engineering, Bilkent University (2017-2019) Ph.D. in Computer Science, KTH Royal Institute of Technology (2019-present) Her research focuses on deep probabilistic programming languages and their applications in clinical data analysis . She has contributed to bioinformatics research through her work on epistasis test prioritization using diverse SNP selection algorithms. Notable teaching experiences include workshops and courses in Computer Hardware Engineering , Computer Organization and Components , and Algorithms & Programming at KTH and Bilkent University. Her 2020 publication at the International Conference on Research in Computational Molecular Biology demonstrates her interdisciplinary work bridging computer science and computational biology.

Jakub BijakView profile

Professor

• Statistical Demography
• Migration Modeling
• Demographic Uncertainty
• +4 more

Jakub Bijak is Professor of Statistical Demography at the University of Southampton's Department of Social Statistics and Demography, with nearly 25 years of academic and international civil service experience. He actively contributes to the Statistical Sciences Research Institute (S3RI) and Centre for Population Change, leading research at the intersection of demographic theory, statistical methodology, and policy application. His work bridges demographic science with practical migration governance challenges across European institutions. He earned his PhD in economics with specialisation in demography from the Warsaw School of Economics, establishing his foundation in quantitative demographic analysis. His doctoral research directly informs his ongoing focus on methodological innovation in population studies. Professor Bijak's research centers on statistical demography, migration modeling under uncertainty, and demographic forecasting. He pioneers agent-based simulation approaches for migration decision-making and develops Bayesian frameworks for population projection. His work spans from paleodemographic analysis of ancient populations to contemporary migration governance, with particular emphasis on quantifying uncertainty in demographic processes. Recent projects explore digital trace data for real-time migration monitoring and kinship dynamics across the life course. Analysis of his 2024-2025 publications reveals three dominant trends: (1) Methodological innovation in migration forecasting using mixed-frequency data and agent-based modeling, (2) Integration of digital footprint analysis for timely migration estimates, and (3) Development of policy-relevant frameworks for managing demographic uncertainty. His work increasingly connects demographic modeling with robotics and aging populations, demonstrating cross-disciplinary impact. No scientific awards are documented in the provided source materials. Professor Bijak currently supervises two PhD students: Andrew Peter Hind (MPhil/PhD Social Statistics) and Md Sifat Ar Salan (PhD Social Statistics and Demography). He leads or co-leads multiple major research projects including QuantMig (European Union-funded), Bayesian Agent-based Population Studies (BAPS), and ESRC/EPSRC grants on population change. His projects frequently involve international consortia addressing migration scenarios, demographic uncertainty, and generational connections. He maintains active roles in the Statistical Sciences Research Institute and Centre for Population Change, contributing to interdisciplinary teams developing the QuantMig microsimulation platform and kinship modeling frameworks. His collaborative networks span demography, computer science, and policy institutions across Europe.

• Artificial Intelligence
• Statistical Relational Learning
• Probabilistic Reasoning
• +4 more

David Poole is a Professor of Computer Science at the University of British Columbia, where he has established himself as a leading researcher in Artificial Intelligence with an h-index of 52 and over 19,000 total citations. His academic career spans more than three decades, with publications dating from 1985 to 2025, demonstrating sustained research productivity and relevance in the field. Professor Poole's research focuses on the integration of logical and probabilistic approaches to artificial intelligence. His work bridges symbolic AI and statistical methods, creating frameworks that handle uncertainty while maintaining logical expressiveness. He has made seminal contributions to probabilistic Horn abduction, Bayesian networks, and statistical relational artificial intelligence. His theoretical work on the Independent Choice Logic (ICL) has provided foundational frameworks for representing and reasoning with uncertainty in complex domains. His applied research spans geological analysis, educational technology, and mineral exploration. Professor Poole has authored influential textbooks including 'Computational Intelligence: A Logical Approach' (1998, 1,915 citations) and 'Artificial Intelligence: Foundations of Computational Agents' (2010, 1,750 citations), which have shaped AI education globally. His recent publications (2020-2025) demonstrate continued innovation in knowledge hypergraphs, relational neural networks, and geological hazard mapping applications. His most impactful research areas include: Statistical Relational Learning (as evidenced by the 2016 book 'Statistical Relational Artificial Intelligence') Knowledge Graph Embeddings and Hypergraphs CP-nets for preference representation (1,293 citations) Lifted probabilistic inference techniques Applications of AI in geological hazard prediction Professor Poole maintains active research collaborations with institutions worldwide, including Google DeepMind, universities in Hong Kong, Germany, and the United States. His work continues to influence both theoretical foundations and practical applications of artificial intelligence.

