جستجوی استاد راهنمای دانشگاهی

Oscar P. Bruno is a Professor of Applied and Computational Mathematics at the California Institute of Technology (Caltech). He holds a Licenciado from the University of Buenos Aires (1982) and a Ph.D. in Mathematics from New York University's Courant Institute (1989). Since 1998, he has been a Professor at Caltech, previously serving as Associate Professor (1995–98) and Executive Officer for Applied Mathematics (1998–2000). His research focuses on developing high-performance numerical methods for solving partial differential equations (PDEs), addressing challenges in complex geometries, singularities, and high-frequency phenomena. Key contributions include the Fourier Continuation (FC) method and integral-equation techniques, enabling solutions to previously intractable PDE problems in science and engineering. Prof. Bruno's expertise spans computational electromagnetics, computational fluid dynamics (CFD), solid mechanics, and mathematical physics. His work integrates numerical analysis, multiphysics modeling, and computational science to solve real-world problems in geophysics, optics, and fluid dynamics. He has received numerous awards, including membership in the National Academy of Sciences of Argentina (2020), the Vannevar Bush National Security Science and Engineering Fellowship (2016), and SIAM Fellow (2013). Bruno serves on editorial boards for journals like SIAM Journal on Scientific Computing and SIAM Journal on Applied Mathematics, and participates in national science advisory roles. His teaching includes advanced courses on applied mathematics methods (ACM/IDS 101 ab), emphasizing theoretical foundations and numerical techniques for PDEs. His research group develops cutting-edge solvers with applications in shock dynamics, optical tomography, and geophysical fluid dynamics.

• Partial Differential Equations
• Computational Science
• Numerical Analysis
• +۵ مورد دیگر

John F. Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering at the University of California, Berkeley, in the Department of Electrical Engineering and Computer Science (EECS). He joined the faculty in 1987 and is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Center for New Media (BCNM), and other research institutes. His research focuses on artificial intelligence, robotics, human-computer interaction, and computational geometry. Canny is renowned for developing the Canny edge detector and contributions to motion planning, privacy-preserving algorithms, and real-time simulation. Education: B.Sc. in Computer Science and Theoretical Physics (Adelaide University, 1979), B.E. (Hons) in Electrical Engineering (Adelaide University, 1980), M.S. and Ph.D. in Electrical Engineering (MIT, 1983 and 1987). Research Interests: His work spans AI, robotics (including universal planar manipulation), HCI (e.g., MultiView video conferencing), and security (privacy in collaborative filtering). He has pioneered algorithms for edge detection, non-linear FEM simulation, and computational algebra. Awards: ACM Fellow (2020), Okawa Research Grant (2003), AAAI Classic Paper Award (2002), NSF PYI (1989), Packard Fellowship (1988), and ACM Doctoral Dissertation Award (1987). Advising & Grants: Advised over 30 Ph.D. students, many of whom hold prominent roles in academia and industry. His grants include NSF and Packard funding for foundational research in robotics and computational methods. He currently teaches CS188: Introduction to Artificial Intelligence. Labs/Teams: Leads research at the Berkeley Institute of Design (BiD), focusing on RISC robotics, activity-based computing, and privacy-enhanced telepresence systems.

