جست‌وجوی استاد و پژوهشگر

Evangelia Georgia Kostakiمشاهده پروفایل

مدرس

• Epidemiology
• Molecular Epidemiology
• HIV/AIDS Research
• +۷ مورد دیگر

Evangelia Georgia Kostaki is a Biostatistician and Epidemiologist serving as a Lecturer in the Department of Hygiene, Epidemiology, and Medical Statistics at the School of Medicine, National and Kapodistrian University of Athens (NKUA). She holds a PhD in Molecular Epidemiology from NKUA and has extensive teaching experience in medical statistics, epidemiology, and public health at the undergraduate and postgraduate levels. Her research focuses on molecular epidemiology of infectious diseases (HIV, HCV, HBV, and SARS-CoV-2), with emphasis on public health applications such as outbreak response and transmission network analysis. Education: Bachelor’s degree (NKUA) MSc in Biostatistics (NKUA) PhD in Molecular Epidemiology (NKUA) Research interests include HIV transmission dynamics, antiretroviral resistance surveillance, and pandemic response strategies. She has contributed to over 50 peer-reviewed articles and 2 book chapters, with active participation in global initiatives like the European AIDS Clinical Society (EACS) YING network and the Greek National Action Plan for HIV. Notable achievements include a New Investigator Scholarship (NSRF 2014-2020) and over 160 conference presentations internationally. Advisory roles include supervising 25+ MSc theses and supporting PhD candidates. Grants include co-investigator roles in national/international projects. She actively contributes to scientific societies, editorial boards, and guideline development for HIV testing protocols in Europe. Labs/Teams: Collaborates with the European Virus Bioinformatics Center (EVBC) and global networks addressing HIV and viral hepatitis epidemiology.

Jean Ponceمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robotics
• +۳ مورد دیگر

Jean Ponce is a Professor of Computer Science at Ecole Normale Superieure (ENS) in Paris and a Part-Time Global Distinguished Professor at New York University's Courant Institute of Mathematical Sciences and Center for Data Science (CDS). He previously served as Director of the ENS Computer Science Department (2011-2017) and held positions at Inria (2017-2022), University of Illinois at Urbana-Champaign (1998-2006), MIT, Stanford, and Inria (1982-1985). Academic Leadership: Scientific Director of PRAIRIE Interdisciplinary AI Research Institute in Paris Startup Involvement: Co-founder and CEO of Enhance Lab (2022) Editorial Roles: Senior Editor-in-Chief of International Journal of Computer Vision (2019-2022) Conference Leadership: Chair of IEEE CVPR (1997,2000), ECCV (2008), and upcoming ICCV (2023) Research Focus: Computer vision, machine learning, robotics, and AI with applications in exoplanet imaging, 3D reconstruction, and image quality assessment. His work bridges statistical learning and deep learning approaches. Awards: IEEE Fellow (2003) ELLIS Fellow (2019) ERC Advanced Grant (2011) IEEE CVPR Longuet-Higgins Prizes (2016,2020) ICML Test-of-Time Award (2019) Patents & Publications: Co-author of influential textbook Computer Vision: A Modern Approach (translated into Chinese, Japanese, Russian). Holds two US patents and one pending French patent. Google Scholar h-index of 78 with over 55,000 citations.

• Autonomous Systems
• Signal Processing
• Vehicle Health Management
• +۷ مورد دیگر

Dr Ivan Petrunin is a Research Professor in Signal Processing for Autonomous Systems and a DARTeC Fellow at Cranfield University's School of Aerospace, Transport and Manufacturing. His work focuses on advancing sensor technologies, data fusion, and decision-making systems for Cyber-Physical Systems, with applications in aerospace, ground-based autonomous systems, and urban air mobility. Key areas include Position, Navigation and Timing (PNT), vehicle health management, and AI-driven fault detection. He leads research at facilities like the Muti-User Environment for Autonomous Vehicle Innovation (MUEAVI) and collaborates with industry partners like Airbus, Rolls-Royce, and Thales. Education: BSc and MSc in Design of Electronic Equipment from National Technical University of Ukraine (1996–1998), followed by a PhD in Signal Processing for Condition Monitoring from Cranfield University (2013). Prior to Cranfield, he was a Lecturer in Digital Signal Processing at NTU Ukraine (2001–2005). Research Interests: Autonomous Systems & Sensor Fusion Machine Learning in Navigation and Safety GNSS Integrity & Urban Air Mobility Condition Monitoring & Structural Health Multi-Agent Reinforcement Learning Publications: Over 100 journal/conference articles and book chapters, with recent works emphasizing hybrid sensor fusion, resilient navigation architectures, and AI-driven solutions for GNSS-denied environments. Notable contributions include multi-sensor fusion frameworks for UAVs and Bayesian filter innovations. Awards: FRIN Fellowship, SMAIAA Membership, IEEE and ION Fellowships, and FHEA recognition. His work is supported by ESA, Innovate UK, and EPSRC. Advising & Labs: Supervises PhD students in UAV navigation and machine learning. Leads Cranfield's facilities for autonomous systems experimentation and advanced timing node infrastructure.

