Thomas Eiter is a Professor at TU Wien's Institute of Logic and Computation. His research focuses on declarative programming paradigms, knowledge representation, and artificial intelligence. He leads projects in neurosymbolic systems, answer set programming (ASP), and stream reasoning, with applications in visual question answering, scheduling optimization, and semantic scene generation. Eiter has contributed to foundational work in ASP semantics, computational complexity, and hybrid reasoning frameworks. His work bridges logical formalisms with practical AI challenges, emphasizing explainability and scalability. Projects like ALASPO and neurosymbolic integration showcase his focus on advancing both theoretical and applied aspects of AI. Projects: HumanE AI Network, WASP, REWERSE Research Themes: Neurosymbolic AI, Answer Set Programming, Stream Reasoning Notable achievements include pioneering work on semiring-based reasoning frameworks and developing efficient ASP solvers like Alpha. His contributions span over 471 publications, emphasizing interdisciplinary applications in computer vision, robotics, and automated planning.
Vivek F. Farias is the Patrick J. McGovern (1959) Professor at the MIT Sloan School of Management , where he is affiliated with the Operations Management group and the Operations Research Center (ORC) . His work bridges high-dimensional optimization, reinforcement learning, and stochastic modeling, with applications in commerce, healthcare, and biology. Education: Ph.D. in Electrical Engineering, Stanford University (Advisor: Prof. Benjamin Van Roy) Research Focus: Vivek’s research spans Reinforcement Learning , Dynamic Optimization , Approximation Algorithms , and Inference in Large-Scale Stochastic Systems . Recent applications include commerce platforms, protein networks, and healthcare operations. He has published extensively in top-tier venues like Management Science , Operations Research , NeurIPS , ICML , and Nature Communications . Editorial & Service Roles: Co-Editor, Data Science Area, Management Science Editorial Board, Operations Research Editorial Board, INFORMS Journal on Optimization Scientific Awards: 2022 RMP Jeff McGill Student Paper Award (First Prize) 2022 Applied Probability Society Student Paper Prize (First Prize) 2016 INFORMS MSOM Best Paper in Management Science 2015 INFORMS Revenue Management and Pricing Section Prize Best Simulation Publication Award, INFORMS Simulation Society (2014) George Nicholson Student Paper Competition, First Place (2017) Advising & Mentorship: Vivek has advised 14+ Ph.D. students and numerous MS students, many now faculty at top institutions (Columbia, NYU, CMU, INSEAD) or leaders in industry (Uber, Facebook, Lyft, McKinsey). His lab focuses on scalable algorithms for real-world decision-making under uncertainty. Industry & Entrepreneurship: He co-founded and served as CTO of Celect (acquired by Nike) and currently advises multiple technology startups in retail, healthcare, and finance.
Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.
Oana Lang is a Lecturer in Mathematics at Babeş-Bolyai University and a former STUOD Research Associate at Imperial College London. Her research focuses on stochastic analysis, particularly nonlinear stochastic partial differential equations (SPDEs) and their applications in fluid dynamics and data assimilation. She holds a PhD from Imperial College London (2020), with a thesis on stochastic transport equations and data assimilation. Affiliations: Academic Women in Mathematics, Mathematics of Planet Earth, Stochastic Analysis Research Group at Imperial Education: PhD in Mathematics, Imperial College London (2016–2020) MRes in Mathematics of Planet Earth, Imperial College London (2015–2016) MSc in Applied Mathematics, University of Bucharest (2013–2015) BSc in Mathematics, University of Bucharest (2010–2013) Her research interests emphasize SPDEs driven by transport noise, particularly in ocean and climate modeling. She has developed analytical frameworks for stochastic Euler equations, rotating shallow water models, and their applications in data assimilation. Key contributions include proving well-posedness for transport SPDEs and advancing calibration methods for stochastic fluid models. She organizes the Stochastic Analysis Seminar at Imperial College and the STUOD SPDEs Seminar. Recent work includes invited sessions on stochastic models in fluid dynamics at international conferences and editorial roles for Emergent Scientist . Awards: MRes degree with distinction (Imperial College London, 2016), multiple conference organization roles, and active grant-funded research in stochastic fluid dynamics. Labs/Teams: STUOD research group, Mathematics of Planet Earth Centre for Doctoral Training (MPE CDT), and collaborations with institutions like Reading University and the University of Aachen.