• Artificial Intelligence
• Robotics
• Human-Computer Interaction
• +۵ مورد دیگر

John DeNero is the Giancarlo Teaching Fellow and Associate Teaching Professor in UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department. He joined UC Berkeley in 2014 to focus on undergraduate education in computer science and data science. He teaches and co-develops introductory courses like CS 61A (Computer Science) and Data 8 (Data Science), which serve thousands of students annually. His research spans natural language processing and education innovation, with notable contributions to textbooks like Composing Programs and Computational and Inferential Thinking . Education: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley (2010) M.A. in Philosophy, Stanford University (2002) B.S. in Mathematical & Computational Science and Symbolic Systems, Stanford University (2001) Research interests focus on advancing AI education and curriculum design. His work emphasizes scalability, equity, and student success in large courses. Notable contributions include the Pac-Man projects for AI education and the Berkeley Data Science Education Program. Recent trends in publications highlight AI-assisted education tools, machine translation improvements, and large-scale student feedback systems. Awards include the UC Berkeley Distinguished Teaching Award (2018) and multiple accolades for teaching excellence. Advising and grants: While currently not taking new students, his team supports massive course infrastructures. Labs include the Berkeley Artificial Intelligence Research (BAIR) Lab and contributions to Data Science undergraduate studies.

• Artificial Intelligence (AI)
• Computer Science Education
• Data Science
• +۳ مورد دیگر

Adam Yala is an Assistant Professor of Computational Precision Health, Statistics, and Electrical Engineering and Computer Science at UC Berkeley and UCSF. He is also the Founder & CEO of Voio Inc., a company focused on clinical translation of AI tools. PhD in Computer Science from MIT (2022) His research lies at the intersection of Machine Learning and Precision Medicine, with a focus on robust AI tools for clinical deployment, personalized screening policies, and private data sharing. Current work includes multi-modal imaging analysis, decision guarantees in clinical workflows, and prospective trials in oncology and radiology. Recent publications highlight advancements in AI for cancer risk prediction, vision-language models in healthcare, and data privacy techniques. Tools like Mirai are implemented in 66 hospitals across 30 countries. Bakar Fellows Spark Award (2024) Eppy Award: Investigative Reporting (2022) Falling Walls Finalist: Life Science (2022) NSF Fellowship (2016) He advises PhD students in AI-driven healthcare and collaborates with hospital systems globally. His lab emphasizes clinical translation of machine learning methods in radiology and oncology.

• Machine Learning
• Artificial Intelligence
• Precision Medicine
• +۵ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Antonio González is a full Professor at the Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Computer Architecture. He leads the Microarchitecture and Compilers (ARCO) research group, focusing on robust and energy-efficient computing systems, including general-purpose processors, GPUs, and cognitive computing architectures. His research spans computer architecture, microarchitecture, compilers, GPU design, and deep learning acceleration, with a strong emphasis on energy efficiency and memory optimization. His work integrates hardware and compiler techniques to enhance performance in modern computing systems. The recent publications highlight a strong trend in energy-efficient DNN acceleration, GPU microarchitecture, memory systems, and near-data processing. His team explores novel quantization methods, adaptive caching, and architectural extensions for vision and AI workloads, published in top-tier venues such as MICRO, ISCA, and IEEE/ACM journals. ACM Fellow (2020) ICREA Academia Award (2014, 2019, 2024) UPC Duran Farell Award (2008) HiPEAC 2024 Paper Award (twice) González has supervised several PhD students and leads competitive R&D+i projects, including those funded by ICREA and the European Research Council. His research group collaborates extensively within UPC and with international institutions. He is actively involved in advancing computer architecture through innovation in simulation, hardware design, and AI acceleration. He is a member of IEEE and ACM, and his work continues to influence both academic and industrial developments in high-performance and energy-efficient computing.

• Computer Architecture
• Microarchitecture
• Compilers
• +۷ مورد دیگر

Fei Ding is a Senior Researcher at National Renewable Energy Laboratory (NREL) specializing in Power Systems Engineering within the Grid Modernization Research Topic. Her work focuses on integrating renewable energy into electrical grids, optimizing distributed energy resources, and enhancing grid resilience. Education: PhD in Electrical Engineering from Case Western Reserve University Her research spans: Distribution grid modeling and control strategies Grid-edge resource optimization for reliability and cybersecurity Photovoltaic hosting capacity analysis Recent publications emphasize: AI-driven voltage regulation with neural networks Microgrid formation for grid restoration Data fusion techniques for outage detection Award highlights: NREL Chairman’s Award (2018) NREL President's Award (2020) NREL Distinguished Member of Research Staff (2021) She actively contributes to IEEE as a Senior Member and leads projects developing advanced distribution management systems.

