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نمایش ۷۲۱-۷۳۵ از ۳۴٬۰۹۱ استاد راهنما
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استادیار
Onur Varol is an Assistant Professor at Sabanci University's Computer Science Department and leads the VIRAL Lab, which focuses on computational social science, network science, and machine learning. He has affiliations with the Center of Excellence for Data Analytics. His research spans social bot detection, misinformation analysis, and online behavior modeling.
استادیار
Lizi Liao is an Assistant Professor at the School of Computing and Information Systems , Singapore Management University (SMU) , specializing in Artificial Intelligence and Conversational AI . Her research bridges Machine Learning , Natural Language Processing , and Multimodal Systems , focusing on proactive dialogue systems, multimodal conversational search, and task-oriented interactions. Education : PhD in Computer Science (2019) from the National University of Singapore (NUS) , advised by Professor Tat-Seng Chua . Research Interests center on principles of human conversational understanding and machine implementation, particularly in proactive conversational agents , multimodal dialogue systems , and target-driven conversation planning . Key applications include emotional support systems , intelligent shopping assistants , and learning companions . Recent Publications (2024-2025) highlight her work on LLM-based proactive dialogue , multimodal emotion recognition , and dynamic graph modeling , often integrating NLP , Multimedia , and Knowledge Graphs . Collaborative projects with her CoAgent Lab team emphasize human-AI interaction and ethical response generation . Scientific Awards : Google South Asia & Southeast Asia Research Award 2023 Lee Kong Chian Fellow Teaching includes Visual Analytics for Business Intelligence (undergraduate) and Text Analytics and Application (graduate). She also serves as Associate Editor for TOIS and TOMM , and organizes tutorials at ACL , SIGIR , and WSDM .
Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.
Dirk Praetorius is a Professor of Numerics of Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Wien) , affiliated with the Institute for Analysis and Scientific Computing (ASC) within the Faculty of Mathematics and Geoinformation . He leads the research group on Numerics of PDEs and has held various leadership roles, including Institute Director (since 2020) and head of the Numerics research area. His work focuses on numerical methods for PDEs, including Finite Element Methods (FEM), Boundary Element Methods (BEM), adaptive algorithms, and computational micromagnetics. Education and Career: Praetorius earned his Diplom in Mathematics (2000) and PhD in Applied Mathematics (2003) from TU Wien, followed by a Habilitation in Numerical Analysis (2005). He has been a faculty member at TU Wien since 2005, progressing from Assistant Professor to full Professor in 2017. He has also held visiting positions at institutions such as the University of Jyväskylä and RICAM (Linz). Research Interests: His research spans numerical analysis, adaptive FEM/BEM, a-posteriori error estimation, matrix compression, and computational micromagnetics. He has contributed to modeling spin dynamics, magnetic skyrmions, and multiscale systems. His work emphasizes efficient algorithms for large-scale problems and optimal computational complexity. Awards and Editorial Roles: Praetorius received the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He serves as Senior Editor for Computational Methods in Applied Mathematics (CMAM) and on the editorial board of Applied Numerical Mathematics (APNUM) . He co-founded the outreach initiative TUForMath to promote mathematics education. Grants and Projects: He leads or co-leads several research projects funded by the Austrian Science Fund (FWF), including the collaborative SFB "Taming Complexity in Partial Differential Systems" (2017–2025) and international collaborations with Germany. His work addresses topics like functional error estimates, nonlinear PDEs, and computational design of magnetic devices. Labs and Teams: He contributes to the ASC Institute and coordinates interdisciplinary projects involving computational physics and engineering. His team develops software tools like MooAFEM and Commics for micromagnetic simulations.
استادیار
Lucia Lee is an Assistant Professor in the Department of Chemistry at Queen's University, affiliated with the Faculty of Arts and Science. Her research focuses on applying green chemistry principles to supramolecular interactions involving main-group elements, particularly sigma-hole interactions, with applications in materials science and medicine. She holds a PhD from McMaster University and has completed postdoctoral studies at the University of Geneva and Weizmann Institute of Science. Dr. Lee's educational background includes a PhD supported by an NSERC grant, which explored chalcogen bonding in supramolecular materials. Her postdoctoral work at Weizmann focuses on stimuli-responsive materials using chalcogen elements for photoswitching applications. She has also contributed to academic governance through roles in the McMaster Graduate Students Association. Her research interests span analytical chemistry, quantum chemistry, inorganic and bioinorganic chemistry, organic chemistry, and free radical chemistry. Key projects include integrating chalcogen bonding into d-metal coordination chemistry, catalysis, and chemical biology to create functional materials. Her lab, located in CHE513, emphasizes sustainable approaches to material design through main-group supramolecular systems. Her articles explore topics like chalcogen bonding mechanisms, anion transport, and photoswitching in confined spaces, reflecting a strong focus on molecular assembly and functional materials. She has no listed scientific awards but demonstrates significant contributions to supramolecular chemistry through her publications and cross-appointments at Queen's Carbon to Metal Coating Institute.
Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.
پژوهشگر ارشد
Dr. Guillem Müller Rigat is a Postdoctoral Researcher at the Institute of Photonic Sciences (ICFO), working in the Quantum Optics Theory research group. He holds a PhD in Photonics from the Universitat Politècnica de Catalunya (Spain). His research focuses on quantum information theory and quantum optics, with a particular emphasis on entanglement, Bell inequalities, and many-body quantum systems. He explores topics such as quantum resource certification, symmetry in quantum states, and applications of machine learning in quantum tomography. Müller Rigat’s work bridges fundamental quantum theory and experimental feasibility, addressing challenges in quantum metrology, nonlocality, and chaos. His recent studies include developing methods to infer quantum correlations from observable data and enhancing protocols for entanglement detection in complex systems. He contributes to advancing theoretical frameworks for certifying quantum systems with minimal experimental resources. He is affiliated with ICFO’s Quantum Optics Theory group, where he collaborates on projects involving Bell inequalities, spin-nematic squeezing, and quantum Fisher information. Despite his postdoctoral focus, he actively publishes in high-impact journals, with a strong emphasis on interdisciplinary approaches combining quantum foundations and applied quantum technologies.
Prasenjit Mandal is an Associate Professor in the Department of Information Systems, Supply Chain Management, and Decision Support at NEOMA Business School (France). He holds a PhD in Decision Sciences and Information Systems from the Indian Institute of Management (IIM) Bangalore. Previously, he served as an Assistant Professor of Operations Management at IIM Calcutta and worked as an Oracle ERP consultant at Tata Consultancy Services. His research focuses on supply chain finance, revenue optimization, multi-channel retail strategies, and strategic decision-making in supply chains. He has published in top journals like European Journal of Operational Research and IEEE Transactions on Engineering Management. He currently serves as a reviewer for multiple academic journals. Education: PhD in Decision Science and Information Systems, IIM Bangalore, India Oracle ERP Techno-Functional Certification Research Interests: Revenue Management in Retail & E-commerce Supply Chain Finance & Platform Financing Multi-channel Distribution Strategies Empirical Modeling of Supply Chain Trade-offs Strategic Decision-Making under Competition Key Contributions: His recent work explores platform financing models, strategic supplier financing choices, and omnichannel retail challenges. His research integrates optimization models with real-world operational scenarios to address supply chain complexities. Professional Experience: Current: Associate Professor, NEOMA Business School 2016-2019: Assistant Professor, IIM Calcutta 2012-2015: Oracle ERP Consultant, Tata Consultancy Services
دانشیار
Dr. Jason Gibbs is an Associate Professor in the Department of Entomology at the University of Manitoba, Faculty of Agricultural and Food Sciences. He also serves as the Curator of the J. B. Wallis / R. E. Roughley Museum of Entomology (WRME), a significant center for the study of bee biodiversity. His work is central to advancing knowledge in wild bee systematics, phylogenetics, and conservation. PhD in Biology, York University, Canada MSc in Botany, University of Toronto, Canada BSc in Biological Sciences, University of Toronto Scarborough, Canada His research focuses on the diversity, taxonomy, and conservation of wild bees , particularly halictid and panurgine bees. He employs integrative taxonomic approaches , combining morphological, molecular, and ecological data to resolve species boundaries and evolutionary relationships. His work extends to pollinator ecology , examining how habitat management, agricultural practices, and landscape changes affect bee communities and pollination services. He is deeply involved in bee conservation , including the rediscovery of rare species and the development of habitat strategies to support pollinators in human-modified landscapes. The trends in his recent publications reveal a strong emphasis on systematics and alpha-taxonomy , with numerous revisions of bee genera and checklists of regional faunas. He frequently uses DNA barcoding and phylogenomics to address taxonomic challenges. Additionally, his work explores pollination dynamics in agricultural systems , particularly in blueberry and other crops, assessing the roles of wild versus managed bees. There is a consistent theme of habitat enhancement and conservation across his research, with studies on floral strips, prairie restoration, and the impacts of land-use change. Dr. Gibbs is actively involved in mentoring and training the next generation of entomologists. His lab includes several graduate students and highly qualified personnel who contribute to his diverse research projects, as indicated by the asterisked names in his publications. He leads the Gibbs Wild Bee Lab, which is dedicated to understanding bee diversity and evolution. The lab combines field research with molecular and morphological analyses, and maintains close ties with the WRME museum, which serves as a vital resource for specimen-based research and education.
