Ajit Rajwade is a Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay. His research spans Artificial Intelligence , Compressed Sensing , and Medical Imaging , with affiliations to the Centre for Machine Intelligence and Data Science and the Koita Centre for Digital Health . Education : PhD in Computer and Information Science and Engineering, University of Florida (2010) MSc in Computer Science, McGill University (2004) BTech in Computer Engineering, University of Pune (2002) His research interests focus on intelligent data acquisition, particularly in neural network analysis , graph signal processing , and medical imaging . He develops compressed sensing algorithms for inverse problems like tomography and MRI reconstruction , alongside applying group testing to pandemic response. Scientific awards include the Prof. S. P. Sukhatme Award (2024) and Departmental Teaching Excellence (2019). His publications demonstrate expertise in image restoration , noise modeling , and epidemiological algorithms . He has advised PhD students like Sabyasachi Ghosh and Jerin Geo James , with a focus on computationally efficient methods in medical imaging and machine learning .
Sidi Wu is a Researcher affiliated with ETH Zürich's Institute of Cartography and Geoinformatics. Their primary role is as Staff of the Professorship for Cartography, contributing to academic research and technical operations within the department. They are based at HIL G 23.2, Stefano-Franscini-Platz 5 in Zürich, Switzerland, and can be reached at sidiwu@ethz.ch. Research interests center on advancing AI-driven cartographic methods, historical map analysis, and environmental spatial dynamics. Specific focuses include generative AI applications in map-making, semantic segmentation of historical documents, and leveraging digitized maps for ecosystem studies. They also explore steganography in image translation and cross-domain adaptation techniques for geospatial data. Recent work emphasizes innovations in automated map storytelling systems, spatio-temporal context modeling using transformers, and weakly supervised learning approaches for map segmentation. Their studies frequently bridge cartography with environmental science disciplines like hydrology and urban morphology. No scientific awards or grants are explicitly listed in the provided information. While no advisees are documented here, their research collaborations likely involve student contributions. They are part of the core team at the Institute of Cartography and Geoinformatics, contributing to cutting-edge projects in geomatics and computational cartography.
Ahmed El Alaoui is an Assistant Professor in the Department of Statistics and Data Science at Cornell University, with a secondary affiliation in the Department of Computer Science. He joined Cornell in 2021 after completing a postdoctoral fellowship at Stanford University under Andrea Montanari. His research focuses on high-dimensional statistics, probability theory, algorithms on random structures, and statistical physics, with particular emphasis on spin glasses and algorithmic thresholds. El Alaoui holds a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2018), advised by Michael I. Jordan, and a Master's from Ecole Normale Supérieure/Ecole des Ponts Paristech. His work bridges theoretical and applied domains, addressing challenges in high-dimensional inference, computational trade-offs, and probabilistic models. He has contributed to foundational results in random matrix theory, detection limits in spiked models, and algorithmic approaches to complex systems. His teaching includes courses on high-dimensional statistics, probability models, and theoretical computer science. He has been recognized for his innovative approaches to sampling and optimization in disordered systems, with publications in top journals like Annals of Probability and venues such as NeurIPS and FOCS. El Alaoui's research explores the interplay between statistical physics principles and algorithm design, aiming to uncover computational barriers in high-dimensional problems. His lab develops methodologies for analyzing complex systems and improving the efficiency of statistical estimation in challenging scenarios.
Ben Bloem-Reddy is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. His research focuses on statistical theory and applications in machine learning, particularly in causal inference, Bayesian methods, neural networks, and probabilistic models. He advises current students Quanhan (Johnny) Xi, Kenny Chiu, and Gian Carlo Diluvi. His work bridges foundational statistical theory with practical machine learning challenges, including causal discovery, model identifiability, and uncertainty quantification. Recent research explores topics such as latent variable models, generative processes, and symmetry in data and algorithms. His contributions span interdisciplinary areas like particle physics applications and information theory-based compression techniques. Ben’s research trends emphasize advancing theoretical guarantees for modern machine learning systems while addressing real-world problems. His publications frequently intersect with algebraic topology (e.g., cocycles in causal inference) and nonparametric methods. He maintains an active lab within the Department of Statistics, fostering collaborations across UBC’s academic ecosystem. No scientific awards are explicitly listed in the provided information. His advising and grant activities focus on statistical methodology development, as evidenced by his student supervision and published work. His office is located in ESB 3168, and he can be reached at benbr@stat.ubc.ca.
Assoc. Prof. Zehra Eksi-Altay holds a position at the Institute for Statistics and Mathematics at Vienna University of Economics and Business (WU). Her research focuses on financial mathematics, stochastic modeling, and partial information control problems in finance. She has expertise in credit risk modeling, derivatives pricing, and commodity markets. Eksi-Altay has a PhD in Financial Mathematics (2011) and completed her Habilitation in 2017. She has advised one doctoral thesis and has published extensively in top-tier journals like Quantitative Finance and Journal of Computational and Applied Mathematics . Her work bridges theoretical advancements with practical applications in areas such as regime-switching models, optimal portfolio strategies, and liquidity analysis. Education: BSc, MSc (2005), PhD (2011) Habilitation: 2017 Key Research Themes: Partial Information Models, Stochastic Control, Credit Risk, Algorithmic Trading Her recent work explores regime-switching affine term structures, optimal trading strategies under uncertainty, and dark pool liquidity analysis. Eksi-Altay has received one academic prize, though its specific name is not detailed in the provided text. Her contributions span both theoretical developments and applied finance, often collaborating with institutions like WU’s Institute for Statistics and Mathematics.
Karen Bandeen-Roche is a Professor and the Hurley-Dorrier Professor and Chair of the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, with joint affiliations in the School of Medicine and the School of Nursing. She is a leading expert in biostatistical methodology, particularly in latent variable models, longitudinal analysis, and multivariate survival methods applied to aging and gerontology. Her research focuses on developing statistical models for unobservable processes such as frailty, resilience, and functional status in older adults. She has made significant contributions to the measurement of aging-related constructs and has extensive collaborative work in ophthalmology and neurology. Her methodological work includes mixture models, measurement error correction, and latent class modeling. The recent publications highlight a strong trend in gerontological biostatistics, with a focus on frailty, dementia risk, resilience, and multisystem physiological responses in aging. Her work integrates complex data from observational cohorts and clinical studies, often employing innovative latent variable frameworks to address measurement challenges in health outcomes. Scientific Awards and Honors: Marvin Zelen Leadership Award in Statistical Science (2016) Fellow of the American Statistical Association (2001) Brookdale National Fellow (1997) Golden Apple Award for Excellence in Teaching (2010) Garland Clay Award (1999) Chair, NIH BMRD Study Section (2006–2008) President, Eastern North American Region, International Biometric Society (2011–2013) Executive Board, International Biometric Society (2015–2022) Board of Directors, National Institute of Statistical Sciences (2020–2023) Karen Bandeen-Roche has been deeply involved in advising and training the next generation of researchers. She co-directs a training program in Biostatistics and Epidemiology of Aging and has received multiple teaching and mentoring awards. She has served on numerous academic committees, including appointments and promotions, faculty senate, and ethics committees at Johns Hopkins. Her grants and collaborative research span aging, dementia, ophthalmology, and cardiovascular health, often supported by NIH and other federal agencies. She leads the Center on Aging and Health and is actively involved in interdisciplinary research initiatives that bridge biostatistics, medicine, and public health. Her lab and research team focus on developing and applying advanced statistical methods to understand the biological and social determinants of healthy aging.
