جست‌وجوی استاد و پژوهشگر

Zhenyu Yangمشاهده پروفایل

مدرس

• Urban Transportation Network Modeling
• Travel Demand Management
• Transport Economics
• +۵ مورد دیگر

Zhenyu Yang is a Lecturer and Postdoctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering through the Department of Civil Engineering and the Urban Transport Systems Laboratory (LUTS) . He holds a PhD in Industrial System Engineering from the National University of Singapore (2022), an M.Eng from Beijing Jiaotong University, and a Diploma in Transportation Engineering from Huazhong University of Science and Technology. PhD, Industrial System Engineering, National University of Singapore (2022) M.Eng, Beijing Jiaotong University Diploma, Transportation Engineering, Huazhong University of Science and Technology His research focuses on urban transportation network modeling , travel demand management , and traffic information provision , with a strong emphasis on handling uncertainty and optimizing shared mobility systems. Recent work explores reinforcement learning applications, vehicle-drone cooperative delivery , and dynamic incident-responsive traffic systems . His publications highlight advancements in ridesourcing algorithms , congestion pricing , and multi-modal transport regulation . As a lecturer, he teaches Transportation Economics , covering demand-supply dynamics, welfare analysis, and environmental policy in transport systems. He is affiliated with EPFL's Urban Transport Systems Laboratory (LUTS) and contributes to the SGC-ENS teaching unit.

Katherine Milkmanمشاهده پروفایل

استاد

• Behavioral Economics
• Judgment and Decision Making
• Behavior Change
• +۳ مورد دیگر

Katherine L. Milkman (Katy) is the James G. Dinan Professor at the Wharton School of the University of Pennsylvania, with secondary appointments in Penn's Perelman School of Medicine and School of Arts & Sciences. She co-founded and co-directs the Behavior Change for Good Initiative, a research center dedicated to advancing the science of lasting behavior change. Her work integrates economics and psychology to address challenges like savings, exercise adherence, vaccination rates, and discrimination through large-scale field experiments. Education: PhD in Computer Science and Business from Harvard University; Bachelor's degree (summa cum laude) in Operations Research and Financial Engineering from Princeton University. Research Focus: Milkman's research leverages big data and behavioral science to understand decision-making failures (e.g., self-control, discrimination) and design scalable interventions. Her recent work emphasizes: Nudge-based strategies for education, health, and finance Diversity enhancement in organizational settings Habit formation through incentive structures Publication Trends: Her 15 most recent articles (2022-2025) primarily involve megastudies testing behavioral interventions. Key themes include: leveraging email/reminders to improve math education and vaccination rates; addressing loan delinquency through nudges; and using stereotyping dynamics to increase diversity in hiring. Over 80% employ field experiments across healthcare, finance, and education sectors. Awards & Honors: Thinkers50 Top Management Thinker (2021, 2023) Schmidt Futures Innovation Fellow (2022) Fellow, Association for Psychological Science (2020) Multiple teaching awards from Wharton (2015, 2016) William F. O’Dell Award for impactful research (2017) Advisory & Grants: Milkman has advised major organizations including The White House, Google, Walmart, and the U.S. Department of Defense. Her Behavior Change for Good Initiative secures funding for large-scale social impact research. She hosts Schwab's behavioral economics podcast Choiceology and contributes to policy through op-eds in The New York Times and Scientific American . Labs & Teams: Co-directs the Behavior Change for Good Initiative, collaborating with interdisciplinary researchers (e.g., Angela Duckworth, Sendhil Mullainathan) on longitudinal studies. The initiative designs and tests interventions across health, education, and savings domains.

