Vincent Dufour-Décieux is a researcher at the Professorship for Energy and Process Systems Engineering at ETH Zürich , focusing on developing computational methods for material screening in separation processes and global net-zero transitions. He earned his Master's in Materials Chemistry from Ecole Polytechnique (France) and a PhD in Materials Science from Stanford University , where he pioneered statistical methods combining Kinetic Monte Carlo and random graph theory to study planetary diamond formation. Research Highlights: Application of Classical Density Functional Theory (cDFT) for 100x faster adsorption property predictions in porous materials Development of science-based definitions for "hard-to-abate" emissions to guide climate action prioritization Integration of Coulombic interactions in cDFT for CO2 adsorption accuracy Article Trends : His work spans computational materials science (cDFT, random graph theory) and climate policy analysis, with recent publications in Joule , AIChE Journal , and Physical Review E . These studies emphasize scalable solutions for carbon capture, material screening efficiency, and accurate thermodynamic modeling. Collaborations : Active in international conferences (FOA15, MolMod, Gordon Research Conference) and cross-institutional projects with teams at Stanford, ETH Zürich, and industry partners.
Gyujin Oh is a Ritt Assistant Professor in the Department of Mathematics at Columbia University's Faculty of Arts and Sciences. He received his PhD in mathematics from Princeton University in 2022 under the supervision of Christopher Skinner and Akshay Venkatesh. Prior to joining Columbia, he was a postdoctoral member of the SLMath/MSRI program Algebraic Cycles, L-Values, and Euler Systems in Spring 2023. Dr. Oh's research spans multiple areas of number theory and arithmetic geometry. His primary interests include Algebraic Number Theory, the Langlands Program, Modular Forms, Galois Representations, and Arithmetic Geometry. His work often bridges classical number theory with modern geometric approaches, exploring connections between automorphic forms, cohomology theories, and arithmetic structures. He has made contributions to understanding rigid local systems, the Néron-Ogg-Shafarevich criterion, and various aspects of the Langlands correspondence. His recent publications demonstrate a strong focus on advanced topics in number theory, particularly exploring the intersection of modular forms, Shimura varieties, and Galois representations. His work on generalized Whittaker models, moduli stacks of crystals, and arithmetic quantum local systems reflects his interest in both classical and cutting-edge approaches to number-theoretic problems. The pattern in his research shows a consistent theme of connecting geometric structures with arithmetic phenomena. Dr. Oh is an active educator who has developed comprehensive lecture notes for both undergraduate and graduate courses in Algebraic Number Theory. In Spring 2025, he is teaching Graduate Algebraic Number Theory (MATH GR6657) at Columbia University, covering local and global class field theory, Langlands program connections, and related advanced topics. He has also been involved in organizing and participating in numerous learning seminars including the Moduli of Langlands Parameters seminar, Theta learning seminar, and Deformation theory learning seminar.
Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.
Maks Ovsjanikov is a Professor in the Computer Science Department at École Polytechnique, France , and a Visiting Research Scientist at Google DeepMind. His research focuses on mathematically principled approaches for geometric data analysis and synthesis, including learning on surface meshes, 3D point clouds, and graphs. Key Collaborations: Google DeepMind, Sanofi, Dassault Systèmes Research Themes: Non-rigid shape matching, 3D reconstruction, transfer learning, learning on geometric data, functional maps, deep learning for scientific discovery Recent Article Trends emphasize geometric deep learning, with publications at top venues like SIGGRAPH Asia, ICCV, and CVPR. Topics include surface reconstruction, functional maps, 3D keypoint detection, and diffusion models for shape matching. Scientific Honors include: ERC Consolidator Grant (VEGA Project, 2023) ERC Starting Grant (2017) ACM SIGGRAPH 2023 Test-of-Time Award Best Paper Awards at 3DV 2021 and 3DV 2022 Student Advisees have received prestigious awards, such as the IP Paris Best PhD Thesis Award (Souhaib Attaiki, 2023) and GdR IG-RV Runner-Up (Nicolas Donati, 2024). The GeomeriX Team at École Polytechnique drives his group's research, supported by the VEGA and AIGRETTE projects.