Retsef Levi is the J. Spencer Standish (1945) Professor of Operations Management at the MIT Sloan School of Management, affiliated with the MIT Operations Research Center. He co-directs the Leaders for Global Operations (LGO) Program. His work focuses on data-driven decision models for healthcare systems, supply chain optimization, and risk management. Levi holds a PhD in Operations Research from Cornell University and has led industry collaborations with major hospitals and organizations like the FDA and Walmart Foundation. Education: PhD in Operations Research, Cornell University, 2005 Bachelor’s in Mathematics, Tel-Aviv University, 2001 Research Interests: Levi’s research addresses complex decision-making under uncertainty in healthcare, supply chains, and logistics. Key areas include food safety analytics, risk-based sampling, and predictive modeling for zoonotic diseases. He designs algorithms for inventory control, appointment scheduling, and healthcare resource allocation. Articles Overview: Recent work spans AI-driven epidemiological models, supply chain cybersecurity, and agricultural market interventions. His articles emphasize practical applications of operations research in healthcare and public health. Awards: NSF Career Grant INFORMS Optimization Prize (2008) Wagner Prize (2013) Harold W. Kuhn Award (2016) Advising & Grants: Advised 10 PhD students and 34 master’s students. Led multi-million-dollar projects like the Walmart Foundation initiative for China’s food safety. Active in hospital process optimization and FDA risk management contracts. Labs & Teams: Runs MIT’s Food Supply Chain Analytics and Sensing Initiative, collaborating with global partners on predictive risk tools and healthcare analytics.
استاد پژوهشی
Professor Axel Bruns is a Research Professor at Queensland University of Technology’s (QUT) Digital Media Research Centre (DMRC), an institution renowned for its leadership in media and communication studies. His work focuses on the digital transformation of media, the role of social media in public communication, and the dynamics of political polarization in online environments. Bruns is an internationally recognized innovator in computational methods for social media analysis, emphasizing interdisciplinary mixed-methods approaches to study complex societal phenomena. Research Interests: His research addresses critical challenges such as polarization’s threat to democracy, the role of algorithms in shaping public discourse, and the spread of disinformation. Notable areas include filter bubbles, platform governance, and the interplay between social media and political systems. He has pioneered concepts like 'gatewatching' to analyze news curation practices of digital intermediaries. Key Achievements: Bruns leads a prestigious Australian Laureate Fellowship project examining polarization drivers and dynamics. His work combines rigorous methodological innovation with policy relevance, evidenced by submissions to parliamentary committees on social media regulation. He has mentored numerous doctoral students who have become leading methodologists in their fields. Awards and Collaborations: Recipient of the Australian Laureate Fellowship (2023), Bruns collaborates widely with institutions globally, including Algorithm Watch, the Centre for Responsible Technology, and the Alexander von Humboldt Institute for Internet and Society. These partnerships enable cross-border research into digital media’s societal impacts.
Pierre KELSEN is a Full Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM). His research focuses on Software Engineering, Formal Methods, Model-Driven Engineering, and Algorithmic Graph Theory. He leads the LASSY Laboratory for Advanced Software Systems, emphasizing model decomposition, regulatory compliance, and formal verification techniques. Education: PhD in Computer Science (1993, University of Illinois at Urbana-Champaign), M.Sc. (1989, UIUC), and Diploma (1986, University of Karlsruhe). Postdoctoral work at the University of British Columbia and Max-Planck-Institut für Informatik. Research Interests: - Development of formal modeling languages (e.g., VCL, F-Alloy) - Model transformation and validation frameworks - Algorithms for compliance and complexity challenges - Visual and modular design methodologies Funding: - ASINE (FNR Pearl, 2013–present): Architecture-based service innovation - MaRCo (FNR Core, 2010–2013): Business-centric regulatory compliance Publications span model-driven engineering, formal methods, and algorithmic foundations, with recent work exploring AI integration in domain modeling and compliance analysis. Labs/Teams: LASSY Laboratory, collaborating on tools like Lightning and Democles for executable modeling frameworks.
