جست‌وجوی استاد و پژوهشگر

Gharad Bryanمشاهده پروفایل

دانشیار

• Development Economics
• Behavioral Economics
• Experimental Economics
• +۵ مورد دیگر

Gharad Bryan is an Associate Professor of Economics at the London School of Economics and Political Science (LSE) , affiliated with the STICERD Economic Organisation and Public Policy Programme. He holds a PhD from Yale University and specializes in Development Economics, Behavioral Economics, and Experimental Economics. His research explores topics such as migration, climate adaptation, urbanization, and mental health interventions in low-income settings. Education: PhD in Economics, Yale University (2011) MPhil and MA, Yale University (2010 and 2007) LLB and BCom with Honors, University of Melbourne (2004 and 2003) Research Interests: Bryan’s work focuses on applying experimental methods to real-world challenges, including seasonal migration in Bangladesh, cognitive behavioral therapy’s impact on poverty reduction in Ghana, and the distributional effects of urban infrastructure in Tanzania. He also studies land market design in Uganda and Kenya to improve resource allocation. Key Projects: Lead on the IGC Cities Research Programme, addressing urban growth in climate-vulnerable areas. Co-designed interventions to boost seasonal migration in Bangladesh and evaluate slum redevelopment in Addis Ababa. Explores climate migration adaptation strategies in Bangladesh and sustainable shrimp farming in climate-sensitive regions. Awards & Honors: British Academy Mid-Career Fellowship (2015–2016) Kauffman Dissertation Fellowship (2008–2011) Raymond Powell Teaching Prize (2007–2008) Grants & Collaborations: Bryan has secured funding from DFID, J-PAL, IPA, World Bank, and others for projects totaling over $2 million. He collaborates with institutions like Innovations for Poverty Action and the Jameel Poverty Action Lab (J-PAL). Labs & Teams: Active in LSE’s Department of Economics and the STICERD initiative, leading interdisciplinary teams on urban development, climate policy, and behavioral interventions.

Alex Voorhoeveمشاهده پروفایل

استاد

• Distributive Justice
• Rational Choice Theory
• Political Philosophy
• +۴ مورد دیگر

Alex Voorhoeve is a Professor in the Department of Philosophy, Logic and Scientific Method and the LSE School of Public Policy at the London School of Economics and Political Science. He holds visiting positions at Harvard, Princeton, National Institutes of Health, Erasmus University Rotterdam, and Nuffield College, Oxford. He studied economics and philosophy at Erasmus University, Cambridge University, and UCL. Research interests focus on distributive and procedural justice in health, rational choice theory, moral psychology, and Epicureanism. Key areas include: Theory and practice of fair decision-making in health financing Moral psychology of ethical judgments and biases Philosophical foundations of economic policy His publications analyze ethical frameworks for health policy, pandemic response strategies, and moral decision-making under uncertainty. Recent work emphasizes procedural fairness in universal health coverage implementations. Professional engagements include advisory roles for the World Health Organization, World Bank, Norwegian Institute of Public Health, and the Bank of England's Academic Advisory Group for the digital pound. He will become Vice-President for Faculty Development at LSE in 2025. Research outputs demonstrate consistent focus on integrating ethical principles with economic policy design, particularly in health financing systems.