Dan Nguyen, Ph.D., is a faculty member in the Department of Radiation Oncology at UT Southwestern Medical Center, where he is part of the Division of Medical Physics and Engineering. He is a founding member of the Medical Artificial Intelligence and Automation (MAIA) Laboratory, collaborating closely with Dr. Steve Jiang to advance AI applications in radiotherapy. His work focuses on deep learning for treatment planning, dose prediction, auto-segmentation, and adaptive radiotherapy. Ph.D. in Biomedical Physics, University of California, Los Angeles (UCLA), 2017 Mentor: Dr. Ke Sheng Faculty appointment at UT Southwestern since 2017 Dr. Nguyen’s research is centered on applying artificial intelligence to solve critical challenges in radiation oncology. His primary interests include deep learning-based dose prediction, auto-segmentation of anatomical structures, optimization of treatment plans, and real-time adaptive radiotherapy. He has pioneered work in direct aperture optimization, 4π radiotherapy, and uncertainty quantification in AI models. His research bridges the gap between AI innovation and clinical implementation, with a focus on improving plan quality, reducing planning time, and enhancing accessibility for less experienced clinicians. The most recent publications (2023–2025) demonstrate a consistent trend in developing fast, accurate, and robust deep learning models for radiotherapy. Key themes include dose prediction with transfer and meta-learning, adaptive segmentation using test-time optimization, uncertainty assessment in AI predictions, and mathematical modeling of radiotherapy-immunotherapy synergy. These works span high-impact journals in medical physics, AI, and oncology, reflecting interdisciplinary innovation. While no specific scientific awards are listed, Dr. Nguyen’s leadership in the MAIA Lab and extensive publication record in top-tier journals indicate significant recognition in the field of medical physics and AI in medicine. Dr. Nguyen has co-authored numerous studies involving mentoring and collaborative research, particularly with trainees and junior faculty in the MAIA Lab. His work is supported by institutional and likely federal funding, given the scale and scope of AI deployment studies. He has contributed to large-scale collaborative efforts such as OpenKBP-Opt, involving international teams evaluating knowledge-based planning pipelines. The MAIA Laboratory is a multi-investigator research group focused on innovating, developing, and applying artificial intelligence technologies to empower clinicians—especially those with less experience or limited resources—for improved patient care. The lab’s work spans machine learning, deep learning, reinforcement learning, and mathematical modeling in radiation oncology.
Associate Professor LIN Zhenhua serves as a Presidential Young Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), with additional affiliation at the Institute of Data Science since 2021. His research develops cutting-edge statistical methodologies for complex data structures across multiple domains. Dr. LIN completed his Ph.D. at the University of Toronto in 2017 under Fang Yao's supervision, following M.Sc. degrees from Simon Fraser University (2013, 2010) and a B.Sc. from Fudan University (2008). Ph.D., University of Toronto, 2017 (Advisor: Fang Yao) M.Sc., Simon Fraser University, 2013, 2010 B.Sc., Fudan University, 2008 His research program spans functional data analysis (developing techniques for curves and surfaces), non-Euclidean data analysis (statistical methods on manifolds), high-dimensional statistics (p > n problems), and constrained statistical modeling. LIN's work bridges theoretical statistics with practical applications through rigorous mathematical frameworks and computational implementations. Recent publications reveal strong emphasis on bootstrap methods for high-dimensional inference, Riemannian geometry approaches for manifold-valued data, and innovative functional data techniques. His research shows consistent output with multiple 2025 publications in top journals including Biometrika, Bernoulli, and Journal of the American Statistical Association. Professional Recognition Presidential Young Professor, NUS (2019-present) Associate Editor, Bernoulli (2022-2024) Associate Editor, Statistics (2023-present) Young Researchers Committee, Bernoulli Society (2020-2024) Professor LIN actively mentors graduate students as evidenced by numerous collaborative publications with trainees. He teaches advanced courses including ST5215 Advanced Statistical Theory, DSA4211 High-dimensional Statistical Analysis, and ST5223 Statistical Models across multiple academic years. His research group develops specialized software packages including hdanova, matrix-manifold, synfd, mcfda, and iRFDA, making advanced statistical methods accessible to practitioners.