• Data Provenance
• Database Systems
• Data Cleaning
• +5 more

Boris Glavic is a Professor at Illinois Institute of Technology, Chicago, specializing in database systems with a strong research focus on data provenance, uncertain data management, and database optimization techniques. His work bridges theoretical database concepts with practical applications in data cleaning, debugging, and ML integration. Glavic's research primarily centers on data provenance, where he has developed innovative techniques for tracking data lineage, optimizing provenance computations, and applying provenance to various database tasks. His work spans theoretical foundations of provenance in database query languages to practical systems like GProM (a 'Swiss Army Knife' for provenance needs) and applications in data debugging, uncertain data management, and ML pipeline robustness. Key contributions include provenance-based data skipping, reenactment techniques for transaction debugging, and frameworks for explaining query answers and non-answers. Analysis of Glavic's recent publications reveals a clear evolution from foundational provenance research toward integration with machine learning systems and addressing data quality challenges. His work increasingly focuses on practical applications where provenance techniques enhance data reliability in ML pipelines, improve debugging of complex data workflows, and enable efficient handling of uncertain and incomplete data. The publications demonstrate strong interdisciplinary connections between database theory, data management systems, and machine learning. While specific awards aren't documented in the provided information, Glavic's extensive publication record in top-tier venues (VLDB, SIGMOD, ICDE) over more than a decade demonstrates significant recognition within the database research community. His work has clearly influenced both theoretical and practical aspects of data management systems. Glavic has mentored numerous researchers who have become frequent collaborators, including Seokki Lee, Xing Niu, Su Feng, and Pengyuan Li. His research has been supported by grants enabling substantial contributions to data provenance, database debugging, and uncertain data management. The collaborative nature of his work is evident through extensive co-authorship networks spanning multiple institutions and research groups. Though specific lab affiliations aren't detailed in the provided information, Glavic's research appears to be conducted within a vibrant database research group at Illinois Institute of Technology, with strong connections to other leading database research centers. His work on systems like GProM suggests an active research laboratory focused on practical database tools and techniques.

• Theoretical Computer Science
• Coalgebras
• Category Theory
• +12 more

Stefan Milius is a Senior Lecturer (Akademischer Direktor) at the Chair of Theoretical Computer Science (Computer Science 8) within the Faculty of Engineering at Friedrich-Alexander University Erlangen-Nuremberg (FAU). He is actively involved in research, teaching, and academic leadership, with a strong emphasis on theoretical foundations of computer science. His research interests are centered on coalgebras, category theory, universal algebra, formal verification, semantics of iteration and recursion, and logic in computer science . He investigates algebraic and categorical methods for modeling and reasoning about computational systems, particularly through the lens of fixed points, automata, and logical semantics. His recent publications (2022–2025) span top-tier venues such as LICS, CALCO, ICALP, MFPS, POPL, and CONCUR, showcasing a consistent focus on algebraic language theory with effects, nominal automata, graded semantics, bialgebraic reasoning, and coalgebraic algorithms . The work often involves deep categorical constructions and has applications in program equivalence, formal verification, and automata minimization. Stefan Milius has received several prestigious awards, including: Ackermann Award (2006) for his PhD thesis Best Theory Paper at FM 2019 EATCS Best Paper Award at MFCS 2017 CALCO 2015 Best Paper Award Braunschweig Prize for Outstanding Academic Achievements (2000) He plays a significant role in the academic community as Editor-in-Chief of Logical Methods in Computer Science (since 2020), member of the advisory board of TheoretiCS , and editorial board member of Applied Categorical Structures . He has served on numerous program and steering committees, including FoSSaCS, LICS, MFPS, CALCO, and CMCS. He has also supervised student projects and thesis topics in theoretical computer science, though specific student names are not listed. He has been involved in externally funded research, including the BMBF project VerSyKo at TU Braunschweig (2011–2012), focusing on formal verification of synchronous software components. His current work continues to advance foundational methods in theoretical computer science with broad applicability.