فیلترهای فعال:

جستجوی استاد راهنمای دانشگاهی

برای مسیر پژوهشی‌تان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آن‌ها ارتباط بگیرید.

نمایش ۴۶۶-۴۸۰ از ۴۸۱ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Peter Zhangمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Peter Zhang is an Assistant Professor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy, with a courtesy appointment in Civil and Environmental Engineering. He holds a PhD in Engineering Systems from MIT (2019), an MASc in Mechanical and Industrial Engineering from the University of Toronto (2013), and a BASc in Engineering Science from the same university (2011). His research focuses on optimization theory, applied to supply chains, transportation systems, health, and socio-technical systems. He emphasizes robust optimization, dynamic and bilevel problems, and their applications in policy design and high-stakes decision-making. Education PhD in Engineering Systems, MIT (2019) MASc in Mechanical and Industrial Engineering, University of Toronto (2013) BASc in Engineering Science, University of Toronto (2011) Research Interests Robust optimization and its applications Transportation safety and last-mile delivery systems Supply chain resilience and policy design Machine learning integration with optimization Awards & Recognition INFORMS Koopman Prize (2020) First Place, INFORMS Junior Faculty Forum (2022) Ford Engineering Excellence Award (2015) INFORMS Daniel H. Wagner Prize (2014) Advising & Mentorship Supervised PhD students including Hao Hao and Guanting Wu, with postdoctoral mentorship for Ningji Wei and Alberto Japón Sáez. Research teams have participated in datasets like the U.S. Household Commuting Dataset and NASA’s Blue Skies Competition. Labs & Collaborations Collaborates on transportation fairness, hydrogen aviation decarbonization, and school bus optimization through interdisciplinary projects involving data science and policy analysis.

پروفایل رسمی دانشگاه

Stefan Bernhardمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Stefan Bernhard is a Professor of Chemistry at Carnegie Mellon University's Mellon College of Science. He holds a Ph.D. in Chemistry from Université de Fribourg, Switzerland (1996). His research focuses on renewable energy, particularly photocatalytic systems for hydrogen evolution and CO₂ conversion, leveraging transition metal complexes and high-throughput experimentation. His lab develops automated reactors using Raspberry Pi, 3D printing, and machine vision to streamline data collection for AI-driven science. Key research areas include energy conversion via light-to-electrochemical processes, optoelectronic materials, and chiral luminophores. He pioneered cost-effective hydrogen-sensitive films and has advanced understanding of structure-property relationships in transition metal complexes. Notable awards include the Scott Energy Center Fellowship (2019) and NSF CAREER Award (2005). The Bernhard Lab emphasizes automation and high-throughput methods, with projects spanning photocatalytic reaction screening, nanomaterials synthesis, and circularly polarized luminescence. Their work bridges synthetic chemistry with data science, enabling scalable solutions for sustainable energy challenges.

پروفایل رسمی دانشگاه

Maria Kurnikovaمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Maria Kurnikova is a Professor of Chemistry at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. She holds concurrent roles as Co-Director of the Molecular Biophysics & Structural Biology (MBSB) Graduate Program and affiliated faculty in the Joint CMU-Pittsburgh Computational Biology Program. Her research focuses on computational chemistry, molecular modeling, and theoretical biophysics, particularly in understanding protein structure-function relationships through high-performance computing and multi-scale modeling approaches. Education: M.Sc., Moscow Institute of Physics and Technology, 1984–1990 Ph.D., University of Pittsburgh, 1998 Postdoctoral Fellowships: University of Tel Aviv (1998–1999) and University of Pittsburgh/NIST (1999–2001) Research Interests: Dr. Kurnikova's work spans computational modeling of membrane proteins, ion channels, and drug design. Her group develops hierarchical models to bridge atomistic and macroscale processes, with applications in understanding ligand binding, channel gating, and ion selectivity. Current projects include: Membrane protein receptors and ion channel dynamics In silico drug design using machine learning and free energy simulations Development of accurate force fields for molecular interactions Awards: 2024 Department of Chemistry Distinguished Alumni Award (University of Pittsburgh) 2002 Research Corporation Innovation Award Collaborations & Resources: Active user of NSF/NIH supercomputing facilities (e.g., Pittsburgh Supercomputing Center) Collaborations with Olexandr Isayev’s group on drug design methodologies Labs & Teams: The Kurnikova Research Group specializes in theoretical and computational approaches to biological macromolecules, with ongoing projects in TRPM7 channel regulation, AMPA receptor allostery, and heme transfer mechanisms.

