Find Academic Supervisors

Jean OhView profile

Researcher

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +5 more

Jean Oh is a Researcher at the Robotics Institute of Carnegie Mellon University (CMU) , leading the interdisciplinary Bot Intelligence Group (BIG) . Her work focuses on developing persistent robots that co-exist and collaborate with humans in shared environments, emphasizing continuous improvement through training, exploration, and human interaction. Education: Ph.D. in Language and Information Technologies, CMU M.S. in Computer Science, Columbia University B.S. in Biotechnology, Yonsei University Oh's research integrates vision, language, and planning systems in robotics, with applications in human-robot teaming , self-driving cars , disaster response , eldercare , and creative robotics . She has pioneered projects like socially-compliant robot navigation in human crowds and AI-driven robotic painting systems. Recent publication trends highlight her work in vision-language planning , social navigation , computational creativity , and human-robot collaboration . Notable contributions include the StyleCLIPDraw algorithm for text-to-art generation and Social-PatteRNN for human-like trajectory prediction. Scientific Awards: Best Paper Award in Cognitive Robotics (ICRA'18, ICRA'15) Best Systems Paper Finalist (HRI'25) Best Oral Paper Finalist (Humanoids'24) Best Paper in Entertainment (IROS'24) Argoverse Challenge Winner (CVPR'24) Best Student Paper (AIAA'24) Best Demo Finalist (RoboSoft'24) Oh mentors a diverse team of PhD, MS, and undergraduate students from CMU departments including Robotics, Computer Science, and Mechanical Engineering. Her research is funded by US Army Research Lab , DiDi Chuxing , and DARPA , with collaborations across industry and academia .

Deva Kannan RamananView profile

Professor

• Computer Vision
• 3-D Vision and Recognition
• Visual Servoing and Tracking
• +8 more

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.

Stephen LichtView profile

Associate Professor

• Marine Robotics
• Biologically Inspired Propulsion
• Nonlinear Attitude Control
• +5 more

Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.

Luca CarloneView profile

Associate Professor

• Robotics
• Autonomous Systems
• Computer Vision
• +6 more

Luca Carlone is the Boeing Career Development Associate Professor in the Department of Aeronautics and Astronautics at MIT and a Principal Investigator at the Laboratory for Information & Decision Systems (LIDS) . He leads the SPARK Lab , focusing on developing certifiable perception algorithms for autonomous systems. PhD in Mechatronics (Polytechnic University of Turin, 2012) Research spans robotics, computer vision, and optimization Research Interests : Certifiable Perception algorithms for high-integrity systems High-level Perception (geometric, semantic, physical understanding) Efficient Perception methods for resource-constrained robots Scientific Contributions include: 2024 Outstanding Systems Paper Award (RSS) 2023 IEEE Transactions on Robotics King-Sun Fu Award 2021 NSF CAREER Award 2020 AIAA Advising Award 2019 Amazon Research Award Advising : Teaches graduate courses like Visual Navigation for Autonomous Vehicles and Robotics: Science and Systems . Collaborates with institutions including JPL, Caltech, and KAIST through the DARPA SubT Challenge.

Michael KaessView profile

Associate Professor

• Robotics
• Computer Vision
• SLAM (Simultaneous Localization and Mapping)
• +6 more

Michael Kaess is an Associate Professor at the Robotics Institute, Carnegie Mellon University (CMU), within the School of Computer Science. He leads the Robot Perception Lab (RPL) and contributes to the Field Robotics Center (FRC) and Computer Vision Group (CV). His research focuses on efficient perception algorithms for mobile robots, particularly in 3D mapping, SLAM, and sensor fusion using vision, LiDAR, inertial, and sonar data. Kaess holds a PhD in Computer Science from Georgia Tech and was a postdoc at MIT's Marine Robotics Lab. Education: Georgia Institute of Technology, PhD in Computer Science (2008) MIT, Postdoctoral Associate (2008–2010) Research Interests: Kaess develops algorithms for robust and efficient inference in robotics, emphasizing factor graphs and linear algebra. His work spans underwater robotics, aerial systems, tactile SLAM, and multi-sensor integration. Key areas include SLAM with planes/lines, imaging sonar reconstruction, and neural field methods for LiDAR-visual fusion. Publications: Over 145 papers, including work on EDPLVO (visual odometry), HoloOcean (underwater simulation), and neural radiance fields with LiDAR. Recent trends focus on robust incremental smoothing, acoustic-optical fusion, and real-time volumetric mapping. Awards: Recognized with the RSS Test of Time Award (2020), Outstanding Associate Editor (2022), and paper awards at ICRA/ICRA. Active in conference organization (IROS/ICRA program committees). Advising & Grants: Supervises 10+ current PhD/MSc students, with past advisees contributing to CoRL/ICRA work. Manages grants in perception, autonomy, and marine robotics. Teaches courses like Robot Localization and Mapping (16-833). Labs/Teams: Directs RPL, collaborates with FRC on field robotics. Develops open-source tools like GTSAM (GNU Toolkit for Smoothing and Mapping).

