Takeshi Ikenaga is a Professor at Waseda University’s School of Fundamental Science and Engineering and Graduate School of Information, Production and Systems . He earned his Ph.D. in Information & Computer Science from Waseda University in 2001, following B.E. and M.E. degrees in Electrical Engineering (1988–1990). His career spans roles at NTT LSI Laboratories (1990–2002), Kitakyushu Foundation for Advancement of Industry, Science and Technology (FAIS) (1999–2002), and visiting researcher at the University of Massachusetts (1999–2000). Research Interests : Application-specific SoCs for video/image processing, including compression (H.264/AVC, H.265/HEVC), filters (super-resolution, noise reduction), recognition systems (feature detection, object tracking), and communication (UWB, LDPC). He also works on many-core processor design, ultra-low-delay vision systems, and sports analytics (volleyball, figure skating) with real-time 3D pose estimation and ball tracking. Awards : Recipient of the Furukawa Sansui Award (Waseda University, 1988) IEICE Research Encouragement Award (1992) Multiple Best Paper/Presentation Awards (2006–2022) at conferences including DAC/ISSCC, LSI IP Design, ISOCC, ISPACS, and CVIT APSIPA Distinguished Lecturer Certificate (2015) Waseda University Presidential Teaching Award (2020)
Professor Alexandra M. Schmidt is a leading academic in Biostatistics at McGill University, holding an endowed University Chair. She specializes in spatial and spatio-temporal modeling, particularly in epidemiology and environmental health. Previously, she served as a Full Professor at the Federal University of Rio de Janeiro (2012–2016). Her research focuses on Bayesian methodologies for analyzing complex processes, including disease spread, environmental hazards, and socio-economic disparities. She has authored influential books such as Spatio-Temporal Methods in Environmental Epidemiology with R (2023) and contributed to over 150 peer-reviewed articles. Key awards include the ISBA Fellowship (2024), ASA Fellowship (2020), and the Abdel El-Shaarawi Award (2008). Education: PhD in Statistics (2001, University of Sheffield, UK), MSc and BSc in Statistics (Federal University of Rio de Janeiro, Brazil). Research interests span Bayesian inference, spatial statistics, and environmental epidemiology. She has advised numerous PhD/MSc students and collaborated on projects linking statistical methods to public health challenges, such as modeling dengue outbreaks and air pollution impacts. Active in academic service, she has chaired major conferences (e.g., 2022 ISBA World Meeting) and serves on editorial boards of top journals like Bayesian Analysis and Canadian Journal of Statistics . Teaching includes advanced courses on generalized linear models, spatial epidemiology, and Bayesian analysis. Her work bridges theoretical statistics with practical applications, addressing global health issues through innovative spatio-temporal modeling techniques.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, specializing in machine learning and statistics. Her research focuses on developing methods for uncertainty quantification in data analysis, Bayesian nonparametrics, and scalable inference algorithms. She leads a research group advising PhD students and postdocs in statistical machine learning. Her work spans Bayesian modeling, variational inference, spatial statistics, and applications in epidemiology and environmental science. Recent projects involve uncertainty-aware forecasting, robustness analysis of statistical methods, and efficient algorithms for high-dimensional inference. Broderick teaches Bayesian Modeling and Inference and contributes to MIT's statistics and data science initiatives.
Xi Gong is an Associate Professor in the Department of Biobehavioral Health and the Institute of Computing and Data Sciences at Pennsylvania State University, where he leads the Gong Lab. His research focuses on Geospatial Data Science, integrating GIScience, computational methods, and statistical analysis to study environmental health and social dynamics. He holds a PhD in Geographic Information Science from Texas State University, an M.Sc. from the University of Chinese Academy of Sciences, and a B.Eng. from Wuhan University. Dr. Gong's research interests include spatio-temporal data mining, environmental exposure modeling, and visual analytics for big data. His work bridges Environmental Health Science (EHS) and Spatially Integrated Social Science (SISS), addressing public health concerns through geospatial modeling and interdisciplinary collaboration. He currently accepts graduate students and postdoctoral scholars for projects in Geospatial Data Science and EHS. Education: PhD (2016, Texas State University), M.Sc. (2011, University of Chinese Academy of Sciences), B.Eng. (2008, Wuhan University) Labs/Teams: Director of Gong Lab, focused on geospatial big data analysis for health and environmental studies Advising: Mentors PhD and MS students in Biobehavioral Health and related fields
Vincent Kaufmann is an Associate Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) , leading the Urban Sociology Laboratory (LaSUR) . Since 2010, he serves as Scientific Director of the Mobile Lives Forum in Paris. His career spans teaching at institutions like Lancaster University , Ecole des Ponts ParisTech , and Tongji University , with research focusing on mobility sociology , urban dynamics , and public policy evaluation . Master's in Sociology from University of Geneva PhD at EPFL on transport modal practices His work explores mobility-capital interactions , motility , and rhythmanalysis in contemporary societies. Key themes include urban lifestyle transformations , cross-border regions , and mobility-sustainability nexus , with recent emphasis on new proximities and slow cities . Teaching roles span EPFL departments like Teaching (EDAR) , Architecture and Urban Sciences (SGC) , and Human and Social Sciences (CDH) . He has directed numerous PhD and master's theses on urban mobility topics.