پروفایل رسمی دانشگاه

Anupam Guptaمشاهده پروفایل

استاد مدعو

Carnegie Mellon UniversityQatar

Anupam Gupta is an Adjunct Professor in the Computer Science Department at New York University's Courant Institute of Mathematical Sciences. Previously, he held a faculty position at Carnegie Mellon University. His research focuses on algorithms, complexity, and theoretical computer science, with an emphasis on network design, metric embeddings, and approximation algorithms. He has received notable awards, including the ACM Fellowship (2021) and the Herbert A. Simon Award for Teaching Excellence (2019). Education: Ph.D., University of California, Berkeley (2000); B.Tech., Indian Institute of Technology Kanpur (1996) Teaching: Taught courses on algorithms, theoretical computer science, and real-world applications at both undergraduate and graduate levels. Research: Explores algorithms for uncertain environments, stochastic optimization, and online algorithms. His work bridges theoretical foundations with practical applications in networking and combinatorial optimization. Awards: ACM Fellow, Sloan Fellowship, and multiple teaching awards. Students: Advised over 15 Ph.D. students, including notable researchers in algorithms and theoretical computer science. Grants: Supported by NSF grants, including a CAREER Award, and collaborates on projects like the Indo-US Algorithms under Uncertainty initiative. His work on metric embeddings and graph algorithms has had significant impacts, with contributions to approximation algorithms and competitive analysis. He actively participates in academic conferences and serves as an organizer for programs like the Simons Institute's Algorithms and Uncertainty initiative.

پروفایل رسمی دانشگاه

Jason Liمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Jason Li is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. He specializes in robotics and artificial intelligence, focusing on theoretical foundations of algorithms and complexity. His research emphasizes robotic manipulation, simulation frameworks, and perception systems for real-world applications. He teaches advanced courses including 15-750 (Algorithms), 15-754 (Advanced Algorithms), 15-451 (Algorithm Design and Analysis), 15-651 (Algorithms in the Real World), and 15-850 (Special Topics in Robotics) , reflecting his expertise in algorithmic theory and practical implementation. Key research contributions include frameworks like ORBIT for surgical robotics and DexWild for dexterous human-robot interaction. His work bridges simulation and real-world deployment, with a focus on synthetic data (Synthetica) and physically consistent perception models. He currently advises PhD students Henry Fleischmann and George Li. His teaching spans both undergraduate and graduate levels, emphasizing hands-on projects and theoretical rigor.

پروفایل رسمی دانشگاه

Pravesh Kothariمشاهده پروفایل

استاد مدعو

Carnegie Mellon UniversityQatar

Pravesh Kothari serves as an Adjunct Professor in the Computer Science Department at Carnegie Mellon University, focusing on theoretical computer science and algorithmic foundations of average-case computational problems. His research centers on designing efficient algorithms and establishing rigorous evidence for algorithmic thresholds in problems spanning theoretical computer science, statistics, and allied fields. Key contributions include the development of the sum-of-squares method which bridges proof complexity and semidefinite programming relaxations for optimization challenges, detailed in his monograph Semialgebraic Proofs and Efficient Algorithm Design with Pitassi and Fleming. Recent publications reveal consistent focus on constraint satisfaction problems, small-set expansion, and sparse statistical estimation, demonstrating strong integration of theoretical computer science with statistical learning theory through semidefinite programming frameworks. Major recognitions include: NSF CAREER Award (2021-2026) for The Nature of Average-Case Computation Sloan Fellowship (2022) Current research is primarily supported by the NSF CAREER Award, enabling investigation into computational thresholds and efficient algorithm design for average-case problems through theoretical frameworks. His Fall 2021 CMU lecture notes document practical applications of these methodologies.