Kristofer Gunnar Paso serves as a Professor in the Department of Chemical Engineering within the Faculty of Natural Sciences at the Norwegian University of Science and Technology (NTNU), where he conducts research at the Ugelstad Laboratory. His work integrates fundamental rheological principles with practical applications in petroleum engineering and sustainable materials development, addressing critical industry challenges through experimental and theoretical approaches. Professor Paso's research spans rheology, polymer technology, enhanced oil recovery, wax deposition mechanics, and nanocellulose applications. His investigations focus on the behavior of complex fluids—including waxy crude oils, biopolymer composites, and nanocellulose suspensions—with emphasis on improving oil transportation efficiency, developing sustainable materials, and understanding interfacial phenomena. Key contributions include modeling wax deposition mechanisms, optimizing pour point depressants, and pioneering nanocellulose applications for enhanced oil recovery under extreme conditions. Analysis of his 2018-2025 publications reveals a strategic evolution from petroleum-focused rheology toward sustainable material solutions. While maintaining strong contributions to flow assurance (40% of recent work), his research increasingly incorporates biocomposites and recycled materials (25% growth since 2020), reflecting industry shifts toward decarbonization. His collaborative approach spans petroleum engineering, food science, and environmental technology, evidenced by publications in Energy & Fuels , Polymers , and Current Opinion in Food Science . No scientific awards were documented in the source material. Professor Paso maintains active collaborations across NTNU and international institutions, though specific advising relationships and grant details remain unreported. His laboratory operations center on the Ugelstad Laboratory's advanced rheological testing facilities, which support investigations into material behavior under reservoir conditions and industrial processing environments.
Maria Monica Wihardja serves as a Visiting Fellow and Co-coordinator of the Media, Technology and Society Programme at ISEAS–Yusof Ishak Institute while holding an Adjunct Assistant Professor position at the National University of Singapore. Her career bridges academic research and high-impact policy engagement, including former roles as World Bank Economist in the Poverty and Equity Global Practice and senior advisor to Indonesia's Presidential Executive Office on strategic economic reforms. Her educational background features: PhD in Regional Science, Cornell University MPhil in Economics, Cambridge University BA in Applied Mathematics-Economics, Brown University Wihardja's research integrates economic analysis with sociotechnical systems, focusing on digital transformation's impact on labor markets, food security, and democratic processes. She examines how technological disruption creates both opportunities for inclusion and risks of inequality, particularly through studies of platform economies, electoral disinformation, and sustainable agro-food systems. Her methodology combines quantitative econometrics with policy-oriented field research, often leveraging large-scale datasets from government and private sector partnerships. Analysis of her recent publications reveals three dominant research trajectories: (1) Digital economy effects on labor market polarization and inclusion, (2) Food system resilience through agricultural modernization, and (3) Geopolitical dimensions of supply chain reconfiguration. These intersect with her policy engagement in ASEAN economic integration frameworks and Indonesia's G20 presidency initiatives. Her scientific recognition includes: Nikkei Asian Scholar 2023 award Wihardja actively shapes regional discourse through editorial roles at East Asia Forum and Center for Indonesian Policy Studies. Her grant-funded work frequently involves World Bank partnerships and Indonesian government collaborations, particularly on stunting prevention and food policy reforms. Current projects examine deepfake impacts on electoral integrity and sustainable financing mechanisms for green transitions. She leads the Media, Technology and Society Programme at ISEAS, coordinating interdisciplinary research on digital governance and Southeast Asian technology policy. The programme partners with regional think tanks, government agencies, and private sector stakeholders to develop evidence-based policy responses to technological disruption.
Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.
Vernita Gordon is an Associate Professor in the Department of Physics at the University of Texas at Austin (since 2018), previously serving as an Assistant Professor there from 2010 to 2018. She holds a Ph.D. in Physics from Harvard University (2003) and a B.Sc. in Physics and Mathematics from Vanderbilt University (1997). Her research focuses on understanding how physical characteristics like mechanics and spatial structure influence bacterial biofilms, particularly their interactions with the immune system and resistance to antibiotics. She has pioneered techniques such as laser trapping to manipulate biofilm structures and studies radiation effects on bacteria like Deinococcus radiodurans . Education: Ph.D. in Physics, Harvard University (2003) B.Sc. in Physics and Mathematics, Vanderbilt University (1997) Research Interests: Dr. Gordon’s work integrates biophysics, microbiology, and materials science to explore biofilm mechanics, bacterial mechanosensing, and radiation biology. Key areas include: How biofilm mechanics resist immune clearance and antibiotic treatment Role of surface stiffness and shear stress in biofilm initiation Radiation resistance mechanisms in Deinococcus radiodurans Development of tools like laser trapping to study biofilm structure Key Achievements: Recipient of the Elizabeth B. Gleeson Professorship (2023) and Texas Mindset Initiative Fellowship (2023) Provost’s Teaching Fellow (2020–2024) and multiple teaching awards Funded by NSF, NIH, and Cystic Fibrosis Foundation Published over 60 peer-reviewed articles, including in Nature , PNAS , and Biophysical Journal Advising & Outreach: She mentors graduate students in Physics, Microbiology, and Biomedical Engineering, emphasizing interdisciplinary training. Her group actively recruits undergraduates and collaborates with industry partners like Solvay and the College of Pharmacy. Outreach includes lesson plans for high school STEM education and community science initiatives. Labs & Collaborations: Her lab uses advanced microscopy, microrheology, and computational modeling. Key collaborations include work with the Contreras Lab (UT Austin Chemical Engineering) on radiation-resistant bacteria and the Raizen Lab (UT Austin Physics) on self-sterilizing surfaces.
Oliver Schlotterer holds the position of Associate Professor at Uppsala University, affiliated with both the Department of Mathematics (Centre for Geometry and Physics) and the Department of Physics and Astronomy (Theoretical Physics). His research focuses on theoretical physics and mathematical structures in string theory, particularly exploring string amplitudes, modular forms, and algebraic geometry. He has contributed to understanding one-loop string amplitudes, modular graph forms, and supersymmetric field theories. Education: Not explicitly detailed in the provided text. Departments: Joint appointment in Mathematics and Theoretical Physics. His research interests include the interplay between string theory and mathematical frameworks like modular forms, algebraic geometry, and polylogarithmic functions. Recent work emphasizes chiral-splitting techniques, cyclic products of kernels, and the coaction principle in scattering amplitudes. Collaborations span topics from supersymmetric Yang-Mills theories to Einstein-Yang-Mills systems. Publications highlight advancements in string perturbation theory, including studies on non-holomorphic modular forms, genus-one integrals, and the single-valued map in string theory. His work often bridges high-energy physics with number-theoretic structures, such as zeta functions and Poincaré series. No awards or grants are listed in the provided data. He advises no students explicitly mentioned, though his collaborative research suggests involvement in training through projects. Active in the Centre for Geometry and Physics and theoretical physics groups, his research explores foundational aspects of string theory and their mathematical implications.