• Medical Physics
• Radiation Oncology
• Proton Therapy
• +۳ مورد دیگر

Sara Margareta Cecilia Pilskog serves as an Associate Professor in the Department of Physics and Technology at the University of Bergen, Norway, with dual affiliation at Haukeland University Hospital's Department of Cancer Treatment and Medical Physics. Her research bridges theoretical medical physics and clinical oncology applications, focusing on precision radiotherapy techniques and biological optimization. Her primary research interests center on proton therapy innovation, where she investigates biological optimization strategies using linear energy transfer (LET) and relative biological effectiveness (RBE) modeling. She develops adaptive radiotherapy frameworks to address inter-fractional motion in pelvic cancers, particularly prostate and rectal malignancies. Her work also pioneers neutron-based in-vivo range verification systems and statistical deformation models for dose accumulation. Current projects emphasize reducing treatment margins through anatomical robustness and optimizing biological dose distributions for organ sparing. Analysis of her 15 most recent publications reveals a dominant focus on improving proton therapy precision for pelvic cancers through biological modeling and motion management. Approximately 70% of her work addresses prostate cancer applications, with significant contributions to adaptive strategies for inter-fractional changes and biological optimization techniques. Her research consistently employs Monte Carlo simulations (particularly FLUKA) and clinical data analysis to translate theoretical models into clinically viable solutions. No scientific awards were documented in the provided materials. While specific advising details remain unreported, her collaborative patterns indicate active mentorship within the University of Bergen's medical physics research ecosystem. Her publications consistently involve junior co-authors from clinical physics teams at Haukeland University Hospital, suggesting hands-on supervision of technical staff and research fellows in radiotherapy innovation projects. Grant funding appears primarily channeled through institutional hospital-university partnerships focused on clinical translation of advanced radiotherapy techniques. Dr. Pilskog operates within the University of Bergen's medical physics research cluster that maintains close operational ties to Haukeland University Hospital's radiotherapy department. This integrated academic-clinical environment enables direct implementation of her research on adaptive proton therapy and biological optimization into clinical workflows, with particular emphasis on pelvic cancer treatment protocols. The team utilizes advanced Monte Carlo simulation platforms and clinical treatment planning systems to develop and validate next-generation radiotherapy approaches.

Brian Fisherمشاهده پروفایل

استاد

• Cognitive Science
• Visual Analytics
• Human-Computer Interaction
• +۲ مورد دیگر

Brian Fisher is a Professor at Simon Fraser University's School of Interactive Arts and Technology, where he leads the Interaction Science Research Group. He holds affiliate positions at the University of British Columbia and serves as Associate Director for the Media And Graphics Interdisciplinary Centre (MAGIC). His research investigates human reasoning in responsive environments through cognitive science approaches, developing visualization systems that support individual and distributed cognition. Fisher's work spans personalized health, aircraft safety analysis, space object classification, and emergency management. His teaching integrates perceptual, cognitive, and social science knowledge into design education. He actively participates in science policy through organizations including NSERC, Defence R&D Canada, EUREKA Eurostars, and IEEE committees. Fisher has been recognized as a Fellow of the Psychonomics Society and chaired major conferences including IEEE VIS 2019. His research is characterized by strong interdisciplinary collaboration and real-world applications in high-stakes decision domains.

Rasmus Waagepetersenمشاهده پروفایل

استاد

• Spatial Statistics
• Quantitative Genetics
• Markov Chain Monte Carlo Methods
• +۳ مورد دیگر

Rasmus Waagepetersen is a Professor in the Department of Mathematical Sciences at Aalborg University, affiliated with The Faculty of Engineering and Science. His research focuses on spatial statistics, quantitative genetics, and statistical methodology for spatial point processes. He leads and participates in interdisciplinary projects such as urbanLab (spatial data analysis for urban planning) and studies on microbiome interactions in agricultural systems. Key research areas include spatial point processes, Markov chain Monte Carlo methods, and statistical inference for complex ecological and biomedical data. His work frequently involves collaborations with environmental and biological scientists, as seen in projects analyzing root microbiota assemblies in legumes and climate data for building simulations. Waagepetersen has contributed to methodological advancements in spatial statistics, including goodness-of-fit tests, likelihood-based inference for log Gaussian Cox processes, and quasi-likelihood approaches for case-control point pattern data. His research has been supported by grants from institutions like the Villum Foundation. Key Projects: urbanLab, Klimadata til fugtsimuleringer, Nod factor signaling in plant microbiota. Grants: Multiple projects funded by the Villum Foundation and Danish research councils. His recent publications address topics such as space-time point processes, microbiome data analysis, and statistical modeling in education. Waagepetersen maintains an active research group and collaborates internationally on both theoretical and applied statistical problems.

