جست‌وجوی استاد و پژوهشگر

Tak-Wah Lamمشاهده پروفایل

استاد

• Algorithms
• Bioinformatics
• Big Data Analytics
• +۳ مورد دیگر

Professor Tak-Wah Lam is a leading academic at the University of Hong Kong (HKU), serving as Deputy Director of the School of Computing & Data Science and former Head of the Computer Science Department (2018–2023). He joined HKU in 1988 after earning his PhD in Computer Science from the University of Washington. His research spans algorithm design, bioinformatics, and big data analytics, with a focus on applications in health informatics and genomics. He has published over 200 articles, including influential work on bioinformatics software like MEGAHIT and Clair3. He is a Charivate Highly Cited Researcher (2017) and has received multiple grants, including ITF-funded projects totaling over HKD 33 million for research in genomic databases, AI-driven RegTech, and bioinformatics solutions for clinical genetics. Education: BSc from The Chinese University of Hong Kong (CUHK), MS and PhD from the University of Washington. Research interests include algorithms, bioinformatics, and big data analytics. His work bridges theoretical computer science with practical applications in healthcare, such as developing tools for variant calling, genome assembly, and disease risk prediction. He co-founded a startup in 2014 to advance bioinformatics software for clinical DNA sequencing. Key grants include: ITF Tier-2 Project (2021–2023): HKD 7 million for automated karyotype analysis ITF Tier-2 Project (2019–2021): HKD 5 million for AI-driven RegTech ITF Tier-2 Project (2015–2020): HKD 7.6 million for precision medicine genomic databases He is also recognized for teaching excellence, having received awards from HKU's Faculty of Engineering and the Department of Computer Science.

• Computational Biology
• Single-Cell Genomics
• Machine Learning
• +۶ مورد دیگر

Dana Pe'er is Chair of the Computational and Systems Biology Program at the Sloan Kettering Institute (SKI) and an Investigator at the Howard Hughes Medical Institute (HHMI). She holds the Alan and Sandra Gerry Endowed Chair and leads an interdisciplinary lab combining single-cell genomics, machine learning, and computational modeling to study cancer biology, immunity, and development. Pe'er earned her PhD at the Hebrew University in Jerusalem and focuses on cellular plasticity, epigenetic regulation, and tumor-immune interactions. Her lab develops tools like CellRank , Wishbone , and SEACells to analyze single-cell data and uncover mechanisms in cancer progression and immunotherapy. Key research areas: Computational Biology, Single-Cell Genomics, Cancer Systems Biology, Epigenetics, Immunotherapy Recent trends: Articles from 2025-2024 emphasize spatial transcriptomics, tumor microenvironment mapping, and regulatory network inference using machine learning. Scientific honors include the NIH Director’s Pioneer Award , AACR Academy Induction , and Packard Fellowship . Her work has direct clinical implications for precision medicine and cancer immunotherapy. Labs & Teams: Leads the Dana Pe'er Lab at SKI, directs the Single Cell Research Initiative (SCRI), and collaborates with the SAIL program.

Arno Onkenمشاهده پروفایل

استادیار

• Data Science
• Machine Learning
• Computational Neuroscience
• +۷ مورد دیگر

Dr. Arno Onken is a Lecturer (Assistant Professor) in Data Science for Life Sciences at the School of Informatics, University of Edinburgh, where he is also affiliated with the Institute for Adaptive and Neural Computation. He leads a research group focused on developing machine learning and statistical methods for modeling neural activity and analyzing large-scale neuroscience data. His work bridges artificial intelligence and computational neuroscience. His research interests lie at the intersection of machine learning, statistics, and neuroscience. He develops flexible probabilistic models such as copulas and Gaussian processes, deep learning architectures like Vision Transformers for brain activity prediction, and matrix/tensor factorization techniques for dimensionality reduction in neural datasets. His group aims to uncover interpretable structure in complex neural recordings and understand how behavior and cognition are encoded in population activity. The recent publications reflect a strong trend in combining modern deep learning with classical statistical modeling to analyze large-scale neural recordings. His work spans from foundational methods in copula modeling and information theory to applications in predicting visual cortex responses and modeling brainstem-hippocampus interactions across sleep states. The research has been published in top venues including NeurIPS, CVPR, eLife, and PLoS Computational Biology. Dr. Onken actively supervises PhD students and has developed several open-source scientific software packages, including the Mixed Vine Toolbox and Population Spike Train Factorization Toolbox. He teaches core courses in Machine Learning and Pattern Recognition and Data Mining and Exploration at the University of Edinburgh.

