Chris Holmes is a Professor of Biostatistics at the University of Oxford and Programme Director for Health and Medical Sciences at The Alan Turing Institute. His work bridges Bayesian statistics, machine learning, and genomic sciences, with cross-appointments in the Department of Statistics and the Nuffield Department of Clinical Medicine through the Wellcome Trust Centre for Human Genetics. He is affiliated with St Anne's College and actively leads research in Statistical Genomics under a Medical Research Council Programme Leaders Grant. PhD in Bayesian statistics from Imperial College London Former postdoctoral researcher and lecturer at Imperial College Industry experience in scientific computing for defense and SCADA systems Research Interests: Focus on Bayesian statistics , nonparametric methods , genetic epidemiology , and treatment effect heterogeneity . His methodologies address challenges in malaria pharmacodynamics, health equity in medical devices, and pandemic preparedness. Publication Trends: Recent work applies machine learning to reclassify multiple sclerosis progression , analyze genomic data for health equity , and develop tools for emergency admission prediction in Scotland. Collaborative efforts span Nature Medicine , Nature Reviews Genetics , and Trials . Scientific Awards: Medical Research Council Programme Leaders Grant in Statistical Genomics Advising: Mentors PhD students Oscar Clivio , Sahra Ghalebikesabi , and Natalia Garcia Martin in areas like Computational Statistics and Statistical Genetics .
Dr. John Maheu is a Professor and Business Research Chair at the DeGroote School of Business, McMaster University. His research focuses on financial econometrics, time series forecasting, and stochastic volatility modeling, with a particular emphasis on structural breaks and Bayesian methodologies. University: McMaster University School: DeGroote School of Business Department: Finance and Business Economics Maheu’s work explores how macroeconomic and financial factors influence market volatility, bull/bear cycles, and portfolio allocation. His models incorporate flexibility to adapt to unforeseen changes, such as those observed during the COVID-19 pandemic. He advocates for the use of disaggregated data to sharpen risk estimates and improve investment decisions. His recent publications analyze topics like infinite hidden Markov models, Bayesian forecasting frameworks, and jump processes in financial markets. These works highlight advancements in modeling market regimes and volatility dynamics under uncertainty. Scientific awards include the Social Sciences and Humanities Research Council (SSHRC) grant, which supports his long-term research initiatives. This funding enables him to address complex financial and economic challenges through innovative econometric modeling. Maheu’s research bridges theory and practice, offering insights into market risk, structural instability, and the efficacy of volatility models. His contributions extend to advising on financial decision-making tools that account for evolving economic conditions.
Mostafa Mashayekhi is an Associate Professor of Actuarial Science in the Department of Finance at the University of Nebraska-Lincoln. He holds a Ph.D. from Michigan State University and an M.S. from London, with expertise in actuarial science and stochastic processes. Education: Ph.D. (Michigan State University), M.S. (London), B.S. (London) His research focuses on compound and empirical Bayes decision theory, credibility theory, survival models, and stochastic calculus applications in actuarial mathematics. He teaches courses in survival models, credibility theory, simulation, and actuarial forecasting techniques. His publications, including works on optimal insurance under ambiguity and parametric empirical Bayes estimation, reflect his engagement with advanced statistical and actuarial methodologies. He has served on institutional committees like the Undergraduate Committee (2017-2020) and Assessment Committee (2008-2015). Scientific Awards: Associateship of the Society of Actuaries
Michele Cascella is a Full Professor in Theoretical Chemistry at the Department of Chemistry, University of Oslo, and a Principal Investigator at the Hylleraas Centre for Quantum Molecular Sciences. His research focuses on multi-scale computational modeling of (bio)chemical systems in condensed phases, combining ab initio, classical, and hybrid QM/MM molecular dynamics with coarse-grained and mesoscale approaches. PhD in Statistical and Biological Physics from SISSA (2004) Postdoc at EPFL (Switzerland) Assistant Professor at University of Bern (2005-2014) His research explores soft matter systems at the interface between molecular and mesoscale resolutions using density-field methods, with applications in physics, chemistry, and biochemistry. He leads a DFG-funded research network on multiscale soft matter modeling and collaborates internationally. Recent publications highlight advancements in hybrid particle-field molecular dynamics, soft matter simulations under pressure, and software development for exascale computing. His work spans computational methodologies for surfactants, lipid bilayers, transition metal complexes, and biological membranes. Scientific Awards Recipient of Swiss SNF-Professorship fellowship Marie-Skłodowska Curie Actions grant He supervises computational science projects related to multiscale modeling of soft matter systems and hybrid particle-field methodologies. Current grants include funding from the German Research Foundation and leadership of the National Centre of Excellence (Hylleraas Centre).
