Find Academic Supervisors

• Autonomous Driving
• Sensor Fusion
• Intelligent Transportation Systems
• +5 more

Prof. Dr.-Ing. Christoph Stiller is a full professor at the Karlsruher Institut für Technologie (KIT) and serves as the director of the Institute of Measurement and Control Technology (Institut für Mess- und Regelungstechnik, MRT). His work focuses on autonomous driving, sensor fusion, probabilistic estimation, HD mapping, motion planning, and intelligent transportation systems. Education: Details on his academic degrees are not provided in the text, but he holds the title of Dr.-Ing. indicating a doctoral degree in engineering. Research Interests: Prof. Stiller's research spans a wide array of topics critical to the development of autonomous vehicles. His work includes: Sensor Fusion: Integrating data from LiDAR, cameras, and radar to create robust perception systems. HD Mapping & Localization: Developing high-definition maps and precise localization techniques for urban and highway environments. Motion Planning & Decision Making: Creating algorithms for safe and efficient trajectory planning under uncertainty. Machine Learning & AI: Applying deep learning and reinforcement learning to perception, prediction, and control tasks. Publication Trends: His recent publications (2023–2025) emphasize robust traffic light detection, image stitching for panoramic views, motion prediction using redundancy reduction, and safety-enhanced model predictive control. The work increasingly integrates learning-based methods with classical control and estimation theory. Scientific Awards: No specific awards are listed in the provided text. Teaching & Supervision: Prof. Stiller teaches foundational and advanced courses in measurement and control systems, probabilistic estimation, and autonomous driving. He holds regular office hours during both summer and winter semesters and is actively involved in advising students and researchers. Labs & Teams: He leads the Institute of Measurement and Control Technology (MRT) at KIT, which is engaged in cutting-edge research in autonomous systems. The institute collaborates with industry and academia on large-scale projects such as UNICARagil and various European initiatives.

• Smart Sustainable and Resilient Cities
• Energy Efficient Communities & Cities
• Crowdsensing-based Infrastructure Monitoring (CbIM)
• +4 more

Mustafa Gül is a Professor in the Department of Civil and Environmental Engineering at the University of Alberta’s Faculty of Engineering. He also serves as Director of Internationalization at the Faculty of Engineering’s Deans Office. His research focuses on smart, sustainable, and resilient cities, with an emphasis on infrastructure monitoring and energy-efficient systems. Education: PhD in Civil Engineering (University of Central Florida, 2009), MSc in Electrical Engineering (University of Central Florida, 2011), MSc in Civil Engineering (Boğaziçi University, 2004), BSc in Civil Engineering (Boğaziçi University, 2002). Dr. Gül’s research spans two primary domains: Crowdsensing-based Monitoring of Built and Natural Environments (CoMBiNE) using AI, signal processing, and data analytics for infrastructure health; and Energy-Efficient Smart Cities through solar PV integration, IoT applications, and net-zero energy homes. His work bridges structural engineering, machine learning, and sustainable urban development. Recent publications highlight advancements in smartphone-based damage detection, UAV-assisted disaster assessment, and AI-driven energy systems. His team’s work on crowdsensing bridges, solar PV optimization, and smartphone analytics has been widely recognized in journals like Structural Control and Health Monitoring and Energy and AI . Notably, his 2017 paper earned the Best Paper Award at ISARC. Students supervised include Azim, R. , Keskin, M. , and Do, N. T. , among others. Scientific Awards: Best Paper Award, 34th International Symposium on Automation and Robotics in Construction (ISARC 2017) Dr. Gül’s projects often involve interdisciplinary collaboration, leveraging sensor networks, computer vision, and optimization algorithms to address urban resilience and energy sustainability. His leadership extends to course CIV E 779 and ongoing research initiatives in Alberta, Canada.

