جستجوی استاد راهنمای دانشگاهی

Chua Tat Sengمشاهده پروفایل

استاد

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +۶ مورد دیگر

Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

Scott D. Saganمشاهده پروفایل

استاد

• Nuclear Weapons Policy
• International Security
• Arms Control
• +۳ مورد دیگر

Scott D. Sagan is the Caroline S.G. Munro Professor of Political Science at Stanford University and a Senior Fellow at the Freeman Spogli Institute for International Studies. He serves as Co-Director of the Center for International Security and Cooperation (CISAC) and holds the position of Bass University Fellow in Undergraduate Education. Sagan also co-chairs the American Academy of Arts and Sciences' Committee on International Security Studies, demonstrating his significant influence in the field of international security. Sagan received his Ph.D. in Political Science from Harvard University in 1983 and his B.A. in Government from Oberlin College in 1977. Prior to joining Stanford, he taught at Harvard University and served as special assistant to the director of the Organization of the Joint Chiefs of Staff in the Pentagon, providing him with valuable practical experience in defense policy. Professor Sagan's research primarily focuses on nuclear weapons policy, international security, arms control, and nonproliferation. His work examines the organizational, political, and ethical dimensions of nuclear strategy, with particular attention to the risks of nuclear accidents, the effectiveness of nonproliferation policies, and public attitudes toward nuclear weapons use. He has pioneered important theoretical frameworks regarding the organizational causes of nuclear accidents and the normative constraints on nuclear weapons use. Sagan's publications reveal a consistent focus on nuclear deterrence theory, the organizational aspects of nuclear command and control, and the ethical dimensions of nuclear weapons policy. His work bridges theoretical analysis with practical policy implications, often challenging conventional wisdom about nuclear strategy and proliferation. Recent publications show increasing attention to public opinion on nuclear weapons use, the evolving nature of nuclear deterrence in the contemporary security environment, and the relationship between international law and nuclear strategy. Thérèse Delpech Memorial Award (2022) Susan Strange Award, International Studies Association (2017) William and Katherine Estes Award, National Academy of Sciences (2015) International Security Studies Section Distinguished Scholar Award (2013) Stanford's Dean's Award for Distinguished Teaching (1998-99) Stanford's Hoagland Prize for Undergraduate Teaching (1996) International Studies Association's Innovative Teaching Award (2008) Monterey Institute for International Studies' Nonproliferation Education Award (2009) Sagan has been instrumental in shaping the research agenda at CISAC, mentoring numerous students and junior scholars in the field of international security. His work has been supported by various research grants from foundations and government agencies focused on nuclear security issues. At Stanford, he has led numerous research initiatives examining nuclear weapons policy, arms control, and nonproliferation challenges. As Co-Director of CISAC, Sagan oversees one of the world's leading research centers on international security, nuclear policy, and arms control. The center brings together scholars, practitioners, and students to address pressing security challenges through interdisciplinary research, policy-relevant analysis, and educational programs. Under his leadership, CISAC has maintained its position as a premier institution for security studies research and policy engagement.

Daria Davittiمشاهده پروفایل

دانشیار

• Human Rights Law
• Public International Law
• Environmental Law
• +۳ مورد دیگر

Daria Davitti is a Senior Lecturer and Associate Professor in Public International Law at Lund University's Department of Law. Her research focuses on intersections of international economic law and human rights, particularly in contexts of migration, climate breakdown, and privatized public goods. She leads major projects like the ERC-funded 'Refugee Finance: Histories, Frameworks, Practices' and the Formas-funded 'Screening for Sustainability'. Awards include the 2021 Juridiska Föreningens pedagogiska pris and the title of Docent in Public International Law. She advises on EU migration policies and environmental law compliance, and collaborates with institutions like GLAN and the International Code of Conduct Association for private security service providers. Her work examines refugee finance mechanisms, sustainable investment frameworks, and legal accountability in transnational corporate activities. Key areas include refugee entrepreneurship, EU Taxonomy sustainability criteria, and human rights due diligence in international investment. She has published extensively on topics ranging from refugee bonds to litigation finance in investment arbitration.

Eduardo Araralمشاهده پروفایل

دانشیار

• Public Policy
• Collective Action
• Institutional Analysis
• +۹ مورد دیگر

Eduardo Araral is an Associate Professor at the Lee Kuan Yew School of Public Policy, National University of Singapore. With over 30 years of experience in academia and government, he holds a PhD in Public Policy from Indiana University-Bloomington under Nobel Laureate Elinor Ostrom. His research focuses on collective action, institutions, and water governance, with publications in Nature Human Behaviour , Public Administration Review , and World Development . He has held fellowships at Stanford University and centers associated with Coase, Ostrom, and Stigler. Research Interests : Collective action, institutional analysis, water governance, digital technologies in public policy, political economy, and socio-ecological systems. Scientific Awards : Ostrom Prize for the Governance of the Commons, Fulbright PhD Scholarship, and Presidential Award for Outstanding Overseas Filipinos. Leadership Roles : Vice Dean for Research, Assistant Dean for Academic Affairs, and Co-Director of the Institute of Water Policy (2016-2022) at LKYSPP. Global Engagement : Advised 20+ international organizations including ADB, World Bank, UNDP, and Fortune 500 companies, while lecturing to 7,000+ government officials across 50+ countries.

