جستجوی استاد راهنمای دانشگاهی

Chengmo Yangمشاهده پروفایل

استاد

• Embedded Systems
• Design Automation
• Computer Architecture
• +۶ مورد دیگر

Dr. Chengmo Yang is a Professor in the Department of Electrical and Computer Engineering at the University of Delaware, with a joint appointment. She received her PhD and MSc in Computer Science from the University of California, San Diego, and a BSc in Microelectronics from Peking University, China. PhD, University of California, San Diego MSc, University of California, San Diego BSc, Peking University, China Her research focuses on enhancing security, reliability, non-volatility, and energy efficiency in embedded systems, cyber-physical systems (CPS), and Internet-of-Things (IoT). She leads the Computer Architecture, Reliability, and Security (CARES) group and has authored over 70 peer-reviewed publications. Recent publications emphasize neural network accelerators, non-volatile memory (NVM) optimization, and security frameworks for embedded systems. Her work explores fault tolerance, wear leveling, and side-channel attack mitigation in NVM-based FPGAs, STT-RAM, and SSDs. National Science Foundation Career Award (2013) University of Delaware Mid-Career Faculty Excellence in Scholarship Award (2020) Best paper award at ICESS (2019) Three best paper nominations at DAC, DATE, and GLSVLSI (2019) Dr. Yang serves as Associate Editor for IEEE TCAD and ACM TODAES. She is the Diversity & Inclusion Representative for IEEE CEDA and has organized numerous conferences including Technical Program Chair for CODES-ISSS 2022. Her lab has hosted current and former students like Fanruo Meng (PhD candidate), Fateme Hosseini (Qualcomm), and Patrick Cronin (Intel), focusing on graduate-level research in hardware security and reliability.

Stefano Ermonمشاهده پروفایل

دانشیار

• Machine Learning
• Generative AI
• Computational Sustainability
• +۷ مورد دیگر

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

Deva Kannan Ramananمشاهده پروفایل

استاد

• Computer Vision
• 3-D Vision and Recognition
• Visual Servoing and Tracking
• +۸ مورد دیگر

Deva Kannan Ramanan is a Professor at the Robotics Institute of Carnegie Mellon University , focusing on computer vision , machine learning , and human-centered robotics . His work bridges neurorobotics and visual perception , with applications in autonomous driving and 4D reconstruction . Research Topics Computer Vision 3-D Vision and Recognition Visual Servoing Neurorobotics Human-Centered Robotics Graphics & Creative Tools His recent publications in CVPR , ICRA , and ICCV emphasize 4D human reconstruction , neural rendering , and vision-language models for autonomous systems. He serves as General Chair of CVPR 2027 and Program Chair of CVPR 2018 , with IARPA funding for aerial-ground rendering (2023-2027). Current students include PhD candidates Sally Chen, Kangle Deng, and Zhiqiu Lin, while past advisees like Arun Vasudevan and Olga Russakovsky now hold positions at Amazon and Meta respectively.

Jason D. Leeمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Jason D. Lee is an associate professor of Electrical Engineering and Computer Sciences (EECS) and Statistics at the University of California, Berkeley. Previously, he held academic positions at Princeton University as an associate professor, and was a research scientist at Google DeepMind. He completed his Ph.D. in Computational and Mathematical Engineering at Stanford University under the advisement of Trevor Hastie and Jonathan Taylor. For students and collaborators, his primary contact email is jasonlee@princeton.edu, though prospective students and postdocs are asked to include "filter_student" in the subject line. Ph.D., Computational and Mathematical Engineering, Stanford University (2015) B.Sc., Mathematics, Duke University (2010) Lee's research lies at the intersection of machine learning, statistics, and optimization, focusing on the theoretical foundations of artificial intelligence. His work addresses fundamental questions in deep learning, including optimization landscapes, representation learning, and reinforcement learning theory. He has made significant contributions to understanding how gradient descent operates in neural network training and has developed provably efficient algorithms for various learning scenarios. His ten most recent publications represent a diverse yet coherent body of work across machine learning theory, focusing on topics such as Gaussian multi-index models, transformer learning capabilities, shallow neural networks, and optimization techniques. These publications appear in top venues including COLT, ICML, NeurIPS, and JMLR. Among his notable accolades are: Samsung AI Researcher of the Year Award (2023) NSF Career Award (2022) ONR Young Investigator Award (2021) Sloan Research Fellow in Computer Science (2019) NIPS Best Student Paper Award (2016) Princeton Commendation for Outstanding Teaching (ECE538B) Lee has advised numerous students including Alex Damian, Wenhao Zhan, Eshaan Nichani, Tianle Cai, Zixuan Wang, and Yunwei Ren. Former advisees have gone on to positions at institutions like NYU Courant, Facebook AI Research, UW, MIT, Duke, and Microsoft Research. His research group and collaborators span multiple institutions, working on theoretical and applied aspects of machine learning and artificial intelligence, with a particular focus on the optimization and learning dynamics of neural networks and transformer models.

