Ioannis Panageas is an Assistant Professor in Computer Science at UC Irvine's Donald Bren School, directing the GOALLab. His research develops theory for learning in multi-agent systems, game dynamics, and optimization. Funded by NSF and NRF, he focuses on last-iterate convergence guarantees in games, efficient equilibrium computation, and multi-agent reinforcement learning. Recent Work: Provides first exponential lower bounds for fictitious play in potential games (NeurIPS 2023), efficient Nash equilibrium computation methods (ICLR 2023), and semi-bandit learning dynamics with no-regret guarantees (ICML 2023). Teaching: Offers courses in Algorithmic Game Theory and Optimization for Machine Learning. Currently advising 3 PhD students and 2 MS students.
Benjamin Grimmer is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University. He is affiliated with the Mathematical Institute for Data Science (MINDS) and the Data Science & AI Institute. His research focuses on designing and analyzing algorithms for continuous optimization, particularly in nonconvex, nonsmooth, and adversarial settings. Grimmer’s work bridges classical optimization theory and modern machine learning challenges, leveraging computer-assisted proof techniques to advance algorithmic foundations. He earned his PhD in Operations Research from Cornell University, advised by Jim Renegar and Damek Davis. His doctoral work was supported by a National Science Foundation fellowship. Grimmer has held research positions at Google and the Simons Institute, exploring adversarial optimization and continuous-discrete optimization interfaces. His current work is supported by the Air Force Office of Scientific Research and a 2024 Alfred P. Sloan Fellowship. Research interests include algorithm design for stochastic/nonconvex/nonsmooth optimization, computer-aided proof methods, and meta-optimization tools like stepsize schedules. His recent studies, including work on gradient descent acceleration via long steps, were highlighted in Quanta Magazine (2023). Education: PhD in Operations Research, Cornell University (advisor: Jim Renegar and Damek Davis) Awards: Alfred P. Sloan Fellowship in Mathematics (2024) National Science Foundation Graduate Fellowship (PhD support) Dr. Grimmer advises a research group including PhD candidates Ning Liu, Thabo Samakhoana, Alan Luner, Yue Wu, and others. His lab explores optimization algorithms through both theoretical and applied lenses, collaborating closely with industry and academic partners.
Ron Peled is a Full Professor in the School of Mathematical Sciences at Tel Aviv University , currently on leave to serve as the Brin Professor in the Department of Mathematics at the University of Maryland starting summer 2024. During 2022–2024 he was a Member at Princeton University and the Institute for Advanced Study . Education & Career: While explicit degrees are not listed, his trajectory shows appointments at NYU (2009–2010), UC Berkeley and Tel Aviv University as a teaching assistant, followed by faculty positions culminating in full professorship. Research Interests: His work lies at the intersection of probability theory, statistical physics, and combinatorics . Key themes include: Disordered systems and random environments (random-field Ising, spin glasses) First-passage percolation and random metrics Random surfaces and height functions Loop models and critical phenomena Random matrices and band matrices Geometric probability and allocation problems Publications & Impact: With over 70 papers in top journals such as Annals of Mathematics , Annals of Probability , Inventiones Mathematicae , and Communications in Mathematical Physics , his recent work explores minimal surfaces in random environments, localization in random band matrices, and quantitative disorder effects in low-dimensional spin systems. Grants & Awards: Research has been continuously funded by: Israel Science Foundation (grants 1048/11, 861/15, 1971/19, 2340/23) ERC Starting Grant LocalOrder ERC Consolidator Grant Transitions Marie Skłodowska-Curie International Reintegration Grant SPTRF Teaching & Mentoring: Prof. Peled has taught a broad spectrum of courses at Tel Aviv University (Brownian motion, probability, percolation, random matrices, stochastic calculus) and NYU (combinatorics, discrete mathematics). He has supervised 13 post-doctoral fellows and 8 graduate students (PhD & MSc) to date. Service & Outreach: He co-organizes the Joint Israeli Probability Seminar and has organized numerous international workshops and conferences including at Oberwolfach, Technion, and Tel Aviv University.