Professor Alex Copley holds the position of Professor of Tectonics at the Department of Earth Sciences, University of Cambridge. His research focuses on understanding Earth's crustal deformation, tectonic forces, and earthquake dynamics across scales from microcrystalline to continental. He employs integrated approaches combining field geology, geophysical data, numerical modeling, and petrological analysis. His work addresses key questions on earthquake controls, tectonic force origins, and crustal material properties, with global field projects spanning Asia, the Middle East, Europe, Africa, and South America. Research interests include: Earthquake mechanics and seismic hazard mitigation Continental tectonics and mountain belt evolution Crustal rheology and lithospheric dynamics Metamorphic petrology and continental collision processes Large-scale controls on critical mineral distributions Recent publications highlight studies on fault mechanics in Iran, Himalayan shortening, and the thermal evolution of mountain ranges. His work bridges fundamental geoscience with societal applications, including earthquake resilience and tectonic influences on resource formation. Affiliations include Bullard Laboratories and collaborations with global institutions. No formal awards are listed in the provided text, though his research has been published in high-impact journals like Nature and Geophysical Research Letters .
Halina Frydman is a Professor in the Department of Statistics and Operations Research at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since 1978. Her academic work bridges statistical theory and real-world applications in finance and labor economics. Institution: New York University School: Leonard N. Stern School of Business Department: Department of Statistics and Operations Research Academic Rank: Professor Email: hf2@stern.nyu.edu Education: Ph.D. in Mathematical Statistics, Columbia University, 1978 M.A. in Mathematical Statistics, Columbia University, 1974 B.S. in Physics and Mathematics, Cooper Union, 1972 Research Interests: Professor Frydman specializes in survival analysis and Markov processes , with a strong focus on their applications in financial modeling and labor market dynamics . Her work explores mixture models of Markov chains to capture heterogeneity in longitudinal data, particularly in the context of corporate credit rating migrations and employment/unemployment transitions. She also contributes to methodological advances in stochastic modeling and statistical inference for time-to-event data. Publication Trends: Her recent research, reflected in reconstructed articles, demonstrates a consistent focus on developing and applying advanced statistical models—particularly survival models, Markov chains, and mixture models—to problems in finance and economics. There is a clear progression toward more complex, data-driven models incorporating Bayesian methods, high-dimensional estimation, and time-varying effects. Scientific Awards: No awards explicitly mentioned in the source text. Advising and Grants: While specific advisees and grant funding are not listed in the available text, Professor Frydman's long-standing research program and publications in premier journals such as the Journal of the American Statistical Association and The Journal of Finance suggest a significant scholarly impact and likely history of research sponsorship. She teaches core courses including Regression & Forecasting Models , Stochastic Processes I , and Stochastic Models in Finance , indicating active engagement in graduate education. Labs and Research Teams: No specific laboratories or research groups are mentioned in the provided content. However, her research aligns with interdisciplinary efforts in financial statistics and econometric modeling, potentially involving collaboration within NYU’s broader quantitative research community.
James A. Evans is the Max Palevsky Professor of Sociology and Data Science at the University of Chicago, where he is a faculty member in the Department of Sociology within the Division of the Social Sciences. He is the director of Knowledge Lab and the Faculty Director of the Masters Program in Computational Social Science . He holds additional affiliations as an External Professor at the Santa Fe Institute , External Faculty at the Complexity Science Hub, Vienna , and Visiting Faculty Researcher at Google . Education: B.A. in Anthropology, Brigham Young University (1994) M.A. in Sociology, Stanford University (1999) Ph.D. in Sociology, Stanford University (2004) His research centers on the collective system of thinking and knowing , exploring how ideas emerge, spread, and evolve through social and technical systems. He investigates innovation, collective intelligence, and the science of science , using large-scale data modeling, machine learning, generative AI, and network analysis to study knowledge creation. His work spans domains including science, technology, law, and religion, with a focus on how AI is reshaping discovery processes. The most recent publications highlight trends in AI and scientific discovery , with a strong emphasis on innovation, knowledge systems, and human-machine intelligence . His research increasingly explores AI as a transformative agent in science , including the concept of 'alien intelligence' and the development of complementary AI to augment human capacity. Projects like the $20M NSF-funded APTO initiative aim to build language models that predict technological outcomes by analyzing historical data. Scientific Recognition and Funding: Research supported by the National Science Foundation (NSF) , National Institutes of Health (NIH) , Air Force Office of Scientific Research (AFOSR) , and philanthropic sources Work published in Nature, Science, PNAS , and leading social science journals Featured in The New York Times, The Economist, The Atlantic, Wired, NPR, BBC, Le Monde , and others James Evans advises on science policy and funding strategies, emphasizing the importance of diversity, interdisciplinary collaboration, and demographic balance in fostering innovation. He critiques current academic incentives and proposes alternative discovery regimes. He leads Knowledge Lab , a collaborative research environment that conducts seminars, grants, and employment opportunities in computational social science and AI.
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