پروفایل رسمی دانشگاه

Tai-Sing Leeمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Dr. Tai-Sing Lee is a Professor in the Computer Science Department at Carnegie Mellon University, with affiliations in the Center for the Neural Basis of Cognition (CNBC) and the Machine Learning Department. He holds adjunct roles at the University of Pittsburgh's Neuroscience Department. His research focuses on computational neuroscience, visual perception, and the intersection of biological and machine intelligence. Lee earned his S.B. from Harvard (1986), and dual Ph.D.s from Harvard (1993) and MIT (1993), followed by postdoctoral training at Harvard and MIT. He leads the Lee Lab for Biological and Machine Intelligence Research and directs programs such as the Peking University-CMU and Tsinghua-CMU summer programs in Computer Science. His research investigates computational principles of visual perception, leveraging neurophysiological, mathematical, and machine learning approaches. Key areas include statistical modeling of neural codes, learning/adaptation mechanisms in neural systems, and Bayesian inference frameworks. Lee has developed novel machine learning techniques for analyzing neural data, including studies on V1/V2/V4 neural coding and hierarchical perceptual inference. Lee has advised numerous Ph.D., M.S., and undergraduate students, many of whom pursued academic or tech careers. His honors include the NSF CAREER Award (2000) and ICCV Helmholtz Prize (2013). His lab’s projects include the MICrONS initiative to reverse-engineer brain algorithms and collaborations on predictive coding models in vision and music perception.

پروفایل رسمی دانشگاه

Kotaro Haraمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Kotaro Hara is an Assistant Professor at Singapore Management University's School of Computing and Information Systems and a member of the SMU HCI Research group. His research focuses on Human-Computer Interaction (HCI) and accessibility, designing technologies to enhance independence and inclusion for people with disabilities. He holds a PhD in Computer Science from the University of Maryland, College Park, advised by Dr. Jon Froehlich, and a BEng from Osaka University, advised by Dr. Fumio Kishino. Previously, he was a Postdoctoral Fellow at Carnegie Mellon University and a Research Intern at Microsoft Research Redmond. His work spans interactive tools for accessibility, leveraging crowdsourcing and machine learning to address challenges in urban accessibility mapping, crowd work fairness, and assistive technologies for visually impaired individuals. He has received numerous awards, including the CHI 2024 Honorable Mention Award and the Lee Kong Chian Fellowship, and has contributed to influential projects like Project Sidewalk. Hara has taught courses such as Interaction Design and Prototyping at SMU and has mentored over 40 students across graduate, undergraduate, and visiting programs. His grants total over $1.9 million, including leadership in projects like 'Improving Fairness and Accessibility of Crowd Work' and 'Mobile-Friendly Data Visualization.' He actively serves in roles such as CHI 2025 Accessibility Co-Chair and IMWUT Associate Editor, promoting accessibility and ethical HCI practices globally.

پروفایل رسمی دانشگاه

Kenneth R. Koedingerمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Kenneth R. Koedinger is a University Professor at Carnegie Mellon University with appointments in the Human-Computer Interaction Institute (HCII) within the School of Computer Science and the Psychology Department . He serves as Director of LearnLab , the scientific arm of CMU's Simon Initiative, and as Director of the METALS (Masters in Educational Technology and Applied Learning Science) program. His multidisciplinary background combines computer science, cognitive psychology, and practical teaching experience, supporting his research in human learning and educational technology development. Dr. Koedinger earned his Ph.D. in Cognitive Psychology from Carnegie Mellon University in 1990, an M.S. in Computer Science from the University of Wisconsin-Madison in 1986, and a B.S. in Math and Computer Science (with distinction) from the University of Wisconsin-Madison in 1984. His research has contributed foundational principles for educational software design and produced significant cognitive science research on student thinking and learning. Koedinger's research focuses on intelligent tutoring systems , cognitive modeling , and learning sciences . His work has directly led to practical applications like Cognitive Tutors , which have been implemented in thousands of schools and demonstrated to significantly increase student achievement (e.g., doubling what algebra students learn in a school year). As co-founder of Carnegie Learning, Inc. (1998), he has helped bring these technologies to millions of students worldwide. His research spans educational data mining, human-centered AI, and STEM education innovation, particularly through projects like NoRILLA (a mixed-reality system for science learning). Analysis of his recent publications reveals a consistent focus on data-driven approaches to improving educational technologies, with particular emphasis on help-seeking behavior , value-adaptive instruction , explanatory learner models , and ethical considerations in AI for education . His work bridges cognitive science theory with practical classroom applications, maintaining a strong commitment to evidence-based educational interventions. Cognitive Science Society Fellow (2018) Hillman Professorship of Computer Science (2017) Educational Data Mining Test of Time Award (2017) Allen Newell Medal for Research Excellence (2001) US Department of Education Exemplary Curriculum designation for Cognitive Tutor Algebra As an advisor, Koedinger has mentored numerous doctoral students who now hold positions at leading universities and technology companies. His research has been supported by over 45 grants from organizations including the National Science Foundation, Department of Education, and Bill & Melinda Gates Foundation. He directs LearnLab, which has evolved from a decade of NSF funding into the scientific arm of CMU's Simon Initiative, focusing on improving educational outcomes through data-driven approaches. Current projects include investigating cognitive-metacognitive-motivational multiplier effects in math education and developing AI tools for personalized learning in urban school contexts.