David NoyceView profile

Professor

• Transportation Safety
• Traffic Operations
• Vehicle-to-Infrastructure Communication
• +5 more

David Noyce is a Professor and Executive Director of the Traffic Operations and Safety (TOPS) Laboratory at the University of Wisconsin-Madison's Department of Civil & Environmental Engineering. He also serves as Associate Director of the Safety Research Using Simulation (SaferSIM) Center, a University Transportation Center. His work focuses on transportation safety, traffic operations, and automated/connected vehicle systems. Key roles include leading over $35M in research since 2003 through the TOPS Lab and advancing technologies like V2V/V2I communication, winter maintenance strategies, and work zone safety. Education: PhD (Texas A&M, 1999), MS (Civil Engineering, UW-Madison, 1995), MBA (UW-Whitewater, 1994), BS (UW-Madison, 1984). Research interests span traffic control devices, driver behavior analysis, crash reconstruction, and multi-modal transportation systems. He pioneered the national implementation of flashing yellow arrow signals and developed the Wisconsin Connected and Automated Transportation Consortium. Current projects include C-V2X localization frameworks, automated vehicle safety testing, and winter maintenance efficiency. Recognized with 20+ awards, including ITE and ASCE Fellowships and the Dr. Arthur F. Hawnn Professorship. His labs offer full-scale driving simulators and field-testing capabilities for real-world validation of innovations.

Shoudong HuangView profile

Professor

• Mobile robot navigation
• Simultaneous localization and mapping (SLAM)
• Underwater robotics
• +4 more

Shoudong Huang is a Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney, and Deputy Director of the UTS Robotics Institute. His research focuses on mobile robot navigation , SLAM , nonlinear state estimation , and surgical robotics . He has published over 200 papers and is recognized as one of the 100 Most Influential Scholars in Robotics (Aminer, 2018). PhD in Automatic Control, Northeastern University (China) Postdoctoral Research Fellow, University of Hong Kong (1998-2000) Research Fellow, Australian National University (2001-2003) Full-time academic roles at UTS since 2004 His work addresses challenges in robot localization across extreme environments (underwater, underground mining, surgical settings) and develops globally optimal SLAM algorithms with guaranteed performance. He has secured over $4 million AUD in external funding, including ARC Discovery grants and industry partnerships. Recent publications emphasize cross-modal calibration (camera-LiDAR), interval analysis for bounded noise , and template-based deformable surface reconstruction . These span applications in autonomous driving, surgical navigation, and UAV guidance. Chancellor’s Medal for Research Excellence (2020) Supervisor of the Year (2023) Best Paper Award (2016 ICARCV) Huang serves as Associate Editor for IEEE Transactions on Robotics and International Journal of Robotics Research , and has held leadership roles in top robotics conferences like IROS and RSS. His collaborations span MIT, USC, Zhejiang University, and industry partners including PMSW Research Pty Ltd and Multiplex Constructions Pty Ltd.

Sanjiv SinghView profile

Research Professor

• Aerial Robotics
• Robotics in Agriculture and Forestry
• Mining Robotics
• +11 more

