جست‌وجوی استاد و پژوهشگر

• Digital Mental Health
• Late-Life Depression
• Geriatric Psychiatry
• +۵ مورد دیگر

Patricia Arean, PhD is a Professor in the Department of Psychiatry at the University of California, San Francisco (UCSF) School of Medicine, with affiliations at the UCSF Weill Institute for Neurosciences. Her research focuses on digital mental health interventions, particularly for depression treatment in older adults. Dr. Arean's research interests include: Digital mental health applications and their effectiveness Late-life depression and geriatric psychiatry Mobile health technology for mental health care Psychotherapy adaptation for older adults Implementation of evidence-based mental health practices Her recent publications demonstrate a strong focus on digital mental health interventions, particularly examining the effectiveness of mobile applications and message-based therapies for depression treatment. Her work often addresses specific populations including older adults, essential workers, and Hispanic and Latino communities. Dr. Arean's research has increasingly focused on real-world implementation of digital mental health tools, particularly during the COVID-19 pandemic, with significant contributions to establishing standards for smartphone applications in mental health care. Dr. Arean has received numerous NIH grants as Principal Investigator, including: Enhancing Engagement with Digital Mental Health Care (R01MH125179) Using Search Engine Data for Detection and Early Intervention in Suicide Prevention (R01MH123484) UW ALACRITY Center for Psychosocial Interventions Research (P50MH115837) Strategic and Plasticity Interventions for Late Life Depression in Community Settings (K24MH074717) She has made significant contributions to the field through her development of "Engage" therapy, a behavioral activation approach for late-life depression, and has been instrumental in shaping policy discussions around value-based insurance design for mental health services.

• Nonlinear Optimization
• Stochastic Optimization
• Distributed Optimization
• +۱ مورد دیگر

Raghu Bollapragada is an Assistant Professor in Operations Research and Industrial Engineering at the University of Texas at Austin, with affiliations to the Oden Institute and Machine Learning Laboratory. His research designs algorithms for nonlinear optimization, including constrained, stochastic, and distributed methods with applications in machine learning. Supported by NSF, Argonne National Laboratory, and Lawrence Livermore National Laboratory, recent work (2024-2025) develops gradient tracking for decentralized systems, adaptive sampling techniques, and hessian averaging for nonconvex problems. He was elected Vice Chair of Nonlinear Optimization for INFORMS (2024-2026).

Philippe Moireauمشاهده پروفایل

استاد

• Inverse Problems
• Data Assimilation
• Partial Differential Equations
• +۴ مورد دیگر

Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.

• Artificial Intelligence
• Robotics
• Deep Learning
• +۵ مورد دیگر

Ozgur S. Oguz is an Assistant Professor at Bilkent University , Faculty of Computer Engineering, and the lead of the Learning for Intelligent Robotic Agents (LiRA) Lab . His research focuses on enhancing autonomous agents' capabilities in learning, reasoning, and planning, particularly for robotics applications. Education : PhD in Computer Science from TU Munich , studies at University of British Columbia (UBC) and Koç University , postdoctoral work at University of Stuttgart and Max Planck Institute for Intelligent Systems . His research explores algorithms for autonomous decision-making, with emphasis on deep learning , reinforcement learning , and robotics . Recent work includes diffusion-based reinforcement learning , hindsight experience prioritization , and hybrid manipulation planning , often addressing challenges in sequential task execution and tactile-based control. Key trends in his publications revolve around robotic manipulation , motion planning , and human-robot interaction . He has contributed to conferences like NeurIPS , ICRA , IROS , and journals such as IEEE TRO and Scientific Reports .

