جست‌وجوی استاد و پژوهشگر

• Statistical Machine Learning
• Bayesian Experimental Design
• Probabilistic Programming
• +۳ مورد دیگر

Tom Rainforth is an Associate Professor of Statistical Machine Learning at the University of Oxford's Department of Statistics, leading the RainML Research Lab (rainml.uk). He holds a Tutorial Fellowship at Mansfield College and is Principal Investigator of the ERC Starting Grant 'Data-Driven Algorithms for Data Acquisition' (2024–2029). Previously, he held roles including a postdoc under Yee Whye Teh (2017–2019), Junior Research Fellow at Christ Church College (2019–2019), and Florence Nightingale Bicentennial Fellow (2020–2024). He earned his MEng in Mechanical Engineering from the University of Cambridge and his D.Phil from Oxford under Frank Wood and Michael Osborne, focusing on probabilistic programming and Monte Carlo methods. He briefly worked in Ferrari's Formula 1 team. Research Interests : Bayesian experimental design, probabilistic and data-efficient machine learning, active learning, deep learning (with a focus on probabilistic approaches), probabilistic programming, and Monte Carlo methods. His work emphasizes statistical efficiency and adaptive algorithms. Publications : Recent contributions span modern Bayesian experimental design, adaptive importance sampling (Daisee), and applications of probabilistic methods in LLMs and generative models. His research bridges theory and practice, addressing challenges in scalability and robustness. Awards : ERC Starting Grant (2024–2029), highlighting his leadership in foundational AI research. Advising & Grants : Supervises 15 graduate students, including work on Bayesian neural networks, generative flows, and experimental design. His ERC grant supports cutting-edge data-driven algorithm development. Labs & Teams : Directs the RainML Lab, which develops scalable Bayesian methods and probabilistic AI systems.

David Duvenaudمشاهده پروفایل

دانشیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۵ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

• Trustworthy Machine Learning
• Conformal Prediction
• Explainable Artificial Intelligence
• +۲ مورد دیگر

Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.

Paweł Chmuraمشاهده پروفایل

استاد

• Exercise Physiology
• Sports Science
• Football Performance Analysis
• +۳ مورد دیگر

Professor Paweł Chmura is a Full Professor at Wrocław University of Health and Sport Sciences, specializing in Sports Sciences with a focus on football performance analysis. He serves in the Department of Individual and Team Sports, where he conducts research on exercise physiology, training load monitoring, and physical activity analysis in soccer players. With over 130 publications, Professor Chmura is a recognized expert in sports science and also holds a UEFA A coaching license. Professor Chmura's research interests center on exercise physiology, particularly in the context of team sports. His work extensively examines repeated maximum effort capabilities, physical activity analysis in soccer players, and monitoring training loads. He has developed innovative concepts such as the Psychomotor Fatigue Threshold and investigates how environmental factors like temperature and air quality affect athletic performance. His research bridges the gap between theoretical sports science and practical coaching applications, making significant contributions to football performance optimization. Professor Chmura's extensive publication record demonstrates a consistent focus on soccer performance analysis, with recent work expanding into multidisciplinary approaches including physiological, psychological, and tactical aspects of the game. His research shows increasing sophistication in methodology, incorporating advanced monitoring technologies and comprehensive analysis frameworks that consider multiple performance indicators simultaneously. This evolution reflects broader trends in sports science toward integrated, holistic approaches to athlete performance. Professor Chmura actively supervises the Motosoccer Student Research Club and invites master's and doctoral students to collaborate on research projects related to football, exercise physiology, game analysis, and motor preparation. His mentorship extends beyond formal academic supervision to practical coaching guidance, leveraging his UEFA A coaching certification to provide students with both theoretical knowledge and practical field experience. As a UEFA A-licensed football coach and tennis instructor, Professor Chmura maintains strong connections between academic research and practical sports applications. He leads the Motosoccer Student Research Club, which provides hands-on research opportunities for students interested in sports science. His dual expertise in academic research and practical coaching creates a unique environment for applied sports science research that directly impacts coaching methodologies and athlete development.

• Language Models
• Diffusion Models
• Theory of Deep Learning
• +۶ مورد دیگر

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.

• statistical machine learning
• optimization
• sparse methods
• +۵ مورد دیگر

Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.