Jeff Beck is an Associate Professor at the School of Hospitality Business, Michigan State University. His expertise spans Event & Meeting Management and Sales & Sales Management within the hospitality industry. He holds leadership roles, including serving as the 73rd president of the International Council on Hotel, Restaurant, and Institutional Education (ICHRIE). Beck actively engages with industry through events like the Hilton Hackathon and has been recognized with the Stevenson W. Fletcher Award for his contributions. His research bridges hospitality management with interdisciplinary work in cognitive neuroscience and machine learning, as evidenced by publications exploring probabilistic inference, neural computation, and decision-making processes. Beck’s work also extends into healthcare quality analysis and food chemistry, showcasing a unique academic profile that integrates business and scientific domains. Education details are not explicitly provided in the text, but his professional trajectory indicates advanced training in both hospitality management and quantitative disciplines. His research interests combine practical hospitality challenges with computational models of human behavior, reflected in collaborations such as the 'How To Build A Brain' white paper and studies on Bayesian predictive coding in neural systems. Beck’s academic service includes mentorship roles and industry partnerships, exemplified by his classroom collaboration with Walt Disney Parks and Resorts executive Gregg Chapman. His scholarly contributions span diverse fields, with recent work focusing on machine learning applications in wine authentication, predictive modeling of sensory systems, and the neurobiological underpinnings of decision-making. These articles collectively highlight methodological innovations in probabilistic modeling and their implications for understanding both natural and artificial intelligence systems. Beck’s leadership in ICHRIE underscores his commitment to advancing academic-practice linkages in hospitality education.
Shafi Goldwasser is a Professor of Computer Science at the University of California, Berkeley, and Director of the Simons Institute for the Theory of Computing. She holds affiliations with the Berkeley Center for Responsible, Decentralized Intelligence (RDI) and the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB). Her research focuses on cryptography, computational number theory, complexity theory, and probabilistic proof systems. Goldwasser has received numerous accolades, including the ACM A.M. Turing Award (2012) and multiple Test of Time Awards (2021). Her educational background includes a Ph.D. (1984), M.S. (1981) in Computer Science from UC Berkeley, and a B.S. in Mathematics and Science from Carnegie Mellon (1979). Research Contributions: Goldwasser’s work spans foundational areas in theoretical computer science, with emphasis on cryptographic protocols, distributed computing reliability, and algorithmic efficiency. Her research has influenced modern security frameworks and computational complexity theory. Awards & Honors: ACM A.M. Turing Award (2012) Multiple Gödel Prizes (1993, 2001) FOCS/STOC Test of Time Awards (2021) Labs/Teams: Active leadership at the Simons Institute, fostering collaborations in theoretical computing and interdisciplinary research.
Marco De Angelis is a Lecturer at the Centre for Intelligent Infrastructure within the Department of Civil and Environmental Engineering at the University of Strathclyde's Faculty of Engineering. His work focuses on computational methods for handling uncertainty in engineering systems, with applications in structural reliability and health monitoring. Education: PhD in Risk and Uncertainty (2015) from University of Liverpool's Institute for Risk and Uncertainty Master of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Bachelor of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Dr. De Angelis specializes in computing with imprecision, developing methods to propagate uncertainty through models using interval analysis, probability bounds, and other mathematical frameworks. His research enables rigorous inference with scarce empirical data and builds trust in simulation for structural reliability assessment. His work intersects civil engineering, computer science, and statistics, with particular emphasis on practical applications in infrastructure monitoring and risk assessment. His recent publications demonstrate a strong focus on high-dimensional uncertainty analysis, optimization under uncertainty, and verified computational methods for reliability engineering. The research shows increasing sophistication in handling complex uncertainty representations while maintaining computational tractability for real-world engineering problems. Scientific Awards: Best student paper (June 18, 2025) The NASA and DNV Challenge on Optimization under Uncertainty (June 17, 2025) Bronze poster award (July 27, 2021) Teaching and Learning Award (May 17, 2017) ISIPTA-IJAR Young Researcher Award (August 2015) Dr. De Angelis teaches structural engineering theory, computer programming, interval computation, probability theory, and machine learning to undergraduate students. He has developed teaching materials from scratch for advanced dynamics courses. He serves as Co-investigator on the REUN project (Reduction of Uncertainties in risk assessment of structures and infrastructures against Natural hazards) funded by the Royal Society of Edinburgh, running from April 2025 to March 2027. His professional activities include conference participation, journal peer review, and invited talks in his specialty areas. He is actively involved with the Centre for Intelligent Infrastructure, where he contributes to research on digital twins and computational methods for infrastructure monitoring and assessment.