Amir Tofighi Zavarehمشاهده پروفایل

استادیار

• Biomedical Engineering
• Medical Device Design
• Signal Processing
• +۴ مورد دیگر

Amir Tofighi Zavareh is an Assistant Professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University, specializing in Electronic Systems Engineering Technology. He is affiliated with the Center for Remote Health Technologies and Systems and Biomedical Engineering. His research focuses on analog and mixed-signal bioelectronic design, biomedical imaging, and wearable technology for healthcare applications. Dr. Tofighi Zavareh earned his Ph.D. in Electrical and Electronics Engineering from Texas A&M University in 2019. His work bridges biomedical engineering and signal processing, with a strong emphasis on developing innovative medical devices and wearable systems for real-time health monitoring. His research interests include wearable biosensors, machine learning-driven diagnostics, and improving mental health support in educational settings through technology. Notable projects involve eye-tracking metrics for online learning accessibility and stress-alleviation systems using emotion recognition. His publications span advancements in optical coherence tomography (OCT), retinal imaging systems, and AI-powered mental health tools. He collaborates across disciplines to address challenges in healthcare technology and education equity.

Hugo Curielمشاهده پروفایل

استادیار

• Stimulus Preference Assessments
• Reinforcer Assessment Procedures
• Gender Representation in Academic Journals
• +۴ مورد دیگر

Hugo Curiel is an Assistant Professor in the Department of Psychology at Western Michigan University , specializing in behavior analysis. His research focuses on stimulus preference assessments, reinforcer assessments, and gender representation in academic journals. He holds a Ph.D. in Behavior Analysis (2018) from Western Michigan University, an M.A. (2012), and a B.A. (2009) from California State University, Fresno. Dr. Curiel is a Board Certified Behavior Analyst with clinical experience in early intervention for children with autism and other developmental disabilities, emphasizing culturally responsive care through his bilingual (Spanish/English) expertise. His work integrates technology with traditional behavioral assessment methods and examines diversity in authorship across behavior-analytic journals. Education : Ph.D. in Behavior Analysis, Western Michigan University (2018) M.A. in Psychology, California State University, Fresno (2012) B.A. in Psychology, California State University, Fresno (2009) Research Interests : Development and validation of stimulus preference assessment tools Technology-enhanced reinforcer identification for children with autism Gender equity in behavior-analytic authorship and editorial roles Efficient skill acquisition methods like matrix training His recent articles explore global trends in behavior-analytic publications, cross-cultural research collaborations, and the integration of video-based interventions. Dr. Curiel actively promotes diversity in academia and has advised graduate students in applied behavior analysis programs. His work contributes to both theoretical advancements and practical applications in behavioral interventions.

Jason Gierschمشاهده پروفایل

دانشیار

• Education Policy
• School Choice
• High-Stakes Testing
• +۴ مورد دیگر

Jason Giersch serves as Associate Professor in the Department of Political Science and Public Administration at the University of North Carolina at Charlotte within the College of Humanities & Earth and Social Sciences. He concurrently holds the position of associate director for the university's Public Policy program. His research centers on education policy with concentrated expertise in school choice mechanisms, high-stakes testing implications, segregation dynamics, and teacher quality assessment. Complementing this, he investigates how political ideology filters individuals' interpretations of messages across diverse contexts including media and political communication. His methodological approach integrates quantitative analysis with experimental designs, bridging political science, public administration, and educational research frameworks. Recent publications demonstrate persistent focus on educational inequality through race, gender, and socioeconomic lenses. His work critically examines charter school impacts, segregation patterns, and policy effects on student outcomes while incorporating international comparisons from Finland, Sweden, and Kenya alongside U.S.-centric analyses. Dr. Giersch actively engages with academic communities through board service for the North Carolina Association for Research in Education and North Carolina Political Science Association. He collaborates with UNC Charlotte's Center for Racial Equity in Education (CREED), notably contributing to discussions on school segregation. Prior to his university appointment, he accumulated practical experience teaching across public, private, and charter high schools plus college-level instruction in Pennsylvania and Nairobi, Kenya—providing applied perspective to his scholarly work.

