جست‌وجوی استاد و پژوهشگر

Andrew Friendمشاهده پروفایل

پژوهشگر

• Carbon Cycle
• Ecosystem Ecology
• Climate Science
• +۵ مورد دیگر

Andrew Friend is a Researcher at the University of Cambridge , affiliated with the Department of Geography . His work focuses on terrestrial ecosystem dynamics, particularly the interplay between vegetation, carbon fluxes, and climate systems. He develops process-based computer models to simulate vegetation growth, competition, and carbon cycle responses, while also conducting experimental and field studies in collaboration with institutions like the Sainsbury Laboratory, NIAB, and Forestry England. Friend’s research integrates plant physiological knowledge with global vegetation modeling . Key interests include source-sink carbon interactions, physiological diversity in ecosystems, and the impact of climate extremes on forest resilience. His models address challenges such as drought, temperature changes, and deforestation, with applications in predicting future carbon balances and informing climate mitigation strategies. The 15 most recent articles highlight his contributions to understanding Amazon deforestation tipping points , wood formation mechanisms , and fire/disturbance impacts on ecosystems . These works span climate science , forest ecology , and biogeochemical cycles , emphasizing multi-scale approaches from cellular processes to global systems. Friend collaborates with institutions like Forestry England (Thetford Forest studies), Sainsbury Laboratory , and the C-CLEAR Doctoral Training Partnership . His work bridges computational modeling, experimental validation, and field data to advance terrestrial carbon cycle science.

• intelligent systems
• high performance distributed computing
• resource management
• +۸ مورد دیگر

Professor Omer Rana serves as Professor of Performance Engineering and International Dean for the Middle East at Cardiff University's School of Computer Science and Informatics. He also holds the prestigious position of Cross-Council Research Director for the UK National Edge AI Hub, demonstrating his leadership in national research initiatives. Previously, he led the Complex Systems research group and served as Dean of International for the Physical Sciences and Engineering College at Cardiff University. Professor Rana is a Fellow of both the Learned Society of Wales and the Higher Education Academy, and serves on the Advisory Board of the Welsh Ethnic Minority Professors Initiative (WEMPI). His research expertise centers on the intersection of intelligent systems and high performance distributed computing, with particular focus on applying intelligent techniques to resource management in distributed systems. His scholarly contributions span edge computing, cloud computing, Internet of Things (IoT), artificial intelligence, cybersecurity, federated learning, privacy-preserving systems, and sustainable computing. Professor Rana has published extensively in top-tier journals and conferences, with recent work emphasizing practical applications in industrial automation, smart buildings, and sustainable computing solutions. Professor Rana's publication record demonstrates consistent leadership in edge computing and distributed systems research, with a growing emphasis on practical implementations across diverse application domains. His work bridges theoretical computer science with real-world technological challenges, particularly in industrial automation, smart environments, and sustainable infrastructure. Fellow of the Learned Society of Wales Fellow of the Higher Education Academy Professor Rana actively supervises postgraduate students and has secured significant research funding for projects related to edge computing, IoT, and distributed systems. His international collaborations span Europe, Asia, and the Middle East, reflecting his global influence in the field. He serves as a sought-after keynote speaker, workshop chair, and panel moderator at major international conferences including IEEE/ACM Utility and Cloud Computing (UCC) and IEEE Edge Computing. He leads multiple research initiatives, most notably as Cross-Council Research Director for the UK National Edge AI Hub, where he shapes national research directions in edge computing and AI. His work has practical applications across industrial automation, smart building management, electric vehicle infrastructure, and sustainable computing solutions.

Keely Duganمشاهده پروفایل

استادیار

• Personality Psychology
• Attachment Theory
• Personality Development
• +۳ مورد دیگر

