جست‌وجوی استاد و پژوهشگر

Wolfgang Lutzمشاهده پروفایل

استاد

• Clinical Psychology
• Psychotherapy Research
• Precision Mental Health Care
• +۷ مورد دیگر

Prof. Dr. Wolfgang Lutz is a Full Professor and Head of the Department of Clinical Psychology and Psychotherapy at the Faculty of Psychology, University of Trier, Germany. He also serves as Director of the Outpatient Clinic and Postgraduate Clinical Training. Additionally, he holds an Adjunct Professor position at the University of Western Australia and is a Fellow of the Association for Psychological Science (APS). Dr. Lutz's research focuses on advancing clinical psychology and psychotherapy through precision mental health care, treatment personalization, and feedback-informed psychological therapy. His work emphasizes using data-driven approaches to optimize treatment outcomes, with particular attention to depression, anxiety disorders, and PTSD. He has pioneered the development of the Trier Treatment Navigator (TTN), a system for feedback-informed treatment that helps match patients to the most effective therapeutic approaches. His recent publications reveal a strong emphasis on integrating technology and artificial intelligence into psychotherapy research and practice. This includes developing algorithms for personalized therapy, using large language models to analyze therapy sessions, and implementing routine outcome monitoring systems. His research shows how temporal dynamics in therapy processes affect outcomes and how clinical microskills can predict therapeutic alliance and success. Fellow of the Association for Psychological Science (APS) Editor of Psychotherapy Research He leads initiatives to develop a European Psychotherapy Consortium (EPoC) to standardize outcome measurement across countries and promote cooperation in psychotherapy research. His laboratory focuses on precision mental health care, developing tools for treatment personalization, and investigating the mechanisms of change in psychotherapy.

Angela Boatmanمشاهده پروفایل

دانشیار

• Higher education policy
• college access and student success
• remedial and developmental education
• +۵ مورد دیگر

Angela Boatman is an Associate Professor in the Department of Educational Leadership & Higher Education at Boston College specializing in higher education policy evaluation, with emphasis on improving access and success for underrepresented student populations through evidence-based interventions. Her educational credentials include: Ed.D. in Higher Education from Harvard University M.P.P. and M.A. in Higher Education from the University of Michigan B.A. from the University of Minnesota Dr. Boatman's research program systematically examines college access barriers and success pathways, focusing on remedial education reform (particularly mathematics), financial aid design, community college transfer policies, and behavioral interventions to reduce loan aversion. She employs rigorous quasi-experimental and experimental methodologies to evaluate policy impacts on student outcomes, with particular attention to equity implications for traditionally marginalized groups. Her work bridges academic research and practical policy implementation through sustained engagement with federal and state education agencies. Analysis of her recent publications (2019-2025) reveals consistent thematic focus on remediation models (corequisite vs. traditional, high school vs. college timing), behavioral economics applications in financial aid, and cross-institutional transfer policies. Her research spans diverse contexts including U.S. community colleges, technical institutions in Chile, and rural-serving universities, demonstrating methodological versatility through large-scale administrative data analysis, randomized trials, and comparative international studies. Dr. Boatman has secured substantial external research funding from the Institute for Education Sciences, Bill & Melinda Gates Foundation, Lumina Foundation, and Peabody College. She actively contributes to national discourse as a faculty affiliate of the Center for the Analysis of Postsecondary Readiness (CAPR) and the Center for Education Policy Research (CEPR) at Harvard University, frequently presenting findings to policymakers and practitioners through congressional briefings, Federal Reserve conferences, and national education forums.

Sriraam Natarajanمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Healthcare Applications
• +۵ مورد دیگر

