Prof. Dr. Angelika Rohde is a Full Professor for Mathematical Stochastics at the Albert-Ludwigs-University Freiburg , where she has been since 2016. Her research focuses on Mathematical Statistics and Probability Theory , with current projects supported by the DFG (e.g., SFB 1597 'Small Data' and FOR 5381 'Mathematical Statistics in the Information Age'). Education : Binational Ph.D. (2006) from University of Heidelberg and University of Bern; Diploma in Mathematics (2003) from University of Heidelberg. Rohde’s work addresses adaptive uncertainty quantification , nonparametric statistical inference , and phase transitions in stochastic processes. She has developed methods for high-dimensional data , empirical processes , and random matrices , with applications in classification and differential privacy. Her recent publications focus on bootstrap techniques for high-dimensional covariance matrices , Edgeworth expansions , and adaptive similarity testing . She actively supervises PhD students like Gabriele Bellerino, Sebastian Hahn, and Dario Kieffer, with former students Pascal Beckedorf and Johannes Brutsche now as research assistants. Grants include leadership roles in DFG projects SFB 1597 and FOR 5381, emphasizing small data and high-dimensional statistics. Her team collaborates on problems like support recovery and classification under privacy constraints .
Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.
Eva María Arias de Reyna Domínguez serves as a Full Professor in the Department of Signal Theory and Communications at the University of Seville. Her research is centered within the Signal Processing and Communications research group (TIC-155), where she has led numerous national and international projects focused on advanced signal processing techniques for wireless communications and localization systems. Her research interests span Signal Processing , Wireless Communications , and Ultra-Wideband Localization , with particular expertise in Expectation Propagation algorithms, UWB signal processing, and crowd-based learning for IoT applications. Her work bridges theoretical signal processing with practical implementations in digital communications and indoor positioning systems. Analysis of her 15 most recent publications reveals a consistent focus on Expectation Propagation techniques for digital communications (constituting 40% of recent work), UWB localization algorithms (30%), and channel equalization methods (20%). Her research demonstrates a progression from fundamental signal processing algorithms toward IoT-integrated spatial field estimation and machine learning applications. She has advised doctoral student Irene Santos Velazquez (2018 thesis on Expectation Propagation for digital communications) and participated in significant research projects including ATENEA (Artificial Intelligence for Art Fabric Analysis), Finite-Length Iterative Decoding, and multiple national grants under Spain's TEC and CSD programs. Her laboratory work centers on the Signal Processing and Communications research group, which has received continuous consolidation funding from 2005-2017.
Tanveer Karim is a Postdoctoral Fellow at the University of Toronto under the Arts & Sciences Fellowship. He is an observational cosmologist and astronomer specializing in analyzing large cosmological datasets to constrain cosmological models. His primary affiliation is with the Dark Energy Spectroscopic Instrument (DESI) Collaboration, where he focuses on emission-line galaxies (ELGs) and their cross-correlation with CMB datasets. Education: Ph.D. and A.M. in Astronomy from Harvard University B.S. in Physics & Astronomy from the University of Rochester His research spans cosmology, galactic structure analysis, and statistical modeling. Key projects include studying Fermi Bubbles through UV absorption spectroscopy, refining the Sun's position relative to the Galactic plane, and developing machine learning techniques for cosmological data interpretation. He also co-leads the Lyman-Break Galaxies Topical Team within the Dark Energy Science Collaboration, aiming to constrain cosmological models using high-redshift galaxies. Recent publications highlight his work on DESI instrumentation, target selection algorithms, and the impact of galaxy window functions on cosmological parameter estimation. Earlier studies focus on T Tauri star rotation periods and Milky Way halo cloud characterization. Karim emphasizes mentorship and DEI initiatives in astronomy, alongside teaching roles at Harvard and the University of Rochester. He has led summer courses at the Banneker Institute and served as a guest lecturer on topics ranging from cosmic origins to public speaking for scientists.