• Renewable Energy Grid Integration
• Distributed Energy Resources
• Grid Resilience and Security
• +۳ مورد دیگر

Zaklina Spalevic is a distinguished Professor at Singidunum University, specializing in the intersection of law, technology, and business. With a strong academic background in criminal law and extensive research in cyber law, artificial intelligence applications in legal systems, and tourism law, she has established herself as a leading scholar in digital legal frameworks. Her work spans multiple disciplines, connecting traditional legal principles with emerging technological challenges. Dr. Spalevic earned her doctoral degree in Criminal Law from the University of the Academy of Economics in Novi Sad (2008-2012), following basic studies in General Law at the University of Pristina (1996-2001). Her educational foundation in Science and Mathematics from First Pristina High School (1992-1996) provided the analytical basis for her interdisciplinary approach to legal scholarship. Her research interests focus on the evolving landscape of cyber law, particularly the legal implications of artificial intelligence in judicial systems, electronic governance, and tourism industry regulation. Dr. Spalevic has pioneered work on digital evidence processing, algorithmic justice, and the integration of green law principles into sustainable business practices. Her publications reveal a consistent pattern of addressing emerging legal challenges in the digital age, with increasing emphasis on AI applications across various legal domains. Analysis of her recent publications shows a clear trend toward interdisciplinary research that bridges law, technology, and business. Her work increasingly focuses on practical applications of legal frameworks for emerging technologies, particularly in tourism, agriculture, and judicial systems. The integration of technical solutions with legal regulation represents a distinctive feature of her scholarly contributions. Dr. Spalevic has authored numerous publications including three books: 'Fundamentals of Law with Special Reference to Business and Tourism Law' (2021, 2024 editions) and 'Legal Aspects of Cyberspace' (2018). Her extensive publication record includes over 100 journal articles and conference papers spanning cyber security, electronic governance, intellectual property in digital environments, and legal frameworks for emerging technologies. She actively contributes to academic discourse through conference presentations and collaborative research projects, particularly focusing on the legal implications of artificial intelligence across various sectors. Her work demonstrates a commitment to developing practical legal frameworks that address real-world technological challenges while maintaining foundational legal principles.

• Cyber Law
• Artificial Intelligence and Law
• Business Law
• +۷ مورد دیگر

Jan Oliver Borchers is a full professor of computer science and head of the Media Computing Group at RWTH Aachen University, where he holds the endowed Chair in Media Informatics and Human-Computer Interaction (Computer Science 10). He established his research group in 2003 and pioneered modern HCI academic research and teaching in Germany, becoming a leading lab in terms of archival publications at CHI, the top international conference in HCI. Before joining RWTH, he held faculty positions at Stanford University and ETH Zurich. His research focuses on Human-Computer Interaction with particular interest in new user interfaces for soft robotics, textile user interfaces, 3D printing and personal fabrication, augmented reality, wearable and tangible computing, interfaces for software development, deceptive patterns, and interactive guides and exhibits. He opened Germany's first Fab Lab in 2009 and has been instrumental in advancing digital fabrication and maker culture in academia. His work bridges theoretical HCI research with practical applications, as evidenced by his consulting for major companies including AirBus, Apple, ARD, Bayer, Children's Museum Boston, Daimler, Handelsblatt, OTIS, Scout24, TEDx, and Deutsche Telekom. His recent publications show a strong focus on deceptive patterns in UI design, textile interfaces, and the intersection of AI with human interaction. The research demonstrates consistent innovation in tactile interaction techniques, with numerous honorable mentions at top conferences like CHI. His lab has produced significant contributions to eyes-free interaction, textile computing, and the ethical implications of dark patterns, particularly regarding children's experiences with deceptive designs. IDC 2025 Best Work in Progress for research on children's assessment of deceptive designs CHI'25 Student Games Competition Winner for The Deceptive Dungeon RWTH-Wissenschaftsnacht 2024 Best Science Slam for Usability: 4 Prinzipien guter User Interfaces Multiple CHI Honorable Mentions spanning from 2008-2021 Best Note and Best Demo awards at ITS and UIST conferences Borchers has served in numerous leadership roles including heading the Computer Science Department's Examination Board (2015-2025), coordinating the department's public relations since 2010, and organizing the weekly Faculty Lunch since 2003. He is an active member of the ACM, SIGCHI, SIGCHI Germany, and GI, and introduced the Interactivity format to the CHI conference in 2005. His PhD thesis, A Pattern Approach to Interaction Design, became the first book to bring design patterns to HCI. The Media Computing Group under his leadership has developed significant expertise in physical computing, textile interfaces, and fabrication technologies, with a strong emphasis on both theoretical contributions and practical applications. His lab continues to push boundaries in interactive technologies while maintaining a strong ethical stance on user experience, particularly regarding deceptive design patterns.