Cesare Franchini is a full Professor at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics research group. His work focuses on theoretical understanding and computational modeling of quantum materials using first principles methods, particularly VASP. He maintains an active research program with numerous postdocs, PhD students, and collaborations across multiple institutions including the University of Bologna. Professor Franchini's research centers on quantum materials with many interacting degrees of freedom (lattice, spin, and electron orbital) that enable novel electronic and magnetic phases. His specific interests include metal-insulator transitions, polaron physics (electron-phonon interactions), non-collinear spin orderings, topological Dirac/Weyl phases, multiferroism, and superconductivity. He has increasingly incorporated machine learning data-driven tools and diagrammatic Monte Carlo techniques into his computational approaches. Analysis of his recent publications (2024-2025) reveals a strong focus on polaron physics across multiple material systems, with significant work on hematite, titanium dioxide, and quantum paraelectrics like KTaO3. His research increasingly integrates machine learning with traditional first-principles methods, particularly for studying hydrogen diffusion, surface science phenomena, and electronic structure calculations. There's also substantial work on single-atom catalysis and the application of advanced computational techniques to understand fundamental charge transport mechanisms in energy materials. Professor Franchini actively supervises numerous PhD students and postdocs, including Andrea Angeletti, Viktor Birschitzky, Lorenzo Celiberti, and several others working on diverse aspects of computational materials physics. He leads or participates in major research projects including TACO (Taming Complexity in Materials Modeling), DCAFM (Doctoral College Advanced Functional Materials), and the recently launched Spin-orbit entangled anharmonic polarons project. His group maintains strong collaborations with experimentalists at Charles University, Technical University of Vienna, and other international institutions.
David Alan Goldberg is an Associate Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University, part of Cornell Engineering. He joined Cornell in 2017 and previously held the A. Russel Chandler III Associate Professorship at Georgia Tech’s Industrial and Systems Engineering department. Goldberg earned his Ph.D. in Operations Research from MIT (2011) and a B.S. in Computer Science from Columbia University (2006). Education: B.S. in Computer Science, Columbia University (2006) Ph.D. in Operations Research, MIT (2011) Research Interests: Goldberg’s work focuses on applied probability and stochastic processes, including optimal stopping, inventory and queueing models, combinatorial optimization, and robust optimization. He develops algorithms and insights for complex systems, addressing challenges like the curse of dimensionality. His research spans applications in data science, operations research, and stochastic modeling. Notable contributions include distributionally robust inventory control and high-dimensional decision-making frameworks. Awards and Honors: 2025 Community-Engaged Practice and Innovation Award (David M. Einhorn Center) 2023 Sunny Yau ’72 Teaching Award (Cornell) 2019 INFORMS Applied Probability Society Best Publication Award 2015 NSF CAREER Award Multiple INFORMS Nicholson Student Paper Competitions (First Place, 2019 & 2015) Teaching and Service: Goldberg leads Cornell ORIE’s undergraduate research program, connecting students to real-world applications of OR and data science. He teaches courses in probability modeling, stochastic models, and academic skills for PhD students. He chairs the INFORMS Applied Probability Society and serves on editorial boards for Operations Research and Stochastic Systems . At Cornell, he advises the Undergraduate ORIE Society and directs undergraduate studies in ORIE. Labs & Collaborations: Goldberg’s research integrates theoretical rigor with practical applications, often involving collaborations across disciplines. His work bridges operations research, statistics, and computer science to address modern challenges in inventory systems, queueing networks, and decision-making under uncertainty.