Sakari Melanderمشاهده پروفایل

استاد

• Criminal Law
• EU Criminal Law
• Constitutional Law
• +۴ مورد دیگر

Sakari Melander is a Professor at the Faculty of Law , University of Helsinki, specializing in Criminal Law , with a focus on constitutionalization, Europeanization, and sentencing policies. His work bridges theoretical criminal law with practical legislative and policy challenges. Key research areas include EU criminal law, constitutional constraints on criminal legislation, and general doctrines of criminal law. He has extensive experience in parliamentary legal consultation, providing expert opinions to Finland’s Legislative Committees (e.g., on immigration control, anti-corruption directives, and digital evidence handling). Recent publications address topics like EU corporate sanction policies, constitutionalization of criminal law, and reforms in Finnish sentencing systems. His 2025 article in the New Journal of European Criminal Law explores balancing administrative and criminal sanctions in the EU. Scientific awards include: Hilkka ja Otto Brusiin Foundation Thesis Prize (2001) Magister Bonus – Good Teacher Award (2014) Primustohtori Award (2010) Year’s Teacher (2010) Year’s Opponent (2008) He leads research projects such as Criminal Law Under Pressure: Differentiation and Harmonization (2012-2015) and Assessment of Human Trafficking Sanction Practices (2021-2022), and supervises doctoral theses on criminal law topics.

• Health Informatics
• Medical Data Privacy
• Electronic Health Records
• +۴ مورد دیگر

Johan Gustav Bellika is a Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway, based in Tromsø. His work bridges clinical medicine with health informatics, focusing on practical applications that improve healthcare delivery and patient outcomes. Current Position: Professor, Department of Clinical Medicine Institution: UiT The Arctic University of Norway Location: Tromsø, Norway Contact: johan.gustav.bellika@uit.no Professor Bellika's research spans several critical areas in modern healthcare. His primary focus is on health informatics with particular emphasis on medical data privacy, electronic health records, and the application of artificial intelligence in clinical settings. He has made significant contributions to understanding chronic pain management through technology, primary care research networks, and patient-centered care models. His work often involves large population studies such as the Tromsø Study, examining how digital health tools impact healthcare utilization and patient outcomes. His research demonstrates a consistent thread connecting technological innovation with practical clinical applications. Bellika has been instrumental in developing privacy-preserving architectures for healthcare data analysis, which enable researchers to gain insights from sensitive health information without compromising patient privacy. His work on federated learning frameworks represents cutting-edge approaches to analyzing medical data while maintaining strict privacy controls. Health Informatics and Medical Data Privacy Chronic Pain Management through Technology Primary Care Research Networks (PraksisNett) Electronic Health Records and Clinical Decision Support Patient-Centered Digital Health Solutions Federated Learning Applications in Healthcare Professor Bellika's publication record shows a strong trend toward interdisciplinary research that combines clinical medicine, computer science, and public health. His recent work increasingly focuses on the intersection of artificial intelligence and healthcare, particularly how machine learning can be applied to chronic conditions while maintaining rigorous privacy standards. The geographical scope of his research extends across Norway with particular emphasis on Northern Norwegian populations, providing valuable insights into healthcare delivery in Arctic and remote regions. His collaborative approach is evident through numerous co-authored publications with researchers across multiple institutions and disciplines. While specific awards aren't detailed in the available information, his extensive publication record in reputable journals suggests recognition within his field. Professor Bellika has been actively involved in several major research initiatives including the Tromsø Study and PraksisNett, Norway's nationwide practice-based research network. His work on privacy-preserving architectures for healthcare data has significant implications for how medical research can be conducted while respecting patient confidentiality. He appears to be particularly focused on translating research findings into practical tools for clinicians, as evidenced by his work on audit and feedback systems for antibiotic prescribing.

Navid Azizanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Control Systems
• +۵ مورد دیگر

Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.