Keely Dugan serves as an Assistant Professor in the Department of Psychology at the University of Missouri, directing the Personality, Attachment, and Change (PAC) Lab in McReynolds Hall. She holds a PhD in Social/Personality Psychology from the University of Illinois at Urbana-Champaign (2023) and completed an NIMH T32 Postdoctoral Fellowship at the University of Minnesota (2024). Her research investigates dynamic changes in personality traits and attachment styles across time, life experiences, and social contexts. Using advanced statistical methodologies, she examines how individual differences manifest in everyday environments, emphasizing how cumulative "little moments" shape long-term development. This work bridges personality psychology, attachment theory, and contextual behavioral science. Recent 2024 publications reveal three interconnected research strands: quantifying life events' impact on personality trajectories, testing attachment theory's canalization hypothesis through within-subject variations, and conducting systematic reviews of queer/minority identities in relationship science. These studies demonstrate her interdisciplinary approach combining longitudinal analysis, computational modeling, and inclusive relationship research. Dr. Dugan's scientific recognition includes: NIMH T32 Postdoctoral Fellowship (2024) She actively recruits graduate students for Fall 2025 and teaches PSYCH 9330 (Graduate Research Methods), PSYCH 8620 (Graduate Seminar in Personality Psychology), and PSYCH 2320 (Introduction to Personality Psychology). Current projects include NIH-funded personality-environment interaction studies and development of AI-assisted coding methodologies for behavioral research. The PAC Lab, located in McReynolds Hall's Lower Level, currently spearheads a groundbreaking project analyzing 3D living room scans to predict personality traits through environmental cues. This initiative employs both human coders and machine learning algorithms to examine how physical spaces reflect and influence individual differences in attachment and personality expression.

Lars Engstrandمشاهده پروفایل

استاد

• Microbiome Research
• Women's Reproductive Health
• Gastroenterology
• +۷ مورد دیگر

Lars Engstrand is a Professor in infection control at Karolinska Institutet, heading the Translational Microbiome Research and Pandemic Preparedness group within the Department of Microbiology, Tumor and Cell Biology. His research spans multiple disciplines with a focus on translational microbiome research and pandemic preparedness. Engstrand's research interests center on understanding the human microbiome's role in health and disease, particularly in women's reproductive health and gastroenterology. His group has established multiple large-scale clinical studies including SweMaMi (Swedish Maternal Microbiome project), BASIC (associations between microbiota and preterm birth), VaMiGyn (Vaginal Microbiota in Gynaecological health), and several others investigating the vaginal microbiome's relationship to pregnancy outcomes, HPV infection, and cervical cancer. His team also conducts significant research on gut microbiome in inflammatory bowel disease and colorectal conditions through projects like PopCol and KOLBIBAKT. Analysis of Engstrand's most recent publications reveals a strong focus on the relationship between microbiome composition and women's health outcomes. His research consistently examines how vaginal, gut, and oral microbiomes influence pregnancy complications, preterm birth, HPV infection, and mental health during pregnancy. The work often employs large cohort studies with comprehensive sampling strategies across multiple body sites and time points, providing robust data for understanding microbiome dynamics in health and disease. Engstrand leads the Microbiome Exploration and Development for Intervention (MEDI) initiative and the National Pandemic Center (NPC) at Karolinska Institutet. The NPC conducted large-scale sequencing during the COVID-19 pandemic and has amassed over 1.5 million samples. His research group includes multiple specialized teams focusing on culturomics, bioinformatics, labcore operations, women's health research, and pandemic preparedness.

Rahul Mangharamمشاهده پروفایل

استاد

• Formal Methods
• Machine Learning
• Control Systems
• +۳ مورد دیگر

Rahul Mangharam is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania's School of Engineering and Applied Science, with a secondary appointment in Computer and Information Science. He directs the Safe Autonomous Systems Lab (mLAB) and is a founding member of the PRECISE Center. Mangharam serves as Penn Director for the Safety21 DoT National University Transportation Center ($20MM), Director of the Autoware Center of Excellence, and leads the F1Tenth Autonomous Racing Community. Education: Ph.D. in Electrical & Computer Engineering, Carnegie Mellon University M.S. in Electrical & Computer Engineering, Carnegie Mellon University B.S. in Electrical & Computer Engineering, Carnegie Mellon University His research bridges formal methods, machine learning, and control systems with applications in medical devices, autonomous systems, and energy-efficient buildings. Key focus areas include safety verification for autonomous vehicles, real-time control systems, and patient-specific cardiac modeling for clinical applications. Recent work explores conformal prediction for safe perception, differentiable control barrier functions, and explainable autonomous systems. Mangharam's publication trends show strong emphasis on autonomous systems safety (control synthesis, uncertainty quantification) and biomedical applications (cardiac modeling, clinical decision support). His 2022-2023 publications demonstrate cross-disciplinary approaches combining control theory, machine learning, and formal methods for robust autonomous systems. Awards and Honors: Presidential Early Career Award (PECASE) 2016 IEEE Benjamin Franklin Key Award 2014 NSF CAREER Award 2013 Intel Early Faculty Career Award 2012 National Academy of Engineers US Frontiers of Engineering (2012, 2018) Stephen J. Angelo Term Chair (2008-2013) He leads multiple major grants including NSF CAREER, DoT Safety21 Center ($20MM), DoE Energy-Efficient Building Hub ($160MM), and DARPA HACMS. Current PhD students include Zirui Zang. Mangharam founded the F1Tenth autonomous racing platform used globally for education and hosts international competitions through the Autoware Center of Excellence.