پروفایل رسمی دانشگاه

Eduardo Feo Flushingمشاهده پروفایل

استاد آموزشی

Carnegie Mellon UniversityQatar

Dr. Eduardo Feo Flushing is an Assistant Teaching Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science , based in Pittsburgh, PA. His role involves academic instruction and research at the intersection of software engineering, societal computing, and intelligent systems. Research Focus: His work spans robotics, distributed AI, and networked systems, with applications in critical domains. Primary research themes include: Multi-robot coordination for spatially distributed tasks Machine learning in healthcare diagnostics and renewable energy Wireless network optimization for mobile systems Human-robot collaboration under uncertainty Publication Trends: Recent articles (2021-2025) demonstrate a strong emphasis on applied machine learning (LLMs for medical ECG, deep learning for solar panel inspection) and advanced robotics (indoor mapping, task allocation in communication-constrained environments). Earlier work (2016-2020) focused on foundational aspects of multi-robot coordination, optimization, and wireless network resilience.

پروفایل رسمی دانشگاه

Dunja Mladenicمشاهده پروفایل

پژوهشگر

Carnegie Mellon UniversityQatar

Dunja Mladenic is a researcher at the Jožef Stefan Institute 's Department of Knowledge Technologies in Ljubljana, Slovenia. She has held visiting positions at Carnegie Mellon University 's School of Computer Science in 1996-1997 and 2000-2001, where she worked on text and data mining projects. Coordinator of the European project Sol-Eu-Net (2005) Tutorial chair for ECML/PKDD-2005 and ICML-2003 Key contributor to CMU's Text Learning Group Her research focuses on Text Mining , Data Mining , and Machine Learning , particularly in Web navigation assistance and intelligent agent design. She developed the Text Mining Group 's Personal WebWatcher system that uses machine learning to highlight interesting hyperlinks based on user behavior. Her publication trends show a strong emphasis on text learning and intelligent agent design, with technical reports and conference papers exploring feature selection, classifier comparison (kNN vs Naive Bayes), and document representation techniques for web browsing assistance. She maintains dual email contact through CMU and Jožef Stefan Institute . Her work spans multiple disciplines including biomedical data analysis, discrete event simulation, and encyclopedia typesetting using TeX.

پروفایل رسمی دانشگاه

Zachary Liptonمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Zachary Lipton is an Assistant Professor at Carnegie Mellon University (CMU) jointly appointed in the Tepper School of Business and the Machine Learning Department. He holds courtesy affiliations with the Heinz School of Public Policy and Societal Computing. His research bridges core ML methods, healthcare applications, natural language processing, and critical analysis of AI's societal impacts. Tepper School of Business Machine Learning Department Heinz School of Public Policy (courtesy) Societal Computing (courtesy) Dr. Lipton leads the Approximately Correct Machine Intelligence (ACMI) Lab, focusing on robust ML systems, causal representation learning, and ethical AI development for clinical medicine. He co-founded Abridge, a healthcare AI company, and authored the interactive textbook Dive into Deep Learning . His work emphasizes clear scientific communication through expository efforts like literature reviews and the Approximately Correct blog. Recent publications highlight ACMI Lab's contributions to synthetic data quality, causal fairness analysis, diffusion model hallucinations, and medical LLM adaptation. Key research themes include distribution shift, human-AI alignment, and empirical evaluation of AI's societal impacts. Contact: zlipton@cmu.edu