Assoc Prof Ying Chen is an Associate Professor at the National University of Singapore , affiliated with the Department of Mathematics, Asian Institute of Digital Finance (as Academic Director of PhD Program in Digital FinTech 2022–2024), Risk Management Institute (2019–2023), Department of Statistics and Data Science (2019–2023), and Department of Economics (2018–2023). She also contributes to NUS Graduate School for Integrative Sciences and Engineering since 2016. Research Interests include: AI forecasting and quantum computing for finance Nonstationary time series and functional data analysis Energy data analytics and precision medicine Network autoregression and spatial-temporal modeling Explainable AI and citation metrics Portfolio liquidation and market-making algorithms Article Trends demonstrate expertise in: Adaptive forecasting for gas flows and electricity prices Blockchain network influence detection Quantum computing applications in finance Functional autoregression with mixed predictors Credit rating fairness and explainability High-resolution implied volatility modeling Scientific Awards include: ISI Elected Member (2016–) International Statistical Institute Council (2023–2027) IASC Scientific Secretary (2017–2019, 2023–2025) Advisory roles for EU FIN-TECH and xAIM projects
François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.
Jingtong Hu is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh, where he also holds the William Kepler Whiteford Faculty Fellowship. His research focuses on Cyber-Physical Systems and Infrastructure Security, with significant contributions to embedded systems, non-volatile memory architectures, and hardware/software co-design for energy-constrained environments. Dr. Hu received his PhD from the University of Texas at Dallas (2007-2013) and his Bachelor of Engineering from Shandong University (2003-2007). His research spans multiple interdisciplinary areas including energy harvesting systems, non-volatile processors, FPGA acceleration, and machine learning at the edge. His recent publications demonstrate a strong focus on algorithm-hardware co-design, particularly for vision transformers, federated learning, and non-volatile memory systems. His work often addresses the challenges of implementing AI on resource-constrained edge devices, with emphasis on energy efficiency, reliability, and performance optimization. The trend in his publications shows increasing focus on sustainable AI processing, heterogeneous computing architectures, and personalized machine learning for IoT applications. Selected Awards: IEEE Transactions on Computer-Aided Design Donald O. Pederson Best Paper Award (2021) ACM SIGDA Meritorious Service Award (2019) Multiple Best Paper Award nominations at top conferences including DAC, ASP-DAC, and CODES+ISSS Dr. Hu's research has been supported by multiple grants focusing on energy-efficient computing, non-volatile memory systems, and hardware acceleration for machine learning. His collaborative work spans numerous institutions and involves interdisciplinary teams working at the intersection of computer architecture, embedded systems, and artificial intelligence. His publications show extensive collaboration with researchers at the University of Pittsburgh, particularly with Albert K. Jones and Yiyu Shi. His laboratory work focuses on implementing practical systems for energy harvesting powered devices, non-volatile processors, and hardware accelerators for machine learning applications. Current projects appear to emphasize sustainable AI processing at the edge, heterogeneous FPGA acceleration, and personalized federated learning for health monitoring applications.
Manuel Kaufmann is a Lecturer in the Department of Computer Science at ETH Zürich. His work focuses on advanced 3D human motion capture, sensor-based systems, and computer vision applications. He is affiliated with the Institute of Informatics (inf.ethz.ch) and contributes to research in real-time motion tracking, dataset development, and machine learning integration for human-robot interaction. Research interests include holistic human-scene reconstruction from monocular videos, gaze estimation using EEG signals, and expressive avatar creation. His projects emphasize practical applications in robotics, sports analytics, and biomedical engineering, often leveraging electromagnetic and inertial sensors for high-precision data acquisition. His publications reflect a trend toward multi-modal data fusion, real-world dataset creation (e.g., WorldPose, ARCTIC), and addressing challenges in loose garment modeling (Reloo). These efforts aim to improve markerless motion capture, crowd analysis, and human-robot collaboration. No scientific awards or grants are explicitly listed. He has no documented advisees, though his research may involve collaborations with students or teams. His office is located at OAT X 23, Andreasstrasse 5, Zürich, Switzerland, and contact details include a phone number and professional email.
Shih-Fu Chang is the Dean of Columbia Engineering and holds the Morris A. and Alma Schapiro Professorship at Columbia University. His research focuses on computer vision, machine learning, and multimedia information retrieval. He is recognized as a foundational figure in the field of content-based visual search and has pioneered innovations in image/video search engines, crime prevention systems, and brain-machine interfaces. His leadership roles include Chair of Columbia's Electrical Engineering Department (2007-2010), Editor-in-Chief of the IEEE Signal Processing Magazine (2006-2008), and Senior Executive Vice Dean at Columbia Engineering, where he drives strategic planning and international collaboration. Dr. Chang has received prestigious awards including the ACM Multimedia Technical Achievement Award, IEEE Signal Processing Technical Achievement Award, and IEEE Kiyo Tomiyasu Award. He is a Fellow of AAAS, ACM, and IEEE, and an Academician of Academia Sinica. His recent work emphasizes multimodal reasoning, few-shot learning, and vision-language systems, with applications in healthcare diagnostics and multimedia benchmarking. His research spans cross-modal understanding, event extraction, and adaptive AI systems. Key contributions include systems like Ferret-v2 for multimodal grounding and RESIN for schema-guided event tracking. He has advised multiple startups and actively contributes to curriculum development in AI and engineering education.
Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.
Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).
Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .
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.