Andrea Lecchini Visintiniمشاهده پروفایل

دانشیار

• Systems Modelling and Control
• Aerospace Engineering
• Biomedical Applications
• +۱ مورد دیگر

Dr. Andrea Lecchini Visintini is an Associate Professor at the School of Electronics and Computer Science , University of Southampton. He specializes in systems modelling and control with applications in aerospace engineering and biomedical domains, utilizing Monte Carlo methods for stochastic optimization. Cyber-Physical Systems Research Group Institute for Life Sciences Research Focus: His work bridges computational methods with practical applications in: Neurovascular coupling and brain tissue pulsation analysis Advanced control strategies for aerospace systems Stochastic optimization in machine learning and fault detection Medical imaging and diagnostic protocol development Publication Trends: Recent work emphasizes interdisciplinary approaches combining computational neuroscience with engineering, focusing on brain hemodynamics, MIMO system control, and data augmentation techniques for imbalanced datasets. Supervision: Currently supervising PhD student Xuankun Cai in Computer Science.

• Neurosurgery
• Functional Neurosurgery
• Neuromodulation
• +۳ مورد دیگر

Zion Zibly, MD, MBA is an Associate Professor in the Department of Neurosurgery at Yale School of Medicine . He holds multiple leadership roles including Director of the Center of Neuromodulation , Director of the Center of Neurosurgical Cancer Pain , and Head of Stereotactic & Functional Neurosurgery and the Focused Ultrasound Institute . Previously served as Chair of Neurosurgery at Sheba Medical Center after graduating from Technion’s Faculty of Medicine (MD) and Coller School of Management (MBA). Research Interests: Specializes in Neuromodulation for movement disorders (Parkinson’s, tremors, dystonia), Deep Brain Stimulation , Gene Therapy for pediatric neurodegenerative conditions, Oncological Neurosurgery , and Neurological Pain Management . Combines Functional Neurosurgery with Focused Ultrasound technology. Scientific Contributions: Participated in pioneering Alzheimer’s brain stimulator procedures and Gene Therapy applications. Active member of the North American Association of Functional Neurosurgery and Israeli Neurosurgical Society . Clinical Expertise: Implantation of electrostimulators for Parkinson’s and essential tremor, treatment of Benign/Malignant CNS Tumors , and management of Neurological Pain Conditions . Affiliated with Yale Cancer Center and Center for Brain & Mind Health .