Joseph Halpern is a Professor in the Department of Computer Science at Cornell University. His research focuses on reasoning about knowledge and uncertainty, distributed computing, AI, security, and game theory. He holds a Ph.D. in Mathematics and has made foundational contributions across interdisciplinary fields, blending computer science with economics, philosophy, and logic. Education: Ph.D. in Mathematics (specific institution not explicitly stated in provided text). Professional Experience: Extensive contributions to theoretical computer science, including seminal work on knowledge representation, distributed systems, and causality. Authored influential textbooks like Reasoning About Knowledge and Reasoning About Uncertainty . Research Interests: His work bridges AI, distributed systems, and economics, addressing challenges in formal verification, game theory, and ethical AI. He investigates topics like causal models, decision-making under uncertainty, and security protocols. Awards: Over 30 awards, including the Gödel Prize (1997), ACM/AAAI Newell Award (2008), and membership in the National Academy of Engineering (2019). Recognized for contributions to theoretical computer science and interdisciplinary research. Teaching: Regularly teaches courses such as CS 2800 (Discrete Structures) and graduate courses on reasoning about knowledge and uncertainty. Collaborates with economists and philosophers on interdisciplinary projects. Grants and Funding: Supported by NSF, ARO, and Open Philanthropy Foundation. Active in promoting safe AI frameworks and formal methods for distributed systems.
Philippe Rigollet is the Cecil and Ida Green Distinguished Professor of Mathematics at the Massachusetts Institute of Technology, where he was appointed full professor in July 2020. He is also affiliated with MIT's Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). His academic journey spans positions at Princeton University (2008-2014) and Georgia Tech (2007-2008) before joining MIT in 2015. Rigollet's educational background includes a PhD in mathematical statistics from the University of Paris VI (now Sorbonne University) in 2006, an M.Sc. in Statistics & Actuarial Science from ISUP in 2003, and a B.Sc. in Applied Mathematics from the University of Paris VI in 2002, along with a B.Sc. in Statistics in 2001. His research spans statistics, machine learning, and optimization, with particular focus on high-dimensional problems, statistical limitations of learning under computational constraints, statistical optimal transport, and the mathematical foundations of transformer models. Rigollet approaches interdisciplinary problems at the intersection of computer science and statistics, bringing insights from each field to the other. His work has evolved from theoretical investigations of statistical versus computational trade-offs to practical applications in areas like cryoelectron microscopy and biological data analysis. Analysis of his recent publications reveals a strong trend toward developing mathematical frameworks for understanding modern AI architectures, particularly transformer models, through the lens of optimal transport and dynamical systems. His work connects deep learning theory with classical statistical methods, creating bridges between seemingly disparate mathematical domains. Notable scientific achievements: NSF CAREER Award (2011/2015) for Large Scale Stochastic Optimization and Statistics Elected Fellow of the Institute of Mathematical Statistics (2021) for contributions to statistical versus computational trade-offs, theory of aggregation, and statistical optimal transport Frank E. Perkins Award for Excellence in Graduate Advising (2023) Best Paper Award at Conference On Learning Theory (COLT) (2013) Invited Speaker at International Congress of Mathematicians (2026) Rigollet has advised numerous PhD students, many of whom have secured prestigious academic positions at institutions including Yale, NYU, Georgia Tech, and Duke. His research group at MIT includes current PhD students and postdoctoral researchers working on topics ranging from mathematical foundations of transformers to biological applications of optimal transport. He maintains active collaborations with the Broad Institute, particularly through the Eric and Wendy Schmidt Center, applying novel mathematical methods to genomic data analysis.