Sheng Shenمشاهده پروفایل

استاد

• Nanophotonics
• Nanoscale Energy Transport and Conversion
• Advanced Manufacturing
• +۷ مورد دیگر

Sheng Shen is a Professor in the Mechanical Engineering Department at Carnegie Mellon University (CMU) , with courtesy appointments in the Departments of Electrical and Computer Engineering and Materials Science and Engineering . He earned his Ph.D. in Mechanical Engineering (Minor in Electrical Engineering) from Massachusetts Institute of Technology (MIT) , and his B.S. and M.S. from Huazhong University of Science and Technology in China. Prior to joining CMU in 2011, he conducted postdoctoral research at UC-Berkeley . Education: Ph.D., Mechanical Engineering, MIT (2010) B.S. & M.S., Power Engineering & Engineering Thermophysics, Huazhong University of Science and Technology (2000 & 2003) Research interests include nanophotonics , nanoscale energy transport and conversion , nanofabrication , and advanced manufacturing , with applications in thermal management , light sources and devices , thermal emission control , solar energy conversion , infrared sensing , and multifunctional materials . His work leverages interdisciplinary expertise in thermal and optical measurements , material synthesis , device fabrication , and theoretical modeling . Recent publications highlight advancements in infrared radiation control , thermal interface materials , metasurface engineering , and graphene-based nanosystems . His scientific awards include: NSF CAREER Award DARPA Director's Fellowship DARPA Young Faculty Award Elsevier/JQSRT Raymond Viskanta Award CMU Dean's Early Career Fellowship Philomathia Foundation Research Fellowship Hewlett-Packard Best Paper Award Best Paper Award, Julius Springer Forum Advising spans Ph.D. and postdoctoral researchers in nanoscale energy systems, with alumni contributing to solar energy conversion , infrared sensing , and flexible electronics . His lab receives funding from ARL, DARPA, DOE, DTRA, NASA, NSF, and ONR , and recently secured a DURIP award for instrumentation.

Francesca Stradoliniمشاهده پروفایل

مدرس

• Bioelectronics
• IoT for Medical Monitoring
• Anesthesia Delivery Systems
• +۳ مورد دیگر

Francesca Stradolini is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Electrical Engineering Doctoral School (EDEE). She holds a PhD in Electrical Engineering from EPFL (2018), with prior education in Bio Engineering and Neuroscience from the University of Genoa (2012, 2015). Her current work focuses on R&D engineering for medical devices, emphasizing system lifecycle management, human factors engineering, and compliance with CE/FDA regulations. As an invited guest, she contributes to usability engineering education and interdisciplinary research at EPFL. PhD: IoT Bio-Electronic Multi-Panel Device for On-line Monitoring of Anaesthesia Delivery (EPFL, 2018) MSc: Bio Engineering (University of Genoa, 2015) BSc: Bio Engineering (University of Genoa, 2012) Her research spans bioelectronics, IoT-enabled medical monitoring, and electrochemical sensor development for clinical applications. Key areas include anesthesia delivery systems, wearable diagnostics, implantable devices, and human factors engineering. Her work integrates fluidic systems, noble metal nanostructures, and open-source hardware (e.g., Raspberry Pi) to address challenges in traceability, fouling mitigation, and pharmacokinetic modeling. Scientific recognition includes the Ville de Lausanne Award (2019) , EPFL Outstanding PhD Thesis Distinction (2018) , and a Best Paper Award at MOBIHEALTH (2016) . She has published extensively in journals like IEEE TBIOCAS, Sensors and Actuators B, and IEEE Sensors Journal, with collaborative studies on propofol monitoring, carbon nanoallotrope drug detection, and implantable sensor validation. Her technical expertise includes managing medical device development lifecycles, designing IoT architectures for intensive care, and optimizing electrochemical sensors for stability in undiluted biological fluids. She has contributed to conferences such as IEEE BioCAS, ISCAS, and MOBIHEALTH, focusing on translational studies in personalized medicine and 3D-printed medical systems.