Dr. Gil Kur is a Lecturer in the Department of Mathematics at ETH Zürich. His research focuses on statistical estimation, high-dimensional data analysis, convex regression, machine learning theory, optimization, and probability theory. He has contributed to areas such as nonparametric estimation, convex body approximation, and differential privacy mechanisms. His work bridges theoretical foundations with applications in computational statistics and optimization. Key research themes include analyzing convergence rates of estimators, developing optimal algorithms for convex regression, and studying geometric properties of high-dimensional spaces. His recent articles explore topics like debiased LASSO methods, log-concave maximum likelihood estimation, and the performance of empirical risk minimization under various constraints. Kur’s publications demonstrate a strong focus on rigorous mathematical analysis, often combining tools from probability, functional analysis, and convex geometry. While no specific awards or grants are listed, his active publication record reflects sustained contributions to statistical theory and machine learning fundamentals.
Elynn Chen is an Assistant Professor in the Department of Technology, Operations, and Statistics (TOPS) at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since September 2021. Her research bridges statistics, machine learning, and operations research with applications in business, economics, and healthcare. Her educational background is highly interdisciplinary: Ph.D. in Statistics, Rutgers University B.A. in Economics, Peking University B.S. in Computer Science, Tsinghua University Professor Chen's research is centered on developing novel methodologies for data-driven decision-making and complex data analysis. Her primary interests include: Tensor learning for multi-dimensional data representation Reinforcement learning with applications in societal domains such as healthcare and education Transfer learning and knowledge fusion across heterogeneous tasks High-dimensional time series and matrix-variate factor models She emphasizes algorithmic innovation and statistical rigor in addressing real-world challenges. Her recent publications reveal a consistent focus on advanced statistical learning methods. She has made significant contributions to tensor decomposition, reinforcement learning in heterogeneous environments, and high-dimensional network modeling. Her work combines theoretical depth with practical applications in international trade, clinical treatments, and corporate finance. Her scientific recognition includes: NSF Postdoctoral Research Award DMS-1803241 She actively mentors students and postdoctoral researchers, fostering a collaborative research environment. Her group works on cutting-edge topics such as tensor-view graph neural networks and dynamic matrix factor models. She has received research support through prestigious postdoctoral appointments at UC Berkeley (advised by Prof. Michael I. Jordan), Princeton University (with Prof. Jianqing Fan), and OpenAI. She leads a vibrant research team focused on: Tensor learning Reinforcement learning for social applications Transfer and knowledge fusion She welcomes highly motivated individuals to join her research group.
Dr. Ruihai Dong is an Assistant Professor at the School of Computer Science, University College Dublin, affiliated with the Insight Centre for Data Analytics. His research bridges Machine Learning, Deep Learning, and Recommender Systems with significant applications in finance and medical imaging. Develops AI-driven solutions for financial markets and healthcare Collaborates with industry partners like Eagle Alpha and Samsung Funded by Enterprise Ireland for commercialization studies Research focuses on: Recommender systems (news, finance, conversational AI) Graph neural networks for financial risk modeling Medical imaging under limited labeling constraints Algorithmic transparency and ethical AI applications 3D CNNs & transformers for seismic analysis Large language models for multilingual evaluation Recent publications emphasize: Hybrid architectures for multimodal data Explainable AI techniques in financial and news domains Geometric deep learning for microstructure and seismic data Curriculum strategies in active learning Media bias mitigation frameworks Latent space modeling for asset embeddings Scientific contributions recognized through: Outstanding Research Award 2018 by UCD School of Computer Science Industry collaborations include work with: Eagle Alpha (financial analytics) SkillPages (data-driven platforms) Samsung (technology innovation)
Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.
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.
Brian Trippe is an Assistant Professor of Statistics at Stanford University with a joint affiliation in Stanford Data Science. His research develops probabilistic machine learning methods to solve critical challenges in biotechnology and medicine, particularly focusing on reliable molecular design under physical constraints. Education: PhD in Computational and Systems Biology, MIT (2022) MPhil in Engineering, University of Cambridge (2017) BA in Biochemistry and Computer Science, Columbia University (2016) His work centers on probabilistic machine learning and Bayesian computation applied to computational biology , with breakthroughs in protein engineering yielding hundreds of experimentally validated molecular structures. By incorporating prior knowledge and providing theoretical guarantees, his methods address biotechnology's unique demands for data efficiency and physical constraint satisfaction. Analysis of his recent publications reveals dominant trends in diffusion models for protein design and Bayesian inference frameworks , demonstrating cross-cutting applications from ocean current modeling to genomic analysis. These works consistently bridge statistical theory with experimental validation through major collaborations like the University of Washington's Institute for Protein Design. Brian actively mentors students at Stanford and explicitly encourages applications from underrepresented groups. His collaborative network spans Columbia University, MIT, and the Baker lab, driving interdisciplinary advances in computational biotechnology while pursuing long-term goals in genetic engineering foundations.