Carl Yang is an Assistant Professor in the Department of Computer Science at Emory University since 2020. He holds courtesy appointments as Assistant Professor in the Center for Data Science (Nell Hodgson Woodruff School of Nursing) and the Department of Biostatistics and Bioinformatics (Rollins School of Public Health). His research focuses on data mining, knowledge graphs, and trustworthy AI with applications in healthcare, neuroscience, and biomedicine. He has received prestigious awards including the NSF CAREER Award (2025), NIH K25 Career Award (2023), and the Best Paper Award at ICDM 2020. Yang earned his Ph.D. from the University of Illinois, Urbana-Champaign under Prof. Jiawei Han, and his B.Eng. from Zhejiang University under Prof. Xiaofei He. Yang’s work spans federated learning for graph data, brain network analysis, and healthcare informatics. He leads initiatives like the FedKDD workshop series and co-organized FedGraph conferences. His research is funded by NSF, NIH, Microsoft, and OpenAI. Notable contributions include FedSage (federated graph learning), BrainGB (fMRI analysis benchmark), and KG-LLM co-learning frameworks. He advises multiple Ph.D. students whose work has been recognized with awards such as the MCBIOS Young Scientist Excellence Award and SDM Doctoral Forum honors. Yang’s current projects include NSF-funded research on diabetes heterogeneity and brain graph mining, NIH grants for health informatics, and collaborations with Stanford, Oxford, and NTU. He also serves as a visiting faculty at Google Research/DeepMind (part-time since 2024) and has held visiting roles at Oxford and Zhejiang University.
Annie Liang is an Associate Professor of Economics and courtesy Associate Professor of Computer Science at Northwestern University. She holds a PhD from Harvard University (2016) and dual SB degrees in Mathematics and Economics from MIT (2011). Her research bridges economic theory, machine learning, and behavioral economics, focusing on welfare implications of algorithms, improving economic models via ML, and dynamic information strategies. She has held tenured positions since 2024 and previously taught at University of Pennsylvania and University of Pennsylvania (2017–2020). Education: PhD in Economics (Harvard, 2016); SB Mathematics & SB Economics (MIT, 2011). Professional roles include Program Committee roles at EC conferences, NSF grants (CAREER award 2022–2027), and service as a referee for top journals like *American Economic Review* and *Econometrica*. Teaching includes courses on data economics, information economics, and algorithmic decision-making. Research highlights include work on algorithmic fairness trade-offs, model completeness measurement, and information aggregation dynamics. Her work has been recognized with the Economic Theory Fellow award (2022) and the Kravis Teaching Award (2018–2019). Grants/awards include $1.2M NSF CAREER grant (SES-2145352) and multiple conference recognitions (e.g., EC '21 Exemplary Paper). Active in interdisciplinary initiatives such as the Machine Learning in Economics Summer Conference (2024) and NeurIPS workshops on AI/ML societal impact.
Peng Liu is a Lecturer in the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex. He holds a PhD from Nankai University (2015), an MSc from Nankai University (2012), and a BSc from Zhengzhou University (2009). Before joining Essex in 2020, he served as a Postdoctoral Fellow at the University of Waterloo (2018–2020) and a Senior SNSF Researcher at the University of Lausanne (2016–2018). His research focuses on quantitative risk management, stochastic processes, and extreme value theory, with applications in finance and insurance. Key areas include robust risk metrics, Gaussian processes, and risk-sharing mechanisms. His work often addresses ambiguity, dependence uncertainty, and practical challenges in financial modeling. Peng has published extensively, with recent contributions on lambda quantiles, distortion risk metrics, and queueing systems. His research bridges theoretical advancements and practical applications in risk assessment and management.
Dimitris Fotakis is Professor of Computer Science at the National Technical University of Athens (NTUA) and Collaborating Senior Researcher at the Archimedes Unit/ATHENA RC. His research in theoretical computer science focuses on algorithmic game theory, approximation algorithms, and facility location problems. He leads research projects including BALSAM (Beyond Worst-Case Analysis in Approximation Algorithms) funded by HFRI. With publications in ICALP, NeurIPS, and STOC, his work develops efficient algorithms for optimization under uncertainty. Fotakis serves on program committees of major conferences including ICALP and SAGT.