Lenka Zdeborováمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

• Machine Learning
• Computer Vision
• Medical Imaging
• +۷ مورد دیگر

Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.

Andrea Meilán-Vilaمشاهده پروفایل

استادیار

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +۳ مورد دیگر

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

Noel Cressieمشاهده پروفایل

استاد

• Spatial Statistics
• Environmental Informatics
• Bayesian Statistics
• +۳ مورد دیگر

Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.

Boris Shorمشاهده پروفایل

دانشیار

• Political Science
• State Legislatures
• Political Polarization
• +۷ مورد دیگر

Boris Shor is an Associate Professor of Political Science at the Hobby School of Public Affairs, University of Houston, where he conducts research on state legislatures, political polarization, representation, and health policy. He is associated with multiple interdisciplinary initiatives and contributes to major data infrastructure in political science. Education: Ph.D. and M.A. in Political Science, Columbia University B.A., Princeton University Shor specializes in American politics with a focus on legislative behavior, ideological measurement, and health policy. His work combines quantitative methods, roll call analysis, and survey data to understand polarization and representation across U.S. states. He has developed one of the most comprehensive datasets on state legislative ideology, influencing both academic research and policy analysis. His recent publications span top journals such as American Political Science Review , American Journal of Political Science , and Political Analysis , with a thematic focus on state-level political dynamics, methodological innovation, and health policy politics. The research consistently explores how partisanship, ideology, and institutional structures shape policy outcomes. Scientific Awards and Honors: Robert Wood Johnson Scholar in Health Policy, UC Berkeley Fellow, Center for the Study of Democratic Politics, Princeton University Shor has secured major external funding from the National Science Foundation and the Russell Sage Foundation . He has advised undergraduate, master’s, and doctoral students throughout his career. His ongoing book project examines the politics of health policy in the American states, building on his interdisciplinary background in political science and public health. He is affiliated with research centers focused on democratic governance and public policy, contributing to collaborative academic networks across institutions.

Hsiao-Dong Chiangمشاهده پروفایل

استاد

• Nonlinear System Theory
• Power System Stability and Control
• Optimal Power Flow
• +۱۲ مورد دیگر

Hsiao-Dong Chiang is a Professor in the School of Electrical and Computer Engineering at Cornell University. He holds a Ph.D. in Electrical Engineering from the University of California, Berkeley, and has made significant contributions to nonlinear system theory and power system stability. His research spans theoretical development and practical applications in electric power systems, nonlinear optimization, and machine learning. B.S., Electrical Engineering, National Taiwan University, 1979 M.S., Electrical Engineering, National Taiwan University, 1981 Ph.D., Electrical Engineering, University of California, Berkeley, 1986 Chiang's research interests focus on nonlinear system theory , power system stability and control , nonlinear optimization , and their applications to modern power grids with high penetration of inverter-based resources. He is renowned for developing the BCU method and TRUST-TECH methodology , which have enabled fast direct stability assessment and global optimization in complex systems. His work bridges fundamental theory with industrial deployment through his companies, Bigwood Systems, Inc. and Global Optimal Technology, Inc. His recent publications (2024–2025) reflect a strong trend toward integrating machine learning and deep neural networks with power system analysis , particularly in state estimation, optimal power flow, and voltage control. There is a clear emphasis on handling uncertainty, non-convexity, and multi-scale dynamics in active distribution networks and integrated energy systems . His work increasingly focuses on resilience , real-time control , and user-centered methodologies for modern grid operations. Chiang has received numerous scientific honors, including: IEEE Fellow (1997) United States Presidential Young Investigator Award (1989) Multiple DOE Grid Optimization Challenge Awards (2020–2023) Best Paper Awards from IEEE Transactions and Conferences Outstanding Education Award, Cornell University (1990) He has successfully managed over 100 research projects and holds 28 U.S. and international patents. As the founder of Bigwood Systems, Inc., he has commercialized advanced software for utility companies across the U.S. and Japan. His team has published over 480 refereed papers and received more than 17,500 citations. He advises a large research group and leads innovations in computational methods for energy systems. His lab is actively involved in developing next-generation tools for grid security, optimization, and machine learning integration.

Andrew Ngمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Robotics
• +۴ مورد دیگر

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.