• River Ecology
• Wetland Conservation
• Conservation Biology
• +۷ مورد دیگر

Professor Richard Kingsford is a distinguished river ecologist and conservation biologist at the University of New South Wales (UNSW), serving as Director of the Centre for Ecosystem Science within the School of Biological, Earth & Environmental Sciences. With a career spanning over three decades since earning his PhD from the University of Sydney in 1986, he has established himself as a leading expert on Australian river systems. His research focuses on river ecology and conservation biology, particularly in the wetlands and rivers of the Murray-Darling Basin and Lake Eyre Basin. Kingsford's work integrates field ecology with policy development, having previously worked for the NSW Government Environment agency from 1986-2004 before transitioning to academia. He has made significant contributions to understanding how river regulation impacts ecosystems and biodiversity. Kingsford's recent publications demonstrate his expertise in freshwater ecosystem science, adaptive management approaches, and conservation interventions. His research spans from fundamental ecological studies of river systems to applied conservation projects including the Wild Deserts rewilding initiative in Sturt National Park and the Platypus Conservation Initiative. He has developed expertise in using emerging technologies like drone imagery and remote sensing for ecosystem monitoring. As an influential researcher, Kingsford has served on numerous state and federal advisory committees, helping translate scientific findings into river policy and management practices across Australia. His work bridges the gap between academic research and practical conservation outcomes through community engagement and strategic adaptive management approaches. Director of the Centre for Ecosystem Science, UNSW Leader of Wild Deserts rewilding project Expert in Murray-Darling Basin and Lake Eyre Basin ecosystems Specialist in freshwater conservation and adaptive management

Maryellen L. Gigerمشاهده پروفایل

استاد

• Computer-Aided Diagnosis
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Maryellen L. Giger, Ph.D. is the A.N. Pritzker Distinguished Service Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago. She serves as Vice-Chair of Radiology (Basic Science Research) and was the immediate past Director of the CAMPEP-accredited Graduate Programs in Medical Physics/Chair of the Committee on Medical Physics. Her career spans over 30 years of pioneering research in computer-aided diagnosis, machine learning, and deep learning applications in medical imaging. Dr. Giger's research focuses on computational image-based analyses for cancer risk assessment, diagnosis, prognosis, and response to therapy, particularly in breast cancer, lung cancer, prostate cancer, lupus, bone diseases, and more recently, COVID-19. Her work has evolved from developing computer-aided diagnosis systems to utilizing 'virtual biopsies' in imaging genomics association studies for discovery. She has made significant contributions to quantitative imaging, radiomics, and AI applications in medical imaging, with emphasis on translating research into clinical practice. Her publication record shows a clear trajectory from foundational work in computer vision for medical imaging to cutting-edge AI and deep learning applications. The recent publications demonstrate her leadership in large-scale collaborative efforts like the Medical Imaging and Data Resource Center (MIDRC), focus on health equity through AI analysis, and expansion into diverse applications including gynecological imaging, lung cancer screening, and trauma assessment. Her work consistently bridges technical innovation with clinical relevance. Dr. Giger has received numerous prestigious honors including membership in the National Academy of Engineering, the William D. Coolidge Gold Medal (the highest award from AAPM), and being named one of the 50 most impactful medical physicists in the last 50 years. She is a Fellow of multiple professional societies including AAPM, AIMBE, SPIE, SBMR, and IEEE. Her 2019 TIME magazine recognition for QuantX, the first FDA-cleared machine-learning-driven system for cancer diagnosis, highlights her translational impact. As an educator and mentor, Dr. Giger has guided over 100 graduate students, residents, and medical students throughout her career. She has secured substantial research funding including NIH R01 grants and serves as contact PI for the NIH NIBIB-funded & ARPA-H-funded Medical Imaging and Data Resource Center (MIDRC). Her leadership extends to former presidencies of the American Association of Physicists in Medicine and SPIE, and she was the inaugural Editor-in-Chief of the SPIE Journal of Medical Imaging. Dr. Giger co-founded Quantitative Insights, Inc. through the University of Chicago's New Venture Challenge, which developed QuantX - the first FDA-cleared AI system for cancer diagnosis. She leads the Medical Imaging and Data Resource Center (MIDRC), a critical resource for AI development in medical imaging that received the 2023 DataWorks Prize. Her research laboratory bridges engineering, physics, and clinical medicine to develop and validate quantitative imaging biomarkers and AI tools for precision medicine.