Michael L. Austinمشاهده پروفایل

مدرس ارشد

• Sound and music in interactive media
• Ludomusicology
• Sound studies
• +۷ مورد دیگر

Dr. Michael L. Austin is a Senior Lecturer in Music and Sound at Edge Hill University, serving as Programme Leader in Music Production since January 2023. He previously held academic roles at Louisiana Tech University, Howard University, and others. His research focuses on sound and music in interactive media, including video games, social media, and smart devices. He is Executive Director of the Society for the Study of Sound and Music in Games and a Research Associate at the Library of Congress's Radio Preservation Task Force. Education: PhD in Humanities (Aesthetic Studies) from the University of Texas at Dallas (2011), MA in Music Theory from UT Austin (2007), and BA in Music Composition from UT San Antonio (2005). Research interests include ludomusicology, cultural representation in media, sound design for UX, and algorithmic bias in emerging technologies. His 2024 monograph Audiovisual Alterity explores identity representation in music videos, while earlier works like Music Video Games: Performance, Politics, and Play (2016) analyze music's role in digital games. Publications span video game music analysis, algorithmic bias, and sound art. His talks include topics like 'Sounding Drag Identities in Video Games' and 'Algorithmic Bias in Emerging Media.' As a sound artist, his work has been exhibited at the Smithsonian and Dallas Museum of Art.

• Labour Economics
• Public Policy
• Industrial Relations
• +۳ مورد دیگر

Pedro Martins is a Full Professor at Nova School of Business and Economics (Nova SBE) and Scientific Director of the Economics for Policy Knowledge Center. He previously served as Secretary of State for Employment in Portugal (2011–2013), leading reforms in employment protection legislation and labor market policies. His research focuses on labour economics, public policy, and industrial relations, with emphasis on employment services, vocational training, and collective bargaining. He holds a PhD in Economics from the University of Warwick (2005) and has held positions at Queen Mary University of London (2004–2021). Education: PhD in Economics, University of Warwick (2005) MSc in Economics, University of York (1998) Postgraduate Degree in European Studies, ISEG (1997) Bachelor in Economics, Nova SBE (1997) Research Interests: His work examines the impact of labor market policies, training programs, and collective bargaining on employment, wages, and productivity. Recent studies include analyses of vocational education in China, employer power dynamics, and the effects of wage cyclicality on labor markets. Awards: EALE Prize for best article in Labour Economics (2021) Conselho para a Produtividade/Banco BPI Award (2021) UTL/Santander Totta Award (2010) Advising & Grants: Supervised PhD students including Yong Yang and Li Dai. Principal investigator for grants totaling over €1.5M, including projects on vocational education, intergenerational equity, and labour market reforms. Collaborates with institutions like the European Commission, World Bank, and OECD. Labs/Teams: Directs the Economics for Policy Knowledge Center and co-leads the Public Administration Leadership Programme. Active in executive education programs on policy evaluation and leadership in public administration.

Zhiyi Huangمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithmic Game Theory
• Online Algorithms
• +۳ مورد دیگر