Todd Rogersمشاهده پروفایل

استاد

• Behavioral Science
• Public Policy
• Education Policy
• +۳ مورد دیگر

Todd Rogers is the Weatherhead Professor of Public Policy at the Harvard Kennedy School of Government , with dual appointments at Harvard University. He co-founded the social enterprises Analyst Institute (serving on its board) and EveryDay Labs (as Chief Scientist). At Harvard, he directs the Behavioral Insights Group and chairs the executive education program Behavioral Insights and Public Policy . He also holds affiliations as Senior Scientist at ideas42 and Academic Advisor at the Behavioural Insights Team . His education includes a joint Ph.D. from Harvard’s Psychology Department and Harvard Business School, and a B.A. in Religion and Psychology from Williams College. Education: Ph.D. in Psychology & Harvard Business School B.A. in Religion & Psychology, Williams College Todd Rogers specializes in behavioral science with applications to student attendance , democracy strengthening , and communication optimization . His research bridges behavioral economics, public policy, and education, focusing on scalable interventions like automated reminders and communication redesign. His work addresses truancy reduction , voter behavior , health compliance , and decision-making biases . Todd’s recent publications emphasize behavioral interventions in education and health, communication simplification , and technology-driven nudges . Key themes include student absenteeism , vaccination compliance , AI-assisted writing , and behavioral barriers in policy implementation . His studies often leverage randomized controlled trials and large-scale field experiments to evaluate the effectiveness of behavioral tools like SMS reminders, commitment devices, and normative feedback. Leadership Roles: Co-founder & Equity Holder, Analyst Institute Co-founder, Chief Scientist (unpaid), EveryDay Labs Faculty Director, Behavioral Insights Group Senior Scientist, ideas42 Academic Advisor, Behavioural Insights Team

Qian Zhangمشاهده پروفایل

استادیار

• Climate Change Mitigation
• Sustainable Mining
• Life Cycle Assessment
• +۷ مورد دیگر

Dr. Qian Zhang serves as Assistant Professor in the Robert M. Buchan Department of Mining at Queen's University's Smith Engineering, leading the Green Mining Value Chain (GreeMVC) Lab. His research develops strategic frameworks for sustainability and resilience throughout mining value chains, with emphasis on climate change mitigation and resource efficiency in global mineral systems. His academic foundation includes a Ph.D. in Urban Engineering from the University of Tokyo (awarded Japanese Government MEXT Scholarship), complemented by MSc and BSc degrees in Environmental Science plus a Minor in Economics from Peking University. Prior to his current role, he conducted postdoctoral research at the University of Victoria and University of Tokyo while consulting for the World Resources Institute on climate-energy initiatives. Dr. Zhang's expertise spans carbon footprint analysis , life-cycle assessment , and industrial ecology applied to mining systems. He employs advanced methodologies including input-output analysis and material flow accounting to model environmental pressures across urban infrastructure and mineral supply chains. His work specifically addresses greenhouse gas accounting, water-energy nexus challenges, and circular economy implementation in resource-intensive sectors. Recent publications reveal strong methodological convergence between artificial intelligence and environmental assessment, particularly in optimizing mining operations through reinforcement learning and geospatial analysis. Key thematic clusters include carbon accounting standardization, critical mineral sustainability, and policy-oriented modeling of environmental pressures throughout mineral value chains. His research program is supported by major competitive grants: NSERC Discovery Grant (2022-2027) SSHRC Institutional Grant (2023, 2025) NSERC Alliance Missions Grant (2023, 2024) Mitacs Accelerate Grant (2023, 2025) NFRF Exploration Grant (2025-2027) NRCan Energy Innovation Program (2025) Dr. Zhang actively mentors a dynamic research group comprising 10+ graduate students and postdocs, securing collaborative funding through institutional and federal channels. His GreeMVC Lab maintains active partnerships with industry leaders and government agencies to translate research into practical sustainability solutions for the mining sector, with current projects focusing on AI-driven fleet management and life-cycle assessment of mineral supply chains. The GreeMVC Lab operates as a multidisciplinary hub with structured mentorship programs, regular industry engagement events, and international collaborations including the COM symposium on sustainable circularity. The lab's physical space in Goodwin Hall supports advanced computational analysis of mining value chains while fostering innovation in green mining technologies through student-led research initiatives.