• Robotic Systems
• Intelligent Computing
• Artificial Intelligence
• +۳ مورد دیگر

Nuno Miguel Fonseca Ferreira is a Full Professor at the Instituto Superior de Engenharia de Coimbra (ISEC), part of the Polytechnic of Coimbra, where he currently serves as President of the Scientific Council. His academic career spans over 25 years at ISEC, progressing from Assistant to Professor Coordenador Principal. He has held significant leadership positions including Vice-President of ISEC (2001-2005), Pro-President of the Polytechnic of Coimbra (2009-2010), President of ISEC (2010-2013), and Vice-President of the Polytechnic of Coimbra (2013-2017), where he was responsible for internationalization initiatives. His educational background includes a degree in Electrical Engineering from the University of Porto (1996), a Doctorate in Electrical Engineering from the University of Trás-os-Montes and Alto Douro (2006), and a Habilitation Title (Aggregation) from the same institution (2020). His research focuses on Robotic Systems, with specialization in cooperative robotic systems as evidenced by his Habilitation work. Professor Ferreira's research spans multiple domains of robotics and intelligent systems, with particular emphasis on multi-robot coordination, environmental applications, and medical robotics. His work bridges theoretical control systems with practical applications across diverse fields including forestry, healthcare, manufacturing, and education. He has developed innovative approaches to robotic manipulation, sensor integration, and human-robot interaction, often incorporating advanced techniques from artificial intelligence and machine learning. His recent publications demonstrate a strong trend toward practical applications of robotics in real-world environments, particularly in forestry maintenance, industrial automation, and medical applications. The research shows progression from theoretical control systems to applied robotics in challenging environments, with increasing integration of computer vision, deep learning, and collaborative systems. His work spans both fundamental robotics research and immediate industrial applications, reflecting a balance between academic inquiry and practical implementation. Professor Ferreira has supervised two doctoral theses and participated in numerous research projects with substantial funding. His leadership extends to coordinating 15 of the 33 national and international R&D projects he has participated in, demonstrating significant grant acquisition and management capabilities. His international collaborations through Erasmus+ and other European programs highlight his role in fostering global research partnerships. He is an integrated member of GECAD (Research Group in Engineering and Intelligent Computing for Innovation and Advanced Development), a Portuguese R&D unit classified as Excellent by the Portuguese Science and Technology Foundation. Additionally, he is a member of LASI (Associated Laboratory for Intelligent Systems), the Portuguese laboratory associated with Artificial Intelligence, connecting him to a broader national research ecosystem.

Kantaro Fujiwaraمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Neural Data Analysis
• Machine Learning
• +۱ مورد دیگر

Kantaro Fujiwara serves as Associate Professor at the Graduate School of Medicine, The University of Tokyo, with concurrent appointments at the International Research Center for Neurointelligence (IRCN) and the Department of Mathematical Informatics, Graduate School of Information Science and Technology. He also manages the Data Science Core infrastructure for IRCN. His academic background includes a Ph.D. in Information Science and Technology from the University of Tokyo (2008), followed by postdoctoral research at the University of Tokyo (JSPS) and University of Cambridge, then assistant professorships at Saitama University and Tokyo University of Science before joining the University of Tokyo faculty. Dr. Fujiwara's research bridges computational neuroscience and neural data analysis through mathematical modeling of neural networks, development of neural data analysis methodologies, and exploration of brain-inspired machine learning. His work extends to biological information processing with specific applications in pancreatic beta cell modeling for diabetes research, establishing connections between theoretical frameworks and experimental neuroscience. His publication record (2017-2023) reveals consistent interdisciplinary contributions applying echo state networks, recurrence analysis, and nonlinear dynamics to neural data classification, physiological signal processing, and disease modeling. These works demonstrate strong integration of computer science, neuroscience, and biomedical engineering methodologies to solve complex neurobiological problems. As Data Science Core Manager at IRCN, he oversees computational infrastructure and software resources that enable advanced neurointelligence research across the University of Tokyo ecosystem, providing critical support for data-intensive neuroscience projects.

Ross Kingمشاهده پروفایل

استاد

• Automation of Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Professor Ross King is a faculty member at the University of Cambridge, affiliated with the Department of Chemical Engineering and Biotechnology. His research focuses on the automation of scientific discovery, machine learning applications in biology and chemistry, and DNA computing. Developed the first autonomous 'Robot Scientist' systems (Adam, Eve, Genesis) capable of hypothesis generation, experimental design, and execution using AI Pioneer in DNA computing, demonstrating the first physical Nondeterministic Universal Turing Machine (NUTM) 35+ years of expertise in machine learning, particularly relational learning for complex biological/chemical data Organizer of the international 'Nobel Turing Grand Challenge' for AI scientists His work in computational biology spans eukaryotic cell modeling, cancer signaling pathways, and AI-driven drug discovery for neglected tropical diseases like malaria and Chagas disease. The Genesis system aims to automate 10,000 simultaneous closed-loop experiments using micro-chemostats to model cellular complexity. The DNA computing research demonstrates exponential theoretical advantages over classical and quantum computing architectures for NP-complete problems, utilizing Thue string rewriting systems and polymerase chain reaction techniques. This work has significant implications for computer science, physics, and practical computing resource utilization. King's machine learning contributions include active learning strategies for compound selection in drug design and meta-learning approaches to optimize ML applications in bioinformatics and chemoinformatics.

Joergen Kornfeldمشاهده پروفایل

پژوهشگر

• Connectomics
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Joergen Kornfeld is a researcher at the University of Cambridge, affiliated with the MRC Laboratory of Molecular Biology (LMB) in the Connectomics of Learned Behaviour group. His work focuses on understanding how learned behaviors are encoded in neural circuits through connectomic analysis. Institution: University of Cambridge Role: Connectomics Researcher Research Interests: • Connectomics and synaptic connectivity mapping • High-throughput 3D electron microscopy • Deep learning applications in neural network analysis • Behavioral memory storage mechanisms • Comparative neuroanatomy of learned behaviors • Computational modeling of neural circuits. Recent publications highlight his expertise in developing deep learning tools (e.g., DeepFocus, SyConn2) for connectomic reconstruction, with applications in zebra finch song learning and larval zebrafish neural circuits. His work bridges advanced imaging techniques, computational methods, and behavioral neuroscience. Techniques: High-throughput 3D electron microscopy, flood-filling networks Model Systems: Zebra finch, larval zebrafish

Curtis Lee Bakerمشاهده پروفایل

استاد

• Visual Perception
• Neurophysiology
• Computational Modeling
• +۷ مورد دیگر

Curtis Lee Baker is a Professor in the Department of Ophthalmology & Visual Sciences at McGill University's Faculty of Medicine, with an associate appointment in the Department of Biomedical Engineering. His research focuses on understanding human visual perception through neural mechanisms relevant to real-world visual processing. His laboratory investigates how early visual processing detects complex cues like contrast, texture, and motion to establish figure-ground relationships and depth perception. Key research areas include: Neural mechanisms of second-order vision Texture and motion processing Figure-ground segregation Depth perception from motion parallax Computational modeling of visual cortex Dr. Baker employs diverse methodologies including single-unit electrophysiology, optical imaging, human psychophysics, and machine learning. His recent publications (2022-2014) demonstrate consistent focus on neural processing of visual boundaries, texture perception, and motion-based depth cues, with increasing integration of computational approaches like convolutional neural networks. His work bridges neuroscience, engineering, and computational modeling to understand fundamental visual processing mechanisms. Current students include Ana Ramirez Hernandez, Jinani Sooriyaarachchi, and Ethan Pirso, with several alumni having completed graduate work in neuroscience, physiology, and biomedical engineering. The lab actively recruits students with quantitative backgrounds for projects involving signal processing, machine learning, and neurophysiological data analysis.