• Robotics
• Computer Vision
• Sensor Systems
• +3 more

Dr. Olga Vysotska is a Researcher affiliated with the Professorship for Robotic Systems at ETH Zurich's Department of Mechanical and Process Engineering. Her work focuses on advancing robotic systems through research in sensor-based navigation, SLAM (Simultaneous Localization and Mapping), and autonomous systems. She holds a doctoral degree and is based in Zurich, Switzerland. Her email is olga.vysotska@inf.ethz.ch. Research Interests: Olga's research spans robotics, computer vision, and autonomous navigation. She specializes in LiDAR-based place recognition, SLAM algorithms, and cross-modal localization using 3D scene graphs. Her work addresses challenges such as environmental changes, sensor fusion, and data association in dynamic environments like agriculture and underground exploration. Key themes include robust localization, loop closure detection, and adaptive algorithms for real-world robotic applications. Publications Overview: Olga's recent work emphasizes diffusion-based LiDAR place recognition (2025), 4D spatial-temporal mapping for agricultural robots (2023), and SceneGraphLoc for cross-modal localization (2024). Her research trends highlight innovation in sensor integration, algorithmic robustness, and practical applications in challenging environments. Earlier contributions include exploration of catacombs with mobile robots (2013) and SLAM enhancements using public map data (2017). Grants & Advising: While specific grants or student advisement details are not listed, her active publication record suggests involvement in funded research projects. Her work often collaborates with industry and academic partners to advance robotic autonomy in complex scenarios. Labs/Teams: As part of the Robotic Systems Professorship, she likely contributes to ETH Zurich's robotics labs focused on SLAM, sensor systems, and autonomous navigation. Her projects may intersect with the Department's broader initiatives in mechanical and process engineering.

SangHyung AhnView profile

Lecturer

• Construction Process Modelling
• Building Information Modelling (BIM)
• Agent-Based Simulation
• +3 more

SangHyung Ahn is a Lecturer at the School of Civil Engineering , University of Queensland (UQ), since 2017. He joined UQ as a postdoctoral research fellow in 2015 after earning his PhD in Civil Engineering (Construction Engineering and Management) from Purdue University, USA. Prior to his academic career, he worked as an assistant manager at Hyundai Engineering and Construction Co., Ltd. (2003-2007) and holds an MBA in international business from Hanyang University and a B.Sc in Civil Engineering from Korea University. Research Focus: Construction process modelling with virtual reality, decision support systems for construction, automation of data-driven simulation modelling, sensor-based operations analysis, and integration of Building Information Modelling (BIM). Teaching: Coordinates undergraduate courses Introduction to Project Management (CIVL3510) and Construction Engineering Management (CIVL4522) . Research Trends: His recent publications highlight interdisciplinary work in transportation engineering, structural design, and AI-driven simulation tools. Key themes include application of machine learning to car-following models, drone-based vehicle identification, and optimization of public transport systems using agent-based simulations. Supervision: Available for supervision, with completed supervision of PhD and Master’s theses on topics such as BIM-LCA integration, pedestrian trajectory analysis, and AI-driven driving behavior models.

Chin Hoong ChorView profile

Professor

• Transportation Systems Modelling
• Transportation Safety
• Congestion Management
• +6 more

Professor Chin Hoong Chor is a Professor in the Department of Civil Engineering at the National University of Singapore (NUS), affiliated with the Faculty of Engineering. He specializes in transportation systems modeling, safety assessment, and congestion management. As a registered professional engineer and road safety auditor, he has contributed to numerous projects with Singapore’s land authorities and private companies. He holds leadership roles including Vice Chairman of the Chartered Institute of Logistics and Transport and Editorial Board membership for the International Journal of Transport Management. Qualifications include a BEng and MEng from NUS and a PhD in Transportation Engineering from the University of Southampton. His research interests span vehicle detection using image processing, traffic conflict analysis, and public transport optimization. Notable awards include the NUS Faculty Innovative Teaching (Gold) Award (2001/2002) and the UK Institution of Civil Engineers Webb Prize (2003). Education: BEng in Civil Engineering, National University of Singapore MEng in Civil Engineering, National University of Singapore PhD in Transportation Engineering, University of Southampton His research focuses on quantitative safety analysis, traffic flow modeling, and smart city initiatives. Recent work explores maritime collision risk modeling and sentiment analysis for transportation planning. He actively contributes to government committees and has authored over 40 peer-reviewed papers on traffic management and safety. Awards: Faculty Innovative Teaching (Gold) Award, 2001/2002 UK Institution of Civil Engineers Webb Prize 2003 Prof Chin has undertaken traffic impact studies and road safety reviews in Singapore, collaborating with public and private sectors. His work bridges academic research and practical policy implementation, emphasizing sustainable urban mobility solutions.