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning and StaRLing Lab at The University of Texas at Dallas (UTD), part of the Erik Jonsson School of Engineering & Computer Science. He holds additional roles as a hessian.AI Fellow at TU Darmstadt and an RBCDSAI Distinguished Faculty Fellow at IIT Madras. His expertise spans Artificial Intelligence, Machine Learning, and their applications in healthcare, with a focus on Relational Learning, Reinforcement Learning, and Graphical Models. He has been honored as an AAAI Fellow (2025), elected to the AAAI Executive Council, and recognized with the UTD Outstanding Graduate Teaching Award. Education: Completed his PhD in Computer Science at Oregon State University in 2007 under Prof. Prasad Tadepalli. Postdoctoral work at the University of Wisconsin-Madison with Professors Jude Shavlik and David Page. Previously served as faculty at Indiana University and Wake Forest School of Medicine. Research: Active in developing AI systems for healthcare, including predictive models for gestational diabetes and cardiac arrest in children. His work emphasizes integrating human knowledge into machine learning (e.g., Human-in-the-Loop systems) and advancing statistical relational AI frameworks like Markov Logic Networks and Probabilistic Circuits. Publications: Over 100 peer-reviewed articles, including notable works on causal learning, relational reinforcement learning, and knowledge graph construction. Recent focuses include explainable AI and scalable probabilistic models. Awards: AAAI Fellow, RBCDSAI Distinguished Fellowship, UTD Teaching Excellence Award. Advising and Collaboration: Mentored over 30 students, many now in academia and top institutions like IBM Research, Facebook, and Microsoft. Collaborates globally on projects like GLAD (Glocalized Anomaly Detection) and StaRLing Lab initiatives. Labs/Teams: Leads the StaRLing Lab, focusing on statistical relational AI, and directs UTD's Center for Machine Learning. Engaged in interdisciplinary projects with healthcare, robotics, and data science communities.

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۵ مورد دیگر

Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.

Christian Messierمشاهده پروفایل

استاد

• Forest Ecology
• Tree Resilience to Global Changes
• Urban Forestry
• +۵ مورد دیگر

Christian Messier is a Professor at the University of Quebec in Outaouais (UQO) in the Department of Natural Sciences and at the University of Quebec in Montreal (UQAM) in the Department of Biological Sciences. He serves as Scientific Director of the Institute of Temperate Forest Sciences (ISFORT) and holds two prestigious research chairs: the Canada Research Chair on Tree Resilience to Global Changes and the NSERC/Hydro-Québec Chair on Tree Growth Control. His academic career spans over three decades since completing his Ph.D. in forest sciences from the University of British Columbia in 1991. Dr. Messier's research focuses on two primary areas: the complex functioning of managed natural forest systems to develop management approaches that promote resilience in the face of global changes, and the study of trees and urban forests to reconcile the needs of minimizing negative impacts of trees on human infrastructure while maximizing ecosystem services. His work integrates field studies, simulation modeling, and network theory to address pressing challenges in forest ecology and management. His recent publications demonstrate a strong emphasis on urban forest resilience, functional diversity in forest ecosystems, and nature-based solutions for climate adaptation. The research shows a clear trajectory toward more applied, solution-oriented approaches that bridge fundamental ecological understanding with practical forest management applications across diverse spatial scales from individual trees to entire landscapes. Francqui Foundation Chair (Belgium) - 2020 Member of The Royal Society of Canada - 2019 Personality of the Year 'Radio-Canada/Le Droit' - 2017 Humboldt Research Award - 2016 Prix Acfas – Michel-Jurdant - 2010 Canadian Forestry Scientific Achievement Award - 2006 Dr. Messier has supervised over 40 graduate students throughout his career and currently leads multiple major research projects including the Canada Research Chair on Forest Resilience, the NSERC/Hydro-Québec Chair on Tree Growth Control, and the SylvCIT project for urban forest immunization against global change. His research has been supported by significant grants from NSERC, Canada Research Chairs program, Hydro-Québec, and other major funding agencies. He directs the Messier lab with research assistants Kim Bannon and Fanny Maure, and collaborates with numerous researchers across Canada and internationally. His team studies diverse aspects of forest ecology including soil dynamics, functional composition of tree communities, nature-based solutions for climate adaptation, and ecophysiology of maple water flow. The lab maintains strong connections with the International Diversity Experiment Network with Trees (IDENT) and other major forest research initiatives.