Joshua B. Tenenbaum is a Professor of Computational Cognitive Science in MIT's Department of Brain and Cognitive Sciences, a Principal Investigator at CSAIL, and Research Thrust Leader at CBMM. His work bridges human cognition and machine intelligence, focusing on perception, learning, and reasoning. He holds a PhD from MIT (1999) and previously taught at Stanford (1999–2002). His research develops algorithms used globally in science/engineering, emphasizing human-like AI grounded in computational models of mind. Awards include the Troland Research Award (NAS), APA's Early Career Contribution Award, and Society of Experimental Psychologists' Early Investigator Award. He is a Fellow of the Cognitive Science Society and Society of Experimental Psychologists. Key research interests span intuitive physics, theory of mind, probabilistic models of cognition, and neuro-symbolic AI. His lab explores how humans and machines learn from limited data, reason with abstract concepts, and generalize knowledge. Publications emphasize interdisciplinary approaches to intelligence, with recent work on neuro-symbolic systems, embodied reasoning, and ethical AI frameworks. He advises MIT's AI initiatives and collaborates internationally on cognitive science projects. His lab's work has led to open-source tools like probabilistic programming frameworks and benchmark datasets for evaluating human-like capabilities in machines. Current projects include understanding moral judgment mechanisms and developing physically plausible AI systems.
Yixin Wang is an Assistant Professor of Statistics at the University of Michigan, affiliated with the Department of Statistics within the College of Literature, Science, and the Arts. His research focuses on Bayesian statistics, causal inference, and machine learning. Previously, he was a postdoctoral researcher at UC Berkeley under Michael Jordan and earned his Ph.D. in Statistics from Columbia University (2020) and B.Sc. in Mathematics & Computer Science from Hong Kong University of Science and Technology (2014). Education: Ph.D., Columbia University (2020); B.Sc., HKUST (2014) Research Interests: Probabilistic generative modeling and Bayesian statistics, including large language models and diffusion models Causal machine learning, including causal representation learning and causal inference for language models Applications in recommender systems, computational biology, and human-AI interactions His work emphasizes robust statistical methods and causal approaches to address real-world challenges, such as bias mitigation in algorithms and equitable treatment allocation in healthcare. He currently leads a research group seeking to advance causal machine learning and probabilistic models, with postdoctoral openings available. Key contributions include developing robust Bayesian methods, causal inference frameworks for unstructured data, and applications in electronic health records and materials discovery. He has been instrumental in bridging theory and practice in areas like feedback loop mitigation in recommenders and causal fairness assessment.
Athina Spiliopoulou is a Chancellor's Fellow and Lecturer at the Usher Institute within the University of Edinburgh's College of Medicine and Veterinary Medicine. She leads the Genetic Targets and Precision Medicine Research Group alongside Paul McKeigue and maintains affiliations with both the Institute of Genetics and Cancer and the Diabetes Medical Informatics and Epidemiology Research Group under Helen Colhoun. Education: PhD in Probabilistic Models for Melodic Sequences, University of Edinburgh (2013) MSc in Artificial Intelligence, University of Edinburgh (2008) Her research program focuses on understanding autoimmune disease pathogenesis through probabilistic modeling and computational approaches. She develops methods for predictive and causal inferences using large-scale genomic and clinical datasets, with primary applications in rheumatoid arthritis and type 1 diabetes. Her work bridges machine learning expertise with genetic epidemiology to enable precision medicine applications. Analysis of her 15 most recent publications reveals strong focus on trans-effects analysis, Mendelian randomization, and genomic prediction across autoimmune conditions. Her research consistently integrates genetic data with clinical outcomes to identify drug targets and biomarkers, with increasing emphasis on real-world data applications through projects like Scotland's national rheumatology data linkage study. Scientific Awards: Versus Arthritis Career Development Fellowship (2025-2030) for precision medicine approach in rheumatoid arthritis Academy of Medical Sciences Grant (2021-2024) for combining omics biomarkers with Bayesian predictive models She currently leads 8 research projects totaling over £2M in funding, including the active "Exploiting methodological and technological innovations for targeted treatment" project. Her national rheumatology data linkage study employs novel clinic-based data capture tools, with an app scheduled for NHS Lothian implementation in June 2025. She actively supervises PhD students and collaborates extensively across UK biobanks and international consortia. Spiliopoulou co-leads development of the GENOSCORES platform for genomic prediction and maintains several open-source software tools including MRhevo for Bayesian Mendelian randomization and nestedCV for high-dimensional data analysis.