Eldan CohenView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Optimization
• +6 more

Eldan Cohen serves as an Assistant Professor of Industrial Engineering within the Department of Mechanical & Industrial Engineering at the University of Toronto's Faculty of Applied Science and Engineering. His academic journey includes a PhD from the same department followed by a postdoctoral fellowship in Computer Science at the University of Toronto and the Vector Institute for Artificial Intelligence. His educational background is detailed as follows: PhD in Mechanical & Industrial Engineering, University of Toronto Postdoctoral Fellowship in Computer Science, University of Toronto and Vector Institute for Artificial Intelligence Dr. Cohen's research centers on machine learning, deep learning, heuristic search, and optimization with strong emphasis on interpretable and human-compatible AI systems. His work bridges theoretical advancements with practical applications in healthcare (e.g., patient-physician interaction analysis, surgical safety diagnostics), automated planning, natural language processing, and software engineering. Recent projects develop interpretable clustering methods for medical data and optimization techniques for constrained sequence generation. Analysis of his 2023-2025 publications reveals a concentrated focus on healthcare AI applications, particularly using large language models for clinical text analysis and diagnostic support systems. Significant work also addresses interpretable machine learning for medical imaging, diverse plan selection in optimization, and constrained sequence generation in domains like vehicle routing. No major scientific awards or fellowships are documented in the available information. As an academic advisor, Dr. Cohen mentors graduate students in mechanical and industrial engineering, guiding research in optimization and machine learning. His OptiMaL research group fosters collaboration between computer science and industrial engineering to solve real-world decision-making challenges through human-centered AI approaches. The Optimization and Machine Learning (OptiMaL) research group, led by Dr. Cohen, serves as the primary hub for developing scalable, interpretable AI solutions for complex healthcare, planning, and engineering problems, with active projects in medical diagnostics and automated planning systems.

• Structural Health Monitoring
• Computer Vision
• Artificial Intelligence
• +3 more

Roya Nasimi, Ph.D., is an Assistant Professor in the Department of Engineering at California State University, East Bay, where she joined in Fall 2023. Her expertise spans structural engineering, computer vision, and artificial intelligence, with a focus on developing innovative solutions for infrastructure monitoring and safety. Dr. Nasimi's educational background includes: Ph.D. with distinction in Structural Engineering from the University of New Mexico Master’s degree in Structural Engineering from the University of Tabriz Bachelor’s degree in Civil Engineering from the University of Tabriz Her research focuses on structural health monitoring using advanced technologies. She integrates computer vision , artificial intelligence , and machine learning to develop systems for monitoring aging infrastructure, particularly bridges. Her work includes designing low-cost and high-end sensor systems, conducting full-scale bridge experiments, and collaborating on interdisciplinary projects to enhance infrastructure safety and resilience. Her recent publications (2021-2025) demonstrate a strong emphasis on non-contact monitoring techniques using drones, lasers, and computer vision. Key trends include the application of deep learning for displacement measurement, digital twinning for infrastructure, and rockfall prevention through machine learning. Her work bridges civil engineering with cutting-edge technology to address critical infrastructure challenges. Dr. Nasimi's research is supported by multiple grants: U.S. Army Corps of Engineers Transportation Research Board (TRB) Transportation Consortium of South-Central States (Tran-SET) New Mexico Consortium She serves on two TRB standing committees and mentors students in structural health monitoring and infrastructure technology. Dr. Nasimi leads interdisciplinary research teams focused on infrastructure monitoring, utilizing drones, lasers, and computer vision systems. Her work involves field experiments on bridges and rail systems, often in collaboration with government agencies and research consortia.

Yeonghyeon GuView profile

Assistant Professor

• Artificial Intelligence
• Meta Learning
• Transfer Learning
• +2 more

Yeonghyeon Gu serves as Assistant Professor in the Department of Artificial Intelligence Data Science at Sejong University, South Korea, a position held since 2022 after progressing from Principal Researcher (2014-2019) to Acting Professor (2019-2022). He maintains active affiliation with the university's AI Convergence Research Center and has produced 84 research outputs with 795 Scopus citations and an h-index of 14. His academic credentials include: B.A. from Sejong University (2004) M.A. from Sejong University (2006) Ph.D. from Sejong University (2014) Dr. Gu's research centers on Artificial Intelligence with specialization in Meta Learning, Transfer Learning, and Deep Learning methodologies. His work demonstrates strong interdisciplinary application across robotics, agricultural technology, energy systems, and meteorology. Key contributions include district heater load forecasting using parallel CNN-LSTM attention, image-based hot pepper disease diagnosis, and potato late blight prediction models. Analysis of his 2024-2025 publications reveals concentrated innovation in hybrid AI architectures, particularly combining graph networks with reinforcement learning for blockchain security and integrating physical models with deep learning for weather prediction. His work consistently addresses real-world engineering challenges through novel neural network applications while maintaining strong theoretical foundations in transfer learning frameworks. No scientific awards were documented in the source materials. While specific student advisees and grant details weren't listed, his extensive publication record (29 outputs in 2025 alone) and international collaborations suggest active mentorship and research funding. His work shows particular strength in cross-institutional projects with researchers from Turkey, Nigeria, Saudi Arabia, and South Korea. As a core member of Sejong University's AI Convergence Research Center, Dr. Gu contributes to institutional initiatives bridging AI theory with practical implementation across multiple sectors. The center's structure facilitates his interdisciplinary approach, connecting computer science with engineering, agriculture, and environmental science domains through shared computational infrastructure and collaborative research frameworks.