George B. Mertzios is an Associate Professor in the Department of Computer Science at Durham University, affiliated with the Algorithms and Complexity Research Group (ACiD) within the School of Engineering and Computing Sciences. He has held academic positions at Durham since 2011, progressing from Lecturer to Senior Lecturer and then to Associate Professor since 2017. He has also held visiting positions at institutions including the University of Bordeaux/CNRS and the University of Haifa. His research interests lie at the intersection of theoretical computer science and network science, with a strong focus on temporal graphs , algorithmic graph theory , parameterized complexity , and combinatorial optimization . He investigates efficient algorithms for dynamic and evolving networks, geometric graph models, and computational problems in network evolution and connectivity. His work often bridges foundational theory with applications in network design and distributed systems. The recent publications and ongoing activities of George B. Mertzios demonstrate a consistent and impactful research trajectory centered on the algorithmic foundations of temporal and dynamic networks. His work spans complexity analysis, algorithm design, and structural graph theory, with a notable emphasis on temporal vertex cover, sliding window models, and connectivity in time-varying graphs. He frequently publishes in top-tier conferences such as ICALP, MFCS, AAAI, and STACS, as well as leading journals including the Journal of Computer and System Sciences and Algorithmica . Gold Medal, Balkan Mathematical Olympiad, 1998 Distinguish Diploma, Bulgarian National Mathematical Competition 'Chernorizets Hrabar', 1998 Certificate of Merit, Mediterranean Mathematics Competition, 1999 Best paper award of Track C, ICALP 2010 Best student paper award, SAND 2024 George B. Mertzios has been actively involved in research supervision and leadership. He has supervised multiple PhD students to completion and currently advises ongoing doctoral research. He has served as Principal Investigator for EPSRC grants on Algorithmic Aspects of Temporal Graphs and Algorithmic Aspects of Intersection Graph Models , and as a Co-Investigator on projects related to graph coloring. He is a frequent organizer of scientific workshops, including the Algorithmic Aspects of Temporal Graphs series at ICALP and Dagstuhl seminars, and serves on the program committees of numerous international conferences such as MFCS, IWOCA, and SAND. He is a key member of the Network Engineering Science and Theory in Durham (NESTiD) research group, where he coordinates seminar series and fosters collaborative research in network algorithms and theory.
Jorge Lobo is an Assistant Professor at the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Coimbra. His research focuses on computer vision, sensor fusion for mobile robotics, and low power computing, with recent emphasis on quantum computing and bio-inspired computational approaches. B.Sc/M.Sc in Electrical Engineering (University of Coimbra) Ph.D. in Electrical and Computer Engineering (2007, thesis: 'Integration of Vision and Inertial Sensing') Research Interests: Specializes in artificial perception systems combining computer vision and inertial sensing for robotics. Develops probabilistic models for sensor fusion and explores unconventional computing architectures like stochastic circuits and quantum computing for efficient Bayesian inference. Key applications include autonomous robotic grasping and disaster response systems. Scientific Leadership: Principal Investigator for Q-Bet: Bridging classical/quantum computing via HPC and bio-inspired methods Founding member of Quantum@UC interest group Coordinator of quantum computing specialization courses (collaboration with Physics & Computer Science departments) Key Projects: Contributed to European initiatives BACS (Bayesian Cognitive Systems), HANDLE (robotic dexterity), BAMBI (Bayesian inference hardware), and ECOBOTICS.SEA (marine ecosystem robotics). Academic Recognition: IEEE Senior Member and former president of the Portuguese IEEE RAS chapter. Developed innovative remote labs for stochastic computing education.