• Plant Nutrition
• Soil Fertility
• Soil Spectroscopy
• +۳ مورد دیگر

Du Changwen is a Researcher (Professor) at the Nanjing Institute of Soil Science, Chinese Academy of Sciences, serving as Deputy Director of the National Engineering Laboratory for Soil Nutrient Management. He supervises doctoral and master's students in soil science and agricultural technology development. His academic journey includes: Bachelor's degree from Huazhong Agricultural University's College of Resources and Environmental Science (1997) Master's degree from Huazhong Agricultural University's Trace Element Laboratory (2000) PhD from Nanjing Institute of Soil Science, Chinese Academy of Sciences (joint program with Technion - Israel Institute of Technology) (2003) Dr. Du's pioneering research focuses on precision fertilization technologies, particularly polymer-coated controlled-release fertilizers developed through model membrane and water-based reaction film-forming techniques. His work integrates Fourier Transform Infrared spectroscopy (ATR and PAS modes) with engineering mathematics to monitor nutrient release dynamics, soil chemistry processes, and plant nutrition in real-time. This interdisciplinary approach bridges agricultural chemistry, materials science, and environmental engineering to optimize fertilizer efficiency while minimizing ecological impact. Analysis of his 2015-2017 publications reveals a consistent emphasis on spectroscopic methods for soil-plant system analysis, with dominant themes in controlled-release fertilizer development, soil organic matter characterization, and in-situ nutrient monitoring. His work demonstrates strong cross-disciplinary integration between agricultural technology, analytical chemistry, and environmental science. His scientific recognition includes: Special Award of the First China Agricultural Science and Technology Innovation and Entrepreneurship Competition First Prize of Jiangsu Science and Technology Award First Jiangsu Youth Entrepreneurship Award Second Prize of Chinese Academy of Sciences Science and Technology Contribution Award First Prize of China Agricultural Science and Technology Award Dr. Du has secured major research funding including National Natural Science Foundation projects (key, general, youth), National '973' Basic Research Program, '13th Five-Year' R&D Plan sub-projects, '863' High-tech Program sub-projects, and Jiangsu Provincial Science and Technology Support Plan initiatives. His leadership in the National Engineering Laboratory for Soil Nutrient Management drives innovation in fertilizer technology, with significant outputs including 198 academic papers (89 SCI, 35 EI), 6 monographs, 1 international patent, 8 national patents, and 2 software copyrights. His laboratory specializes in advanced spectral analysis of soil-plant systems, utilizing FTIR-ATR and FTIR-PAS technologies for real-time monitoring of nutrient dynamics and polymer membrane reactions. Current research focuses on next-generation controlled-release fertilizers, machine learning-enhanced spectral analysis, and precision nutrient management systems for sustainable agriculture.

Matthieu Cordمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Matthieu Cord is a Professor at Sorbonne University and Scientific Director of valeo.ai, leading research in computer vision, deep learning, and computational cooking. He heads the MLIA team at ISIR Lab, focusing on multimodal models, transformers, and efficient architectures. Research areas include computer vision, large language models with vision, and AI-driven food analytics. Key projects: VISA-DEEP AI chair, Foundation VaViM models, and SmolVLA collaboration with Hugging Face. His recent work examines scalable multimodal models , trajectory prediction , and diffusion-based segmentation , with studies on in-context learning and biased shortcut learning in visual question answering. Articles highlight DeiT variants , fishr for OoD generalization , and STEEX for counterfactual explanations . Scientific awards include IUF Honorary Membership (2009), BMVC 2017 Best Paper, and ICIP 2018 Best Paper. As an advisor, he supervised PhD theses on topics like GAN editing , semantic segmentation , and multimodal retrieval . Current roles involve mentoring the 'Research Band' at MLIA and leading EU-funded initiatives like SCAPE. His work bridges theoretical AI exploration with practical applications in autonomous driving and food technology.

Joseph Tao-yi Wangمشاهده پروفایل

استاد

• Experimental Economics
• Neuroeconomics
• Game Theory
• +۳ مورد دیگر

Joseph Tao-yi Wang is a Distinguished Professor in the Department of Economics at National Taiwan University (NTU). He holds a PhD from UCLA and previously served as a Postdoctoral Scholar and Visiting Associate at Caltech. His research spans experimental economics, neuroeconomics, game theory, and behavioral economics, with a focus on strategic decision-making, market design, and learning in games. Wang directs the Taiwan Social Sciences Experimental Laboratory (TASSEL), which hosts large-scale experimental research and conferences like the 2017 APESA. His work integrates eye-tracking, pupillometry, and machine learning to study cognitive processes in economic decisions. Wang is also active in educational innovation, developing flipped classroom models with experiments for economics courses. His publications consistently explore behavioral deviations from game-theoretic predictions, such as overcommunication in sender-receiver games and learning patterns in auctions. Recent work emphasizes reproducibility in management science and AI applications in education. Wang’s research uses diverse methodologies—from neuroimaging to field experiments—to test economic theories in real-world contexts. Wang mentors through NTU’s Berkeley Economics Student Assistant Program (BESAP) and organizes mini-courses for high school students. He has not received scientific awards per the available data.