Ajit Rajwadeمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Ajit Rajwade is a Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay. His research spans Artificial Intelligence , Compressed Sensing , and Medical Imaging , with affiliations to the Centre for Machine Intelligence and Data Science and the Koita Centre for Digital Health . Education : PhD in Computer and Information Science and Engineering, University of Florida (2010) MSc in Computer Science, McGill University (2004) BTech in Computer Engineering, University of Pune (2002) His research interests focus on intelligent data acquisition, particularly in neural network analysis , graph signal processing , and medical imaging . He develops compressed sensing algorithms for inverse problems like tomography and MRI reconstruction , alongside applying group testing to pandemic response. Scientific awards include the Prof. S. P. Sukhatme Award (2024) and Departmental Teaching Excellence (2019). His publications demonstrate expertise in image restoration , noise modeling , and epidemiological algorithms . He has advised PhD students like Sabyasachi Ghosh and Jerin Geo James , with a focus on computationally efficient methods in medical imaging and machine learning .

• Computer Vision
• Machine Learning
• Human Perception Modeling
• +۵ مورد دیگر

Deva Ramanan is a Professor at the Robotics Institute of Carnegie Melllon University, where he leads research in computer vision and machine learning. His work focuses on modeling human visual perception, leveraging large-scale visual data, and developing systems for 3D understanding, neural rendering, and autonomous systems. He advises a large group of PhD students and has mentored numerous postdoctoral researchers now in leading roles across industry and academia. His research interests include computer vision, machine learning, human perception modeling, 3D scene understanding, neural rendering, autonomous driving, video understanding, and multimodal foundation models. These areas reflect his focus on both foundational models and their application to real-world problems in robotics and AI. The recent publications highlight a strong trend toward multimodal and 3D-aware models, with increasing use of diffusion models, neural fields, and large vision-language systems. Key themes include scene flow, 3D reconstruction from monocular video, autonomous driving perception, and robust evaluation of vision-language models. There is a clear emphasis on both methodological innovation and practical deployment in dynamic environments. Marr Prize, Honorable Mention (ICCV 2021) Best Paper, Honorable Mention (ECCV 2020) Best Paper Finalist (WACV 2024) Best Paper Award (WACV 2016) Best Industrial Paper, Honorable Mention (BMVC 2017) Marr Prize winner (ICCV 2009) Deva Ramanan has advised numerous PhD and master’s students, many of whom are now at top institutions and companies including Apple, Meta, Google, Nvidia, OpenAI, and Princeton. He has received substantial funding from IARPA, DARPA, NSF, Intel, Google, and Facebook for projects in video analytics, dispersed computing, visual cloud systems, and multi-task recognition. His group has developed influential datasets and benchmarks used widely in the community. He leads a vibrant research lab focused on advancing computer vision through deep learning and multimodal integration. His team works on core challenges in perception, including 3D reconstruction, motion modeling, object detection, and scene understanding, with applications in robotics and autonomous systems.

Ram Rajagopalمشاهده پروفایل

دانشیار

• Smart Grids
• Renewable Energy Integration
• Power Systems Optimization
• +۳ مورد دیگر

Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering and Electrical Engineering at Stanford University, and a Senior Fellow at the Precourt Institute for Energy. He leads the Stanford Sustainable Systems Lab (S3L), focusing on large-scale monitoring, data analytics, and stochastic control for infrastructure networks, particularly power systems. His research emphasizes renewable energy integration, smart distribution systems, and demand-side data analytics. Education: PhD in Electrical Engineering and Computer Sciences & MA in Statistics (UC Berkeley), MS in Electrical and Computer Engineering (UT Austin), and BEng in Electrical Engineering (Federal University of Rio de Janeiro). Research interests include power grid optimization, renewable energy systems, and data-driven approaches to infrastructure challenges. He has pioneered work in grid flexibility, distributed energy resources, and machine learning applications for energy systems. His lab develops technologies like Smart Dim Fuses and the EV-EcoSim platform for EV charging infrastructure optimization. Received NSF CAREER Award, Powell Foundation Fellowship, and Berkeley Regents Fellowship Over 30 patents and best paper awards Advises/founded companies in sensor networks, power systems, and data analytics Labs/Teams: Stanford Sustainable Systems Lab (S3L), Powernet Project. His work spans grid resilience, energy equity, and scalable energy solutions.

Adriana Tapusمشاهده پروفایل

استاد

• Robotics
• Human-Robot Interaction
• Machine Learning
• +۷ مورد دیگر

Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.

Sebastian Schererمشاهده پروفایل

استاد پژوهش

• Robotics
• Unmanned Aerial Vehicles
• Autonomous Navigation
• +۱۳ مورد دیگر

Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI), Carnegie Mellon University (CMU), where he leads cutting-edge research in autonomous aerial systems and robotics. His work focuses on enabling unmanned rotorcraft to operate safely and efficiently in cluttered, low-altitude, and extreme environments. Education: Ph.D. in Robotics, Carnegie Mellon University (2010) MS in Robotics, Carnegie Mellon University (2007) BS in Computer Science (Minor in Robotics), Carnegie Mellon University (2004) His research interests span robotics, artificial intelligence, autonomous navigation, obstacle avoidance, SLAM, visual-inertial odometry, energy infrastructure, and public policy . He has made seminal contributions to UAV autonomy, including the first obstacle avoidance for micro aerial vehicles in natural environments (2008) and the first automatic landing zone detection and landing on a full-size helicopter (2010). His recent publications (2023–2025) demonstrate a strong focus on resilient autonomy, multi-robot exploration, foundation models for robotics, and large-scale dataset development. His team has released key datasets like TartanGround , BETTY , and SubT-MRS , and simulation tools like Pegasus Simulator , indicating a systems-level approach to advancing real-world autonomy. The research trends emphasize self-supervised learning, robust perception, risk-aware planning, and multi-modal fusion for off-road and urban environments. Scientific Awards: Popular Science Best of What's New 2010 Award AIAA@Infotech Best Paper Runner-up Award (2010) Siebel Scholar Dr. Scherer has advised numerous students and leads a vibrant research group focused on high-impact robotics applications. He has secured significant grants related to UAV autonomy, energy infrastructure, and urban air mobility. His lab develops experimental infrastructure such as AIrTonomy for testing next-generation autonomous aerial vehicles. He is actively involved in advancing SLAM and localization in extreme environments, notably through participation in the DARPA Subterranean Challenge. His team develops large-scale datasets and benchmarking frameworks to push the boundaries of robustness and generalization in mobile robotics.

Judy Hoffmanمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Domain Adaptation
• +۷ مورد دیگر

Judy Hoffman is an Associate Professor in the College of Computing at Georgia Institute of Technology, with a joint appointment in the School of Interactive Computing and affiliation to the Machine Learning Center . She received tenure in April 2025 after joining Georgia Tech as an Assistant Professor. Her research focuses on enabling AI systems that are reliable, fair, and resource-efficient. PhD in Electrical Engineering and Computer Science (2016, UC Berkeley) Postdoctoral Fellowships at Stanford (2017) and UC Berkeley (2018) Former Research Scientist at Facebook AI Research Her work intersects computer vision and machine learning , with specialization in domain adaptation , adversarial robustness , and algorithmic fairness . She has published over 40 peer-reviewed articles, including the award-winning DeCAF (ICML 2024 Test of Time Award) and co-founded Women in Computer Vision (2015), which has sponsored ~40 women annually to premier conferences. ICML Test of Time Award (2024) NSF CAREER Award (2022) PAMI Distinguished Young Researcher (2023) Samsung AI Researcher of the Year (2021) Dr. Hoffman has served as Program Chair for CVPR 2023, Associate Editor for T-PAMI (2021-2023), and co-organizer of workshops at major AI conferences. She has delivered over 70 invited talks and contributes to open-source projects like cycada_release (567 stars) and lsda (47 stars).