Gautam Kamathمشاهده پروفایل

استادیار

• Differential Privacy
• Machine Learning
• Statistics
• +۴ مورد دیگر

Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, a Faculty Member at the Vector Institute, and a Canada CIFAR AI Chair. He leads The Salon, a research group focused on statistics, algorithms, machine learning, and optimization. His work bridges theoretical foundations with practical applications in data privacy and robustness, contributing to both academic research and real-world deployments that impact millions of users. Dr. Kamath earned his PhD and SM degrees in Electrical Engineering and Computer Science at MIT, where he was advised by Costis Daskalakis. Prior to MIT, he graduated from Cornell University in May 2012 with a degree in Computer Science and Electrical and Computer Engineering, where he worked with Bobby Kleinberg. His academic journey reflects a strong foundation in both theoretical computer science and practical applications. His research focuses on developing solutions for trustworthy and reliable machine learning and statistics, with particular emphasis on data privacy and robustness. He addresses fundamental problems in these areas, revitalizing statistical toolkits for the modern data era where privacy preservation is paramount. His work spans theoretical foundations of differential privacy to practical applications that have been deployed at scale, including contributions to systems that protect the sensitive information of hundreds of millions of individuals. Analysis of his recent publications reveals a strong trend toward addressing the interplay between privacy, robustness, and machine learning performance. His work increasingly examines practical deployment challenges while maintaining theoretical rigor, with growing emphasis on diffusion models, generative AI, and the legal implications of AI systems. There's a clear trajectory from foundational privacy theory toward addressing real-world implementation challenges across diverse application domains. Canada CIFAR AI Chair Ontario Early Researcher Award 2024 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies STOC Best Student Presentation Award ICML 2024 Best Paper Award Microsoft Research Fellow at the Simons Institute for the Theory of Computing Dr. Kamath actively mentors students, with notable successes including Valentio Iverson winning the Germain-Erdős Undergraduate Award and Chris Trevisan receiving the CRA Outstanding Undergraduate Researcher Award. His service to the research community is extensive, serving as Editor-in-Chief of TMLR, on the Executive Committee of the Learning Theory Alliance, and on steering committees for major conferences including ICML, COLT, and ALT. He has organized numerous workshops focused on privacy-preserving machine learning and differential privacy. Through The Salon research group, Dr. Kamath fosters a collaborative environment where postdoctoral fellows, graduate students, and undergraduates work together on cutting-edge problems at the intersection of statistics, algorithms, machine learning, and optimization. The group maintains strong connections with industry partners and participates in major research initiatives, including the Vector Institute's privacy and security research efforts. Looking forward, Dr. Kamath will be moving to the Computer Science department at NYU's Courant Institute of Mathematical Sciences in September 2026, where he plans to expand his research program.

• Robust Statistics
• Dimension Reduction
• Meta-Analysis
• +۳ مورد دیگر

Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.

• Wind Energy
• Wind Turbine Reliability
• Condition Monitoring
• +۱۲ مورد دیگر

James Carroll is a Professor in the Department of Electronic & Electrical Engineering (EEE) at the University of Strathclyde, where he also serves as Director and Principal Investigator of the Wind and Marine Energy Systems and Structures CDT (2019–2027) and the Strathclyde lead for the EnerHy Wind and Hydrogen CDT (2024–2032). He is Co-Lead of the Wind Energy and Control (WEC) Group, one of the largest university-based wind energy research groups in the UK, comprising 8 academics and over 35 researchers. His research interests are centered on wind energy systems, with a focus on: Novel wind turbine concept development Wind turbine reliability and maintenance modeling Cost of energy and O&M cost modeling Drive train selection impact on reliability Condition monitoring and failure prediction of wind turbine components Data-driven machine learning and physical modeling for remaining useful life prediction SCADA and vibration data analytics The recent articles highlight a consistent trend in offshore and onshore wind energy innovation, with strong emphasis on predictive maintenance, cost reduction, digital twins, and novel turbine design. His work bridges engineering, data science, and sustainability, contributing significantly to renewable energy advancement. Scientific recognition includes: 2nd Place, Future Energy Competition (2019) James Carroll has been actively involved in major research projects, including EPSRC-funded CDTs and industry collaborations, focusing on hydrogen integration, digital twins for powertrains, and offshore wind maintenance optimization. He has also contributed to professional activities such as keynote speaking at the Wind Energy Science Conference 2019 and participation in EPSRC scoping workshops. He supervises research students and leads a dynamic team within the WEC Group, driving innovation in wind and marine energy systems. His research group, the Wind Energy and Control (WEC) Group, is a leading UK academic team in wind energy, fostering interdisciplinary collaboration and training the next generation of energy engineers through doctoral training programs.