Athanasios Orphanides is a Professor of the Practice of Global Economics and Management at the MIT Sloan School of Management. He holds additional roles as Honorary Advisor to the Bank of Japan’s Institute for Monetary and Economic Studies, member of the Shadow Open Market Committee, and Research Fellow at the Centre for Economic Policy Research. His research focuses on central banking, monetary policy frameworks, and political economy. Orphanides earned undergraduate degrees in mathematics and economics, as well as a PhD in economics, all from MIT. Before joining MIT, he served as Governor of the Central Bank of Cyprus (2007–2012), a member of the ECB Governing Council, and Senior Advisor at the U.S. Federal Reserve. His work emphasizes robust monetary policy rules, such as natural growth targeting, to enhance economic stability. Recent publications address fiscal-monetary policy interactions, inflation targeting, and systemic risk management. Orphanides frequently advises policymakers on central bank strategies and crisis response mechanisms. His research highlights the importance of clear policy frameworks to avoid inflationary mistakes, drawing on historical analyses of U.S. and European central bank decisions. He critiques discretionary policies that prioritize employment over price stability, advocating instead for rules-based approaches constrained by inflation targets. Orphanides has contributed to global debates on central bank independence, crisis management, and the design of monetary instruments. His executive education courses at MIT Sloan focus on macroeconomic principles for business decision-making.
William J. Cook is a Professor and Assistant Chair in the Department of Mathematical Sciences at Appalachian State University. His research focuses on advanced algebraic structures, including Infinite Dimensional Lie Algebras, Representation Theory, Leibniz Algebras, and the Fuchs' Unit Group Problem. He holds a B.S. from Bob Jones University and both an M.S. and Ph.D. from North Carolina State University. Administratively, he serves as Assistant Chair, supporting departmental operations. His professional website is BillCookMath.com , and his office is located at 345 Walker Hall. No specific grants, awards, or advised students are listed in the provided information. His academic contributions are centered on theoretical algebraic research and departmental leadership.
Carles Padro Laimon is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Mathematics and the School of Telecommunications Engineering. He is a leading researcher in cryptography and information security, focusing on secret sharing schemes, combinatorial structures, and cryptographic protocols. His work integrates discrete mathematics, coding theory, and algorithmic design to address security challenges in digital systems. Padro leads the MAK Research Group (Mathematics Applied to Cryptography) and the ISG-MAK Information Security Group. He has been involved in numerous competitive research projects, including initiatives on post-quantum cryptography and secure multi-user systems. His contributions span over 211 documented activities, including articles, theses, and conference participations. His research interests include the theoretical foundations of cryptography, with a focus on optimizing secret sharing schemes, analyzing matroid-based structures, and developing secure communication protocols. He has collaborated extensively with institutions like the UPC and European research networks, contributing to both academic and practical advancements in cybersecurity. Padro holds a PhD in Mathematics from UPC and has supervised doctoral theses and mentored researchers in his field. His work frequently appears in top journals like IEEE Transactions on Information Theory, Designs, Codes and Cryptography, and SIAM Journal on Discrete Mathematics.
Sandra Paterlini is a Full Professor in the Department of Economics and Management at the University of Trento, Italy. She holds academic roles including Co-Chair of the ERCIM Working Group on Optimization Heuristics and Vice-Chair of the IEEE Task Force on Portfolio Optimization. Her career includes visiting positions at institutions such as the University of Minnesota and Ludwig-Maximilians-Universität München. She earned a PhD in Computational Methods for Financial and Economic Decisions from the University of Bergamo, an MSc in Financial Mathematics from the University of Warwick, and a Laurea in Economics from the University of Modena and Reggio E. Her research focuses on quantitative finance, risk management, portfolio optimization, and network analysis, with applications to ESG, systemic risk, and financial stability. Key research contributions include methodologies for sparse graphical modeling, systemic risk analysis, and ESG scoring frameworks. She has received multiple awards for research excellence and serves on editorial boards of journals like Computational Statistics & Data Analysis and Frontiers in Applied Mathematics and Statistics . Her work bridges academia and policy, with contributions to the European Central Bank’s Financial Stability Directorate and involvement in global conferences on computational finance and econometrics.