Huan Leiمشاهده پروفایل

استادیار

• Computational Mathematics
• Numerical Analysis
• Scientific Machine Learning
• +۴ مورد دیگر

Huan Lei is an Assistant Professor at Michigan State University, holding a joint appointment in the Department of Computational Mathematics, Science and Engineering and the Department of Statistics and Probability. He earned his Ph.D. in Applied Mathematics from Brown University in 2012 under George Karniadakis and a B.S. in Special Class for the Gifted Young from the University of Science & Technology of China in 2005. His research integrates scientific machine learning with numerical analysis to develop structure-preserving algorithms for partial and stochastic differential equations arising in multi-scale systems. His work spans multi-scale modeling , non-Markovian dynamics , coarse-grained molecular simulations , and data-driven parameterization . Recent publications focus on learning generalized Langevin equations with state-dependent memory, consensus-based free energy surfaces, and non-equilibrium coarse-grained models. His team applies these methods to fluid dynamics, biomolecular solvation, and climate systems. NSF CAREER Award (2021) Brown University Dissertation Fellowship (2012) He advises graduate and undergraduate researchers and seeks Ph.D. candidates with expertise in numerical analysis or scientific computing. His group receives funding from NSF, DOE, Ford, and MSU Foundation.

• Computational Physics
• Biophysics
• Light-Harvesting Complexes
• +۷ مورد دیگر

Prof. Dr. Ulrich Kleinekathöfer is a Full Professor of Theoretical Physics at Constructor University (formerly Jacobs University Bremen) in the School of Science. His research focuses on computational physics and biophysics, particularly on light-harvesting complexes, membrane transport, and quantum dynamics in biological systems. He leads the Computational Physics and Biophysics research group and coordinates the MSCA Doctoral Training Network "PhotoCaM". His educational background includes: PhD from Max-Planck-Institut für Strömungsforschung, Göttingen (1996) Diploma in Physics from Universität Göttingen (1993) Habilitation in Physics from Technische Universität Chemnitz (2002) Prof. Kleinekathöfer's research spans multiple areas of computational biophysics and theoretical physics. His primary interests include excitation energy transfer in light-harvesting complexes , molecular transport through membrane channels and nanopores , and quantum dynamics in open systems . His group develops and applies advanced computational methods including molecular dynamics simulations, quantum chemistry calculations, and machine learning approaches to study these phenomena. A significant portion of his work focuses on photosynthetic systems, particularly how energy is transferred and converted in natural light-harvesting complexes, with implications for renewable energy technologies. His recent publications demonstrate a strong trend toward integrating machine learning with traditional computational methods, particularly in the fields of quantum chemistry and molecular dynamics. There's a clear focus on multifidelity approaches that balance computational efficiency with accuracy. His work spans from fundamental quantum dynamics to applied research on antibiotic transport mechanisms, showing remarkable breadth while maintaining depth in computational methodology development. His notable recognition includes: Tan Chin Tuan Exchange Fellowship, NTU Singapore (2019) Prof. Kleinekathöfer has supervised numerous PhD students and postdoctoral researchers, with a current group comprising several PhD candidates and research associates. His research is supported by multiple funding sources including the Deutsche Forschungsgemeinschaft (DFG), European Union through MSCA Doctoral Network PhotoCaM, and previously through the Innovative Medicines Initiative "Translocation" and Marie Curie Training Program "Translocation". His collaborative network spans internationally, with partnerships at institutions in Germany, USA, Greece, and Switzerland. The Computational Physics and Biophysics Group operates within Constructor University's research infrastructure, utilizing high-performance computing resources for their simulations. The group maintains active collaborations with experimental groups to validate and inform their computational models, creating a strong interdisciplinary research environment focused on understanding fundamental biophysical processes at the molecular level.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Prof. Dr. Mehmet Reşit Tolun is a full-time Professor in the Department of Software Engineering at Çankaya University (Turkey) since 2022. Previously held full-time professor positions at Konya Food and Agriculture University (2020-2022), Aksaray University (2013-2017), and TED University (2011-2013), along with a part-time professorship at Başkent University (2017-2020). Specializes in Artificial Intelligence , Machine Learning , and Data Mining , with a focus on deep learning applications in aerospace, biomedical data analysis, and software process improvement. PhD in Computer Science (University of Kent, 1985) MSc in Computer Science (University of Kent, 1982) BSc in Physics and Computer Science (University of Kent, 1981) Research Interests span deep learning frameworks, hybrid expert systems, software engineering methodologies, and biomedical signal processing. Publications emphasize practical implementations in medical diagnostics, robotics, and agricultural pest detection. Scientific Awards include the IEEE Third Millenium Medal (2000). Supervised over 55 graduate students, including Burak Çetin, Uğur Özotuk, and Mahinur Doğan. Collaborated with researchers from Orta Doğu Teknik Üniversitesi , Çankaya University , and Aksaray University .