• Human-Computer Interaction
• Textile User Interfaces
• Soft Robotics
• +۵ مورد دیگر

Devis Tuia serves as Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), holding appointments in the Institute of Environmental Engineering (IIE) within the School of Architecture, Civil and Environmental Engineering (ENAC). He leads the Environmental Computational Science and Earth Observation Laboratory (ECEO) since 2020 and contributes to EPFL's Doctoral Program in Civil and Environmental Engineering. His academic journey began in Lausanne with studies at UNIL and EPFL, culminating in a PhD in remote sensing from UNIL. Postdoctoral research followed at institutions in Valencia, Boulder, and EPFL, focusing on machine learning model adaptation. He progressed from Research Assistant Professor at University of Zurich to Associate and Full Professor at Wageningen University before joining EPFL. Tuia's research bridges Earth observation with artificial intelligence, specializing in interpretable deep learning for environmental applications. His lab develops algorithms for making remote sensing accessible, with particular emphasis on digital wildlife conservation through automated censuses using drone and satellite imagery. Current projects tackle the 'black box' problem in environmental modeling while advancing spatial intelligence for sustainable urban development. His 2023-2025 publication portfolio reveals three dominant trends: (1) species distribution modeling using incomplete observations, (2) multimodal fusion of satellite/drone data with textual descriptions, and (3) interpretable AI frameworks for environmental decision-making. This work consistently addresses real-world challenges like wildfire forecasting and biodiversity monitoring. As an educator, Tuia supervises 12 current PhD students and has graduated 4 former EPFL doctoral candidates. His teaching portfolio includes Frontiers of Deep Learning for Engineers , Sensing and Spatial Modeling for Earth Observation , and Image Processing for Earth Observation courses. The ECEO laboratory maintains active collaborations with ESA-NASA initiatives and conservation organizations globally.