Ximena Arriagaمشاهده پروفایل

استاد

• Intimate Partner Violence
• Sexual Risk-Taking
• Attachment Theory
• +۴ مورد دیگر

Ximena Arriaga is a Professor in the Department of Psychological Sciences at Purdue University. Her research focuses on intimate partner violence, adult attachment dynamics, and social psychological factors in interpersonal relationships. She teaches courses in experimental methods and social psychology, and leads the ARCC Lab (Aggression, Relationships, and Coping Collaborative). Ph.D. from University of North Carolina at Chapel Hill Research interests span three domains: Intimate Partner Violence : Examines victim/perpetrator justification mechanisms, social support responses, and longitudinal impacts. Attachment Theory : Studies relationship security during life transitions (e.g., parenthood) and contextual influences like the COVID-19 pandemic. Social Dynamics : Investigates confrontation scenarios, trust mechanisms, and interdependence theory applications. Her 2019-2025 publications reveal methodological diversity, combining experimental designs, longitudinal analysis, and machine learning techniques to study relational patterns. Themes include violence normalization, coping trajectories, and contextual factors shaping relationship outcomes.

• Computational Complexity
• Graph Algorithms
• Distributed Algorithms
• +۶ مورد دیگر

Aris T. Pagourtzis is a Professor of Computer Science at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), where he also serves as the Head of the Computer Science Division. He is additionally a Lead Researcher at the Archimedes Research Center, Athena RC. His academic career includes positions at the University of Ioannina, the University of Liverpool, the ETH Zuerich, the University of Athens, and the Athens University of Economics and Business. Education: Diploma in Electrical Engineering (1989) and Ph.D. in Electrical and Computer Engineering (1999), both from the National Technical University of Athens Professor Pagourtzis's research spans multiple areas of theoretical computer science, with particular emphasis on computational complexity, graph algorithms, distributed algorithms, approximation algorithms, network algorithms, cryptography, and counting complexity. His work often bridges theoretical foundations with practical applications in network design, security protocols, and optimization problems. He has developed novel algorithms for problems ranging from community detection in networks to Byzantine fault-tolerant protocols and privacy-preserving voting systems. His recent publications show a continued focus on fundamental algorithmic problems while expanding into newer areas like temporal graph analysis, blockchain applications, and privacy-preserving technologies. There's a clear trend toward addressing real-world challenges through rigorous theoretical frameworks, particularly in distributed systems, secure computation, and optimization under constraints. Professor Pagourtzis has served on program and organizing committees for numerous theoretical computer science and cryptography conferences, co-chairing CIAC 2017 and FCT 2021. His research has received funding from diverse sources including US, UK, French, EU, and Greek national resources. He is actively involved in teaching both undergraduate and graduate courses at NTUA, including Algorithms and Complexity, Foundations of Computer Science, Computational Cryptography, and Network Algorithms and Complexity. He leads the Computation and Reasoning Laboratory (corelab) at NTUA, which focuses on theoretical computer science research.

Stefania De Vincentisمشاهده پروفایل

دانشیار

• Digital Art History
• Contemporary Art History
• Digital Museum
• +۷ مورد دیگر

Stefania De Vincentis is an Associate Professor of History of Contemporary Art at the Department of Humanities, Ca' Foscari University of Venice. She teaches across multiple degree programs including History of Contemporary Art, Digital and Public Humanities, and Economics and Management of Arts and Cultural Activities. Her office is located at Malcanton Marcorà (VeDPH, second floor) where she holds student office hours on Wednesdays from 4:00 PM to 6:00 PM. Dr. De Vincentis earned her PhD with honors in Human Sciences from the University of Ferrara. Her educational background includes studies in video art and visual arts at the Academy of Fine Arts in Bologna, the IUAV-Institute of Architecture at the University of Venice, and a Master's Degree in Cultural Heritage Management (MuSeC) at the University of Ferrara. She was a Visiting Student at the Getty Research Institute in Los Angeles and a fellow at the Ermitage Italia Center for Art History. Her research focuses on the intersection of art history and digital technologies, particularly examining how digital tools transform museum experiences and art historical scholarship. Dr. De Vincentis investigates digital museum applications, virtual reality approaches to art collections, and the impact of technologies like IIIF (International Image Interoperability Framework) on art historical research. Her work explores how AI, VR, and other digital methodologies can enhance engagement with cultural heritage while raising critical questions about the nature of digital art history as a discipline. Analysis of her recent publications reveals a consistent focus on practical digital applications in museum contexts, particularly at institutions like the Galleria Borghese in Rome. Her work spans from theoretical reflections on digital art history methodology to hands-on implementations of virtual reality and AI technologies for museum collections. As a founding member of the DiDiART laboratory (Diagnostics and Digital for Art) at the University of Ferrara and a member of the Venice Center for Digital and Public Humanities (VeDPH), Dr. De Vincentis actively contributes to digital humanities initiatives. She has collaborated extensively with cultural institutions including the Ferrara Arte Foundation, Pinacoteca Nazionale di Ferrara, and Estense Castle, bridging academic research with practical museum applications. Her teaching portfolio demonstrates a commitment to preparing students for careers at the intersection of art, technology, and cultural heritage management. She offers specialized courses that equip students with both theoretical frameworks and practical skills for engaging with digital transformations in the cultural sector.

Dustin D. Frenchمشاهده پروفایل

استاد

• Health Economics
• Health Services Research
• Health Policy
• +۴ مورد دیگر

Dustin D. French, PhD, serves as Professor in Ophthalmology and Medical Social Sciences (Determinants of Health) at Northwestern University's Feinberg School of Medicine. His dual appointments bridge clinical ophthalmology with population health research, focusing on healthcare system optimization through economic and policy analyses. Dr. French's educational foundation includes: PhD from The Ohio State University (2001) His research program centers on health economics and outcomes research , with signature expertise in: Comparative and cost-effectiveness methodologies Health informatics and big data applications Health services research and policy evaluation Addressing health disparities in diabetic eye care and rural communities Recent publications reveal a strategic focus on AI-driven clinical decision support and resource optimization, with 2025 studies examining glaucoma identification algorithms, nursing home quality metrics, microhematuria diagnostic pathways, and pediatric obesity interventions. Dr. French's scholarly impact is recognized through prestigious awards: International Society for Pharmacoepidemiology Outstanding Reviewer Award (2016, 2010) Department of Veterans Affairs Distinguished Service Award (2016) International Society for Pharmacoepidemiology Distinguished Article Award (2006) His leadership in health services research extends to mentorship and grant-funded initiatives targeting healthcare quality improvement, particularly in diabetic eye care access for minority populations. Institutional affiliations include the Center for Diabetes and Metabolism, IPHAM's Center for Health Services & Outcomes Research, and NUCATS, where he contributes to translational research infrastructure.

• Cybersecurity
• Blockchain
• Machine Learning
• +۱۲ مورد دیگر

Edgar Weippl is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice Dean and Head of the Research Group Security and Privacy. His work spans cybersecurity, blockchain, and machine learning, with teaching roles in information security and software security courses. Current Positions: Vice Dean (Faculty of Computer Science), Head (Security and Privacy Research Group), Deputy Head (Neuroinformatics & Knowledge Engineering Groups) Research Interests: Cybersecurity, blockchain, IoT security, code obfuscation, privacy technologies, reinforcement learning, and socio-technical systems security Selected Publications: Focus on blockchain privacy, VoWiFi security, code obfuscation, and reinforcement learning applications

Yue Liمشاهده پروفایل

استاد

• Resilient Civil Infrastructure
• Structural Reliability
• Risk Assessment
• +۳ مورد دیگر