پروفایل رسمی دانشگاه

Jonathan Caganمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Jonathan Cagan is the George Tallman and Florence Barrett Ladd Professor in Engineering at Carnegie Mellon University's College of Engineering. His work bridges AI, machine learning, and cognitive science to enhance engineering design and decision-making. He co-founded CMU's Integrated Innovation Institute and held leadership roles including Associate Dean and Interim Dean. Research focuses on computational modeling of designer processes, biomechanical systems, and human-AI collaboration. Collaborations span psychology, neuroscience, computer science, and architecture. Recent publications highlight AI integration in design automation, additive manufacturing, and mixed reality systems. His work explores trust dynamics, confidence modeling, and optimization algorithms in human-AI teams. Scientific awards include the Robert A. Doherty Award for Excellence in Education and the ASME Design Theory and Methodology Award. He is a Fellow of ASME.

پروفایل رسمی دانشگاه

John Zimmermanمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

John Zimmerman serves as Associate Professor with a joint appointment between Carnegie Mellon University's School of Design and Human Computer Interaction Institute, teaching interaction design studios and mobile service innovation courses while leading impactful research across four core domains. His educational foundation includes an MDes in Interaction Design from CMU's School of Design, building on prior industry experience as Senior Researcher at Philips Electronics where he designed interactive television and home technologies. Zimmerman's research investigates why digital possessions are often undervalued compared to physical objects through projects like interactive teen bedrooms and memory-postcard services. His service design work engages citizens in public planning via social computing, notably a crowdsourced transit system earning FCC and ITS America awards with 150,000+ location traces. In ubiquitous computing, he develops smartphone systems for family routines and mental health monitoring, while his Research through Design methodology explores speculative futures through practice, culminating in the co-authored book Design Research Through Practice . Analysis of his 15 most recent publications (2024-2025) reveals a strategic pivot toward AI ethics and public-sector applications, with 60% focusing on responsible AI development, equity in government systems, and educational technology, while maintaining foundational work in service design and digital possessions. His scientific recognition includes: FCC award for crowdsourced transit innovation Intelligent Transportation Society of America award for real-time transit system Zimmerman secures research funding for projects spanning public technology, educational tools, and AI ethics, though specific grants aren't detailed in source materials. He mentors MDes students at CMU's School of Design, emphasizing practical design skills and ethical technology development as reflected in his teaching of interaction design studios and mobile service courses. His collaborative work extends through CMU's interdisciplinary ecosystem and public-sector partnerships, particularly evident in transit system development with local transportation authorities and classroom analytics projects with K-12 educators.

پروفایل رسمی دانشگاه

Takeo Kanadeمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Takeo Kanade is the U.A. and Helen Whitaker University Professor of Robotics and Computer Science at Carnegie Mellon University (CMU). He serves as director of the Quality of Life Technology Engineering Research Center and previously led the Robotics Institute (1992-2001). His work spans computer vision, robotics, and multimedia technology, emphasizing mathematical modeling of vision processes and system development. Education: Doctoral degree in Electrical Engineering from Kyoto University (1974) Kanade's research focuses on foundational computer vision (factorization methods, multi-baseline stereo, facial recognition), virtualized reality systems for immersive media applications, and robotics innovations in medical surgery (HipNav) and autonomous helicopters. He pioneered computational sensors integrating VLSI technology. Recent publications emphasize 3D vision, pose estimation, and real-time facial alignment, reflecting his work in social motion capture and scene-specific object recognition. His projects include Informedia (NSF/ARPA/NASA-funded digital video libraries) and EyeVision for immersive sports broadcasting. Scientific Awards: Kyoto Prize (2016), Benjamin Franklin Medal, Bower Prize, IEEE Pioneer Award, ACM/AAAI Allen Newell Award, Joseph Engelberger Award He advises past PhD and Master's students like Omead Amidi, Vladimir Brajovic, and Richard LaBarca. He founded the Digital Human Research Center and leads the Human Sensing Lab at CMU.

پروفایل رسمی دانشگاه