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Alessio LomuscioView profile

Professor

Imperial College LondonUnited Kingdom

Alessio Lomuscio is a Professor of Safe Artificial Intelligence at Imperial College London, holding the prestigious Royal Academy of Engineering Chair in Emerging Technologies and recognized as an ACM Distinguished Member. He leads the Safe AI Lab, which focuses on developing methods and tools for the verification of AI systems to ensure their safe and secure deployment in applications of societal importance. His research spans verification and robust learning for neural networks and decision trees, robust machine learning in aviation and finance, monitoring of machine learning systems, assurance for autonomous systems and AI, and verification and validation of neuro-symbolic systems. Lomuscio has made significant contributions to formal verification methods for AI systems, particularly in the context of safety-critical applications. His recent publications demonstrate a strong focus on neural network verification techniques, with applications across multiple domains including finance, aviation, and autonomous systems. His work bridges theoretical advances in formal methods with practical applications in real-world AI systems, addressing critical challenges in AI safety and trustworthiness. Scientific Awards: Royal Academy of Engineering Chair in Emerging Technologies ACM Distinguished Member Lomuscio has served in numerous leadership roles, including as Co-Director (2023-present) and Deputy Director (2019-2023) of the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence. He has also held positions as Director of Strategy and Planning (2017-2020), Member of Management Committee (2013-2020), and Deputy Head of Department (2016-2017). His editorial service includes roles as Associate Editor for Artificial Intelligence Journal and Editorial Board Member for Journal of Artificial Intelligence Research and Journal of Autonomous Agents and Multi-agent Systems. His research group actively mentors students and researchers, with current openings for PhD and postdoctoral positions focused on AI verification and safety. Lomuscio's work has established him as a leading figure in the field of safe and verifiable AI systems, with significant contributions to both theoretical foundations and practical applications.

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Simon GodsillView profile

Professor

University of CambridgeUnited Kingdom

Professor Simon Godsill MA PhD FIET FIEEE is a University Professor of Statistical Signal Processing in the Department of Engineering at the University of Cambridge. He heads a research team specializing in statistical signal processing, digital audio restoration, and Bayesian inference. His work addresses the processing and analysis of digital speech, audio, tracking systems, and financial datasets, with a focus on probabilistic modeling and computational methods. Research interests include statistical signal processing , degraded signal restoration , and Bayesian computational methods . Recent publications emphasize Gaussian processes, variational inference, and multi-object tracking for applications in audio enhancement and financial data analysis. He co-founded the audio remastering company CEDAR Audio Ltd in 1988. Scientific awards: Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Institute of Electrical and Electronics Engineers (FIEEE) Outside academia, he enjoys singing, cricket, piano/organ playing, and running. His team at Cambridge's Engineering department focuses on robust tracking algorithms and signal enhancement techniques.

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Jiti GaoView profile

Professor

Monash UniversityAustralia

Professor Jiti Gao is a Donald Cochrane Chair in Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. He leads the Department of Econometrics and Business Statistics, specializing in non- and semi-parametric econometrics, time-series analysis, and panel data methodologies. His research focuses on developing statistical models for climate change, energy demand, and financial forecasting. Affiliations: Monash University, Impact Labs Grants: Multiple ARC Discovery Projects (e.g., 2020–2025 on climate-energy time series, 2017–2020 on econometric model building) Collaborations: CSIRO, Yale University, and international partners from China, Norway, and Singapore Research interests include climate econometrics, financial time series, and policy evaluation. Over 136 publications span econometric theory and applications, with recent work on nonlinear trending models and quantile regression. His grants emphasize methodological advancements in time series and panel data analysis. Awards: Not explicitly mentioned, but recognition includes Australian Professorial Fellow status and international research leadership roles. Advising/Grants: Primary Investigator on multiple ARC-funded projects, focusing on climate modeling and financial econometrics Labs/Teams: Part of Monash's Impact Labs and collaborates with global institutions on climate and econometric initiatives

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Song MeiView profile

Assistant Professor

Brown UniversityUnited States

Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.

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Mikko ValkamaView profile

Professor

Tampere UniversityFinland

Mikko Valkama is a Professor at the Department of Communications Engineering , part of the Faculty of Information Technology and Communication Sciences at Tampere University . His research focuses on advanced wireless communication systems, positioning technologies, and integrated sensing and communication (ISAC). He holds an Orcid ID ( 0000-0003-0361-0800 ) and can be reached at mikko.valkama@tuni.fi . Research interests span 5G/6G networks , RF antenna design , deep learning for signal processing , and millimeter-wave systems . He leads projects on positioning algorithms (e.g., mmWave SLAM, NLOS mitigation), ISAC architectures, and hardware-efficient transmitter linearization. Notable contributions include works on DECT-2020 NR standards, phase-based localization, and RIS-assisted systems. In 2025 alone, his group published over 30 articles on topics such as: Antenna array design for Ka-band and wideband applications Machine learning for power amplifier predistortion Bistatic radio SLAM and mmWave mapping Covert transmission and physical-layer security His work bridges theoretical advancements with practical implementations, often validated through experimental setups (e.g., TUJI1 dataset for indoor localization). No scientific awards were explicitly listed in the provided texts.

