Edriss S. Titi is a University Distinguished Professor and Arthur Owen Professor of Mathematics at Texas A&M University within the College of Arts & Sciences. His research focuses on nonlinear partial differential equations, applied mathematics, and geophysical fluid dynamics. He leads studies on fluid mechanics, atmospheric and oceanic dynamics, data assimilation, and control theory. His work often addresses mathematical rigor in modeling complex systems like climate dynamics and turbulent flows. Research Interests: Nonlinear PDEs and their applications Fluid dynamics and turbulence Data assimilation algorithms Climate and ocean modeling Infinite-dimensional dynamical systems Recent publications emphasize Navier-Stokes equations , primitive equations , and data assimilation in chaotic systems . His methodologies bridge theoretical analysis and computational modeling, with applications to weather prediction and geophysical flows. Collaborations include the Institute for Applied Mathematics and Computational Science (IAMCS) at Texas A&M. Notable contributions include rigorous analysis of global well-posedness for oceanic models and development of CDAnet, a physics-informed deep learning framework for fluid flow downscaling.
Chen Wei Wayne is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. His research focuses on generative design AI, machine learning, uncertainty quantification, and advanced manufacturing. He leads the DIGIT Lab, which develops AI methods for design innovation, automation, and manufacturing integration. Education: Ph.D., Mechanical Engineering, University of Maryland, College Park (2019) M.S., Mechanical Engineering, Chongqing University, China (2015) B.S., Mechanical Engineering, Chongqing University, China (2012) Research Interests: Generative adversarial networks (GANs) for design synthesis Data-driven metamaterials and multiscale systems Uncertainty quantification in engineering design AI-driven design automation Awards & Honors: ASME Journal of Mechanical Design Reviewer of the Year Award (2023) ASME DAC Best Paper Award (2022) Journal of Mechanical Design Editors’ Choice Honorable Mention (2021) Lab Activities: Recent lab milestones include successful completion of TAMUQ Summer Research Programs (2024) Hosts undergraduate researchers like Wisam Gadam and Eddie Guerrero
Valen E. Johnson is a University Distinguished Professor and Dean Emeritus of the College of Science at Texas A&M University, where he has been a faculty member since 2012. He previously held professorships at the University of Texas M. D. Anderson Cancer Center (2004–2012), the University of Michigan (2002–2004), and Duke University (1989–2001). He also served as a Technical Staff Member at Los Alamos National Laboratory (2001–2002). Johnson earned his Ph.D. in Statistics from the University of Chicago (1989), M.A. in Applied Mathematics from the University of Texas at Austin (1985), and B.S. in Mathematics from Rensselaer Polytechnic Institute (1981). His research focuses on Bayesian methodology, including hypothesis testing, variable selection in high-dimensional spaces, latent variable models, and applications in medical imaging, clinical trials, and educational assessment. He has contributed to the development of non-local prior densities and Bayesian diagnostics for MCMC convergence. His work bridges Bayesian and classical statistical approaches, emphasizing reproducibility and rigorous evidence assessment in scientific research. Johnson has held editorial roles, including Co-Editor of Bayesian Analysis (2010–2014) and Associate Editor of the Journal of the American Statistical Association (2011–present). He is a Fellow of the American Statistical Association and the Royal Statistical Society and has served on the Board of Directors of the International Society for Bayesian Analysis. His advocacy for revised statistical significance standards has sparked major debates in scientific methodology. Johnson has supervised numerous doctoral students, including those whose theses won prestigious awards like the Savage Award. He has collaborated on grants addressing medical imaging, system reliability, and cancer symptom management, reflecting his interdisciplinary impact in biostatistics and public health.