• Economics of Law
• Public Economics
• Industrial Economics
• +۳ مورد دیگر

Bruno Deffains serves as Professor of Economic Sciences at Paris-Panthéon-Assas University, where he directs the Master's program in Law and Economics, the Center for Research in Economics and Law (CRED), and the Digital Transformation of Law and Legaltech University Diploma. His academic leadership extends to the Yale-ESSEC-Paris 2 Summer School in Law and Economics and the Legal Security Index initiative. Affiliated with the Faculty of Law, he bridges economic theory with legal practice through extensive research and institutional engagement. Doctor of Economic Sciences (1991) Accreditation to Supervise Research (Habilitation à Diriger des Recherches, 1993) Deffains' research centers on the Economics of Law, examining how legal frameworks influence economic outcomes across diverse domains. His work in Public Economics investigates tax compliance and redistribution mechanisms, while Industrial Economics research analyzes competition policy and market regulation. Recent scholarship explores digital transformation's impact on legal practice, compliance systems, and the cognitive functions of law. He employs rigorous economic modeling to dissect liability rules, dispute resolution mechanisms, and the comparative performance of legal traditions, consistently linking theoretical insights to real-world policy challenges in antitrust, corruption, and judicial efficiency. His publication trajectory reveals deepening engagement with contemporary legal-economic intersections, evolving from foundational work on liability and legal origins toward cutting-edge analysis of digitalization and compliance. The consistent thread across his oeuvre is the application of microeconomic principles to institutional design, with increasing emphasis on behavioral dimensions and technological disruption in legal systems. Institut Universitaire de France Fellowship (2013) As academic director of the Law and Economics Master's program and CRED research center, Deffains oversees numerous doctoral theses and research initiatives. His role in the trilateral Summer School with Yale Law School and ESSEC cultivates international scholarly exchange, while leadership in the French Association of Law and Economics and European Association of Law and Economics demonstrates sustained commitment to field-building. External responsibilities include chairing the Commercial Practices Examination Commission and advising the National Consultative Commission on Human Rights, reflecting policy-relevant scholarship grounded in empirical economic analysis. Deffains leads the Center for Research in Economics and Law (CRED), a prominent research unit specializing in institutional analysis of legal systems. Through the Summer School in Law and Economics and Digital Transformation initiatives, he fosters interdisciplinary collaboration between economists, legal scholars, and practitioners. His directorship of the Legal Security Index project connects academic research with practical legal metrics development, while involvement with the Club des Juristes' digital pole positions him at the forefront of technology-law convergence debates.

• Simulation-Based Optimization
• Supply Chain Management
• Operations Management
• +۴ مورد دیگر

Hamed Jalali is a Full Professor in Supply Chain Management at NEOMA Business School since September 2024, having joined the institution in September 2017. He holds a Master's degree in Statistics and a PhD in Business Economics from KU Leuven, Belgium. His research emphasizes simulation-based optimization, particularly Kriging-based algorithms, and explores intersections between marketing and operations management, as well as sustainable supply chain practices using game-theoretic models. His work has been published in journals like IIE Transactions , European Journal of Operational Research , and International Journal of Production Economics . Dr. Jalali has received numerous teaching awards at NEOMA Business School and collaborates with industries such as IMEC and EuroGijbels on projects like inventory management and rationing strategies. His research spans inventory dynamics, product portfolio management, blockchain applications in supply chains, and coopetition models in closed-loop systems. Education: PhD in Business Economics, KU Leuven, Belgium Master's degree in Statistics, KU Leuven, Belgium His recent articles highlight contributions to sustainable supply chain traceability, bullwhip effect mitigation through product portfolio adjustments, and multiobjective optimization algorithms. He actively reviews for top-tier journals and presents at international conferences. Key Awards: Multiple teaching excellence awards at NEOMA Business School. Industrial Projects: Collaboration with IMEC and EuroGijbels on inventory management and supply chain sustainability initiatives. His research integrates technical simulation methods with real-world supply chain challenges, emphasizing both operational efficiency and environmental sustainability.

Halefom Abrahaمشاهده پروفایل

استادیار

• Artificial Intelligence and Law
• Privacy and Data Protection
• European and International Law
• +۵ مورد دیگر

Dr. Halefom Abraha is an Assistant Professor at the International and European Law (IER) Department of Utrecht University's School of Law. He specializes in Digital Human Rights, focusing on privacy, data protection, AI regulation, and algorithmic management in labor markets. His research addresses cross-border data access for law enforcement and digital sovereignty. Abraha holds a joint PhD from the University of Malta and Edith Cowan University (Australia) for his thesis on 'Cross-Border Data Access for Law Enforcement Purposes,' which earned the 2021 Dean's Award for Academic Excellence. He completed postdoctoral research at the Bonavero Institute of Human Rights, University of Oxford (2021-2024), and has advised the European Parliament’s LIBE Committee. His expertise spans EU and international law, with notable fellowships including Marie Sklodowska-Curie ITN (2017-2020) and AFRINIC (2017). Abraha is affiliated with the Netherlands Institute of Human Rights (SIM) and RENFORCE, focusing on governance challenges in the digital society. His research areas include the Future of Work under digital transformation, Security in Open Societies, and Contesting Governance paradigms. Key publications address workplace data protection frameworks, algorithmic management regulation, and cross-border data governance. Abraha advocates for contextualized legal solutions balancing innovation with human rights protections.