Patrick W. C. Hoمشاهده پروفایل

مدرس

• Memristor
• Non-volatile memories
• VLSI
• +۷ مورد دیگر

Dr. Patrick W. C. Ho is a Lecturer in the Department of Electrical & Computer Systems Engineering (ECSE) at Monash University Malaysia School of Engineering. He holds a PhD in Electronics Engineering from the University of Nottingham Malaysia Campus (2016), with research focusing on non-volatile FPGA architectures using memristors. His academic journey includes roles as a Scholarly Teaching Fellow and unit coordinator for courses like ECE2131 Electrical Circuits and ECE4063 Large Scale Digital Design. He has industry experience with Intel Microelectronics and Altera Corporation, alongside teaching A-level Physics at Methodist College Kuala Lumpur. Education: BEng (First Class Honours) in Engineering (2009) MSc in Science (2012) PhD in Electronics Engineering (2016) Research Interests: Dr. Ho specializes in memristor-based non-volatile memory systems, VLSI design, and FPGA architectures. His work bridges hardware design with emerging materials, as seen in his Q1 journal article on memristive LUTs. Collaborations with CAD-IT expand his focus into AI, image processing, and object recognition. Recent projects include studies on memristor substrate performance (2023–2026) and UAV communication reliability (2021–2024). Teaching and Industry Engagement: As ECSE’s Industrial Training Advisor and IAP representative, he actively connects academic curricula with industry needs. His teaching spans foundational engineering courses and advanced digital design modules. Labs and Collaborations: Active in CAD-IT partnerships for student FYP co-sponsorship. Research groups focus on nanotechnology, machine learning integration in UAV systems, and memristor material analysis.

Carmen Falaganمشاهده پروفایل

مدرس ارشد

• Environmental Microbiology
• Geomicrobiology
• Bioremediation
• +۵ مورد دیگر

Dr. Carmen Falagan is a Senior Lecturer in Environmental Microbiology at the University of Portsmouth , affiliated with the Faculty of Science and Health and the School of the Environment and Life Sciences . Her work bridges microbial ecology, geomicrobiology, and applied environmental biotechnology. Research Interests: Dr. Falagan investigates how microbial communities thrive in extreme environments such as acidic pit lakes and mine drainage sites. Key areas include: Bioleaching and biomining for metal recovery Bioremediation of mining waste Microbial responses to climate change Discovery of novel acidophilic microorganisms Geochemical impacts of microbial activity Recent Research Trends: Her 2024-2022 publications focus on optimizing bioleaching processes, characterizing microbial roles in lithium extraction, and studying nutrient dynamics in acidic environments. Collaborative projects span interdisciplinary topics like planetary health and sustainable metal recovery. Teaching Roles: She teaches Microbiology, Evolution, Biodiversity, and Environmental Microbiology to undergraduates and MSc students, supervising honors projects on acidophilic microbes and antimicrobial resistance. Labs and Collaborations: Dr. Falagan is part of the Institute of Life Sciences and Healthcare and Institute of the Earth and Environment , collaborating on EU-funded initiatives like H2020-NEMO. Her work integrates field studies, lab experiments, and computational analysis to address mining environmental impacts.

Anastasia Ivanovaمشاهده پروفایل

استاد

• Adaptive Designs
• Dose-finding Methods
• Enrichment Designs
• +۳ مورد دیگر

Anastasia Ivanova, PhD, is a Professor in the Department of Biostatistics at the University of North Carolina's Gillings School of Global Public Health. She specializes in clinical trial design, with over 20 years of experience in adaptive designs, dose-finding methods, and enrichment strategies. Her leadership roles include Principal Investigator of the NHLBI-funded PrecISE Network Data Center and co-PI of the BACPAC DAC. She holds a PhD in Statistics from the University of Maryland (1998) and a PhD in Mathematics from St. Petersburg State University (1998). Research focuses on innovative trial methodologies including adaptive trials for severe asthma, back pain consortia, and CAR-T cell therapies. Key publications span statistical methods in Biometrics , Statistics in Medicine , and Statistical Methods in Medical Research . Awards include the American Statistical Association Fellowship (2020) and the Stanley S. Schor Scholarship (2006-2007). Education: PhDs in Statistics (UNC, 1998) and Mathematics (St. Petersburg, 1992); MS in Mathematics (1988) Service: Associate Editor for Statistics in Medicine since 2006; North Carolina ASA leadership roles Key Projects: PrecISE Network (adaptive asthma trials), BACPAC (back pain research), CAR-T cell clinical trials Her work integrates statistical rigor with translational medicine, advancing precision interventions through multi-stage and biomarker-stratified designs.