Daryl Fougnie is an Associate Professor of Psychology and Global Network Associate Professor at New York University Abu Dhabi. He is a core faculty member in the Department of Psychology within the College of Arts and Science, actively conducting research and teaching undergraduate courses in cognitive psychology. His research focuses on the fundamental limits of human cognition, particularly in visual perception, attention, and memory. Through psychophysics and computational modeling, the Fougnie Lab investigates how the brain processes and retains visual information, exploring questions about capacity limits, the nature of mental representations, and the interaction between attention and memory. Key areas include visual working memory, attentional selection, retro-cueing, and memory-guided behavior. The 15 most recent publications reflect a strong thematic focus on the probabilistic and resource-constrained nature of working memory, the strategic control of attention, and the neural and cognitive mechanisms underlying perception and memory. Research often employs innovative paradigms such as betting games and reproduction tasks to probe internal representations and decision-making processes. NYUAD Research Enhancement Fund NYUAD Research Enhancement Fund Daryl Fougnie mentors undergraduate students in the Fougnie Lab and supervises capstone research projects in psychology. His lab is actively funded, as evidenced by repeated Research Enhancement Fund awards, and collaborates widely with researchers such as Kartik Sreenivasan, Clay Curtis, and Edyta Sasin. The lab is currently recruiting postdoctoral researchers, indicating ongoing and expanding research activity. The Fougnie Lab at NYU Abu Dhabi serves as a hub for cognitive science research, integrating experimental psychology with computational approaches. It provides research opportunities for undergraduates and fosters collaboration across disciplines, contributing significantly to the understanding of human cognitive architecture.
Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.
Professor Ben Liang is a faculty member at the Department of Electrical and Computer Engineering , University of Toronto, holding the L. Lau Chair . He has served on editorial boards of IEEE Transactions on Mobile Computing , IEEE Transactions on Wireless Communications , and Wiley Security and Communication Networks . His research focuses on networked systems , mobile communications , and distributed machine learning , with applications in wireless network virtualization, edge computing, and resource optimization. He explores stochastic scheduling , computation-communication co-design , and multi-resource fair allocation . Key publication trends: Wireless federated learning (2024-2025) Network virtualization and MIMO systems (2022-2025) Online distributed optimization (2023-2025) Stochastic resource management (2020-2024) Scientific Awards: Fellow of IEEE Best Paper Award, ACM MSWiM 2013 INFOCOM 2010 Finalist Ontario ERA Award 2007 IFIP Networking 2005 Best Paper Intel Foundation Graduate Fellowship 2000 Polytechnic University valedictorian 1997 Affiliated with IEEE , ACM , and Tau Beta Pi , he teaches courses like ECE368: Probabilistic Reasoning and ECE421: Machine Learning , emphasizing stochastic networks and random processes .
Amrita Basak serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at Pennsylvania State University. Her research focuses on advancing metal additive manufacturing technologies, particularly for gas turbine applications. She maintains her laboratory in 233 Reber Building at University Park, PA. Her primary research interests center on laser-based additive manufacturing processes including Laser Powder Bed Fusion (L-PBF) and Laser Directed Energy Deposition (LDED). Specific expertise spans nickel-based superalloys, melt pool dynamics, microstructure-property relationships, fatigue behavior of additively manufactured components, and AI-driven process optimization. Her work addresses critical challenges in thermal distortion control, surface roughness effects, and high-temperature performance of turbine components. Analysis of her recent publications reveals strong emphasis on integrating machine learning with experimental methods to optimize additive manufacturing processes. Key trends include Gaussian process regression for melt pool modeling, Bayesian optimization for thermal management, reinforcement learning for parameter control, and multi-fidelity modeling approaches. Her research bridges fundamental materials science with practical engineering applications in aerospace and energy sectors. Scientific Awards: NSF CAREER Award (2024) for gas turbine research DARPA Young Faculty Award (2022) for multi-laser additive manufacturing Materials Research Institute Roy Award (2023) Professor Basak actively mentors graduate students including R. Pal, N. Menon, and A. Kushwaha who appear as first authors on multiple publications. Her research is supported by significant grants including NSF CAREER funding, Office of Naval Research grants (2024), and DARPA funding. Current projects include 'On-Demand 3D Printing of Food-Grade Biopolymer-Encapsulated Ferrate(VI) for Individualized and Equitable Access to Drinking Water' and metal additive manufacturing research for gas turbine hot section components.