Mahdi Khodayarمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Mahdi Khodayar is an Assistant Professor of Computer Science at the University of Tulsa . He holds a Ph.D. in Electrical Engineering from Southern Methodist University (2020) and M.Sc./B.S. degrees in Artificial Intelligence and Software Engineering from K.N. Toosi University of Technology (2015, 2013). His research focuses on AI and machine learning applications in power systems, transportation systems, computer vision, and spatiotemporal pattern recognition. B.Sc. in Computer Engineering (2013), K.N. Toosi University of Technology M.Sc. in Artificial Intelligence (2015), K.N. Toosi University of Technology Ph.D. in Electrical Engineering (2020), Southern Methodist University Khodayar’s research bridges deep learning with energy systems , including fault detection in power grids, renewable energy forecasting, and traffic scene understanding. His work leverages graph neural networks , reinforcement learning , and generative models for robust spatiotemporal analysis. Recent publications highlight his focus on graph-based architectures for power systems, hybrid deep reinforcement learning in energy forecasting, and unsupervised domain adaptation in remote sensing. His work integrates physics-informed modeling with probabilistic frameworks . Scientific Awards : NSF ECCS Division Funding (2022) US DOT FHWA Grant (2023) Zelimir Schmidt Award for Early Career Research (2023) Honors Student Award (2015), K.N. Toosi University Khodayar has been funded by the NSF , US Department of Transportation , and the TU Cyber Fellows program . He serves as an associate editor for IEEE Transactions on Transportation Electrification and other journals.

Yin Baoمشاهده پروفایل

استادیار

• Robotics and Controls
• Agricultural Automation
• Precision Farming
• +۴ مورد دیگر

Yin Bao is an Assistant Professor in Plant and Soil Sciences and Mechanical Engineering at the University of Delaware since 2023, previously holding the same position at Auburn University's Department of Biosystems Engineering (2019-2023). He holds a BE in Mechanical Engineering from China Agricultural University (2012) and a PhD in Agricultural and Biosystems Engineering from Iowa State University (2018), followed by postdoctoral research there until 2019. His research focuses on automation technology for agriculture and forestry, leveraging robotics, machine learning, and sensing systems to develop tools for precision farming and plant phenotyping. Key areas include unmanned systems (UGVs/UAVs), spectral imaging, and AI-driven predictive models for crop and livestock management. Recent work emphasizes automated inventory systems for forest nurseries, UAV-based vegetation assessment, and machine learning applications in crop yield prediction. His publications span robotic guidance systems, root segmentation in X-ray CT scans, and equine gait analysis using deep learning. Notable projects include the Robotic Assay for Drought (RoAD) system and the 'smart canopy' sorghum initiative. Collaborative efforts involve integrating multifrequency microwave sensing and electronic nose technologies for crop quality analysis.

Elmira Hassanzadehمشاهده پروفایل

دانشیار

• Hydrology
• Water Resources Engineering
• Climate Change Impact Assessment
• +۴ مورد دیگر

Elmira Hassanzadeh is an Associate Professor at the Department of Civil, Geological and Mining Engineering , Polytechnique Montréal, with a focus on integrated water resource modeling and management under climate and anthropogenic changes. She is affiliated with the Global Institute for Water Security (University of Saskatchewan) and the Experimental and Digital Water Flow Engineering Group (GENIE EAU) . Education: PhD in Civil Engineering, University of Saskatchewan Postdoctoral Fellow, McGill University Research interests include climate change impact assessment, socio-hydrological modeling, stakeholder engagement in water management, and sustainable development of water systems in cold regions. Her work emphasizes decision-making under uncertainty and adaptive strategies for ungauged basins. Recent publications (2024–2020) span coastal flood hazard modeling, climate-driven hydrological changes in tropical and Canadian watersheds, evaluation of agricultural practices under warming climates, and integrated approaches to water quality and system dynamics. Keywords include Hydrology , Climate Change , Water Security , and Socio-Hydrology . Supervision: She has mentored 15+ graduate students in projects related to Lake Urmia, Saskatchewan River Basin, and climate adaptation in Africa and the Pacific.