Walter Jetzمشاهده پروفایل

استاد

• Ecology
• Biodiversity Science
• Conservation
• +۵ مورد دیگر

Walter Jetz is a Professor of Ecology and Evolutionary Biology and the School of the Environment at Yale University, where he directs the Center for Biodiversity and Global Change. He chairs the E.O. Wilson Biodiversity Foundation and co-chairs the GEO BON Species Population Working Group. His work focuses on biodiversity science, conservation, and global change ecology. Education: D.Phil. in Zoology (University of Oxford, 2002), M.Sc. in Integrative Bioscience (Oxford, 1997) Research interests include macroecology, species distribution modeling, and conservation science across spatial scales. His group develops tools like Map of Life , Wildlife Insights , and Half-Earth Project to address biodiversity monitoring and area-based conservation. Current projects explore climate change impacts on tropical ecosystems, movement ecology, and machine learning applications in biodiversity science. Recent publications focus on niche scaling, climate change vulnerability, deep learning for species distribution, and mountain biodiversity monitoring. Awards include being an ISI Highly Cited Researcher since 2014. Over 30 former students hold faculty positions globally. The lab promotes diversity, equity, and inclusion in science and collaborates with NASA, Microsoft, and the Gordon and Betty Moore Foundation.

• Artificial Intelligence in Healthcare
• Digital Health Interventions
• Chronic Disease Prevention
• +۱۵ مورد دیگر

Dr. Yoshimi Fukuoka is a Professor at the University of California, San Francisco (UCSF) School of Nursing , specializing in Physiological Nursing . She holds a PhD in Nursing and Postdoctoral Fellowship from UCSF, with a career focused on AI-driven health interventions , chronic disease prevention , and mobile health (mHealth) applications for diverse populations. Her research bridges artificial intelligence and behavioral health , emphasizing personalized physical activity programs , cardiovascular risk reduction , and diabetes prevention . She has developed AI chatbots to improve heart disease awareness and conducted clinical trials on metabolic syndrome and colorectal cancer survivorship . Recent publications highlight her work on: AI chatbots for heart attack knowledge and physical activity interventions Machine learning in diabetes risk prediction and microRNA analysis Cultural adaptations of mHealth for Asian Americans and Latinos Behavioral analytics in weight loss and exercise adherence Scientific awards include: 2014-2017: National Science Foundation EAGER Grant 2011-2013: UCSF Diabetes Family Fund Award 2010-2012: American Heart Association National Clinical Research Program Award 2008: Marie Cowan Promising Young Investigator Award Her work spans UCSF , with collaborations across NIH , Alzheimer's Association , and Noyce Foundation , addressing health disparities , digital health policy , and precision health through grants like NIH R01HL104147 and K24NR015812 . Media coverage includes USA Today , NHLBI News , and UCSF News for her AI in nursing innovations.