Vaishnav Krishnan, M.D., Ph.D., is an Associate Professor at Baylor College of Medicine with joint appointments in the Department of Neurology, Neuroscience, and Psychiatry and Behavioral Sciences . He also holds an Adjunct Associate Professor position in Electrical and Computer Engineering at Rice University. As a physician-scientist, he leads the Laboratory of Epilepsy and Emotional Behavior , focusing on the neurobiological mechanisms linking epilepsy to psychiatric comorbidities. Education: BS in Neuroscience and Chemistry, New York University (2003) MD-PhD in Behavioral Neuroscience and Molecular Neuroplasticity, UT Southwestern Medical Scientist Training Program (2010) Internship and Residency in Internal Medicine and Adult Neurology, Parkland Memorial Hospital (2011) and Beth Israel Deaconess Medical Center (2014) Postdoctoral Fellowship in Seizures and Autism-Related Behavior, Beth Israel Deaconess Medical Center (2016) His research employs genetically valid mouse models and home-cage behavioral monitoring to study interictal behavioral derangements, using EEG and wearable devices to translate findings to humans. His work has been supported by grants from NINDS , the American Epilepsy Society , Gulf Coast Center for Precision Health , and the Mike Hogg Fund . He has received awards such as the NINDS K08 Career Development Award and American Epilepsy Society Fellow . The Krishnan Lab has published extensively on epilepsy spectrum disorders , circadian and ultradian rhythms , and seizure prediction algorithms , with recent studies in Journal of Comparative Neurology , Brain Communications , and Epilepsia . He collaborates with epilepsy-focused labs, including those of Dr. Jeffrey Noebels and Dr. Berge Minassian. Scientific Awards and Recognitions: Outstanding Resident Teaching Award (Harvard Medical School, 2013) NINDS R25 Award (2014-2015) Clinical Research Training Fellowship in Epilepsy (American Academy of Neurology, 2016-2018) Mentored Clinical Scientist Research Career Development Award K08 (NINDS, 2019-2024) Junior Investigator Award (American Epilepsy Society, 2020-2021) Mike Hogg Fund Award (2021-2022) Gulf Coast Center for Precision Health Pilot Award (2022-2023) Fellow of the American Epilepsy Society (2022) Lab Members and Trainees: The lab includes current researchers like Medical Students Vanuli Arya and Anna Norman , Research Technician Saifina Karedia , and Postdoctoral Associate Arindam Mazumder . Former trainees include Paarth Kapadia (MD, BCM 2023) and Anney Tuo (Rice B.S., now in medical school), among others pursuing careers in medicine, research, and engineering.
Zeda Li is an Assistant Professor of Statistics at the Paul H. Chook Department of Information Systems and Statistics in the Zicklin School of Business at Baruch College, CUNY. She holds a PhD in Statistics from Temple University (2018) and advanced degrees in Biostatistics and Electrical Engineering.
John D. Norton is a Distinguished Professor in the Department of History and Philosophy of Science (HPS) at the University of Pittsburgh, where he has been a faculty member since 1983. He served as Chair of the department from 2000 to 2005 and as Director of the Center for Philosophy of Science from 2005 to 2016. His work bridges the history and philosophy of physics, with deep engagement in Einstein’s relativity, quantum theory, statistical mechanics, and foundational issues in scientific reasoning. His educational background includes a PhD in the School of History and Philosophy of Science from the University of New South Wales (1982) and a Bachelor of Engineering in Chemical Engineering from the same institution (1974). Before transitioning to philosophy, he worked as a technologist at the Shell Oil Refinery in Sydney. Norton is renowned for his development of the material theory of induction , which challenges formalist approaches by asserting that inductive inferences are warranted by domain-specific facts rather than universal logical rules. He has also made significant contributions to the philosophy of spacetime, particularly through his analysis of the hole argument , and has published extensively on thought experiments, causation, and the thermodynamics of computation. His recent publications (2021–2025) reflect a sustained focus on induction, spacetime ontology, and the limits of thermodynamic reversibility and information processing. Key themes include the critique of Bayesianism, the historical development of thermodynamics, and the philosophical implications of time travel models in general relativity. His two major books— The Material Theory of Induction (2021) and its sequel The Large-Scale Structure of Inductive Inference (2024)—form a comprehensive philosophical framework for understanding scientific reasoning beyond formal logic. Co-Founder and Executive Committee Member, philsci-archive.pitt.edu Editor for Philosophy of Physics (Space and Time, General Physics), Stanford Encyclopedia of Philosophy Contributing Editor, Archive for History of Exact Science (1996–present) Associate/Co-Editor, Studies in History and Philosophy of Modern Physics Contributing Editor, Collected Papers of Albert Einstein , Volumes 3 and 4 Norton has advised numerous graduate students and has been instrumental in shaping the academic landscape of philosophy of science through editorial leadership and archival initiatives. His teaching includes graduate seminars on confirmation theory and the popular undergraduate course Einstein for Everyone , for which he maintains a freely available online textbook.