• Earth Observation
• Remote Sensing
• Machine Learning
• +۵ مورد دیگر

Aimee van Wynsberghe is the Alexander von Humboldt Professor for Applied Ethics of Artificial Intelligence at the University of Bonn, where she also serves as Director of the Institute for Science and Ethics (IWE). Her work focuses on integrating ethical values into AI and robotics design, positioning Bonn as a leader in human-centered AI in Europe. She co-founded the Foundation for Responsible Robotics and advises international bodies such as the European Commission and the World Economic Forum. Van Wynsberghe holds a PhD in Applied Ethics from the University of Twente (2012), an MA in Bioethics (Erasmus Mundus, 2008), an MA in Applied Ethics from KU Leuven (2007), and a BSc in Cell Biology from Western Ontario (2006). Her research spans AI ethics, robotics ethics, care ethics, and sustainable technology, with a particular emphasis on value-sensitive design. Recent publications highlight her leadership in sustainable AI, ethical robotics, and multidisciplinary approaches to AI's hidden costs. She has secured significant funding, including the Humboldt Professorship (€3.5M) and Stiftung Mercator support (€3.8M), and leads the Bonn Sustainable AI Lab. Her awards include the L’Oréal UNESCO For Women In Science Award (2018) and the Humboldt Professorship (2021). Scientific Advisory Roles: Member, European Commission High-Level Expert Group on AI (2018–2020) Director, Artificial Intelligence Lab at TU Delft (2019–2021) Co-founder, ALLAI (2019–2021) Board Member, Konrad Zuse Schools of Excellence (2022–) Labs & Teams: She founded the Bonn Sustainable AI Lab and previously coordinated the 4TU Robotics Task Force. Current affiliations include the University of Bonn’s IWE, the German National Academy of Sciences (Leopoldina), and the German Academy of Science and Literature in Mainz.

• AI Ethics
• Robotics Ethics
• Digital Ethics
• +۲ مورد دیگر

Kwang-Sung Jun is an Assistant Professor at the University of Arizona, Department of Computer Science. His research spans interactive machine learning, reinforcement learning, and learning theory, with a focus on multi-armed bandits, Bayesian optimization, and generalized linear models. Education : Ph.D. in Computer Science from the University of Wisconsin-Madison (2015). Research Trends : Kwang-Sung's recent work (2023-2025) emphasizes bandit algorithms with second-order bounds, adaptive experimentation, and PAC-Bayes frameworks. He explores low-rank structures in regression, explainable reward shaping, and environmental risk modeling via probabilistic assessments of postfire debris-flows. His publications often bridge theoretical guarantees (e.g., regret bounds) with practical applications in machine learning and environmental hazards. Expertise : Interactive machine learning Multi-armed bandits Confidence sequences Reinforcement learning Human-machine hybrid systems

• Interactive Machine Learning
• Reinforcement Learning
• Multi-Armed Bandits
• +۷ مورد دیگر

Leslie Ann Goldberg is a Senior Research Fellow at St Edmund Hall and Professor of Computer Science at the University of Oxford. She currently serves as Head of the Department of Computer Science (on sabbatical 2025-26) and focuses on foundational problems in Algorithms and Complexity Theory , particularly randomised algorithms for network communication, machine learning, and statistical physics models. Her research includes solving Aldous' 1987 conjecture on backoff protocol instability (with John Lapinskas), developing rigorous mathematical analysis frameworks for algorithmic efficiency, and advancing approximate counting techniques via Markov Chain Monte Carlo methods (with Andreas Galanis and collaborators). Key projects involve graph homomorphisms , Moran process dynamics , and #BIS complexity class analysis. Recent publications (2023-2024) span topics like Sybil defense mechanisms, low-temperature sampling on random graphs, and parameterised subgraph counting modulo 2. Her work demonstrates cross-disciplinary impact in computational biology, statistical physics, and database theory. Scientific Awards include Best Paper Prizes at ICALP 2016, ICALP 2010, and IPEC 2017. She supervises PhD student Paulina Smolarova and collaborates extensively with researchers in Oxford and beyond.