Michael Bodeمشاهده پروفایل

استاد

• Applied Mathematics
• Ecological Applications
• Conservation Biology
• +۶ مورد دیگر

Michael Bode is a Professor in the School of Mathematical Sciences at Queensland University of Technology (QUT). His research focuses on applying mathematical and computational methods to ecological and conservation challenges, particularly in marine ecosystems and coral reef management. He holds a PhD in Applied Mathematics from the University of Queensland. Research Interests: Professor Bode specializes in larval dispersal modeling, conservation prioritization, and the application of ecosystem models to real-world management scenarios. His work integrates disciplines like applied mathematics, statistics, and ecology to address issues such as coral reef resilience, invasive species control, and climate change impacts. Grants & Projects: Securing Antarctica's Environmental Future (2020) Conserving Coral Reef Fish and Sustaining Fisheries in the Anthropocene (2019) Tackling Pests Using Game Theory (2019) New Methods for Conserving Dispersing Species on Coral Reefs (2017) Awards: 2020 Fenner Medal (Australian Academy of Science) Eureka Prize finalist (2020) Australian Research Council Future Fellowship (2017) Labs & Teams: He leads the QUT Applied Mathematical Ecology Group (QUTAMEG), which develops quantitative tools for ecological and conservation problems. His work is published in journals like Conservation Biology , Nature Sustainability , and PLoS Biology .

Bruno Castro da Silvaمشاهده پروفایل

استادیار

• Reinforcement Learning
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Bruno Castro da Silva is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He holds a PhD in Computer Science from UMass Amherst (2014), and MSc and BSc degrees from the Federal University of Rio Grande do Sul (UFRGS), Brazil. Prior to UMass, he was an Associate Professor at UFRGS and a postdoctoral researcher at MIT's Aerospace Controls Laboratory. His research focuses on reinforcement learning (RL), robotics, and AI safety, aiming to develop algorithms that ensure safe and autonomous task decomposition while meeting user-specified safety criteria. Key areas include hierarchical policies, active learning, and biologically-plausible mechanisms. He has published in top venues like ICML, NeurIPS, and Science, and received awards such as the Best Paper at RLDM 2022 and Distinguished Reviewer distinctions. He teaches courses in reinforcement learning and machine learning at UMass, emphasizing accessibility and safety. His work also extends to fostering diversity in STEM education. He leads the Autonomous Learning Laboratory and collaborates with organizations like Adobe Research and the Laboratory of Computational Neuroscience in Rome.

Dan Sheldonمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Machine Learning
• +۵ مورد دیگر

Dan Sheldon is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, holding a Five College joint faculty position with Mount Holyoke College. His research focuses on developing algorithms to address environmental challenges using large datasets, emphasizing computational sustainability. Key areas include spatial optimization for endangered species conservation, continent-scale bird migration modeling, and interpreting weather radar data for ecological insights. Methodologically, his work leverages probabilistic inference, network modeling, and machine learning. Sheldon earned a PhD in Computer Science from Cornell University and an AB in Mathematics from Dartmouth College. His postdoctoral training at Oregon State University was supported by an NSF Bioinformatics Fellowship. He co-leads the BirdCast project, an NSF-funded initiative applying novel machine learning to avian migration studies. His research affiliations include the Center for Data Science and the Computational Social Science Institute. Research interests span computational biology, machine learning, and data privacy. Notable contributions include algorithms for ecological decision-making, differentially private synthetic data techniques, and Gaussian process applications in environmental forecasting. Awards include an NSF Fellowship in Bioinformatics. Current projects integrate radar data analysis, biodiversity tracking, and privacy-preserving statistical methods. Grants include the BirdCast NSF grant and collaborations in computational sustainability. His work bridges theoretical computer science with applied ecological challenges, emphasizing interdisciplinary approaches to global-scale environmental problems.