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Giancarlo Ferrari TrecateView profile

Adjunct Professor

Swiss Federal Institute of Technology in LausanneSwitzerland

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

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Yannick BARAUDView profile

Professor

University of LuxembourgLuxembourg

Yannick BARAUD is a Full Professor in Mathematics at the University of Luxembourg, leading the group 'Developing Contemporary Mathematical Statistics' within the Department of Mathematics. He holds the ERA-Chair 'SanDAL' in Mathematical Statistics and Data Science, funded by the European Commission. His research focuses on robust estimation, model selection, hypothesis testing, and nonparametric methods. He serves as the Study Programme Director for the Master in Data Science and has held academic positions at the University of Nice Sophia Antipolis and CNRS. His career includes roles as a researcher at École Normale Supérieure (Paris) and a lecturer at the same institution. He earned his PhD from Université Paris-Sud and studied at École Normale Supérieure de Cachan. His work emphasizes rigorous statistical methodologies, including rho-estimation and robust Bayes-like approaches. Notable contributions include advancements in density estimation under shape constraints and robust regression techniques. He has published extensively on topics such as loss functions, empirical processes, and statistical inference, with applications in epidemiology and data science. His leadership in the SanDAL initiative underscores his commitment to bridging mathematical statistics and practical data science challenges. Collaborations and grants further highlight his role in advancing interdisciplinary research.

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Albert G. AssafView profile

Professor

University of Massachusetts AmherstUnited States

Albert G. Assaf is a Professor and Hadelman Family Faculty Fellow in the Department of Hospitality & Tourism Management at the Isenberg School of Management, University of Massachusetts-Amherst . He holds editorial roles in multiple journals including Editor-in-Chief of Tourism Economics and Associate Editor positions in Journal of Hospitality and Tourism Research and International Journal of Hospitality Management . His education includes a PhD in Managerial Economics (University of Western Sydney, 2007), alongside graduate diplomas in Quantitative Methods and Mathematical Science. Prior roles include Assistant and Associate Professor positions at UMass Amherst and Victoria University-Australia. Research focuses on applied economics, statistics, tourism/transport economics, and revenue management . His work integrates Bayesian methods, econometrics, and operations research to analyze hospitality industry dynamics, strategic management, and performance measurement. Recipient of prestigious awards including the Richard M. & Nancy S. Kelleher Teacher Award (2018-2019), Thea Sinclair Award (2016), and multiple Dean Research Excellence Awards. His publications emphasize methodological innovation in tourism and hospitality research. Professional service includes editorial leadership across 10+ journals and academic governance roles. Teaching expertise spans managerial economics, applied statistics, and strategic management courses.

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Panagiotis ZervopoulosView profile

Associate Professor

University of PiraeusGreece

Panagiotis Zervopoulos is an Associate Professor at the Department of Business Organization and Administration within the School of Economics, Business and International Studies at the University of Piraeus. Previously, he served as Associate Professor and Director of the PhD Program in Business Administration at the School of Business Administration of the University of Sharjah in the UAE. His research interests focus on operations research, efficiency and performance measurements, optimization methods, and econometrics. Specifically, he specializes in developing new data envelopment analysis (DEA) techniques, Bayesian methods, parametric and non-parametric econometric models, with innovative applications across multiple disciplines. His methodological contributions have been applied in diverse fields including supply chain management, financial systems, environmental efficiency, and corporate governance. Zervopoulos has published extensively in top-tier journals such as European Journal of Operational Research, Journal of the Operational Research Society, Annals of Operations Research, Journal of Financial Stability, and Journal of Cleaner Production. His recent work (2023-2024) demonstrates continued innovation in efficiency measurement techniques, particularly in network DEA models with bias correction, environmental efficiency analysis, and systemic risk measurement. His publications show strong international collaboration patterns, frequently working with researchers from different countries and institutions. He has served as Guest Editor for journals including Socio-Economic Planning Sciences and IMA Journal of Management Mathematics, demonstrating recognition of his expertise by the academic community. Zervopoulos has held research fellowships at prestigious institutions worldwide, including Peking University (China), the London School of Economics (United Kingdom), the Academy of Athens (Greece), and the Foundation for Economic and Industrial Research (Greece). He also participates in the World Economic Survey and the Economic Expert Group of the Ifo Institute (Germany). Professionally, he has served as Senior Consultant Modeling Statistician at IRi Worldwide and as Project Manager and Expert in Quantitative Analysis and Public Sector Reform through technical support projects with the European Public Law Agency.

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Carl Henrik EkView profile

Professor

University of CambridgeUnited Kingdom

Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.