Jessie P. Buckley, PhD, MPH is an Associate Professor in the Department of Epidemiology at the University of North Carolina Gillings School of Global Public Health. She serves as Director of Chemical Exposure Methodology for the NIH Environmental influences on Child Health Outcomes (ECHO) Program, focusing on data harmonization and pooled analyses of chemical exposures in children's health. Education: PhD in Epidemiology (UNC Chapel Hill, 2014) MPH in Environmental and Occupational Health (George Washington University, 2007) AB in Biology and English (Bowdoin College, 2002) Her research investigates environmental toxicants' health effects, particularly: Exposure assessment of environmental chemicals Methods for estimating effects of exposure mixtures Environmental influences on cardiometabolic and bone health Endocrine-disrupting chemicals Children's environmental health Recent publications analyze: PFAS exposure trends in adolescent bone health Chemical mixture effects using item response theory Perinatal exposure to melamine analogues Diet-exposure interactions in ultra-processed foods Scientific Awards: Teaching Innovation Award (UNC Gillings, 2025) NIEHS ONES Award (2019) Pediatric Loan Repayment Program Awards (2020, 2022, 2023) Dr. Buckley contributes to academic service as: Member of the PhD Admissions Committee (2024-present) Co-Chair of the North America Chapter, International Society of Environmental Epidemiology (2023-present)
Juho Lee is an Associate Professor at the Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST). Previously, he worked as a research scientist at AITRICS and completed his PhD in Computer Science & Engineering at Pohang University of Science and Technology (POSTECH) under Professor Seungjin Choi, followed by postdoctoral research at University of Oxford with Professor François Caron. His research focuses on: Bayesian deep learning Bayesian inference Meta learning Generative models Uncertainty quantification Graph representation learning Professor Lee's work bridges theoretical Bayesian methods with practical deep learning applications. His recent publications demonstrate significant contributions to neural processes, Bayesian optimization, and scalable inference methods. He has developed novel architectures like Set Transformer for permutation-invariant modeling and advanced techniques for uncertainty quantification in deep networks. His research shows a clear trajectory toward making Bayesian principles applicable to large-scale, real-world machine learning problems. Notable contributions include: Set Transformer: A framework for attention-based permutation-invariant neural networks (ICML 2019) Bootstrapping neural processes (NeurIPS 2020) Deep amortized clustering (NeurIPS 2019 workshop) Learning to pool in graph neural networks for extrapolation Professor Lee actively mentors graduate students and has advised numerous PhD candidates who co-author papers with him across NeurIPS, ICML, and ICLR. His research group SIML@KAIST develops scalable and interpretable machine learning methods with strong theoretical foundations.
Jessica J. Fridrich is a Distinguished Professor in the Department of Electrical and Computer Engineering at Binghamton University, part of the State University of New York (SUNY) system. She is affiliated with the T. J. Watson School of Applied Science and Engineering. Her research focuses on steganography, steganalysis, digital forensics, and machine learning, with notable contributions to secure data hiding and patented camera fingerprinting techniques approved for legal evidence. Education: PhD in Electrical and Computer Engineering from Binghamton University Her research interests include steganography and steganalysis of digital images, digital forensics for linking photos to cameras via sensor fingerprints, signal estimation and detection, and applications of machine learning. Earlier work explored chaotic nonlinear dynamical systems and encryption. Her methods have led to over 150 refereed publications and seven successfully commercialized patents. Her articles emphasize advancements in batch steganography, JPEG compatibility, and adaptive embedding strategies. Recent work leverages machine learning for steganalysis and explores security trade-offs in high-dimensional feature spaces. 2006-2007 Chancellor's Award for Excellence in Scholarship and Creative Activities 2002 Chancellor's Award for Outstanding Inventor Narrative on advising and grants: She mentors graduate students and leads projects funded by AFOSR, NSF, and AFRL. Her research addresses challenges in data hiding security, forensic analysis, and optimizing steganographic algorithms. The Digital Data Embedding Lab, which she directs, focuses on algorithmic innovation and empirical validation in steganography and forensics. Labs/Teams: Digital Data Embedding Lab
Charles Poole, ScD, is an Associate Professor in the Department of Epidemiology at the UNC Gillings School of Global Public Health. He specializes in epidemiologic methods, systematic reviews, and meta-analysis. His work emphasizes critical evaluation of research design and statistical interpretation in public health contexts. Education: ScD in Epidemiology, Harvard University (1989) MPH in Health Administration, UNC Chapel Hill (1976) BA in Natural Sciences, Johns Hopkins University (1973) Research focuses on epidemiologic concepts, risk assessment frameworks, and improving methodological rigor in public health studies. He has contributed to debates on statistical inference, including critiques of p-value overreliance and advocacy for nuanced evidence interpretation. Teaching includes courses on epidemiologic logic (EPID 705), quantitative methods (EPID 715), meta-analysis (EPID 731), and advanced epidemiologic methods readings (EPID 719). These courses emphasize critical thinking about study design and data interpretation. Recipient of the 2012 Inaugural Innovation in Teaching Award, recognizing his pedagogical approaches in epidemiology education. He currently chairs the Curriculum Subcommittee within the Gillings School.