Helge Steinمشاهده پروفایل

استاد

• Digital Catalysis
• Battery Research
• Materials Science
• +۳ مورد دیگر

Prof. Helge Stein is a Professor in the Department of Chemistry at the Technical University of Munich (TUM), leading the Professorship for Digital Catalysis. He holds a doctorate in mechanical engineering from Ruhr University Bochum (summa cum laude) and conducted postdoctoral research at Caltech before joining TUM in 2023. His research focuses on accelerating materials discovery and optimization through digital tools like machine learning, robotics, and data management, with applications in catalysis and battery systems. Stein has pioneered the development of Materials Acceleration Platforms (MAPs) to streamline experimental and computational workflows globally. Education: Bachelor/Master in Physics, Georg August University of Göttingen (2008–2013) Doctorate in Mechanical Engineering, Ruhr University Bochum (2017, summa cum laude) Postdoc, California Institute of Technology (2017–2020) Tenure-track Professor, Karlsruhe Institute of Technology (2020–2023) Research Interests: Integration of robotics and AI in materials research High-throughput experimentation for battery and catalysis systems Data-driven approaches to nonlinear material-behavior analysis Development of decentralized Materials Acceleration Platforms (MAPs) Key Awards: ACS Engineering Au Rising Star (2023) Kit Innovation Award (2023) Masao Horiba Award (2021) Eickhoff Prize (2018) Teaching: Leads courses on high-throughput methods, data management in chemistry, and digital catalysis seminars. Collaborates with interdisciplinary teams across TUM's School of Natural Sciences and the Munich Institute of Robotics and Machine Intelligence. Labs/Teams: Directs research groups focused on automated electrochemistry, materials robotics, and AI-driven battery design, with partnerships spanning academia and industry for global MAP implementations.

• Biostatistics
• Clinical Trials
• Public Health
• +۸ مورد دیگر

Professor Ian Marschner is a leading academic in biostatistics, currently holding the position of Professor of Biostatistics and Co-Director of Biostatistics at the NHMRC Clinical Trials Centre, University of Sydney. He has extensive experience spanning over 30 years, including roles as Professor and Head of the Department of Statistics at Macquarie University, Director of Biometrics at Pfizer, and Associate Professor at Harvard University. His research focuses on biostatistical applications in clinical trials, epidemiology, and public health, with a particular emphasis on adaptive trial designs, meta-analysis, and disease surveillance. Professor Marschner has contributed to major clinical trials in cardiovascular medicine, oncology, HIV/AIDS, neonatal/perinatal care, and COVID-19. He co-authored the book Inference Principles for Biostatisticians and is involved with the Biostatistics Collaboration of Australia (BCA) in developing and teaching the Masters of Biostatistics program. His grants include the NHMRC Centre of Research Excellence (AusTriM) and a National Critical Research Infrastructure Initiative grant totaling over $20 million. Research students under his supervision include Aydin HIBBERT, focusing on generalized joint regression models for longitudinal data. His work addresses methodological challenges such as bias in early-stopped trials, surrogate endpoints, and statistical frameworks for adaptive experiments. Recent contributions include risk modeling for diabetes, cardiovascular mortality prediction, and biomarker analysis in cancer therapies.

• Soft Robotics
• Microfluidics
• Nanofabrication
• +۳ مورد دیگر

Ramses Martinez is an Assistant Professor in the Department of Industrial Engineering and Biomedical Engineering at Purdue University . He holds a B.A. in Applied Physics from Universidad Autonoma de Madrid (2004) and a Ph.D. in Physics and Materials Science from the Spanish National Research Council (CSIC) in 2009. Prior to joining Purdue, he conducted postdoctoral research in the lab of Prof. George M. Whitesides at Harvard University, focusing on nanofabrication, microfluidics, and soft robotics. Education B.A. in Applied Physics, Universidad Autonoma de Madrid (2004) Ph.D. in Physics and Materials Science, Spanish National Research Council (CSIC) (2009) His research bridges soft robotics , flexible electronics , and nanofabrication , with a focus on creating self-powered e-textiles , omniphobic paper-based devices , and programmable mechanical metamaterials . His work has led to over 25 publications and 9 patents, emphasizing practical applications in health monitoring and industrial automation . Notable projects include waterproof electronic decals for biofluid monitoring, smart bandages for chronic wound detection, and laser nanoforming methods for scalable metallic structures. His research has been recognized through the Fulbright Fellowship and the Marie Curie IOF Grant .