Karin Allor Pfeifferمشاهده پروفایل

استاد

• Physical Activity Measurement
• Childhood Obesity Prevention
• Exercise Physiology
• +۳ مورد دیگر

Karin Allor Pfeiffer is a Professor in the Department of Kinesiology at Michigan State University (MSU) and Director of the Institute for the Study of Youth Sports. She holds additional membership in the Center for Physical Activity and Health. With a Ph.D. from MSU, her research focuses on physical activity measurement methodologies and population health interventions, particularly among children and adolescents. Her work addresses obesity prevention, environmental design impacts on activity levels, and sociocultural factors influencing youth sport participation. Education: Ph.D. in Kinesiology from Michigan State University Her research interests emphasize: - Quantitative methods for physical activity assessment - Schoolyard redesign strategies and their health impacts - Cardiometabolic risk factors in pediatric populations - Longitudinal tracking of physical fitness and health outcomes Recent work explores accelerometer fragmentation metrics, GPS-linked activity tracking, and disparities in sedentary behavior across demographic groups. She has pioneered interdisciplinary approaches integrating spatial analysis, wearable technology, and policy evaluation. Key contributions include developing the Observational System for Recording Physical Activity in Children and advancing consensus methods for accelerometer data interpretation. Her studies frequently highlight socioeconomic and environmental determinants of health behaviors. Dr. Pfeiffer collaborates with public health agencies and urban planners to translate research into actionable policies. Her lab focuses on scalable interventions for underserved communities, leveraging community-engaged methods to address greenspace accessibility and safety concerns.

Steffen Marburgمشاهده پروفایل

استاد

• Numerical Methods in Vibroacoustics
• Aeroacoustics
• Structural Optimization
• +۶ مورد دیگر

Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.

• Advanced epidemiologic methods
• Causal inference
• Child health
• +۳ مورد دیگر

Michelle Dimitris is an Assistant Professor in the Department of Community Health and Epidemiology at Dalhousie University’s Faculty of Medicine. Her research focuses on advanced epidemiologic methods applied to pregnancy and child health globally, particularly in understanding pregnancy weight gain patterns and their implications for outcomes like gestational diabetes and preterm birth. She holds a PhD from McGill University, an MSc from Queen’s University, and a BScH from Queen’s University in Life Sciences. Her work includes studies on twin vs. singleton pregnancies, methodological challenges in epidemiology during pandemics, and global health research trends. Notable collaborations involve the National Institutes of Health (NIH) grants and fellowships, including a Fonds de Recherche Santé Doctoral Award. She contributes to professional societies like the Society for Pediatric and Perinatal Epidemiologic Research (SPER) and the Society for Epidemiologic Research (SER). Key research themes include causal inference approaches, perinatal epidemiology, and addressing gaps in global health studies. Recent publications analyze gestational weight gain trajectories, pandemic impacts on pregnancy outcomes, and methodological rigor in epidemiological studies.

Anand N.K.مشاهده پروفایل

استاد

• Physics-Informed Neural Networks
• Finite Volume Techniques
• Reduced Order Modeling
• +۵ مورد دیگر

N.K. Anand is a Distinguished Professor of Mechanical Engineering at Texas A&M University, holding the James J. Cain III Regents Professorship. He leads research in advanced computational methods and thermal-hydraulic systems, with affiliations to Multidisciplinary Engineering and Nuclear Engineering programs. His work focuses on physics-informed machine learning, finite volume methods, and aerosol transport in nuclear reactor contexts. Education: PhD (Mechanical Engineering, Purdue University, 1983), M.S. (Kansas State University, 1979), and B.E. (Bangalore University, 1978). Awards include the ASME James Harry Potter Gold Medal (2020) and multiple teaching/administrative excellence awards from Texas A&M. Research emphasizes fluid dynamics modeling (e.g., PINNs for periodic flows, turbulent deposition studies), heat pipe systems, and nuclear reactor thermal-hydraulics. His Versatile Test Reactor (VTR) contributions include cartridge loop designs and aerosol transport experiments. Active in high-temperature reactor safety, with facilities studying pebble beds, helical coil exchangers, and HTGR upper plenum dynamics. Publications span physics-informed ML applications, finite volume techniques, and nuclear thermal systems. Grants supported development of advanced CFD tools and reactor safety infrastructure. His lab collaborates on international nuclear energy projects and emerging AI-driven simulation methodologies.

Erwin Bieblمشاهده پروفایل

استاد

• High-Frequency Technology
• Automotive Radar Systems
• Microwave Communication
• +۳ مورد دیگر

Prof. Dr.-Ing. Erwin Biebl is a Professor in the Chair of High Frequency Engineering at the Technical University of Munich (TUM). His research focuses on high-frequency technology, automotive radar systems, and microwave communication. He has held academic positions since 1999 and has extensive industry experience at Rohde & Schwarz. His work includes developing sensors and communication systems for automotive safety and environment detection. Education: Studied electrical engineering at TUM, earned his doctorate (1991) and habilitation (1994) in high-frequency engineering. Professional Memberships: Senior Member of IEEE, Member of URSI Commission B, and liaison officer for the Hanns Seidel Foundation. Research Interests: Automotive radar systems, antenna design, millimeter-wave measurements, and sensor technology. His contributions include advancements in synthetic aperture radar and inductive compensation techniques for automotive radomes. Awards: Recognized for his teaching (2002 Lecturer Award) and research, including the Dr. Georg Spinner High Frequency Prize (1992). Supervised teams in competitions such as the 2009 International Collegiate Student Safety Technology Design Competition. Labs/Teams: Leads the Chair of High Frequency Engineering at TUM, focusing on interdisciplinary research in high-frequency systems and automotive applications.