Robert LaganièreView profile

Professor

• Computer Vision
• Image and Video Analysis
• Visual Surveillance
• +8 more

Robert Laganière is a Professor at the School of Electrical Engineering and Computer Science at the University of Ottawa, where he has been actively contributing to the fields of computer vision and image analysis. He is a member of the VIVA research laboratory and holds a Ph.D. and M.Sc. from INRS-Telecommunications in Montreal, as well as a bachelor's degree in Electrical Engineering from École Polytechnique de Montréal. Bachelor's in Electrical Engineering: École Polytechnique de Montréal (1987) Master's Degree: INRS-Telecommunications (1990) Doctorate: INRS-Telecommunications (1996) Professor Laganière's research focuses on computer vision, with particular expertise in image and video analysis, visual surveillance, embedded vision systems, and deep learning applications. His work spans fundamental research in feature detection and matching to practical applications in autonomous driving, human recognition, and real-time object tracking. He has made significant contributions to the development of algorithms for pedestrian detection, age and gender recognition, and 3D object localization. His publication trends reveal a consistent focus on practical computer vision applications with a strong emphasis on real-time performance and embedded implementation. Over the past decade, his research has evolved from foundational work in feature matching and homography estimation toward more complex applications in action recognition, human-computer interaction, and intelligent surveillance systems. His work consistently bridges theoretical computer vision with practical engineering constraints, particularly for mobile and embedded platforms. Best Paper Award, IEEE International Conference on Computer and Robot Vision (CRV 2014) Best Paper Award, CVPR Embedded Vision Workshop, Providence, RI, June 2012 Best Real-time Tracker, IEEE International Conference on Computer Vision (ICCV) Workshop on Visual Object Tracking (VOT2015) Professor Laganière has supervised numerous graduate students through the years, with a particular focus on practical applications of computer vision in surveillance, human recognition, and embedded systems. His research has been supported through industry partnerships with companies including CogniVue Corp, NXP, iWatchLife.com, Solink Corp, CBSA Canada, Ross Video, Thales, Habitat Seven, and YouI Labs. He has successfully translated his research into commercial applications through his founding of Visual Cortek (acquired by iWatchLife in 2009) and Tempo Analytics (founded in 2016). As a member of the VIVA research laboratory, Professor Laganière collaborates with colleagues on advanced computer vision projects, particularly those involving intelligent video analytics for security and commerce applications. His work on NAVIRE (Virtual Navigation in Remote Environments) demonstrates his commitment to developing practical solutions for real-world navigation challenges using image-based representations of real environments.