Prof. Tobias Müller is a Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. His academic journey includes previous positions at Utrecht University, CWI (Centrum Wiskunde & Informatica), Tel Aviv University, and Eindhoven University of Technology, with a doctorate from the University of Oxford under Colin McDiarmid. His research focuses on combinatorics, probability theory, random graphs, percolation, discrete and stochastic geometry, and combinatorial game theory. He has contributed extensively to understanding complex networks, hyperbolic models, and geometric random structures. Research Interests: Random Graphs and Percolation Theory Discrete and Stochastic Geometry Hyperbolic Network Models Probabilistic Combinatorics Geometric Probability Graph Algorithms and Connectivity Notable Contributions: Analysis of Voronoi and Poisson-Voronoi percolation in hyperbolic planes. Studies on Mallows random permutations and their cycle structures. Research on component games and logical limit laws in graph theory. Investigations into the geometry and properties of random geometric graphs. Grants & Collaborations: Active in organizing workshops and conferences on random graphs and geometric networks, including the BIRS Workshop on Random Geometric Graphs and the STAR Workshops series. Labs/Teams: Member of the Bernoulli Institute’s research groups, focusing on stochastic studies, combinatorics, and algorithmic methods.
Hannu Toivonen is a Full Professor of Computer Science at the University of Helsinki, affiliated with the Faculty of Science and the Department of Computer Science. He leads the Discovery Research Group and is part of the Helsinki Institute for Information Technology (HIIT) and the Finnish Center for Artificial Intelligence (FCAI). He obtained his PhD in Computer Science from the University of Helsinki in 1996 and has held his professorship since 2002. His research spans Artificial Intelligence , Data Science , Computational Creativity , and Data Mining , with applications in generative art, automated journalism, and bioinformatics. His research focuses on: Developing AI systems for creative tasks (e.g., poetry, music, and art generation) Cross-lingual natural language processing and unsupervised learning Ethical and societal implications of AI in media and labor Analysis of his 15 most recent publications (2021–2025) reveals a strong emphasis on computational creativity and AI-driven content generation , with interdisciplinary applications in digital humanities, journalism, and biology. Trends include ethical AI frameworks, human-AI collaboration, and innovative evaluation methods for generative systems. Scientific Awards & Honors: Knight, First Class, Order of the White Rose of Finland (2019) Best Applied Research Award, IEEE ICDM (1998) Honorary Member of TKO-äly (2010) Member of Finnish Academy of Science and Letters He has supervised over 20 doctoral theses and secured ~7 MEUR in grants (e.g., EU projects: Embeddia , Newseye ). He leads the Discovery Research Group and collaborates with institutions like VUB (Belgium) as a Francqui International Professor (2025).
Professor Steffen Dereich is a leading researcher in mathematical stochastics at the University of Münster's Faculty of Mathematics and Computer Science, where he serves as Professor at the Institute of Mathematical Stochastics. He is an active investigator in the Mathematics Münster cluster of excellence, contributing significantly to the fields of stochastic processes and machine learning theory. His primary research interests span Stochastic Processes , Machine Learning , Deep Learning , Complex Networks , and Stochastic Analysis . Dereich has developed a unique research program that bridges classical probability theory with modern machine learning challenges, particularly focusing on the mathematical foundations of optimization algorithms used in deep learning. His work on stochastic gradient descent methods, especially the Adam optimizer, has provided crucial theoretical insights into convergence properties and optimization landscapes. The 15 most recent publications reveal a strong trend toward mathematical analysis of deep learning, with approximately 70% of his work focusing on neural network optimization, convergence analysis, and theoretical foundations of machine learning algorithms. The remaining publications continue his earlier work on complex networks, stochastic processes, and branching structures, demonstrating how he has successfully connected his foundational work in probability with cutting-edge machine learning research. Professor Dereich actively supervises PhD students and maintains productive collaborations, particularly with Arnulf Jentzen and Sebastian Kassing. His research group at Münster has secured significant funding through the Mathematics Münster cluster, supporting multiple projects including T8: Random discrete structures and their limits, and T10: Deep learning and surrogate methods. His teaching portfolio includes advanced courses on Probability Theory, Stochastic Analysis, Markov Chains, and specialized seminars on Machine Learning and Financial Mathematics, reflecting his dual expertise in theoretical mathematics and applied data science.
Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.
Prof. Dr. Christian Mayer is a Professor in Physical Chemistry at the Faculty of Chemistry, University of Duisburg-Essen. He serves as Head of the working group focusing on origin of life research, nanocapsules, and NMR spectroscopy techniques. His research group is located at Universitätsstraße 5, D-45141 Essen, Germany, with contact information including phone number +49 201 183-2570. Prof. Mayer's research interests primarily focus on the origin of life in deep tectonic fault zones of the first continental fragments, where he collaborates with Prof. Dr. Ulrich Schreiber from the Faculty of Biology and Prof. Dr. Oliver Schmitz from Applied Analytical Chemistry. His work investigates how vesicle formation occurs in tectonic fault systems through cyclic phase transitions of carbon dioxide, creating ideal conditions for molecular evolution. He specializes in pulsed field gradient NMR (PFG-NMR), high-resolution NMR, and solid-state NMR techniques to characterize nanoscale systems including nanocapsules, vesicles, and microemulsions. His recent publication trends reveal a strong interdisciplinary focus spanning physical chemistry, prebiotic chemistry, and astrobiology. The articles demonstrate increasing integration of computational methods with experimental approaches, particularly in analyzing molecular structures and dynamics. His research has evolved from fundamental studies of nanocapsule systems to broader investigations of protocell formation mechanisms under early Earth conditions, with recent work extending to astrobiological contexts including potential life formation on Titan. Prof. Mayer has established significant collaborations across multiple disciplines, particularly with geologists and biologists, to investigate the physical chemical processes that could have led to the emergence of life. His work bridges fundamental physical chemistry with practical applications in nanomedicine, particularly in developing artificial oxygen carriers based on nanocapsule technology. His laboratory utilizes high-pressure facilities to simulate early Earth crust conditions, with a particular focus on supercritical CO 2 environments. The working group combines experimental approaches with theoretical modeling to understand vesicle formation processes and their implications for the origin of cellular life.
Professor Vicky Chondrogianni holds the Personal Chair of Bilingualism and Language Development at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. She is affiliated faculty at CUNY Graduate Center's Department of Speech-Language-Hearing Sciences. Her research focuses on first/second language acquisition, child bilingualism, psycholinguistics, and developmental language disorders. She has held positions at Bangor University, Aalborg University, and the University of Reading before joining Edinburgh. Education: BA in Linguistics and Classics (University of Athens), MPhil in English and Applied Linguistics (University of Cambridge), PhD in Second Language Acquisition (University of Cambridge). Research interests include cross-linguistic aspects of acquisition in typically-developing and impaired children, production/comprehension relationships, and cognitive-language interactions. She currently supervises PhD students in topics like morphosyntax in bilingual children and employs eye-tracking methods. Grants/Projects: Includes studies on Gaelic-medium education, heritage children's eye-tracking data, and language disorders in multilingual settings. She has led over 20 projects since 2002, including work on Welsh-English bilingualism and L2 Greek acquisition. Labs/Teams: Active in Bilingualism Matters, a research center promoting bilingualism awareness. Serves as Editor for Linguistic Approaches to Bilingualism and holds roles in international associations like IASCL.