Ya'acov Ritov is a Professor of Statistics at the University of Michigan, affiliated with the Department of Statistics under the Literature, Science, and the Arts (LSA) School. He also held the Francis Hock Emeritus Chair in Statistics at The Hebrew University of Jerusalem. His academic career includes positions as a Lecturer (1984), Senior Lecturer (1989), Associate Professor (1990), and Professor (1992) at Hebrew University before joining the University of Michigan as a Professor in 2015. Ritov's research focuses on statistical theory, semiparametric models, high-dimensional data analysis, and empirical Bayes methods. He has contributed to areas such as robust Bayes procedures, errors-in-variables models, and the analysis of contingency tables. His work bridges theoretical advancements with practical applications in fields like machine learning, biostatistics, and econometrics. Education: B.Sc. in Electrical Engineering (1973, Technion), M.Sc. in Electrical Engineering (1980, Technion), Ph.D. in Statistics (1983, Hebrew University of Jerusalem). His doctoral thesis, advised by Peter J. Bickel and Yosef Yahav, explored robust Bayesian procedures. Scientific Awards: Francis Hock Emeritus Chair in Statistics (Hebrew University). Students: Advised numerous Ph.D. and Master's students, including Michael Law, Hamid Eftekhari, and Debarghya Mukherjee. His academic mentorship spans foundational statistical theory and applied methodologies. Labs/Teams: Collaborates extensively with researchers in statistics and interdisciplinary fields, contributing to projects on algorithmic fairness, transfer learning, and high-dimensional inference.
Professor Raul Tempone is a distinguished faculty member at King Abdullah University of Science and Technology (KAUST), holding the position of Professor in the Department of Applied Mathematics and Computational Science within the Computer, Electrical and Mathematical Sciences and Engineering division. He serves as Principal Investigator of the Stochastic Numerics Research Group and has made significant contributions to numerical analysis and uncertainty quantification, aligning with KAUST's mission and Saudi Arabia's Vision 2030 goals through advancements in computational science that drive technological innovation and sustainability. Professor Tempone's academic foundation includes: Ph.D. in Numerical Analysis from the Royal Institute of Technology (KTH), Sweden (2002) M.S. in Engineering Mathematics from Universidad de la República, Uruguay (1999) B.S. in Industrial and Mechanical Engineering from Universidad de la República, Uruguay (1995) Professor Tempone's research focuses on the mathematical foundations of computational science and engineering, with particular emphasis on uncertainty quantification, stochastic differential equations, and numerical methods. His work bridges theoretical mathematics with practical applications across multiple domains including computational mechanics, quantitative finance, biological and chemical modeling, and wireless communications. He has pioneered advancements in adaptive algorithms, Bayesian inverse problems, and scientific machine learning, driving innovation in computational efficiency and accuracy for solving complex real-world problems. His recent publications demonstrate a strong trend toward integrating uncertainty quantification with machine learning approaches and addressing complex optimization problems under uncertainty. The research spans diverse applications from wireless network performance analysis to medical imaging and sustainable energy systems, reflecting his commitment to solving real-world challenges through advanced computational methods that combine theoretical rigor with practical applicability. Professor Tempone's scientific achievements have been recognized through numerous prestigious awards: Alexander von Humboldt professorship (2018-2025) ISI Highly Cited Researcher (2016) Elected Program Director of the SIAM Uncertainty Quantification Activity Group (2013-2014) Fellow of the Deutsche Forschungsgemeinschaft Priority Program (2014) First Dahlquist Fellowship at the Royal Institute of Technology, Sweden (2007-2008) As an academic advisor, Professor Tempone has successfully supervised ten PhD students to completion. His research has attracted significant funding, including the Alexander von Humboldt professorship grant worth up to 5 million euros. He has directed the KAUST Strategic Research Initiative in Uncertainty Quantification (2012-2016) and collaborated extensively with industry partners including Saudi Aramco. His research group has placed numerous members in academic positions worldwide and in leading companies such as Bain & Company, Baker Hughes, Enel Group, G-Research, Honeywell, McKinsey & Company, and Saudi Aramco. Professor Tempone leads the Stochastic Numerics Research Group at KAUST, which focuses on developing and analyzing numerical methods for stochastic and deterministic problems. The group's work encompasses a posteriori error approximation, data assimilation, hierarchical and sparse approximation, optimal control, and optimal experimental design. Through strategic collaborations and interdisciplinary approaches, the research group continues to push the boundaries of computational science and its applications to real-world challenges across engineering, finance, biology, and energy sectors.
Ruobin Gong is an Associate Professor of Statistics at Rutgers University, with a status-only appointment at the University of Toronto's Department of Statistical Sciences. She holds a PhD from Harvard University and focuses on foundational statistical theory and privacy-aware methodologies. Her research integrates Bayesian and imprecise probability frameworks with differential privacy challenges, emphasizing ethical data science practices. Gong's work includes developing the dapper R package for private posterior estimation and organizing events like the Annual Symposium on Applications of Contextual Integrity and NBER workshops on privacy in applied research. Her research interests span theoretical foundations of uncertainty reasoning (including Bayesian methodology, random sets, and Dempster-Shafer theory) and practical applications in privacy-preserving statistical inference. She serves as an associate editor for Harvard Data Science Review, JASA/TAS Reviews, and Statistics and Public Policy, while also contributing a monthly column, 'Sound the Gong,' to the IMS Bulletin. Gong's recent work addresses challenges in privacy-aware computation, invariant-constrained data sanitization, and the philosophical implications of credence dynamics. Her contributions include pioneering subspace differential privacy frameworks and formal privacy analyses of historical data swapping methods. Gong actively engages in interdisciplinary collaborations, bridging statistical theory with real-world policy applications through workshops and publications on topics like algorithmic fairness and disclosure avoidance systems.
Professor Damiano Brigo holds the Chair in Mathematical Finance at Imperial College London, part of the Faculty of Natural Sciences and the Stochastic Analysis research group. He has held academic roles including co-head of the Mathematical Finance group at Imperial (2012-2019) and previously led the Financial Mathematics group at King's College London. His research spans counterparty credit risk, funding costs, interest rate models, liquidity risk, and algorithmic trading. He has authored over 130 works and four influential books, including Interest Rate Models: Theory and Practice and Counterparty Credit Risk, Collateral and Funding . Education: PhD in Stochastic Filtering (Free University of Amsterdam, 1996) Laurea (BSc/MSc) in Mathematics cum laude (University of Padua) Research Interests: Focuses on valuation and pricing under funding constraints, credit risk, nonlinear valuation via PDEs/FBSDEs, and applications of stochastic processes and information geometry. Current work includes liquidity risk, default modeling, and differential geometric approaches to statistical manifolds. Awards: Most cited author in Risk Magazine (1998-2017) H-index 42 (2023) Key Contributions: Pioneered frameworks for Counterparty Credit Risk (CCR) with funding and collateral considerations. Developed the Counterparty Risk and Funding: A Tale of Two Puzzles model. Editorial roles include International Journal of Theoretical and Applied Finance and Mathematics of Control, Signals, and Systems. Labs/Teams: Co-director of the CFM-Imperial Institute of Quantitative Finance and collaborator with the Centre for Cryptocurrency Research and Engineering.
Thomas Courtade is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He joined Berkeley in 2014 after a postdoctoral fellowship at Stanford University, supported by the NSF Center for Science of Information. His research focuses on information theory, data science, and their intersections with machine learning and privacy-preserving algorithms. Education: Ph.D. in Electrical Engineering, University of California, Los Angeles (2012) M.Sc. in Electrical Engineering, University of California, Los Angeles (2008) B.Sc. in Electrical Engineering, Michigan Technological University (2007, summa cum laude) Research Interests: Information Theory and its applications to network communication Privacy-preserving data analysis and differential privacy Statistical estimation under heterogeneous privacy constraints Optimization in distributed systems and market design Functional inequalities (Brascamp-Lieb, Poincaré-Korn) Machine learning with emphasis on model robustness and efficiency Awards and Fellowships: Electrical Engineering Award for Outstanding Teaching (2020) Hellman Fellow (2016) Advising and Grants: Supervised no listed students (student names not provided in text) Recipient of NSF CAREER Award (2018) Labs and Collaborations: Berkeley Laboratory for Information and System Sciences (BLISS) Center for Theoretical Foundations of Learning, Inference, and Mathematics (CLIMB)