Ahmet ÖZMENView profile

Professor

• Computer Vision
• Traffic Monitoring Systems
• Sensor Systems
• +6 more

Prof. Dr. Ahmet ÖZMEN is a Professor at Sakarya University's Faculty of Computer and Information Sciences, Department of Software Engineering. He has held various administrative positions including Head of the Software Engineering Department (2019-2028) and Director of the Computer Research and Application Center (2019-2022). With extensive experience in academia since 1991, he has made significant contributions to computer vision, traffic monitoring systems, and sensor technologies. Sakarya University: Professor (2019-present), Associate Professor (2011-2019) Dumlupınar University: Assistant Professor (2001-2011), Research Assistant (2000-2001, 1993-1998) Istanbul Technical University: Research Assistant (1991-1993) Prof. ÖZMEN's research spans computer vision applications for traffic monitoring, indoor air quality systems, parallel computing, and sensor technologies. His work bridges theoretical computer science with practical engineering applications, particularly in developing vision-based systems for nighttime vehicle detection, traffic flow monitoring, and environmental sensing. His interdisciplinary approach combines machine learning, image processing, and embedded systems to solve real-world problems in transportation and environmental monitoring. His publication record shows a clear evolution from parallel and distributed systems in his early career to computer vision and sensor applications in recent years. The majority of his recent work focuses on traffic monitoring systems using computer vision techniques, particularly for nighttime conditions, and indoor air quality monitoring systems using sensor networks. His research demonstrates strong industry and societal relevance, with applications in smart transportation, environmental protection, and educational technology. TÜBİTAK Publication Awards (2006, 2008, 2009, 2010) Physical implementation award from TÜBİDER (2008) Microsoft Certified Professional Certificate (2005) YÖK overseas study scholarships (1993, 1998) Elginkan graduate scholarships (1990, 1991) Prof. ÖZMEN has supervised numerous graduate students across multiple institutions, with a focus on practical engineering problems. His research has been supported by various projects including TÜBİTAK projects, institutional research grants, and industry collaborations. He has led significant research initiatives in traffic monitoring systems, indoor air quality monitoring, and educational technology platforms. His administrative leadership has included directing research centers and shaping curriculum development in software engineering. His work has involved establishing research teams focused on computer vision applications, sensor network development, and educational technology. These teams have produced numerous publications, developed practical systems, and trained the next generation of computer engineers. Current research directions include advanced traffic monitoring systems using deep learning and multi-camera setups for urban planning applications.

Trym Vegard HaavardsholmView profile

Lecturer

• Computer Vision
• Machine Learning
• Robotics
• +3 more

Trym Vegard Haavardsholm is a 20% part-time Lecturer at the University of Oslo (UiO) within the Section for Autonomous Systems and Sensor Technologies. He also serves as Principal Scientist at the Norwegian Defence Research Establishment (FFI) and is a PhD candidate at the Department of Engineering Cybernetics, NTNU. His research focuses on computer vision, machine learning, robotics, and image analysis, with a particular emphasis on multispectral imaging systems and unmanned aerial vehicles (UAVs). Haavardsholm has contributed to advancements in sensor technologies for tactical reconnaissance, autonomous navigation, and real-time data processing. His work spans applications in defense, environmental monitoring, and emergency response systems. Research Interests Haavardsholm’s research integrates interdisciplinary approaches to develop innovative sensor systems and algorithms. Key areas include compact multispectral imaging for small UAVs, in-operation camera calibration, and anomaly detection in hyperspectral data. His contributions emphasize practical applications such as urban feature classification, collaborative indoor navigation, and bioaerosol detection. His work bridges theoretical computer science with applied engineering, addressing challenges in autonomous systems and sensor fusion. Publications His publications highlight trends in multispectral sensor design, UAV imaging, and real-time georeferencing. Recent work includes compact sensor systems for tactical use and advancements in pushbroom image rectification. Earlier research explored band selection algorithms for target detection and GPU-accelerated anomaly detection. Professional Roles As a lecturer, Haavardsholm contributes to academic supervision and teaching in autonomous systems. His dual role at FFI and UiO reflects his commitment to translating academic research into practical defense and civilian applications. His PhD candidacy at NTNU underscores his ongoing academic engagement in engineering cybernetics.

Clinton FookesView profile

Professor

• Computer Vision
• Artificial Intelligence
• Human Behavior Analysis
• +7 more

Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.

Dewei YiView profile

Senior Lecturer

• Computer Vision
• AI-enabled Healthcare
• Hybrid Intelligent Systems
• +16 more

Dewei Yi is a Senior Lecturer (Associate Professor) in the Department of Computing Science, School of Natural and Computing Sciences at the University of Aberdeen, UK. He holds a PhD from Loughborough University and is an active researcher in AI, computer vision, and intelligent systems. He serves as Director of the MSc AI and MSc Robotics and AI programmes. Research Interests: His research spans AI-enabled healthcare, medical image processing, intelligent vehicles, robotics, precision agriculture, remote sensing, and applied machine learning. He focuses on hybrid intelligent systems, personalised AI, federated learning, fairness, and explainability. Recent Publication Trends: His latest work includes medical image quality evaluation using contrastive learning, federated learning for diabetic retinopathy, UAV-based solar panel inspection, vascular image analysis, and emotion recognition from ECG data, reflecting a strong trend toward healthcare and intelligent systems with real-world impact. Scientific Awards: Fellow of the Higher Education Academy (FHEA) Outstanding Reviewer, Transportation Research Part C (TRC) Advising and Grants: Dr Yi supervises multiple PhD students in AI, computer vision, and machine learning. His graduated PhDs include Debinal Bakyavathi Rajan, Sami Hamid Al Sulaimani, and Adinath Abhimanyu Ghadage. He has secured significant funding as PI and Co-PI, including a £408K Smartawl 5.0 project and a £794K Cancer Research UK grant (Co-PI). Labs and Teams: He leads research in AI for healthcare and intelligent vehicles, collaborating with institutions like University of Warwick, Loughborough University, and industry partners such as AVL Powertrain Ltd. His work is supported by interdisciplinary teams focusing on embedded AI, medical applications, and sustainable technologies.