Hamidreza Mahyarمشاهده پروفایل

استادیار

• Machine Learning
• Network Science
• Natural Language Processing
• +۳ مورد دیگر

Hamidreza Mahyar is an Assistant Professor at the Faculty of Engineering , McMaster University , and an Associate Member of the Computing and Software department. His academic journey includes postdoctoral work at Boston University and TU Wien , and a Ph.D. in Computer Science from Sharif University of Technology . Research Focus: Mahyar's work bridges machine learning and network science , emphasizing graph neural networks for applications in social networks , recommendation systems , drug discovery , and generative AI . His research spans industrial AI (Industry 4.0 projects at Infineon Technologies), biomedical engineering (organoid morphology analysis), and semiconductor manufacturing (wafermap modeling). Scientific Recognition: McMaster Teaching Merit Award (2022) Vector Scholarship in AI (2023) NSERC USRA Award (2022) Google Cloud Platform for Research Award (2018) Best Paper Selection, Complex Networks (2018) Academic Leadership: He mentors PhD students (Taraneh Ghandi) and MSc students (Reza Namazi, Mohammad Khodadad, Ali Shiraei), while leading AI initiatives at Mind Lab 56 and BrainMaven . Former mentees include industry leaders at Google, Accenture, and ETH Zurich.

Emtiyaz Khanمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bayesian Inference
• Deep Learning
• +۲ مورد دیگر

Emtiyaz Khan is a Researcher at the RIKEN Center for AI Project in Tokyo, Japan. His work focuses on Bayesian deep learning, optimization, and variational inference methods. He leads research on the Bayesian Learning Rule framework, which bridges deep learning optimization with Bayesian principles. His research interests include developing scalable Bayesian methods for large neural networks, uncertainty quantification in deep learning, optimization algorithms (natural gradients, variational inference), and applications to foundation models. Key areas are efficient adaptation methods, model sensitivity analysis, and Bayesian principles for deep learning. Khan's publications demonstrate strong focus on Bayesian deep learning, optimization techniques, and uncertainty estimation, with applications ranging from large-scale models (GPT-2, ImageNet) to theoretical foundations of variational inference. He leads the Team Approx-Bayes research group focused on approximate Bayesian inference methods and maintains collaborations through JST CREST-ANR and Kakenhi grants.

Johannes Bjervaمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Linguistic Typology
• +۵ مورد دیگر

Johannes Bjerva is a Full Professor at Aalborg University's Department of Computer Science (Campus Copenhagen), leading the Copenhagen branch and conducting interdisciplinary NLP research integrating linguistic typology. His work focuses on low-resource languages, language model security, and societal AI impact. PhD (University of Groningen, 2017): Thesis on multitask/multilingual lexical modeling M.A. & B.A. in Computational Linguistics (Stockholm University) Research interests span linguistically-informed NLP , language model security , and low-resource language technology . Current projects include the DFF Sapere Aude grant (2025) for language model detection security and the LM2-SEC project (2025–2030). His 2024 ACL paper on embedding inversion security and 2024 EMNLP paper on typological diversity exemplify recent work. Scientific awards include: 2021: Teacher of the Year (AAU Computer Science) 2019: Google Cloud research credits 2022: Carlsberg Semper Ardens (5M DKK) 2024: Novo Nordisk Data Science grant (~10M DKK) Supervision includes 8 PhD students across projects like CreoleVal and HiFi-KPI . He serves on the Industrial Researcher Committee at Innovation Fund Denmark and is a member of Det Unge Akademi (2023–2028).

Johannes Soedingمشاهده پروفایل

پژوهشگر

• Computational Biology
• Bioinformatics
• Protein Structure Prediction
• +۹ مورد دیگر

Johannes Soeding is a Research Group Leader in the Computational Biology department at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. His work bridges physics, bioinformatics, and molecular biology, focusing on computational methods for biological data analysis. His research interests include computational biology, protein structure and function prediction, metagenomics, transcriptional regulation, and statistical genomics. He develops widely used software tools such as HH-suite, HHpred, MMseqs2, and Foldseek for protein sequence and structure analysis. The recent publications demonstrate a strong focus on high-throughput biological data, particularly in protein structure search (e.g., Foldseek), metagenomic gene discovery (e.g., MetaEuk), and regulatory genomics. His work combines algorithm development with deep biological insights, often published in top-tier journals like Nature Biotechnology , Science , and Nature Methods . He has been involved in significant methodological advances in sequence clustering, contact prediction, and eQTL analysis, showing a consistent trend toward scalable, data-driven approaches in genomics and proteomics. Soeding has contributed to major projects in gene regulatory networks and RNA biology, often in collaboration with experimental groups. His leadership in developing open, efficient bioinformatics tools has had a broad impact on the scientific community. He is affiliated with several graduate programs including IMPRS Physics of Biological and Complex Systems, Biomolecules: Structure - Function - Dynamics, and Genome Science, indicating active participation in training the next generation of scientists.