• Fatigue strength of aluminum structures
• Fracture mechanics
• Railway bridge assessment
• +۴ مورد دیگر

Dorina Siebert is a Researcher at the Chair of Metal Construction within the School of Engineering at the Technical University of Munich. She has been working as a research assistant at the Chair since 2019, contributing to various research projects related to steel and aluminum construction, fracture mechanics, and additive manufacturing in construction. Education: M.Sc. in Civil Engineering from Technical University of Munich (2012-2019) Affiliation: Chair of Metal Construction, School of Engineering, Technical University of Munich Contact: dorina.siebert@tum.de, Room 0101.Z1.038, +49 (89) 289-22527 Dorina's research primarily focuses on the fatigue strength of aluminum structures, fracture mechanics in railway bridges, and the application of additive manufacturing techniques in construction. Her work on powder bed-based laser beam melting of metal has significant implications for modern construction methods. She also investigates safe operating time intervals for historic steel bridges and has contributed to the development of a mobile vehicle barrier, demonstrating the practical applications of her theoretical work. Her publication record shows a strong trend toward computational and experimental analysis of material behavior under stress, particularly in aluminum alloys and steel structures. She has published extensively on fatigue properties, fracture mechanics calculations, and additive manufacturing applications, with a clear progression toward more complex modeling techniques and practical engineering solutions. Her work bridges theoretical computational models with real-world infrastructure challenges. Dorina teaches courses including 'Constructing with aluminum' for the Summer semester 2025 and 'Fracture mechanics and fatigue' for the Winter semester 2024/25. She also leads a seminar on plate buckling and steel bridge construction, sharing her specialized knowledge with engineering students. Her teaching directly reflects her research expertise, creating a strong connection between theoretical knowledge and practical application for her students.

• Exercise Physiology
• Bone Physiology
• Pediatric Exercise Medicine
• +۴ مورد دیگر

Panagiota (Nota) Klentrou is a Distinguished Professor and Dean of the Faculty of Applied Health Sciences at Brock University, specializing in Kinesiology. Her research focuses on exercise physiology , bone development , and the health implications of sport training in youth , particularly examining cellular mechanisms linking exercise, diet, and lifelong bone health. Supported by NSERC, CIHR, Osteoporosis Canada, and the International Gymnastics Federation Education: PhD, FCSEP (Fellow of the Canadian Society of Exercise Physiology) Her work spans bone physiology , inflammatory responses to exercise , and sclerostin-mediated tissue cross-talk , with recent studies exploring the impact of obesogenic diets , acute exercise , and nutritional interventions on skeletal growth and adaptation. Key findings include the role of sprint interval training in modulating adipose tissue Wnt signaling and the effects of dairy consumption on bone turnover markers. Scientific Awards & Distinctions Fellow, Canadian Society for Exercise Physiology (CSEP), 2020 Marilyn Rose Graduate Leadership Award, 2017 TVO's Best Lecturer nominee, 2010 Chancellor’s Chair for Research Excellence, Brock University, 2009 Award for Distinguished Research & Creative Activity, Brock University, 2006 Dr. Klentrou actively supervises graduate and undergraduate students in projects related to bone physiology , inflammation , and exercise adaptation , and collaborates with organizations like Osteoporosis Canada and the International Gymnastics Federation.

James W. Navaltaمشاهده پروفایل

دانشیار

• Exercise Physiology
• Outdoor Exercise Physiology
• Wearable Technology Validation
• +۳ مورد دیگر

Dr. James W. Navalta is an Associate Professor in the Department of Kinesiology and Nutrition Sciences at the University of Nevada, Las Vegas. His research focuses on physiological responses to outdoor exercise (hiking, trail running) and the validity/reliability of wearable technology. He earned his B.S. in Physical Education and Biology from Brigham Young University–Hawaii, M.S. in Kinesiology from UNLV, and Ph.D. in Exercise Physiology from Purdue University. Education: B.S. - Physical Education & Biology, Brigham Young University–Hawaii M.S. - Kinesiology, University of Nevada, Las Vegas Ph.D. - Exercise Physiology, Purdue University His research portfolio includes: Wearable technology validation for physiological measurements Comparative studies of indoor vs outdoor exercise environments Impact of gender-inclusive approaches on sports science Metabolic and cardiovascular responses to unconventional workouts Psychological benefits of nature immersion Recent publications demonstrate expertise in: Wearable device accuracy testing VO2max and lactate threshold validation Environmental influence on exercise physiology Methodological improvements in data collection Gender-inclusive research design Outdoor activity impact assessment As co-founder and executive editor of the International Journal of Exercise Science, he contributes significantly to academic discourse. He also serves on editorial boards for journals related to digital health and exercise technology.