Sanna Järvelä is a Professor in the Faculty of Education and Psychology at the University of Oulu, where she leads the Learning and Educational Technology Research Lab (LET). She is a leading figure in learning sciences, with a focus on self-regulated learning, collaborative learning, and AI integration in educational contexts. Her research is supported by major international and national grants, including from the Jacobs Foundation. Research Interests: Self-regulated learning and socially shared regulation Computer-supported collaborative learning (CSCL) Artificial intelligence and adaptive technologies in education Development and application of multimodal research methods Her work bridges theoretical advancement with innovative methodological practices, contributing significantly to the understanding of learning in digital environments. While no specific publications are listed, her research output is extensive and highly cited, evidenced by her inclusion in Stanford University’s top 2% most-cited scientists list (2024). Scientific Awards and Honors: Member of the Finnish Academy of Science and Letters (2015) Francqui Chair, Ghent University (2015–2016) Learning Sciences Fellow (2022) Knight First Class of the Order of the White Rose of Finland (2022) Finnish Ostrobothnia Cultural Foundation Science Prize (2023) Stanford University’s Top 2% Scientist List (2024) Advising and Grants: As the head of the LET Lab and co-PI of the Center for Learning and Living with AI (CELLA), funded by the Jacobs Foundation, she mentors researchers and leads large-scale interdisciplinary projects. She also leads the University of Oulu’s Profi 7 Hybrid Intelligence research programme, shaping strategic research directions in AI and education. Labs and Teams: She founded and leads the Learning and Educational Technology Research Lab (LET), a vibrant research group focused on advancing knowledge about technology-enhanced learning. She is also a member of the OECD PISA 2025 ‘Learning in the Digital World’ expert team, influencing global educational policy and assessment frameworks.
Mahnoosh Alizadeh is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), affiliated with the Institute for Energy Efficiency and the Center for Control, Dynamical Systems and Computation (CCDC). She directs the Smart Infrastructure Systems laboratory and focuses on scalable control frameworks, data analytics, and market mechanisms for sustainable cyber-physical systems in smart grids and electric transportation. PhD in Electrical and Computer Engineering from UC Davis (2014) Recipient of the National Science Foundation CAREER award (2019) Associate Editor for IEEE Transactions on Control of Network Systems and IEEE Open Journal of Control Systems Her research spans theoretical work in networks, optimization, and AI, with applications in smart grids , electric transportation , and resilient infrastructure . She has contributed to safe optimization algorithms, decentralized learning, and game-theoretic approaches in resource allocation. Recent publications highlight advancements in safe optimization (safe linear bandits, conservative linear bandits), decentralized learning (robust federated learning), game theory (General Lotto games, resource allocation), and smart charging (mobility-aware EV scheduling). These works emphasize real-time decision-making under constraints, security, and robustness in cyber-physical systems. NSF Early CAREER Award Northrop Grumman Excellence in Teaching Award Her research group includes PhD students Spencer Hutchinson, Arghavan Zibaei, Nanfei Jiang, and Sajjad Ghiasvand, with alumni placed at institutions like Apple, Toyota, and the University of Colorado.
Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.
Albert S. Berahas is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan's College of Engineering. He joined the university in 2020 after completing postdoctoral positions at Lehigh University (2018-2020) and Northwestern University (2018). He holds a PhD in Engineering Sciences and Applied Mathematics from Northwestern University (2018), an MS in Applied Mathematics from Northwestern (2012), and a BSE in Operations Research and Industrial Engineering from Cornell University (2009). His research focuses on designing, developing, analyzing, and implementing algorithms for solving large-scale nonlinear optimization problems. His work spans multiple sub-fields including constrained optimization, optimization for machine learning, stochastic optimization, derivative-free optimization, and decentralized optimization. He is affiliated with the Michigan Institute for Data Science (MIDAS), the Michigan Institute for Computational Discovery and Engineering (MICDE), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). Berahas has received numerous honors including the Charles Broyden Prize (2025), the Air Force Office of Scientific Research Young Investigator Program award (2025), the IISE Operations Research Division Teaching Award (2024), and the North Campus Dean's MLK Spirit Award for Community Building & Impact (2024). His recent publications demonstrate strong activity in developing novel optimization frameworks with theoretical guarantees for challenging problem settings. His research has been supported by significant grants including from the Office of Naval Research (ONR) and the Air Force Office of Scientific Research. He actively mentors PhD students and has successfully advised Jiahao Shi, who defended his dissertation in March 2025 and joined Amazon. Berahas is also engaged in community outreach, particularly through initiatives like Engage Detroit that aim to empower Detroit's next generation of engineers.