Dr. Marion Schrumpf is a Group Leader in the Soil Biogeochemistry research group at the Max Planck Institute for Biogeochemistry, affiliated with the Department of Biogeochemical Processes. Her work focuses on soil carbon dynamics, mineral-organic matter interactions, and climate change impacts on soil systems. Research areas: Soil biogeochemistry, carbon cycling, mineral-soil interactions, nutrient stoichiometry, microbial ecology, and climate modeling. Email: mschrumpf@... Phone: +49 3641 57-6182 Office: B2.015 Her recent publications address themes like mineral control over soil carbon stabilization, drought effects on soil processes, microbial stoichiometric adaptation, and the Jena Soil Model's role in simulating carbon-nutrient interactions. She leads efforts to disentangle the complex relationships between land use, mineralogy, and soil organic matter turnover across diverse ecosystems.
Benjamin Bloem-Reddy is an Assistant Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on probabilistic approaches in statistics and machine learning, emphasizing symmetry, causality, and model-driven scientific knowledge acquisition. Prior to UBC, he completed his PhD under Peter Orbanz at Columbia University and a postdoc with Yee Whye Teh at the University of Oxford. He holds a physics background from Stanford and Northwestern Universities. Education: PhD in Statistics, Columbia University Postdoctoral Research, CSML Group, University of Oxford Physics degrees from Stanford and Northwestern Universities Research Interests: Bloem-Reddy explores symmetry in modeling and inference, causal discovery, and integrating scientific models with statistical frameworks. His work includes developing hypothesis tests for symmetry, causal inference via cocycles, and leveraging invariance properties in neural networks. He collaborates with scientists to apply statistical methods to domain-specific problems. Awards: Best Student Poster Award at NeurIPS 2014 Workshop on Networks Teaching & Advising: He teaches courses like STAT 460/560 (Statistical Inference) and advises a vibrant research group. His students include Boyan Beronov, Kenny Chiu, and Johnny Xi. He emphasizes recruiting curious, mathematically skilled students aligned with his research themes. Grants & Funding: Supported by NSERC, CANSSI, and UBC, with computational resources from ARC at UBC.
Yong Liu is an Assistant Professor in the Department of Agricultural Economics at Texas A&M University. His research focuses on applied econometrics, Bayesian methodologies, machine learning, and agricultural policy with emphasis on crop insurance and risk management. He holds a Ph.D. in Agricultural Economics from the University of Guelph (2016), preceded by dual M.S. degrees in Economics and Agricultural Economics from Yunnan University and the University of Guelph, respectively, and a B.S. in Chemistry from Yunnan University. His research explores econometric techniques for agricultural policy evaluation, including causal inference methods and behavioral economics applications. Recent work analyzes climate change impacts on crop yields, the efficacy of insurance subsidies, and the integration of weather data into risk assessment models. Liu’s publications demonstrate a strong focus on improving crop insurance mechanisms through statistical innovations like Bayesian model averaging and spatial-temporal forecasting. His academic contributions span theoretical advancements in density estimation and practical applications in agricultural finance. While no specific awards or grants are listed, his active publication record indicates sustained research engagement in agricultural economics and quantitative methods.
Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.
Mihai Marasteanu serves as Professor and Miles Kersten Chair in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, with affiliations at the Center for Transportation Studies. His research bridges fundamental material science and practical pavement engineering solutions for Minnesota's infrastructure network. His primary research focuses on asphalt pavement engineering , specializing in fracture mechanics and viscoelasticity applied to low-temperature cracking analysis. Key interests include recycled material integration , asphalt binder-mixture property relationships , and innovative testing methodologies for quality control. His work emphasizes cost-effective solutions for local roads while advancing predictive models for pavement performance. Recent publications (2022-2024) demonstrate strong trends in asphalt mixture optimization , probabilistic density modeling , and nanomaterial-enhanced asphalt (e.g., graphene nanoplatelets). The research consistently targets Minnesota-specific challenges including cold-climate durability, recycled material validation, and field-compaction efficiency. Professor Marasteanu maintains active funding through 9 current projects including: Tools to improve asphalt pavement durability (MN DOT, 2025-2027) Asphalt lift thickness impact on density (MN DOT, 2024-2026) Sawing/sealing joints for cracking control (MN DOT, 2023-2026) EV data for pavement quality assessment (FHWA, 2023-2025) His national leadership includes coordinating pooled fund studies with Wisconsin, Iowa State, and Illinois researchers on low-temperature cracking. While specific lab names aren't documented, his team operates within University of Minnesota's testing facilities, utilizing advanced rheometers and computational models to validate size-effect theories and representative volume element concepts for asphalt mixtures.