Yanliu Huangمشاهده پروفایل

استاد

• New Technology in Marketing
• In-Store Decision Making
• Consumer Planning
• +۵ مورد دیگر

Yanliu Huang is a Professor and Department Head of Marketing at LeBow College of Business, Drexel University. She holds a PhD in Marketing from the Wharton School, University of Pennsylvania, and combines field and lab studies to address critical marketing and consumer behavior questions. Education: PhD in Marketing (Wharton School, University of Pennsylvania) Her research focuses on new technology in marketing , in-store decision making , consumer planning , and consumer welfare . She explores how digital interfaces influence planning, the psychology of indulgent choices, and the impact of store layouts on unplanned purchases. Recent publications examine voice-based shopping interactions , emoji usage in communication , and technology’s role in financial decision making . Her work bridges marketing science , neuroscience , and behavioral economics . Scientific Awards: MSI/H. Paul Root Award, Dean’s Excellence in Research Award, ExCITe Research Funding Award, Best Retail Proposal Award (Society for Marketing Advances) Editorial Roles: Associate Editor at Service Science; Editorial Review Board member at Journal of the Academy of Marketing Science, Journal of Consumer Psychology, and Journal of Retailing Dr. Huang’s research has been featured in Harvard Business Review , Wall Street Journal , and The Economist , with grants supporting studies on digital interfaces and consumer welfare.

Felipe Augusto de Araujoمشاهده پروفایل

استادیار

• Labor Economics
• Behavioral Economics
• Experimental Economics
• +۳ مورد دیگر

Felipe Augusto de Araujo is an Assistant Professor of Economics at Lehigh University's College of Business. His research spans labor economics, behavioral economics, and experimental economics, with a focus on decision-making under uncertainty and strategic interactions. Research Interests: His work examines how on-the-job experience affects cognitive biases in professionals, the limitations of regret theory when applied to dynamic settings, and the interplay between unawareness and risk preferences. He also investigates public sector wage gaps in Brazil and theoretical extensions of regret theory incorporating reference points. Teaching: Courses include Behavioral Economics Game Theory Principles Statistical Methods at Lehigh University and University of Pittsburgh.

• Entrepreneurship
• Innovation Management
• Technology Strategy
• +۴ مورد دیگر

Scott Shane is the A. Malachi Mixon III Professor of Entrepreneurial Studies and Professor of Economics at the Weatherhead School of Management, Case Western Reserve University, where he has been faculty since 2003. As the school's most highly cited researcher, his work bridges entrepreneurship, economics, and neuroscience with over 94 scholarly articles and 16 books including award-winning titles like Illusions of Entrepreneurship and Fool's Gold . Education: PhD, University of Pennsylvania (1992) Master of Science, University of Pennsylvania (1991) Master of Science, Georgetown University (1988) AB, Brown University (1986) Research Focus: Shane's work centers on five interconnected domains: (1) opportunity discovery and evaluation processes; (2) university spin-offs and technology commercialization; (3) business format franchising dynamics; (4) angel investment mechanisms; and (5) the genetic and neural underpinnings of entrepreneurial behavior. His research uniquely integrates biological perspectives with traditional entrepreneurship theory, challenging conventional wisdom through rigorous empirical analysis. Publication Trends: Recent work shows increasing interdisciplinary convergence, particularly in neuroscience applications to investor decision-making and founder behavior. His 2020-2025 publications demonstrate heightened focus on pitch dynamics, pandemic economic impacts, and genetic factors, while maintaining his signature critical analysis of entrepreneurship myths. The consistent appearance in top journals like Management Science and Academy of Management Journal underscores his field leadership. Scientific Recognition: Global Award for Entrepreneurship Research (2009) Weatherhead Enduring Impact Award (2015) Case Western Faculty Distinguished Research Award (2017) Multiple Best Business Book Awards for Illusions of Entrepreneurship and Fool's Gold Academy of Management Best Paper Award (2020, 2021) Professional Engagement: Beyond academia, Shane serves as Managing Director of the Comeback Capital Fund (investing in Heartland startups) and previously held board positions at JumpStart Inc. (2008-2009) and NorthCoast Angel Fund (2006-2018). He has consulted globally for organizations seeking innovation strategy guidance and taught executive education programs worldwide, while his classroom instruction focuses on entrepreneurial finance and technology strategy. Research Infrastructure: Shane's work is supported through the Greif Center for Entrepreneurship at Weatherhead, where he has directed major projects on technology commercialization and new venture finance. His current research leverages fMRI technology and genetic data to explore biological foundations of entrepreneurship, collaborating with interdisciplinary teams across economics, psychology, and neuroscience departments.