Zhiyi Huang is an Associate Professor of Computer Science at the University of Hong Kong, leading the Computer Science Division within the School of Computing and Data Science. He holds a PhD from the University of Pennsylvania (2013) and completed a postdoctoral fellowship at Stanford University (2013–2014). His research focuses on Theoretical Computer Science, Algorithmic Game Theory, Online Algorithms, and Differential Privacy, with notable contributions to Machine Learning and Computer Networks. Education: PhD in Computer and Information Science, University of Pennsylvania (2013) Postdoctoral Researcher, Stanford University (2013–2014) Bachelor's Degree from the Yao Class at Tsinghua University (2008) Research interests span foundational areas including algorithmic game theory, online optimization, and privacy-preserving mechanisms. He has pioneered work on revenue maximization in single-parameter settings and developed novel frameworks for analyzing price of anarchy in game theory. Key Awards: Early Career Award (Research Grant Council of Hong Kong, 2014) Best Paper Award at ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2015) Morris and Dorothy Rubinoff Dissertation Award (2013) Simons Graduate Fellowship in Theoretical Computer Science (2012–2013) Recent grants include studies on algorithmic foundations of Bayesian mechanism design (HK$675,647), online primal dual techniques (HK$496,028), and privacy-preserving mechanisms (HK$931,737). His work bridges theoretical advancements with practical applications in healthcare, autonomous systems, and cybersecurity. Notable Projects: Medical predictive systems for acute cardiopulmonary events AI-driven maritime navigation using AIS data Secure federated learning frameworks with blockchain

Stefan Penczynskiمشاهده پروفایل

دانشیار

• Behavioral Economics
• Behavioral Game Theory
• Information Economics
• +۱۲ مورد دیگر

Stefan Penczynski is Associate Professor in Economics at the School of Economics, University of East Anglia (UEA). He is a member of the Centre for Behavioural and Experimental Social Science and contributes to the Behavioral Economics research group. He also serves as the subject-area coordinator for Economics in UEA’s Vegan Studies Network, reflecting his interdisciplinary engagement. His research lies at the intersection of behavioral and experimental economics, focusing on Behavioral Game Theory , Information Economics , Political Economy , and Development Economics . He investigates economic behaviors such as information disclosure, social learning, auction bidding, jury voting, and trust, using innovative experimental methods that incorporate natural language evaluation. He is particularly interested in how communication and framing affect decision-making in strategic contexts. The recent trend in his publications (2023–2025) centers on game-theoretic modeling of real-world behaviors, including vaccination decisions, team reasoning, trust in institutions, and resource allocation. These works appear in high-quality journals such as Games and Economic Behavior , Experimental Economics , and Quantitative Economics , showcasing a strong methodological emphasis on experimental design and behavioral insights. Stefan supervises PhD research in areas related to behavioral and experimental economics, with a focus on consumption decisions (e.g., nutrition) and the use of natural language in experiments. While no specific grants are detailed beyond a completed project with the Israeli Prime Minister’s Office (2017–2019), his ongoing publication output suggests active research funding and collaboration. He is affiliated with the Centre for Behavioural and Experimental Social Science (CBESS) at UEA, a hub for interdisciplinary experimental research. His work often involves collaboration with scholars across economics and behavioral science, contributing to a dynamic research environment focused on human decision-making in strategic and institutional contexts.

Michal A. Mankowskiمشاهده پروفایل

استادیار

• Transplantation Medicine
• Health Equity in Organ Allocation
• Immunogenetics
• +۷ مورد دیگر

Michal A. Mankowski, PhD, is an Assistant Professor in the Department of Surgery at NYU Grossman School of Medicine, where he conducts impactful research in transplantation medicine. His work bridges clinical needs with data science and policy innovation, focusing on improving equity and efficiency in organ allocation systems. His research interests center on transplantation bioinformatics , HLA immunogenetics , and health equity , with a strong emphasis on kidney and liver allocation. He investigates how electronic health records and large language models can be leveraged—and secured—within clinical transplant settings. His work also explores the impact of patient comorbidities, such as delirium, on transplant access and outcomes. Recent publications reveal a strong trend toward integrating data science , machine learning , and policy modeling in organ transplantation. His research spans technical immunogenetic analysis (e.g., eplet mismatch) to ethical and operational challenges in national allocation systems. He frequently collaborates with leaders in transplant epidemiology and bioinformatics, contributing to high-impact journals such as American Journal of Transplantation and Nature Medicine . Scientific Awards : No awards are listed in the provided text. Advising and Grants : While no specific students or grants are listed, his role as an Assistant Professor and active publication record suggest involvement in mentoring graduate or medical students and securing research funding in transplantation informatics and policy. His collaborations indicate participation in multi-institutional research teams and likely grant-funded projects related to organ allocation and AI in medicine. Labs and Teams : Dr. Mankowski is part of a robust transplant research ecosystem at NYU Grossman School of Medicine, collaborating extensively with teams focused on transplant outcomes, immunogenetics, and health services research. He contributes to projects involving large databases such as the Scientific Registry of Transplant Recipients (SRTR) and Epic Cosmos, suggesting integration within data-driven clinical research networks.

Ramya Korlakai Vinayakمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Inference
• Crowdsourcing
• +۲ مورد دیگر

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. She also holds affiliations with the Department of Computer Science and the Department of Statistics at UW-Madison. Her research program bridges theoretical machine learning with practical applications in data science and crowdsourcing. Education: PhD in Electrical Engineering from California Institute of Technology (Caltech), advised by Prof. Babak Hassibi B.Tech in Electrical Engineering with minor in Physics from Indian Institute of Technology Madras (IIT Madras) Dr. Vinayak's research focuses on developing theoretically grounded machine learning tools for reliable inference using data from human sources. Her work spans machine learning theory, statistical inference, and crowdsourcing systems, with particular emphasis on preference learning, metric learning, and robust dataset construction. She has pioneered methods for learning from limited pairwise comparisons, auto-labeling systems, and human-in-the-loop out-of-distribution detection. Her recent publications reveal a consistent trajectory toward building practical machine learning systems that incorporate human feedback while maintaining theoretical guarantees. The pattern shows increasing focus on ethical considerations in AI, particularly regarding bias in generative models and reliable human-AI collaboration frameworks. Scientific Awards: Faculty for the Future fellowship (2013-2015) from Schlumberger Foundation NSF CAREER Award American Family Funding Initiative Award with Fred Sala Dr. Vinayak leads an active research group with multiple PhD students across ECE and CS departments. She has secured significant research funding including an NSF grant for "Uncovering the cognitive and neural fingerprints that make each of us unique" in collaboration with Tim Rogers, Rob Nowak and Brad Postle. She is also co-organizing the MidWest Machine Learning Symposium and NeurIPS tutorials on dataset construction. Her research group operates at the intersection of theory and practice, developing mathematically grounded frameworks that address real-world challenges in dataset construction, human-AI collaboration, and reliable machine learning systems.

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +۷ مورد دیگر

Shuguang Cui is the X.Q. Deng Presidential Chair Professor in the Department of Electrical and Computer Engineering at the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen. He holds a PhD from Stanford University and has previously served on the faculties of the University of Arizona, Texas A&M University, and UC Davis. His research lies at the intersection of artificial intelligence and communication networks, particularly in AI+Networking , with major contributions in federated learning over wireless systems, intelligent reflecting surfaces, UAV trajectory optimization, and energy-efficient edge computing. His work bridges machine learning theory with practical communication system design. The recent publications reflect a strong trend toward integrating distributed AI with wireless network constraints, focusing on reducing communication overhead, optimizing resource allocation, and enhancing learning efficiency in edge environments. Key themes include federated learning convergence, hybrid VLC-RF systems, and intelligent surface-assisted communications. Award highlights include: IEEE Fellow (2013) IEEE Marconi Prize Paper Award (2023) Fellow of the Royal Society of Canada (2023) Fellow of the Canadian Academy of Engineering (2023) Multiple IEEE best paper awards (Globecom, WCNC, ICC) Thomson Reuters Highly Cited Researcher (2014) Prof. Cui has served in leadership roles such as Vice Chair of the IEEE VT Fellow Evaluation Committee, member of the IEEE ComSoc Award Committee, and currently as Editor-in-Chief of IEEE Transactions on Mobile Computing . He has secured significant research impact through high-visibility publications in top-tier journals and conferences, shaping the future of AI-driven wireless systems. He leads a research group focused on next-generation intelligent networks, supervising graduate students and collaborating across institutions. His team actively contributes to advancing the theoretical and practical foundations of AI-enabled communications.