Dan Mikulincer is the Brian and Tiffinie Pang Assistant Professor at the University of Washington in the Department of Mathematics, College of Arts and Sciences. He previously held a postdoctoral Instructor position at MIT Mathematics and earned his Ph.D. from the Weizmann Institute of Science under Ronen Eldan. He completed his B.Sc. in Mathematics and Computer Science at Ben-Gurion University, where he also studied Cognitive Neuroscience. B.Sc.: Ben-Gurion University (Mathematics, Computer Science, Cognitive Neuroscience) Ph.D.: Weizmann Institute of Science, Faculty of Mathematics Postdoc: MIT Mathematics Current: Assistant Professor, University of Washington, Department of Mathematics His research lies at the intersection of high-dimensional geometry, probability, statistics, information theory, and data science. He is particularly focused on normal approximations, Stein's method, stochastic analysis, and dimension-free phenomena. His work explores foundational aspects of learning theory, random matrices, transportation inequalities, and neural networks, often using probabilistic and analytic tools to derive sharp, robust results in high dimensions. The recent publications reflect a consistent focus on probabilistic methods in high-dimensional settings. Key themes include normal approximation via Stein's method, optimal transport, concentration and anti-concentration inequalities, random graph models, and theoretical aspects of machine learning such as learnability and neural network expressivity. The work spans both pure mathematics (e.g., GAFA, PTRF) and top-tier computer science venues (e.g., COLT, STOC, NeurIPS), highlighting interdisciplinary impact. Although no formal scientific awards are listed in the provided text, his publications in premier journals and conferences (Annals of Probability, STOC, NeurIPS, COLT) indicate significant recognition in the theoretical community. Dan Mikulincer has advised or collaborated with several researchers including Yair Shenfeld, Max Fathi, Ronen Eldan, and Sébastien Bubeck. He has served as a TA for 18.650: Statistics for Applications at MIT and taught programming courses (Java, Python, JavaScript) at the Interdisciplinary Center Herzliya. He is also a senior lecturer at WeCode, a nonprofit providing free programming education to underrepresented youth in Israel, indicating a strong commitment to education and outreach. He has been affiliated with research groups at MIT Mathematics, Weizmann Institute, and Microsoft Research AI, where he spent the summer of 2019 hosted by Sébastien Bubeck. These collaborations span theoretical machine learning, stochastic processes, and algorithmic foundations.
Tselil Schramm is an Assistant Professor in the Department of Statistics at Stanford University, with courtesy appointments in Computer Science and Mathematics. She is actively engaged in research and teaching in theoretical computer science and statistics. Department: Department of Statistics School: School of Humanities and Sciences University: Stanford University Office: CoDa E254 Email: tselil@stanford.edu She earned her PhD from UC Berkeley under Prasad Raghavendra and Satish Rao, followed by postdoctoral work at Harvard and MIT with Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo. Her research lies at the intersection of theoretical computer science and statistics, focusing on high-dimensional estimation, information-computation tradeoffs, sum-of-squares algorithms, and random graph theory. She develops algorithms for statistical problems and investigates the boundaries between what is statistically possible and what is computationally feasible. Her recent publications span topics including the overlap-gap property, discrepancy algorithms, robust message passing, semidefinite programming, spectral clustering, and random geometric graphs, appearing in top venues such as STOC, FOCS, COLT, NeurIPS, and The Annals of Statistics. She teaches a range of courses, including Introduction to Statistics (STATS 60), Theory of Statistics II (STATS 300B), and Machine Learning Theory (STATS 214 / CS 228M), reflecting her expertise in both foundational and advanced statistical theory. Runner-up for Best Paper at COLT 2021 Invited to STOC 2022 special issue of SICOMP Invited to SODA 2016 special issue of ACM Transactions on Algorithms Invited to CCC 2019 special issue of Theory of Computing Tselil Schramm advises and collaborates with numerous students and researchers, including Shuangping Li, Misha Ivkov, and Siqi Liu. She has been involved in multiple research grants and projects, particularly in the areas of high-dimensional inference and algorithmic robustness. Her work often bridges theoretical guarantees with practical algorithmic design. She is affiliated with Stanford’s theoretical computer science and statistics research groups, contributing to a vibrant academic environment. Her future work is expected to further explore the limits of efficient computation in statistical settings, with potential applications in machine learning, signal processing, and network analysis.