• Algorithms
• Complexity Theory
• Randomised Algorithms
• +۵ مورد دیگر

Giorgio Grisetti is a Full Professor at Sapienza University of Rome within the Department of Systems and Computer Science, maintaining active research roles in the RoCoCo lab at Sapienza since November 2010 and the Autonomous Intelligent Systems Lab at Freiburg University where he previously served as a Post Doc under Wolfram Burgard starting in 2006. His educational background includes a M.Sc. in Computer Engineering from the University of Rome (2001) and a Ph.D. from Sapienza University of Rome's Intelligent Systems Lab (2006), supervised by Daniele Nardi. His doctoral thesis focused on SLAM using Rao-Blackwellized particle filters. Dr. Grisetti's research centers on mobile robotics with emphasis on robust solutions for autonomous navigation systems. His work spans theoretical and practical advancements in Simultaneous Localization and Mapping (SLAM), robot localization, path planning, and sensor fusion, particularly leveraging LiDAR and multi-sensor configurations. Recent publications demonstrate strong focus on optimization techniques, sensor calibration, and real-time performance for autonomous systems operating in complex environments. His publication trends reveal deep specialization in LiDAR-based SLAM (7 of 15 recent articles), bundle adjustment methods (4 articles), and sensor calibration/perception (3 articles), with consistent contributions to top robotics venues like IEEE Robotics and Automation Letters and ICRA. Key recognitions include: Nomination for the best IROS paper award (2010) Open Source achievement award from Willow Garage (2010) Best paper award at the International Conference and Exhibition on Unmanned Areal Vehicles (2010) Best Paper award at ICRA 2009 (2009) His research is conducted through the RoCoCo lab at Sapienza University of Rome and the Autonomous Intelligent Systems Lab at Freiburg University, focusing on developing foundational algorithms for mobile robot autonomy. Current projects emphasize robust perception systems, optimization frameworks for sensor fusion, and practical implementations for real-world navigation challenges.

• Mobile Robotics
• SLAM
• Robot Localization
• +۳ مورد دیگر

Hiroshi Ishikawa is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering. He also serves as a Visiting Professor at the National Institute of Informatics since 2016. Previously, he held positions at Nagoya City University from 2004-2010 as Assistant Professor, Associate Professor, and Professor. His academic journey includes being a JST PRESTO Researcher from 2009-2013 and an Associate Research Scientist at New York University's Courant Institute of Mathematical Sciences from 2000-2001. Ph.D. in Computer Science, New York University (2000) Master of Science, Kyoto University Bachelor's degree in Mathematics, Kyoto University Faculty of Science (1991) Hiroshi Ishikawa's research spans perceptual information processing, computer vision, artificial intelligence, deep learning, and discrete optimization. His work focuses on developing algorithms for image restoration, segmentation, and understanding, with significant contributions to energy minimization techniques in computer vision. He has pioneered approaches in sketch simplification, medical image segmentation, and higher-order graph cuts. His research bridges theoretical advances in mathematical optimization with practical applications in medical imaging, computer graphics, and consumer electronics. Ishikawa's recent publications demonstrate a strong focus on leveraging deep learning for image enhancement and understanding. His work spans super-resolution techniques, colorization methods, human avatar generation, and medical image analysis. A notable trend is the increasing integration of attention mechanisms and generative models to solve complex vision problems, with growing emphasis on real-world applications in medical imaging and computer graphics. His research group consistently produces high-impact work that appears in top-tier computer vision conferences. 75th Annual IEICE Best Paper Award (2019) Innovative Technologies 2016 Special Prize for Culture (Ministry of Economy, Trade and Industry) MIRU Nagao Award (Best Paper Award) (2009) Young Author Award (IEEE Computer Society Japan Chapter, 2006) MIRU2006 Excellent Paper Award (2006) Harold Grad Memorial Prize (Courant Institute of Mathematical Sciences, NYU, 2000) As a professor at Waseda University, Ishikawa has mentored numerous students who have become active researchers in computer vision, including Yuya Masuda, Edgar Simo-Serra, and Satoshi Iizuka. His research has been supported by various grants, including JST PRESTO funding from 2009-2013. He has served on editorial boards for prestigious journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and has held leadership roles in major computer vision conferences such as ICCV, CVPR, and ACCV. Ishikawa leads a vibrant research group at Waseda University focused on computer vision and image processing. His laboratory collaborates extensively with researchers at Nagoya City University, National Institute of Informatics, and international institutions. The group maintains strong connections with industry partners, particularly in medical imaging and consumer electronics sectors, translating theoretical advances into practical applications.

• Perceptual information processing
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر