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Researcher
Haobo Wang is a researcher affiliated with Zhejiang University , specifically within the School of Software Technology under the College of Computer Science and Technology . His work bridges Computer Science and Electrical Engineering , focusing on Machine Learning , Signal Processing , and Remote Sensing .
Assistant Professor
Prof. Jingjie Yeo is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University and the Principal Investigator of the J² Lab for Engineering Living Materials. His research integrates computational modeling, machine learning, and biomaterials to design sustainable, adaptive materials for healthcare and environmental applications. He is also a co-instructor at Station1, a nonprofit advancing socially-directed STEM education. Ph.D. and B.Eng., Nanyang Technological University, Singapore Postdoctoral training, Tufts University and Massachusetts Institute of Technology Research Scientist, Institute of High Performance Computing, Singapore Prof. Yeo’s research focuses on computational design of engineered living materials , particularly bacteria-based systems and biopolymers. His lab employs multiscale simulations —from quantum mechanics to continuum modeling—and integrates machine learning and materials informatics to accelerate discovery. Key areas include nanomechanics of mucus-bacteria interactions, mucoadhesive biomaterials, sustainable materials, and living materials for gut health and infrastructure. The lab pioneers AI-driven frameworks for materials-by-design, aiming to advance a "Materials 4.0" paradigm. The lab’s recent publications reveal a strong trend in AI-enhanced materials discovery , bio-inspired soft materials , and biomechanical modeling of biological systems . Themes include antimicrobial surfaces, solid electrolytes, hydrogels, and sustainable carbon materials. The work is highly interdisciplinary, combining physics, biology, and computation. Notable scientific awards include: NSF CAREER Award (2024) Dennis G. Shepherd Excellence in Teaching Award, Cornell College of Engineering (2023) ASME Rising Star in Mechanical Engineering Emerging Investigator, Journal of Materials Chemistry B (2020) Multiple NSF grants including EFRI and Convergence Accelerator awards Prof. Yeo has advised numerous PhD, MS, and undergraduate students , including the lab’s first PhD graduates, Drs. Haoyuan Shi and Tianjiao Li. His research is supported by NSF, Cornell, and international seed grants . He serves on editorial boards for STEM Education and International Journal of AI for Materials and Design , reflecting his dual commitment to research and education innovation. The J² Lab is part of the Cornell Engineered Living Materials Institute and collaborates with researchers at MIT, Montana State University, Zhejiang University, and others. The lab emphasizes interdisciplinary teamwork, computational rigor, and socially impactful science.
Professor
James Booth is a Professor and Department Chair in the Department of Statistics and Data Science at Cornell University, part of the Computing and Information Science school. He holds a joint appointment with the Department of Biological Statistics and Computational Biology in the College of Agricultural and Life Sciences. His research focuses on statistical methodology, including bootstrap methods, clustering, mixed models, and applications in bioinformatics. He has taught numerous courses, including Statistical Methods II and Biological Statistics I, and contributes to Cornell's statistical consulting service. His work spans statistical theory and applications across disciplines like epidemiology and social sciences. Education: PhD from the University of Florida (Department of Statistics), with postdoctoral research at the Australian National University and Colorado State University. Research Interests: James’ methodological work emphasizes computational statistics, including Monte Carlo methods and generalized linear models. His applied research includes bioinformatics, wildlife disease surveillance, and social science data analysis. Key contributions include Bayesian modeling for disease prevalence estimation and statistical tools for proteomic data analysis. Publications: Over 50 peer-reviewed articles, with recent focuses on sample size calculations for wildlife disease studies and statistical methodologies for high-dimensional data. Notable work includes contributions to the Journal of the American Statistical Association and PLoS Computational Biology. Advising: Advised over 15 graduate students, many contributing to statistical methodology and interdisciplinary applications. Labs/Teams: Active in the Cornell Statistical Consulting Unit and collaborates with interdisciplinary groups in biology, public health, and social sciences.
Professor
Prof. Wolfgang Utschick is a full Professor at the Technische Universität München (TUM), holding the professorship for Methods of Signal Processing since 2002. He is affiliated with the TUM School of Computation, Information and Technology and the Department of Electrical Engineering and Information Technology (EI). Since 2017, he has served as Dean of the EI Department, overseeing academic and research activities. His research integrates applied mathematics into signal processing applications across wireless communications, radar technology, vehicle safety, and machine learning. Prof. Utschick earned his doctorate after industry experience and completed postdoctoral studies at TUM and ETH Zurich. He has led numerous industrial and DFG-funded projects, resulting in patents in signal processing and over 150 peer-reviewed publications. Notable contributions include advancements in channel estimation, MIMO systems, and quantum radar technology. His research interests emphasize the intersection of mathematical theory and practical engineering, with a focus on optimizing communication systems and safety-critical applications. Awards include IEEE Fellow (2021), TUM Sabbatical for Teaching Excellence (2014), and multiple IEEE publication awards. Prof. Utschick’s work bridges academia and industry, addressing challenges in 5G/6G networks, radar innovation, and machine learning-driven signal processing. He remains active in curriculum development and departmental leadership at TUM.
Professor
Meng Wang is a Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where she was promoted to Full Professor in June 2025. She received her B.S. and M.S. degrees (both with honors) in Electrical Engineering from Tsinghua University, China, in 2005 and 2007, respectively, and her Ph.D. in Electrical and Computer Engineering from Cornell University in 2012. After a postdoctoral position at Duke University, she joined RPI in December 2012 as an Assistant Professor, was promoted to Associate Professor with Tenure in 2019, and then to Full Professor in 2025. Her research spans machine learning and artificial intelligence, high-dimensional data analytics, power system monitoring, signal processing, and optimization methods. She has made fundamental contributions in sparse signal recovery and monitoring and control of smart grid using high frequency data from phase measurement unit (PMU). More recently, she has collaborated with IBM to produce theoretical guarantees of modern AI architectures such as graph neural networks and transformers used in large language models (LLMs). Wang's recent publications (2023-2025) reveal a strong focus on theoretical foundations of deep learning, particularly transformer architectures and graph neural networks. Her work bridges theoretical guarantees with practical applications in power systems, demonstrating how fundamental insights in machine learning can solve real-world energy challenges. She has increasingly focused on the intersection of AI and energy systems, developing methods for building-level load forecasting, energy disaggregation, and smart grid monitoring with behind-the-meter solar integration. AFOSR Young Investigator Program (YIP) Award (2019) Army Research Office (ARO) YIP Award (2017) James M. Tien '66 Early Career Award and Grant for Faculty (2022) School of Engineering Research Excellence Award (2018) IEEE Signal Processing Society Best Reviewer Award (2018) Professor Wang has mentored numerous Ph.D. students who have gone on to successful careers in academia and industry, including HongKang Li (now postdoc at University of Pennsylvania), Yi Ming (postdoc at University of Michigan), and Shuai Zhang (Assistant Professor at New Jersey Institute of Technology). Her research has been supported by multiple grants from the National Science Foundation, Air Force Office of Scientific Research, Army Research Office, and industry partners including IBM. She is actively involved with research centers including the Center for Future Energy Systems (CFES) and the Center for Materials, Devices, and Integrated Systems (CMDIS), where her group develops cutting-edge methods for power system monitoring and control. Her recent work has increasingly focused on the theoretical foundations of large language models and their applications to energy systems, positioning her at the forefront of AI for critical infrastructure.
Assistant Professor
Ilias Zadik is an Assistant Professor at Yale University's Department of Statistics and Data Science. His research focuses on computational-statistical trade-offs in modern machine learning, high-dimensional statistics, and probability theory. He has held postdoctoral positions at MIT (2021-2023) and NYU (2019-2021), and earned his PhD from MIT (2019), advised by David Gamarnik. He teaches courses like Stochastic Processes and has contributed to numerous conferences and workshops. His awards include MIT's Best Student Paper Honorable Mention (2017) and scholarships from Trinity College and the Onassis Foundation. Education: PhD in Operations Research, MIT (2019) MASt in Mathematics (Part III), University of Cambridge (2014) BA in Mathematics, University of Athens (2013) Internship at Microsoft Research New England (2017) Research Interests: Computational-statistical trade-offs, phase transitions in inference (e.g., All-or-Nothing phenomena), cryptographic methods in statistics, privacy-cost analysis, and algorithmic lower bounds. Awards: MIT Operations Research Best Student Paper Honorable Mention (2017) Trinity College Senior Scholarship (2014) Onassis Foundation Scholarship (2013-2014) SEEMOUS Gold Medal (2011), IMC First Prize (2011) Teaching & Service: Instructor for Yale's S&DS 351 (Stochastic Processes) and advanced courses on computational-statistical trade-offs. Co-organized the MaD+ seminar during the pandemic. Served on program committees for COLT, NeurIPS, and FOCS. Labs/Teams: Active in MIT's NSF/Simons Collaboration on Theoretical Foundations of Deep Learning and NYU's Math and Data group.
Professor
Joseph Camp is a Professor of Comparative Pathobiology and Secretary of Faculties at Purdue University. His work spans interdisciplinary areas including machine learning, robotics, and human-robot interaction. He leads the Global Engineering Program and holds affiliations across technical and administrative roles. His research focuses on reinforcement learning, multi-agent systems, and applying large language models to enhance decision-making and action anticipation in robotics and healthcare contexts. Key research interests include neuro-symbolic systems for short-context action prediction, robust reinforcement learning under noisy feedback, and geometric reasoning for zero-shot robotic grasping. His work also explores disentanglement in concept-residual models and transfer learning in multi-agent environments. He has published extensively on topics ranging from probabilistic imitation learning to bio-inspired robot locomotion design. While no specific awards or grants are listed, his contributions to human-robot interaction (HRI) and AI-driven medical devices reflect a strong focus on practical applications. His lab’s recent work integrates large language models with traditional robotics algorithms to improve adaptability in dynamic environments. No advising relationships or student names are explicitly mentioned in the provided texts.
Associate Professor
Dr. Andrew Wynn is an Associate Professor in the Department of Aeronautics at Imperial College London, Faculty of Engineering. His research focuses on developing computational optimization methods to improve fluid mechanical systems, including active flow control, data-driven modelling, aeroservoelasticity, and semi-algebraic optimization techniques. He leads a research group addressing challenges in fluid-structure interactions, turbulence, and control systems. Wynn's affiliations include the Flow Control Research Group and the Aeroelastics Group. His work intersects interdisciplinary fields such as mechanical engineering, applied mathematics, and aerospace engineering. Key research topics include bluff body drag reduction, scaling laws for fluid flows, and estimator design for high-dimensional systems. Publications highlight contributions to fluid mechanics, optimization algorithms, and control theory. His group employs methods like semidefinite programming and dynamic mode decomposition to analyze and control complex flows. Ongoing projects involve wind farm optimization, global stability of viscoelastic fluids, and Bayesian optimization for experimental fluid dynamics. Education: Not explicitly stated in the provided text. Grants & Awards: Affiliated with Imperial College Research Fellowships (ICRF) and President's PhD Scholarships programs. Labs/Teams: Leads the Wynn Research Group, collaborating with the Flow Control and Aeroelastics Groups.
Professor
Thomas Y. Hou is the Charles Lee Powell Professor of Applied and Computational Mathematics at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. He holds a B.S. from South China University of Technology (1982), M.S. and Ph.D. from UCLA (1985-1987) under Prof. Bjorn Engquist. His research focuses on multiscale analysis, fluid dynamics, singularity formation in Euler/Navier-Stokes equations, and data analysis. Key roles include Executive Officer of Applied & Computational Math (2000-2006), and editorships of SIAM Journal on Multiscale Modeling and Simulation , Advances in Adaptive Data Analysis , and Research in Mathematical Sciences . He teaches ACM/EE 106ab: Introductory Methods of Computational Mathematics and oversees the Computing and Mathematical Sciences Colloquium. His work on singularity formation has led to groundbreaking studies in 3D Euler equations and Navier-Stokes systems, using rigorous numerics and multiscale methods. He has pioneered exponential convergence techniques in multiscale finite element methods and developed sparse time-frequency analysis for nonlinear data. Honors include the 2024 William Benter Prize, SIAM Kleinman Prize (2023), membership in the National Academy of Sciences (2024), and the 2004 Morningside Gold Medal in Applied Mathematics. His research group explores fluid dynamics, turbulence, and mathematical modeling in biological systems like keratocyte evolution. Collaborations include studies on Kolmogorov-Arnold networks (KAN) for high-dimensional data and Bayesian inference frameworks.
Assistant Professor
Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).
Professor
Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it
Professor
Hans Julius Skaug is a Professor in the Department of Mathematics at the University of Bergen (UiB), Norway, with a distinguished career spanning several decades focused on statistical methodology and its applications to biological and ecological problems. Dr. Skaug's research expertise encompasses several interconnected domains: Biostatistics, particularly applications of statistics and probability to marine ecology Computational statistics, with pioneering work on Automatic Differentiation and Laplace approximation for complex model fitting Artificial Intelligence, where he has recently focused on variational autoencoders and diffusion models Development of innovative methods for line transect surveys and close-kin mark recapture (CKMR) His publication record shows a clear progression from foundational statistical methodology to increasingly sophisticated applications. The 2016 paper with Bravington and Anderson on 'Close-kin mark-recapture' in Statistical Science established him as a leader in population estimation methods. Recent publications (2023-2025) demonstrate continued innovation in refining CKMR techniques, applying TMB to diverse fields like insurance claims analysis, exploring sparse Bayesian learning, and addressing measurement errors in marine mammal surveys. His work consistently bridges theoretical statistical development with practical applications to real-world conservation and management problems. Dr. Skaug served as co-Editor in chief of the Scandinavian Journal of Statistics from 2018 to 2021, demonstrating his significant standing in the international statistical community. He teaches STAT110 Basic course in statistics at UiB during both spring and fall semesters and has conducted specialized workshops on CKMR and TMB at institutions including Dalhousie University in Halifax and the Institute of Marine Research in Bergen. He is actively involved in software development for statistical modeling through the TMB project (https://github.com/kaskr/adcomp) and ADMB (http://admb-project.org/), which implement his theoretical work on combining Automatic Differentiation with statistical modeling. His current research trajectory shows increasing integration of AI techniques with traditional statistical approaches, reflecting his observation that backpropagation in deep learning is fundamentally the same computational technique as Automatic Differentiation, which he has worked with for over 20 years.
Associate Professor
Valeria Vitelli is an Associate Professor in Statistics at the Oslo Centre for Biostatistics and Epidemiology, University of Oslo, where she has been faculty since 2018. Her research spans high-dimensional and functional data analysis, Bayesian methods, clustering, and preference learning. As a Principal Investigator, she leads projects within the Norwegian Centre for Knowledge-driven Machine Learning (Integreat), funded by the Norwegian Research Council. Education: PhD in Statistics, Politecnico di Milano, Italy (2012) MSc in Mathematical Engineering (cum laude), Politecnico di Milano, Italy (2008) Bachelor in Mathematical Engineering (cum laude), Politecnico di Milano, Italy (2006) Dr. Vitelli's research focuses on developing statistical methods for complex high-dimensional data, with applications spanning biomedical research, cancer genomics, and precision medicine. Her work integrates Bayesian inference, machine learning, and functional data analysis to address challenges in modern biostatistics. She has made significant contributions to ranking models, clustering algorithms, and methods for analyzing curves and intrinsically smooth processes. Her publication record demonstrates strong interdisciplinary collaboration across medicine, particularly in cancer research, ophthalmology, and neurology. Recent work shows a clear trend toward developing novel statistical frameworks for integrating complex datasets in precision medicine applications, with emphasis on Bayesian approaches and high-dimensional data analysis. Scientific Recognition: Principal Investigator for the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) Awarded major FRIPRO grants from the Research Council of Norway Dr. Vitelli actively mentors students and researchers in statistical methodology development. Her grant portfolio includes significant funding from the Norwegian Research Council for projects focused on Bayesian clustering methods for high-dimensional omics data. She teaches courses including "Introduction to Machine Learning in Biomedical Research" and "Statistical Principles in Genomics" for medical students and PhD candidates. She leads a research group focused on statistical models for high-dimensional and functional data within the Oslo Centre for Biostatistics and Epidemiology, collaborating closely with medical researchers across various specialties to develop and apply advanced statistical methods to biomedical challenges.
Professor
Augustin Kelava is a Professor at the Department of Quantitative Methods, Eberhard Karls University of Tübingen. He has held this position since 2018 and leads the Methods Center as Managing Director. Previously, he was Professor at the Hector Institute for Empirical Educational Research (2013-2018) and Junior Professor at Technical University of Darmstadt (2011-2013). PhD in Psychology (Goethe University Frankfurt, 2009) Diploma in Psychology (Goethe University Frankfurt, 2004) Kelava specializes in latent variable modeling, machine learning in social sciences, and educational research. His work spans dynamic latent class models, Bayesian regularization techniques, and prediction of human behavior using intensive longitudinal data. He contributes to psychometric theory (e.g., item response theory extensions) and applies these methods to diverse fields including sports science and emotion regulation. Editor of "Testtheorie und Fragebogenkonstruktion" (3rd ed., Springer, 2020) Key researcher in the Cluster of Excellence "Machine Learning in Science" Active in methodological conferences (FGME 2017, SEM 2019) Review activities for 20+ journals and foundations including Psychometrika, DFG, and SNSF His recent publications focus on integrating machine learning with psychometrics, addressing identifiability in complex models, and evaluating personality assessment validity for large language models. He collaborates with researchers across psychology, education, and computational fields.
Professor
Dr. Victor Hugo Lachos is a Professor in the Department of Statistics at the University of Connecticut. His research focuses on advanced statistical methodologies for handling complex data structures, including censored regression models, mixed-effects models, and heavy-tailed distributions. He has contributed extensively to Bayesian inference, EM algorithms, and software development for statistical analysis. Multivariate Student-t and skew-normal distributions Longitudinal and spatial data modeling Regularization techniques for high-dimensional data Software packages for censored data analysis His recent publications emphasize robust modeling of censored and irregularly observed data, with applications in medical research (e.g., HIV longitudinal studies) and environmental modeling (e.g., acid rain analysis). His work integrates theoretical advances in distribution theory with practical computational tools in R packages like ‘StempCens’ and ‘mixsmsn’. These contributions are complemented by methodological innovations in EM algorithm applications, influence diagnostics, and semiparametric regression. Dr. Lachos' research has been applied to diverse fields such as medical data analysis, environmental science, and educational measurement. While no explicit awards or student advisement details are listed, his prolific output in top-tier journals and software development underscores his active academic engagement.
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