Shuyang Ling is an Assistant Professor of Data Science at NYU Shanghai and a Global Network Assistant Professor at the Tandon School of Engineering, New York University. He joined NYU Shanghai in September 2019 as an Assistant Professor Faculty Fellow of Data Science, after serving as a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences and Center for Data Science at NYU from 2017-2019. Dr. Ling received his PhD in Applied Mathematics from the University of California, Davis in 2017 under the supervision of Thomas Strohmer. He also earned an MS in Statistics from UC Davis in 2016 and completed his undergraduate studies in Mathematics and Applied Mathematics at Fudan University in Shanghai, China in 2012. Dr. Ling's research focuses broadly on the mathematics of data science, with particular interest in tackling inverse problems from engineering applications and extracting meaningful information from large-scale and heterogeneous datasets. His work spans a broad spectrum of subjects including: Optimization (convex and non-convex) Probability and statistics Computational harmonic analysis Numerical linear algebra Signal processing and machine learning theory His recent publications demonstrate a strong focus on synchronization problems, neural collapse phenomena, and optimization landscapes. He has made significant contributions to understanding the theoretical foundations of spectral methods, non-convex optimization, and convex relaxations in data science applications. His work often bridges theoretical guarantees with practical algorithms for problems in signal processing and machine learning. Dr. Ling has received several prestigious awards and grants including the SIAM Student Paper Prize in 2017, Shanghai Eastern Scholar for Young Professionals (2019), Shanghai Rising Star Program (A-type) (2024), and grants from the Natural Science Foundation of Shanghai and National Key R&D Program of China. He is actively involved in mentoring the next generation of researchers, currently advising three Ph.D. students at NYU Shanghai: Jiayang Yin (co-advised with Prof. Mathieu Lauriere), Wanli Hong, and Ziliang Samuel Zhong. He has also supervised numerous undergraduate thesis projects at NYU Shanghai since 2020. Dr. Ling is part of the Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning at NYU Shanghai, where he organizes seminars and reading groups on data science and machine learning.
Santiago Paternain is an Assistant Professor in the Department of Electrical, Computer and Systems Engineering at Rensselaer Polytechnic Institute's School of Engineering. He joined RPI in 2020 after completing his Ph.D. and postdoctoral work at the University of Pennsylvania, where he developed foundational algorithms at the intersection of machine learning and control theory. His research bridges theoretical rigor with practical applications in robotics, power systems, and autonomous vehicles. His academic credentials include: B.Sc. in Electrical Engineering, Universidad de la República, Uruguay (2012) M.Sc. in Statistics, The Wharton School, University of Pennsylvania (2018) Ph.D. in Electrical and Systems Engineering, University of Pennsylvania (2018) Paternain's research centers on reinforcement learning and control of dynamical systems, with emphasis on safety guarantees, optimization, and real-world deployment. He develops algorithms that integrate model-based control with data-driven methods to overcome limitations of pure reinforcement learning, particularly for constrained and safety-critical applications. Current projects explore in-context learning for robotics, physics-guided AI for power grids, and multi-agent coordination in uncertain environments, always prioritizing theoretical soundness and practical viability. Analysis of his 15 most recent publications reveals a dominant focus on constrained reinforcement learning (60% of works), with growing applications in power systems (20%) and robotics (15%). A clear trend shows increasing integration of foundation models with control theory, particularly for safety-critical decision making. His work consistently addresses scalability challenges while maintaining rigorous safety guarantees, reflecting his commitment to bridging theoretical and applied research. His scientific contributions have earned significant recognition: Best Student Paper Award at ICASSP 2020 Joseph and Rosaline Wolfe Best Doctoral Dissertation Award (2019) Best Student Paper Award at CDC 2017 Best Student Paper Award at I2MTC 2014 Best Teaching Assistant at University of Pennsylvania (2017) CTL's Graduate Fellowship for Teaching Excellence (2018) Paternain actively mentors six doctoral students across diverse research areas including safe reinforcement learning, multi-robot systems, and power grid applications. His research is supported by substantial funding including multiple RPI-IBM Future of Computing Research Collaboration grants (quantum computing and LLM reasoning), a DOE grant for EV battery assessment, an ONR grant for autonomous helicopter refueling, and industry partnerships with Boeing and ARM. He leads a high-impact research group that organizes influential workshops like "Learning under Requirements" at premier conferences including AAAI and L4DC. His laboratory focuses on translating theoretical advances into industrial applications through partnerships with GE Research, The Boeing Company, and national laboratories. Current projects include autonomous helicopter aerial refueling systems, real-time power grid stability assessment tools using graph neural networks, and safety-certified multi-robot coordination frameworks, all emphasizing deployable solutions for critical infrastructure challenges.