Weifeng Li serves as an Associate Professor in the Department of Management Information Systems at the University of Georgia's Terry College of Business. His academic foundation includes a Ph.D. in Management Information Systems from the University of Arizona (2017) and a B.S. from Shanghai Jiao Tong University (2012). Ph.D., Management Information Systems, University of Arizona (2017) B.S., Management Information Systems, Shanghai Jiao Tong University (2012) Dr. Li's research centers on AI security and cybersecurity applications , with methodological expertise in machine learning, natural language processing, and Bayesian modeling. His work spans critical domains including adversarial robustness in AI systems, dark web threat intelligence, disinformation detection, and phishing defense mechanisms. He develops frameworks for proactive cyber defense through generative adversarial learning and interpretable multi-modal models. His publication portfolio reveals a strong trajectory in top-tier venues, with recent work focusing on adversarial robustness (RADAR framework), dark web community analysis, and interpretable AI for security applications. Research consistently bridges theoretical machine learning advances with practical cybersecurity implementations, particularly in financial technology and social media contexts. Dr. Li's research has received funding from the National Science Foundation's Secure and Trustworthy Cyberspace (SaTC) program, supporting his work on AI security frameworks. His collaborations span multiple institutions and research groups focused on cyber threat intelligence. He contributes to cybersecurity infrastructure through systems like the AZSecure text mining platform for dark web monitoring and hacker community analysis. His work enables proactive threat detection through nonparametric topic modeling and generative adversarial approaches to counter cybercriminal tactics.
Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.
Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.
Hugo Paquet is a Researcher at INRIA Paris and a member of the ANTIQUE team at École Normale Supérieure , PSL University. He completed a PhD in Computer Science (2015–2019) at the University of Cambridge under Glynn Winskel , focusing on concurrent game semantics for probabilistic programming. His postdoctoral work includes positions at LIPN, Paris (2022–2024, funded by a Marie Skłodowska-Curie Award) and University of Oxford (2020–2022). He has contributed to conferences including LICS , ESOP , FSCD , and POPL . Education : PhD in Computer Science (University of Cambridge, 2019) Research Interests : Probabilistic programming (semantics, inference algorithms, nonparametric models), categorical semantics (game semantics, concurrency models, adjunctions), combinatorial species, and 2-dimensional categories. Teaching : Category Theory (2023–2024), Bayesian Statistical Probabilistic Programming (2021–2022), Lambda-calculus and Types (2020–2021), and small-group teaching at Cambridge (Logic, Discrete Mathematics, Semantics). Awards : Marie Skłodowska-Curie Award under the Paris Region Fellowship Programme Labs : INRIA Paris, ANTIQUE team (2024–present)
Cristina Butucea is a Professor of Statistics and Machine Learning at ENSAE-CREST, Institut Polytechnique de Paris . She was nominated an IMS Fellow (2019) for her contributions to nonparametric and high-dimensional statistics. She has co-organized major conferences like Fréjus 2018 , Luminy 2019-2020 , and Oberwolfach 2021 , and serves as Associate Editor for ALEA . Fields of interest: Nonparametric statistics, quantum statistics, differential privacy, inverse problems, machine learning. Awards: IMS Fellowship, conference organization leadership. Research trends: Focuses on optimal estimation under privacy constraints, quantum state reconstruction, high-dimensional inference, and adaptive nonparametric methods. Email: Cristina.Butucea@ensea.fr | Cristina.Butucea@ip-paris.fr