Ramina Sotoudehمشاهده پروفایل

استادیار

• Sociogenomics
• Sociology of Culture
• Social Inequality
• +۲ مورد دیگر

Ramina Sotoudeh is an Assistant Professor of Sociology at Yale University with a secondary appointment in Statistics & Data Science. Her research bridges sociogenomics, the sociology of culture, and social inequality, focusing on how genetic and social environments interact to shape human behavior. Education : BA in Social Research and Public Policy from NYU Abu Dhabi, PhD in Sociology from Princeton University Postdoctoral Experience : Fellow at Nuffield College, University of Oxford Ramina’s work in sociogenomics examines how institutional, relational, and genetic contexts influence health outcomes, such as smoking behavior and peer interactions. Her sociology of culture projects use relational methods to explore cultural frameworks underlying attitudes toward science, religion, politics, and marriage. She also investigates health disparities and inequality through interdisciplinary lenses. Her most recent publications analyze genomic population structure, behavioral plasticity, and computational approaches to algorithm selection. Earlier works focus on cultural attitudes, behavioral diffusion in networks, and genetic correlations with education and longevity. These studies span journals like American Sociological Review , PNAS , and Demography .

David Gesbertمشاهده پروفایل

استاد

• Communication theory
• Signal processing for wireless networks
• Information theory
• +۷ مورد دیگر

David Gesbert serves as Professor and Director of EURECOM, a leading research institution in Sophia Antipolis, France, specializing in digital sciences. Previously heading the Communications Systems Department, he now directs EURECOM while leading the Foundations & Algorithms research group within the Communication Systems Department. His leadership spans institutional administration and cutting-edge research supervision. Dr. Gesbert's research portfolio demonstrates exceptional depth across communications theory and wireless networking: Communication theory and information theory fundamentals Signal processing for wireless networks with emphasis on robustness Machine learning applications for decentralized network optimization Connected robotics and UAV-enabled flying radio access networks 6G wireless architecture with focus on AI integration Distributed decision making under information uncertainties His publication trajectory reveals a strategic evolution from classical communication theory toward AI-integrated wireless systems, particularly focusing on UAV-aided networks and connected robotics for 6G. Recent work emphasizes learning-based approaches for network optimization under asymmetric information conditions, with growing emphasis on sustainability and green communications. Dr. Gesbert's scientific recognition includes: Fellow of IEEE (2011) and Asia Pacific Artificial Intelligence Association (2021) Thomson-Reuters List of Highly Cited Researchers in Computer Science Multiple Best Paper Awards at IEEE conferences (EW 2017, ICC 2019) ERC Advanced Grant recipient for the PERFUME project on Smart Device Communications 3IA Chair funding for AI for future IoT Networks 2019 Winner of 'Fundamental research project of the year' by French SCS He actively mentors PhD students and researchers, welcoming collaboration in his research domains. His group secures substantial research funding including ERC grants, 3IA Chairs, and participation in major European projects like the WINDMILL ITN Marie Curie project on Machine Learning in Wireless Communications. Dr. Gesbert serves on editorial boards and regularly delivers keynotes at premier international conferences, establishing himself as a thought leader in next-generation wireless communications.

Nicolas Federico Martinمشاهده پروفایل

دانشیار

• Precision Agriculture
• Crop Science
• Machine Learning Applications in Agriculture
• +۷ مورد دیگر