Zach Shahnمشاهده پروفایل

استادیار

• Causal Inference
• Healthcare Data Analysis
• Critical Care Applications
• +۳ مورد دیگر

Zach Shahn is an Assistant Professor in the Department of Epidemiology and Biostatistics at the CUNY School of Public Health. He holds a PhD in Statistics from Columbia University and a BA in Mathematics from Stanford University. His research focuses on causal inference methods applied to healthcare data, particularly time-varying treatment effects and critical care applications. He has conducted postdoctoral work at Harvard School of Public Health and previously worked at IBM Research in Healthcare and Life Sciences. Education: PhD in Statistics and Probability, Columbia University BA in Mathematics, Stanford University Research interests include developing causal inference frameworks for evaluating treatment efficacy in dynamic healthcare settings, with a focus on critical care outcomes and methodological innovations. Recent work explores applications of causal diagrams, instrumental variables, and machine learning in healthcare decision-making. His studies often involve large-scale healthcare datasets to assess interventions' real-world impacts. Key trends in his articles include advancements in causal effect estimation under complex treatment regimes, bias analysis in observational studies, and methodological contributions to difference-in-differences and N-of-1 trial designs. His work bridges statistical theory with practical healthcare challenges, emphasizing reproducibility and policy relevance. No scientific awards are listed, though his contributions to causal inference methodologies are notable in academic circles. He has advised on healthcare data projects and published extensively without explicitly listed grants. His professional network includes collaborations with institutions like Harvard and IBM. He is affiliated with CUNY's public health programs and actively engages in academic discourse via Twitter and LinkedIn. His lab or team details are not explicitly mentioned in available materials.

• Radiation Biology
• Radiation Carcinogenesis
• Radiation Applications in Medicine
• +۳ مورد دیگر

John Ford is an Associate Professor in the Department of Nuclear Engineering at Texas A&M University. His research focuses on radiobiology, radiation carcinogenesis, and medical applications of radiation. He holds academic appointments within the College of Engineering and contributes to the Health Physics, Radiation Biology & Medical Physics research group. Education includes a B.S. and M.S. in Nuclear Engineering from Mississippi State University (1982, 1986), a Ph.D. in Biomedical Sciences from the University of Tennessee (1992), and postdoctoral training at Oak Ridge National Laboratory (1992–1993). His work emphasizes understanding radiation effects on biological systems, with notable contributions to radiation dosimetry, bystander signaling mechanisms, and dietary modulation of radiation damage. Recent studies explore space radiation monitoring technologies and therapeutic applications of radiation. Awards include the BP Award for Teaching Excellence (2007) and ARRO Educator of the Year (2013–2014). Research spans interdisciplinary areas such as radiation-induced cancer mechanisms, nutritional interventions to mitigate radiation effects, and advanced radiation detection instrumentation. He has advised numerous projects in radiation safety curriculum development and collaborated on NASA-funded studies modeling radiation impacts on astronauts.

• Quantitative PET/CT and SPECT/CT Imaging
• Deep Learning in Medical Imaging
• Motion Correction Algorithms
• +۲ مورد دیگر

Chi Liu is a Professor of Radiology & Biomedical Imaging at Yale School of Medicine . He serves as Associate Director of Biomedical Imaging Technology at the Yale Biomedical Imaging Institute and Director for Research Faculty Affairs in the Radiology & Biomedical Imaging department. Education : PhD from Johns Hopkins University (2008) Postdoctoral Training : University of Washington (2010) Certification : American Board of Science in Nuclear Medicine (Nuclear Medicine Physics and Instrumentation) His research focuses on quantitative cardiac and oncological PET/CT and SPECT/CT imaging , emphasizing deep learning algorithms , reconstruction algorithms , data correction , and dynamic imaging . Key clinical applications include early detection of chemotherapy-induced cardiotoxicity , multimodality imaging of heart failure , and motion variability elimination in therapy response assessment . The 15 most recent publications reveal a strong emphasis on deep learning techniques for low-dose imaging , motion correction , and cross-tracer generalizability in PET/SPECT systems. These works span applications in cardiac imaging , neuroscience , oncology , and theranostics . Scientific Award : Bruce Hasegawa Young Investigator Medical Imaging Science Award (2012) Contact: chi.liu@yale.edu | ORCID 0000-0002-7007-1037