Yue Li is the Leonard Case Jr. Professor in the Department of Civil and Environmental Engineering at Case Western Reserve University. He specializes in resilient and sustainable infrastructure systems, focusing on structural reliability, probabilistic design, and climate change adaptation. His research addresses risk assessment for infrastructure under extreme events, including earthquakes, hurricanes, and climate impacts. Education: PhD in Civil Engineering, Georgia Institute of Technology, 2005 Research Interests: Dr. Li’s work integrates advanced statistical methods and data-driven approaches to enhance infrastructure resilience. Key areas include: Probabilistic modeling of structural systems Risk-informed decision-making for multi-hazard mitigation Climate change impacts on material durability and performance Asset management and lifecycle cost analysis Notable Contributions: His recent publications emphasize data-driven resilience metrics for water systems and seismic risk assessment for bridges. He has pioneered frameworks for evaluating infrastructure vulnerability under climate change, including corrosion effects and extreme weather adaptation. Awards: ABSE Outstanding Paper Award (2023) Case School of Engineering Teaching Award (2020) Nomination for John S. Diekhoff Award (2019) Leadership Roles: Dr. Li serves as Section Editor for the ASCE Journal of Structural Engineering and chairs multiple technical committees on safety and reliability. He leads initiatives to standardize multi-hazard design practices and resilience evaluation methodologies.

• Safe Reinforcement Learning
• Data-Driven Model Predictive Control
• Artificial Intelligence
• +۷ مورد دیگر

Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.

Gabriella Casalinoمشاهده پروفایل

استادیار

• Computational Intelligence
• eHealth
• Data Stream Mining
• +۵ مورد دیگر

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

• Machine Learning
• Data Mining
• Bioinformatics
• +۱۱ مورد دیگر

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

Fotios Petropoulosمشاهده پروفایل

استاد

• Time Series Forecasting
• Judgmental Forecasting
• Statistical Model Selection
• +۳ مورد دیگر

Fotios Petropoulos is a Professor at the University of Bath, holding the Management Chair in Management Science within the School of Management's Information, Decisions & Operations department. He also served as the Spyros Makridakis Chair in Forecasting at the University of Nicosia (2023–2023). His research focuses on time series forecasting, judgmental approaches, and integrating statistical and human judgment in decision-making processes. He has contributed to improving forecasting accuracy through temporal aggregation and hierarchical methods. Petropoulos holds a Doctor of Engineering (2012) and Bachelor of Engineering (2007) from the National Technical University of Athens. Editor of the International Journal of Forecasting (2020–present) Associate Editor of Foresight: The International Journal of Applied Forecasting (2015–2022) Director of the International Institute of Forecasters (2016–2018) His research interests emphasize forecasting processes, model selection, and the role of judgment in statistical models. Key areas include temporal aggregation, forecast reconciliation, and behavioral operations analytics. He has published over 100 peer-reviewed articles, focusing on topics like computational cost optimization, probabilistic forecasting, and scalable reconciliation methods. His work contributes to Sustainable Development Goals related to education and innovation. Recent articles highlight advancements in univariate forecasting efficiency, forecast selection criteria, and dynamic reconciliation. Petropoulos is a member of the Smart Warehousing and Logistics Systems group and actively participates in editorial boards of leading forecasting journals. His academic and professional roles bridge theoretical research and practical applications in operational decision-making.

• Asset Pricing
• Financial Institutions
• Machine Learning
• +۳ مورد دیگر

Xiao Han is a Senior Lecturer (Assistant Professor) in Finance at the Bayes Business School , part of the City, University of London . His research focuses on investor expectations, asset pricing, and the application of machine learning in finance. He holds a PhD in Finance from the University of Edinburgh and a Higher Education Fellowship in the UK. Education: PhD in Finance, University of Edinburgh (2017-2021) MSc Finance with Risk Management, University of Bath (2016-2017) B.A. in Accounting, Dongbei University of Finance and Economics & Curtin University (2012-2016) His research interests include subjective investor expectations , financial institutions and demand-based pricing , and machine learning applications in Fintech . He has held visiting positions at the Wharton School, Peking University, and Shanghai University of Finance and Economics. His recent work explores topics such as return decomposition in financial markets, machine learning-driven earnings analysis, and the impact of investor sentiment on mispricing. His research has been published in top journals like the Journal of Financial Economics and Review of Financial Studies . Awards: Best Paper Award in Investments (Eastern Finance Association) Jacobs Levy Center Research Best Paper Prize 2023 Marshall Blume Prize in Financial Research 2023 Xiao Han serves as a referee for journals including Journal of Financial Economics , Review of Financial Studies , and Management Science . His work bridges theoretical finance with practical applications in Fintech and behavioral economics.