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Patrick BrownView profile

Associate Professor

University of TorontoCanada

Patrick Brown is an Associate Professor at the University of Toronto , affiliated with the Department of Statistical Sciences and cross-appointed to the Centre for Global Health Research and St. Michael's Hospital . His research focuses on spatio-temporal data modeling , Bayesian inference , and non-parametric methods for spatial epidemiology and environmental sciences. Fields of Interest : Spatial Statistics, Cancer Statistics, Statistical Software Education : PhD from University of Lancaster His methodological work encompasses Bayesian inference for non-Gaussian spatial data, Gaussian Markov random fields, and computational techniques like INLA and MRA. Applied research themes include disease mapping, environmental risk assessment, and public health surveillance using real-world data sources such as electronic health records and wastewater monitoring . He has developed key R packages (mapmisc, geostatsp, diseasemapping) supporting spatial statistical applications. Current collaborative projects span diverse fields: Ultra-diffuse galaxy detection with astrophysical applications Multi-pollutant mortality studies in Canadian cities SARS-CoV-2 seropositivity tracking Homelessness population estimation using EHR Geospatial cancer risk tools for Nova Scotia His work bridges statistical innovation with global health challenges , emphasizing computationally efficient solutions for large-scale spatiotemporal datasets.

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Ralf BeckView profile

Professor

Düsseldorf University of Applied SciencesGermany

Prof. Dr.-Ing. Ralf Beck serves as Professor for Control and Regulation Technology and Automation Technology at Hochschule Düsseldorf University of Applied Sciences within the Faculty of Electrical Engineering & Information Technology. His academic responsibilities span multiple degree programs including BEng Electrical Engineering, BEng Industrial Engineering, and MSc Electrical Engineering and Information Technology. His educational background includes Mechanical Engineering studies at TU Braunschweig (1998-2004), followed by doctoral research at RWTH Aachen's Institute of Control Engineering where he earned his Dr.-Ing. in 2010 with a dissertation on predictive energy management for hybrid vehicles. Prior to his current professorship, he held progressive roles at FEV Europe GmbH from 2009-2018, culminating as Senior Project Manager for Vehicle and Powertrain Electronics. Beck's research focuses on control engineering systems with particular emphasis on automation technology, regulation systems, and model-based development approaches. His work bridges theoretical control methodologies with practical automotive applications, especially in hybrid vehicle energy management, multi-robot systems, and intelligent air path control. The Modellfabrik Fab21 serves as his primary experimental platform for model-based development applications. His publication record since 2005 demonstrates consistent contributions to control engineering, particularly in hybrid vehicle systems, emission control optimization, and calibration methodologies. Recent work shows increasing focus on distributed robotics and intelligent transportation systems, reflecting evolving research directions while maintaining core expertise in control theory applications. As an educator, Beck teaches foundational and advanced courses including Electrical Engineering III, Control and Regulation Technology, Model-Based Development, Technical Mechanics, and Advanced Control Engineering at the Master's level. His teaching integrates theoretical concepts with practical laboratory applications through the university's Moodle platform, emphasizing hands-on implementation of control algorithms and system modeling techniques.

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Valentyn PanchenkoView profile

Professor

University of New South WalesAustralia

Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.

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Subhabrata SenView profile

Assistant Professor

Harvard UniversityUnited States

Subhabrata Sen is an Assistant Professor of Statistics at Harvard University, located in Science Center 713, Cambridge. His research focuses on Applied Probability, Statistics of Networks, Signal Detection, and Machine Learning. He holds a PhD from Stanford University (2017), advised by Amir Dembo and Andrea Montanari, and prior degrees from the Indian Statistical Institute, Kolkata. His work bridges statistical theory, high-dimensional data analysis, and applications in networks and physics-inspired methods. Key contributions include foundational studies on spin glasses, community detection, and causal inference in complex systems. His research often employs mean-field techniques and explores universality principles in estimation problems. Selected awards and recognition are not explicitly mentioned in the provided text. His advising and grants include postdoctoral mentoring at Microsoft Research and MIT (2017-19). He collaborates on projects involving spectral methods, random matrix theory, and multi-layer network analysis. Labs/teams: Active in Harvard's Statistics Department research groups focused on statistical theory and network science. Maintains an academic website with preprints and resources.

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Ali ShojaieView profile

Professor

University of WashingtonUnited States

Ali Shojaie is a Professor of Biostatistics and Statistics at the University of Washington, serving as Associate Chair for Strategic Research Affairs in the Department of Biostatistics. He leads the Summer Institute for Statistics in Big Data (SISBID) and the Data Management and Statistics (DMS) Core for the UW Alzheimer's Disease Research Center. His research focuses on developing statistical and machine learning methods for high-dimensional data, with applications in genomics, neuroscience, and public health. Shojaie's work includes advancements in graphical models, Granger causality, and spatial statistics. He has contributed to methodologies for analyzing networks from time series and spatial data, with applications in understanding gene regulatory networks and brain connectivity. His recent projects involve NIH-funded grants exploring gene-phenotype associations using omic data and explainable machine learning for brain stimulation research. Scientific awards include the 2022 Leo Breiman Award from ASA's Statistical Learning and Data Science section, and election as a Fellow of the Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). He serves on editorial boards for journals like the Journal of the American Statistical Association and Biometrika. Shojaie advises numerous PhD students and postdocs, many of whom have secured academic and industry positions. His lab develops open-source software tools, including the netgsa and ngc packages for network analysis and Granger causality estimation.

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