Professor V. Radu Craiu is a distinguished faculty member in the Department of Statistical Sciences within the Faculty of Arts and Science at the University of Toronto. He has served as Chair of the Department for 5 years (2018-2022 and 2023-2024) after joining as an Assistant Professor in 2001, being promoted to Associate Professor in 2006 and to Full Professor in 2013. Ph.D. in Statistics (2001) - University of Chicago M.S. in Mathematics (1996) - University of Bucharest B.S. in Mathematics (1995) - University of Bucharest Professor Craiu's research spans multiple domains of statistics with particular expertise in computational methods. His work has evolved from foundational research on Markov chain Monte Carlo samplers to broader applications in Bayesian statistics, copula models, statistical genetics, and more recently, astronomy. His research demonstrates both theoretical depth and practical applications across diverse fields including genetics, ecology, and astrophysics. His recent publications show a strong focus on advancing computational methodologies while addressing complex real-world problems. The research trends reveal increasing interdisciplinary collaboration, particularly with astronomers working on radio transients and stellar flares, while maintaining strong contributions to core statistical methodology in areas like copula modeling, MCMC algorithms, and dimension reduction. Fellow of the American Statistical Association (2022) Fellow of the Institute of Mathematical Statistics (2020) Faculty Affiliate of the Vector Institute (2020) CJS Award for 'Likelihood Inflating Sampling Algorithm' (2019) CRM-SSC prize from Centre de Recherches Mathematiques and Statistical Society of Canada (2016) Elected Member of the International Statistical Institute (2015) Professor Craiu has supervised numerous doctoral students whose work spans statistical genetics, computational methods, and copula modeling. His editorial service includes positions as Contributing Editor for the IMS Bulletin and Associate Editor for multiple prestigious journals including Harvard Data Science Review, Journal of Computational and Graphical Statistics, Statistics Surveys, The Canadian Journal of Statistics, and Statistical Methods and Applications. His research has been supported by various grants that have enabled extensive collaborations across disciplines.
Fredrik Dahlqvist is a faculty member at the University College London , Department of Computer Science , focusing on theoretical and applied aspects of probabilistic programming , semantics , and formal verification . His work bridges computer science with mathematical logic , machine learning , and programming language theory . Education: PhD in Coalgebraic Logics from Imperial College London (2014) His recent publications (2016–2025) explore model pruning , reparameterisation invariance , probabilistic numerical analysis , and categorical approaches to machine learning . Key themes include optimisation , cosine similarity , and omega-complete cone duality in probabilistic systems. Contact: f.dahlqvist@ucl.ac.uk (institutional) or f.p.h.dahlqvist@gmail.com (private), located at Gower Street, London WC1E 6BT, United Kingdom .
Pieter Audenaert is an Associate Professor at Ghent University's Faculty of Engineering and Architecture, affiliated with the Department of Information Technology and the Internet Technology and Data Science Lab. He simultaneously holds a postdoctoral researcher position at IMEC. His interdisciplinary research bridges computer science, mathematics, and engineering. Primary research domains include: Algorithm design for network optimization (Steiner trees, fiber networks) Computational biology (de Bruijn graphs, genome assembly) Transportation logistics (container drayage, port operations) Telecommunication systems (VLC, network flows) Discrete mathematics applied to network science Recent publications (2019-2025) demonstrate strong focus on optimization algorithms for both biological networks and transportation systems, with increasing applications of probabilistic modeling and simulation techniques. Bioinformatics work frequently employs graph-theoretic approaches to genome analysis. Awards and honors: Knuth Reward Check (2003) Laureaat Vlaamse Wiskunde Olympiade (1996) Leads research at the Internet Technology and Data Science Lab, collaborating extensively with industry partners like Port of Antwerp and Belgian retailers for transportation optimization projects.
Glenn Van Wallendael is an Associate Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . He leads research in video coding, digital watermarking, and immersive media technologies. Academic Focus: Video compression standards (HEVC, H.266), AI for multimedia, virtual reality Key Collaborations: iMinds, imec, European research consortia Research Interests include: Video compression algorithms (HEVC, SVC, MV-HEVC) Digital watermarking for copyright protection Machine learning applications in image/video analysis Quality of Experience (QoE) in immersive environments Recent Publications (2024-2025) show expertise in: Deepfake detection using vision transformers Medical image landmarking tools Lightweight geometric approximation methods AI-driven video quality assessment Doctoral Mentorship includes supervising: 2021: Hannes Mareen (video forensics) 2020: Vasileios Avramelos (light field coding) 2017: Johan De Praeter (adaptive video encoding)