Nicolas Federico Martin is an Associate Professor in the Department of Crop Sciences at the University of Illinois at Urbana-Champaign, with additional appointments as Associate Professor in the Center for Latin American and Caribbean Studies, Center for Digital Agriculture, and the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges traditional agricultural science with cutting-edge computational approaches. Dr. Martin's research focuses on the intersection of agriculture and data science, with particular emphasis on: Precision agriculture and on-farm experimentation methodologies Machine learning applications for crop management and yield prediction Nitrogen and nutrient management optimization Soybean and corn breeding and production systems Remote sensing and UAV applications in agriculture Sustainable agricultural practices including cover crop management His publication record demonstrates a clear trajectory toward increasingly sophisticated integration of artificial intelligence with agricultural science. Recent work shows heavy emphasis on using machine learning algorithms (particularly reinforcement learning, convolutional neural networks, and generalized additive models) to solve practical farming challenges related to crop management decisions, yield prediction, and resource optimization. This research has significant implications for both scientific understanding of crop-environment interactions and practical farm management. Dr. Martin actively collaborates across disciplines and institutions, as evidenced by his extensive co-authorship network spanning agronomy, computer science, environmental science, and economics. His work has garnered attention from numerous news outlets and social media platforms, indicating its relevance to current agricultural challenges. He is a key contributor to the Data-Intensive Farm Management project, which aims to transform agronomic research through on-farm precision experimentation. His affiliation with NCSA provides access to high-performance computing resources essential for processing large agricultural datasets. Additionally, his work in Latin American agriculture (particularly in Mexico and Argentina) reflects his commitment to addressing global food security challenges.

• Game Theory
• Bargaining Theory
• Coalition Formation
• +۴ مورد دیگر

Juan Camilo Gómez is an Associate Professor with tenure at the University of Washington Bothell's School of Business, specializing in game theory, bargaining, and microeconomic theory. He earned his Ph.D. in Economics from the University of Minnesota and his B.Sc. in Mathematics from Universidad de los Andes in Colombia. Education: Ph.D. Economics (1998–2003), University of Minnesota, Minneapolis, MN B.Sc. Mathematics (1990–1996), Universidad de los Andes, Bogotá, Colombia Research Interests: His research focuses on foundational and applied aspects of game theory, including bargaining models, coalition formation, cooperative solution concepts, and general equilibrium. He explores how efficiency can be achieved in strategic interactions, especially when agents may behave manipulatively or when traditional balance conditions do not hold. His work contributes to understanding how coalitions form and how outcomes can be predicted or designed to ensure equitable and efficient allocations. He also investigates the implications of reference points in bargaining and the role of aspirations in shaping cooperative behavior. Publications Overview: Across his publications, a clear trajectory emerges from foundational theoretical work—such as axiomatizing core extensions in cooperative games—to applied models that predict coalition formation and bargaining outcomes. His research consistently bridges rigorous mathematical frameworks with practical economic questions, including market interpretations of cooperative solutions and strategic pricing models like 'Pay What You Want'. Awards and Honors: 2010 MBA Professor of the Year, University of Washington Bothell Teaching and Advising: Gómez has taught a wide range of courses from undergraduate calculus and mathematical economics to graduate-level game theory and quantitative methods for business. He has held teaching roles at institutions including Macalester College, University of Copenhagen, Universidad de los Andes, and University of Minnesota. He has also co-directed undergraduate theses, such as that of Santiago Saavedra, and served on numerous academic committees including MBA Admissions and faculty hiring. Labs and Teams: While no specific lab is mentioned, his collaborative working papers with researchers like Camelia Bejan and P.V. Balakrishnan suggest active participation in research networks focused on game theory and economic modeling.

• Statistical Machine Learning
• Deep Learning
• Computational Biology
• +۲ مورد دیگر

Rui Li is an Associate Professor in the Ph.D. program at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. She directs the Lab for Use-inspired Computational Intelligence (LUCI), focusing on AI applications in computational biology and medical imaging. Education includes: B.Sc. in Computer Science, Harbin Institute of Technology M.Sc. in Computer Science, Tianjin University of Technology Ph.D. in Computing and Information Sciences, RIT Research integrates statistical machine learning with computational biology, medical image analysis, and human visual attention modeling. Current projects include deep learning for histopathology, multimodal medical image registration, and gene network inference. Publications demonstrate consistent focus on medical AI applications, with recent advances in unsupervised image registration, interactive segmentation, and multimodal fusion techniques. Key trends include self-supervised learning, uncertainty-aware models, and human-AI collaboration frameworks. Awards include the NSF CAREER Award for developing adaptive machine intelligence systems. Advises multiple PhD students on projects spanning deep learning architectures, biomedical image analysis, and biological network modeling. Leads several NSF-funded projects including human-centered image understanding systems and gene-protein network inference tools. Directs LUCI lab investigating machine learning for healthcare applications and teaches graduate courses in Statistical Machine Learning and Deep Learning.