• Computer Vision
• Natural Language Processing
• 3D Human Motion Synthesis
• +۳ مورد دیگر

Gül Varol is a permanent researcher at École des Ponts ParisTech's IMAGINE group, an ELLIS Scholar, and Guest Scientist at Max Planck Institute. She holds a PhD from Inria Paris/ENS with awards from ELLIS and AFRIF. Her academic service includes Program Chair at ECCV'24 and Area Chair roles at major conferences. Current affiliations: IMAGINE group (École des Ponts ParisTech), Max Planck Institute Previous roles: Postdoctoral researcher at University of Oxford Her research focuses on vision-language applications, particularly in 3D human motion synthesis, sign language technology, and audio description generation. Key techniques include text-conditioned diffusion models, temporal context modeling, and synthetic data utilization. Scientific contributions recognized through: Google Research Scholar award (2023) ELLIS PhD Award (2020) AFRIF PhD thesis award (2020) Best application paper at ACCV'20 Recent publications demonstrate expertise in: Text-driven 3D motion editing (MotionFix, 2024) Cross-dataset generalization studies (TMR++, 2024) Temporal action composition frameworks (TEACH, 2022) Sign language dense annotation methods (BOBSL, 2022) Zero-shot audio description generation (AutoAD-Zero, 2024) She actively contributes to dataset development including BOBSL (British Sign Language corpus) and SURREACT synthetic action dataset, while pioneering new evaluation metrics for audio description quality and motion retrieval benchmarks.

• Cancer Genomics
• Liquid Biopsies
• Machine Learning in Oncology
• +۷ مورد دیگر

Ash A. Alizadeh is the Moghadam Family Professor of Medicine, Oncology, and Hematology (by courtesy) at Stanford University, where he serves as leader of the Cancer Genomics Program at Stanford Cancer Institute. He holds multiple academic appointments including Professor in Medicine - Oncology, and membership in Bio-X, the Institute for Stem Cell Biology and Regenerative Medicine, and the Maternal & Child Health Research Institute (MCHRI). Dr. Alizadeh received his BS in Biochemistry from UCLA (1994), MD from Stanford Medical School, and PhD in Biophysics from Stanford. He completed additional training at the National Cancer Institute (NCI), the National Institutes of Health (NIH), and the Howard Hughes Medical Institute (HHMI). His primary research focuses on developing and applying genome technologies and computing (machine learning & data science) to problems in human disease, with special emphasis on cancer detection, classification, monitoring, and tumor immunology. His laboratory pioneers noninvasive cancer genomic techniques including CAPP-Seq, PhasED-Seq, and EPIC-Seq for "liquid biopsies" that analyze circulating nucleic acids for early cancer detection and monitoring therapeutic response. Using machine learning approaches, his group studies how cellular compositional variation impacts cancer behavior and therapeutic response, including anti-tumor immunity. His work spans molecular, cellular, organism and population levels of tumor behavior analysis. Dr. Alizadeh has received numerous prestigious awards including the Scholar Award from the American Society of Hematology (ASH), the Leukemia & Lymphoma Society (LLS), the V-Foundation, as well as awards from the American Red Cross, Damon Runyon Cancer Research Foundation, and Doris Duke Charitable Research Foundation. He is an elected member of the American Society for Clinical Investigation (ASCI) and serves on the Scientific Advisory Board of the Lymphoma Research Foundation (LRF). As an educator and mentor, Dr. Alizadeh advises numerous doctoral students, postdoctoral fellows, and medical scholars. He teaches in the Department of Medicine and Immunology and serves on various admissions panels at Stanford. His laboratory, the Alizadeh Lab, is a hub for interdisciplinary cancer genomics research that combines computational biology, molecular genetics, and clinical oncology to develop novel cancer diagnostics and therapeutics.

• Exercise Physiology
• Cardiovascular Health
• Exercise as Medicine
• +۳ مورد دیگر

Ulrik Wisløff is a Professor and Head of the Cardiac Exercise Research Group (CERG) at the Norwegian University of Science and Technology (NTNU), Department of Circulation and Medical Imaging. He also holds an Honorary Professorship at the University of Queensland, Australia, and serves as Head of Physical Performance at Rosenborg FC. His research focuses on exercise physiology, translating experimental findings into clinical applications, and developing health technologies like the PAI (Personal Activity Intelligence) metric. Wisløff leads CERG, a 55-member group renowned for groundbreaking studies on exercise in cardiovascular health, aging, and disease prevention. He has authored over 340 peer-reviewed papers, with 127,000+ citations, and been recognized as one of the world's top-cited exercise scientists. Key achievements include the Fitness Calculator (used by 80.5 million people) and PAI, a free app with 70 million users worldwide. Current projects include NorEx (9,700 patients with myocardial infarction) and ExPlas (Alzheimer’s cognitive function trials). Wisløff has mentored 22 postdocs and 38 PhD students, advancing the next generation of researchers. His leadership extends to innovative ventures like the 'My Medical Digital Twin' project, spun into Mia Health Ltd. Scientific contributions span exercise physiology, cardiovascular risk prediction, and high-intensity training efficacy. Awards include Norway’s highest honor in heart research (2020). Research spans molecules-to-society translational medicine, emphasizing global health impact through physical activity advocacy.