Carolyn Conner SeepersadView profile

Professor

• Design for Additive Manufacturing
• Engineering Design
• Additive Manufacturing
• +4 more

Carolyn Conner Seepersad serves as the J. Mike Walker Professor of Mechanical Engineering at the University of Texas at Austin and directs the Center for Additive Manufacturing and Design Innovation. She holds membership in the U.T. System Academy of Distinguished Teachers and maintains active leadership in the additive manufacturing community through roles such as co-organizer of the Solid Freeform Fabrication Symposium and ASME Design Engineering Division Executive Committee membership. Her academic credentials include: PhD in Mechanical Engineering from Georgia Tech (2004) MA/BA in Philosophy, Politics and Economics from Oxford University (1998, Rhodes Scholar) BS in Mechanical Engineering from West Virginia University (1996) Dr. Seepersad's research centers on computational design methodologies and additive manufacturing innovation , with particular expertise in simulation-based design of complex systems, environmentally conscious product development, and materials engineering. Her work bridges theoretical design frameworks with practical manufacturing applications, emphasizing sustainability and performance optimization across aerospace, automotive, and energy systems. Current projects explore reactive extrusion additive manufacturing, negative stiffness materials, and machine learning integration for process-aware design. Analysis of her 15 most recent publications reveals a dominant focus on process innovation in additive manufacturing (70%), particularly stereolithography and selective laser sintering, with growing emphasis on data-driven design approaches (20%) and sustainable engineering applications (10%). Her work demonstrates consistent progression from fundamental material design toward integrated system optimization and industrial scalability. Her scientific recognition includes: International Outstanding Young Researcher Award in Freeform and Additive Manufacturing (2009) UT System Regents’ Teaching Award (2010) ASME Design Automation Committee Outstanding Young Investigator Award (2010) ASEE Outstanding New Mechanical Engineering Educator Award (2013) Multiple ASME and ASEE best paper awards U.T. System Academy of Distinguished Teachers membership Dr. Seepersad maintains an extensive advising portfolio with 48 graduate students (16 PhD, 24 MS, and 8 current) plus 2 postdoctoral researchers, reflecting sustained research productivity and educational impact. Her Product, Process, and Materials Design Lab fosters interdisciplinary collaboration between mechanical engineering, materials science, and computational design teams.

Zezhou ChengView profile

Assistant Professor

• Computer Vision
• Machine Learning
• AI for Science
• +6 more

Zezhou Cheng is an Assistant Professor of Computer Science at the University of Virginia, leading the Computer Vision Lab. He holds a Ph.D. from UMass Amherst (2023), a postdoctoral position at Caltech, and a Bachelor's degree from Sichuan University (2015). His research focuses on computer vision, machine learning, and their applications in ecology, materials science, and autonomous systems. Key areas include 3D understanding, self-supervised learning, and AI-driven ecological monitoring. He has received awards such as the Best Synthesis Award (2020) and Outstanding Reviewer (CVPR 2021). His work spans publications in top venues like CVPR, ICCV, and ECCV, addressing challenges in 3D reconstruction, generative models, and ecological data analysis. Education: Ph.D. in Computer Science, UMass Amherst (2023) Bachelor's Degree, Sichuan University (2015) Postdoctoral Researcher, Caltech (advised by Georgia Gkioxari) Research Highlights: Developed LU-NeRF for unposed scene reconstruction and camera pose estimation Contributed to AI for ecology via bird roost detection using weather radar data Advanced 3D representation learning through procedural programs and self-supervised techniques Awards & Recognition: Outstanding Reviewer, CVPR 2021 Best Poster Award, New England Computer Vision Workshop 2019 National Scholarship (China, 2014 and 2016) His lab explores cutting-edge topics in computer vision, with a focus on interdisciplinary applications. Teaching roles include leading Caltech's AI Bootcamp and serving as a Teaching Assistant at UMass Amherst. Industry collaborations include internships at Google Research, Snap, and Amazon.