Liz Stephensمشاهده پروفایل

استاد

• Flood Forecasting
• Climate Variability
• Hydrology
• +۳ مورد دیگر

Professor Liz Stephens is a faculty member at the University of Reading's Department of Meteorology, specializing in flood forecasting, climate variability, and disaster risk management. Her work focuses on improving hydrological and meteorological models to enhance flood preparedness and climate adaptation strategies globally. Research Interests: Probabilistic flood forecasting Climate impacts on extreme events Enhancing forecast communication for decision-makers Applications in data-scarce regions like Kenya and Uganda Key Projects: Global Flood Awareness System (GloFAS) World Weather Attribution studies Probabilistic forecast evaluation frameworks Her research bridges academic analysis with practical implementation, collaborating with international organizations like ECMWF and humanitarian agencies to translate scientific insights into actionable disaster preparedness measures.

Hui Zhangمشاهده پروفایل

استاد

• Computer Science
• Networking
• Data-Driven Systems
• +۳ مورد دیگر

Hui Zhang is a Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on data-driven networking systems, video streaming optimization, and network control frameworks. He has contributed to innovations in adaptive resource allocation, real-time analytics, and sustainable strategies for resource utilization. Key research themes include time-state analytics, network anomaly detection, and integrating machine learning for enhanced performance. His work addresses challenges in content delivery networks (CDNs), peer-to-peer systems, and environmental applications like waste management. Recent publications (2021–2024) highlight advancements in neural network-based prediction, timeline frameworks, and sustainable material science innovations. No scientific awards are mentioned in the provided text. His research emphasizes practical solutions for improving video quality of experience (QoE), network efficiency, and cross-disciplinary applications.

Francesco Fabianoمشاهده پروفایل

پژوهشگر ارشد

• Multi-agent systems
• Epistemology
• Autonomous planning
• +۷ مورد دیگر

Francesco Fabiano is a Research Fellow at the Department of Computer Science, University of Oxford, and an Affiliated Faculty Member at New Mexico State University. He previously served as Assistant Professor at New Mexico State University (2023–2024) and held Adjunct Professor roles at Saint Joseph’s University and University of Parma. His research spans neuro-symbolic AI, multi-agent systems, epistemology, and autonomous planning. Postdoctoral Research Associate at University of Oxford (2025–present) Ph.D. in Computer Science from University of Udine (2018–2021) Master’s Degree in Computer Science from New Mexico State University (2017–2018) Bachelor’s Degree in Computer Science from University of Parma (2013–2016) His work focuses on neuro-symbolic architectures that integrate formal logic with machine learning, particularly for multi-agent epistemic planning and ethical decision-making frameworks . He explores hybrid systems combining System-1/System-2 cognitive paradigms to enhance AI trustworthiness. Recent publications analyze knowledge representation in neuro-symbolic systems, the role of large language models in planning, and heuristic optimization in multi-agent epistemic solvers like EFP. His work also addresses ethical AI and explainable data-to-text frameworks . Best Ph.D. Thesis Award by GULP (2022) He collaborates with IBM Watson Research Lab on cognitive theory-inspired AI paradigms and contributes to open-source planning tools like EFP. His teaching experience includes courses in Applied Machine Learning , Automated Planning , and LaTeX programming .

Rani Moranمشاهده پروفایل

مدرس

• Decision Making
• Reinforcement Learning
• Cognitive Maps
• +۵ مورد دیگر

Dr. Rani Moran is a Lecturer in Psychology at Queen Mary University of London's School of Biological and Behavioural Sciences. She is affiliated with the Centre for Brain and Behaviour. Her research focuses on decision-making, memory, and learning mechanisms, employing methods like computational modeling, neuroimaging, and pharmacological interventions. Key interests include reinforcement learning, cognitive maps, and exploration-exploitation dilemmas. Her work integrates experimental designs with advanced statistical analysis to understand flexible behavioral control. Recent studies explore model-based vs. model-free learning, credit assignment mechanisms, and disinformation's impact on learning biases. She has published in top journals such as Psychological Review and Nature Communications . Dr. Moran collaborates on projects involving neurocomputational models of confidence, meta-cognition, and social learning. Her research aims to develop interventions for optimizing cognitive processes, with applications in mental health and decision-making contexts.