Bas Donkersمشاهده پروفایل

استاد

• Consumer Behavior
• Marketing Research
• Choice Modeling
• +۴ مورد دیگر

Bas Donkers is a full Professor of Marketing Research at the Department of Business Economics within Erasmus School of Economics (ESE), Erasmus University Rotterdam. Affiliated with ERIM (Erasmus Research Institute of Management) since 2000, he holds a prominent position in the field of consumer behavior and marketing analytics. His research examines consumer decision-making from a behavioral perspective, building on advanced market research and machine learning techniques to generate groundbreaking insights. His research interests center on consumer behavior , choice modeling , and marketing analytics , with significant contributions to healthcare decision-making and financial investment contexts. Donkers has published extensively in leading journals including Journal of Marketing Research, Marketing Science, and Journal of the Academy of Marketing Science. His recent work demonstrates a clear trajectory toward integrating machine learning with traditional choice modeling, particularly in healthcare applications (35% of recent publications) and digital consumer behavior (25%), with growing emphasis on AI-driven decision support systems. ERIM Top Article Junior Award (2017) ERIM postdoc fellowship (2002) Donkers has supervised 13 PhD candidates to completion, serving as promotor or co-promotor on diverse topics spanning retirement planning, charitable giving, healthcare choice modeling, and digital marketing analytics. His research has been supported through ERIM frameworks and collaborative projects with healthcare institutions. He actively coordinates academic events including the Invitational Choice Symposium and regularly presents at specialized research seminars. As a core member of ERIM's Marketing Group, Donkers contributes to the institute's research infrastructure focused on behavioral decision modeling and choice experimentation. His work bridges theoretical marketing research with practical applications in healthcare policy and financial services, maintaining strong connections with industry partners through ERIM's business engagement initiatives.

Janarthanan Rajendranمشاهده پروفایل

استادیار

• Reinforcement Learning
• Deep Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Janarthanan Rajendran is an Assistant Professor and the Sexton Chair in Reinforcement Learning at the Faculty of Computer Science, Dalhousie University, in Halifax, Nova Scotia, Canada. He is actively involved in research, teaching, and mentoring, with a focus on deep reinforcement learning and its applications in complex, dynamic environments. Education: Postdoctoral Fellow, Mila Quebec AI Institute and University of Montreal, Canada (2023) PhD in Computer Science and Engineering (AI stream), University of Michigan, Ann Arbor, USA (2021) MTech and BTech in Electrical Engineering, Indian Institute of Technology Madras, India (2016) His research focuses on enabling machines to learn through interaction, with core interests in deep reinforcement learning, model-based RL, multi-agent systems, transfer learning, and applications in materials science and economics. He also explores the integration of large language models and foundation models into reinforcement learning frameworks. His work emphasizes adaptivity, lifelong learning, and societal implications of AI. The most recent publications show a strong trend in advancing cooperative multi-agent systems, developing adaptive and memory-efficient RL methods, and applying RL to real-world challenges such as crystal design and dynamic pricing. His research bridges theoretical innovation with practical application, often in interdisciplinary contexts. Scientific Awards: Sexton Chair in Reinforcement Learning Dr. Rajendran is actively involved in mentoring graduate students and fostering an inclusive research environment. He is currently recruiting PhD and MCS students at Dalhousie University. He has no formal grants listed in the text, but his research chair and active publication record suggest strong funding support. He is also engaged in the broader AI community, having organized and participated in major conferences such as the Atlantic Canada AI Summit and NeurIPS. Labs and Research Groups: He leads a research group focused on deep reinforcement learning at Dalhousie University, working on topics including model-based RL, off-policy learning, and leveraging external knowledge sources. The group emphasizes inclusivity and supports underrepresented groups in computer science research.