Sanjiv Singh is a Research Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science. His academic journey at CMU spans from Systems Scientist (1995-2001), to Senior Research Scientist (2001-2003), Associate Research Professor (2003-2007), and finally Research Professor since 2007. He also holds an adjunct faculty position in Mechanical Engineering since 2009. Singh serves as Editor-in-Chief of the Journal of Field Robotics, demonstrating his leadership in the robotics community. His educational background includes a Ph.D. and M.S. in Robotics from Carnegie Mellon University (1995, 1992), an M.S. in Electrical Engineering from Lehigh University (1985), and a B.S. in Computer Science from the University of Denver (1983). Dr. Singh's research focuses on three primary themes: Autonomous Navigation (developing motion planning and control for ground and air vehicles with applications in agriculture, exploration, and low-flying aircraft), Coordinated Multi-Robots (examining team-based tasks like structure assembly and search/rescue operations), and Forceful Interaction with the world (using physical models to enable robots to handle complex, high-force interactions). His work spans aerial robotics, agricultural and forestry robotics, mining robotics, 3D vision, sensing and perception, visual servoing, motion planning, and field service robotics. Analysis of his recent publications (2016-2020) reveals a strong focus on collision avoidance algorithms, sensor fusion techniques, and real-time navigation systems. His research demonstrates consistent advancement in SLAM (Simultaneous Localization and Mapping) technologies, particularly in GPS-denied environments, with increasing sophistication in handling complex aerial maneuvers and multi-robot coordination. Editor-in-Chief of Journal of Field Robotics Dr. Singh has advised numerous graduate students throughout his career, with current advisees including Matt Aasted (Ph.D), Andrew Chambers (M.S), Hugh Cover (M.S), Michael Dille (Ph.D), and Justin Haines (M.S). His past students include prominent researchers like Sebastian Scherer, Joe Djugash, Fred Heger, Geoff Hollinger, and Ji Zhang who have gone on to make significant contributions in robotics. His research has been supported through various projects including CASC (agricultural applications), Riverine, Transformer, Trestle, and Ember. His laboratory work focuses on developing practical robotic systems capable of operating in challenging real-world environments, with particular emphasis on agricultural applications, search and rescue operations, and coordinated multi-robot teams that can work effectively alongside humans.

Olga Sorkine-HornungView profile

Professor

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +5 more

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

• Computer Vision
• Machine Learning
• Autonomous Driving
• +4 more

Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.

• Transportation Safety
• Traffic Engineering
• Bicycle and Pedestrian Safety
• +4 more

Brendan Russo serves as an Associate Professor in the Department of Civil Engineering, Construction Management, and Environmental Engineering at Northern Arizona University, where he conducts influential research in transportation safety and traffic engineering. His work focuses on improving safety outcomes for vulnerable road users through rigorous analysis of crash data, traffic operations, and emerging mobility technologies, with significant contributions to Arizona-specific transportation challenges and national safety practices. Russo's research program centers on bicycle and pedestrian safety, crash severity analysis, and the integration of autonomous systems into transportation networks. He employs advanced methodologies including spatial analysis, statistical modeling (e.g., random parameters bivariate probit models), and observational studies to investigate traffic stress levels, intersection safety, and the impacts of infrastructure treatments. His work consistently bridges theoretical transportation engineering with practical applications for safer community design. Analysis of Russo's recent publications reveals a strong emphasis on emerging transportation technologies and their safety implications, particularly regarding autonomous delivery robots and vehicle-pedestrian interactions, while maintaining core focus on traditional safety concerns like bicycle crash frequency and severity. His research demonstrates increasing integration of spatiotemporal analysis and scenario-based testing methodologies, with a clear geographic concentration on Arizona metropolitan regions that provides valuable localized insights applicable to broader transportation contexts. No scientific awards were mentioned in the provided text. No specific information about advising responsibilities or grant funding was provided in the text, though his extensive publication record and dataset contributions indicate active research leadership. Russo collaborates within a robust research network centered on transportation safety, frequently partnering with colleagues including Gehrke, Smaglik, and Holliday on projects involving field data collection, bicycle infrastructure evaluation, and safety performance metrics. His work leverages both observational studies and simulation approaches to develop data-driven guidance for transportation practitioners, with particular attention to Arizona's unique transportation environment and metropolitan planning challenges.

Sebastian SchererView profile

Research Professor

• Robotics
• Unmanned Aerial Vehicles
• Autonomous Navigation
• +13 more

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.

Shauna BrailView profile

Professor

• Economic Geography
• Urban Planning
• Digital Platform Economy
• +5 more

Shauna Brail is Professor and Director at the Institute for Management & Innovation, University of Toronto Mississauga, with cross-appointments at the Munk School of Global Affairs & Public Policy. As an economic geographer and urban planner, her research examines urban transformation through economic, social, and technological change, particularly focusing on platform economy impacts, mobility innovation, and governance shifts in 21st-century cities. Education: PhD in Geography (University of Toronto), MA in Urban Planning (University of British Columbia), BA in Urban Studies/Geography (University of Toronto). Leadership Roles: Former Associate Director of Mobility Network, Director of Urban Studies Program, Associate Director of Partnerships & Outreach at School of Cities, and Interim Director of Master of Urban Innovation. Her work appears in top journals across geography, urban planning, and urban studies . Recent publications analyze pandemic impacts on cities, platform economy regulation, public housing finance models, and autonomous vehicle governance. She advises governments and civic organizations on housing, transportation, and digital platform policy while serving on editorial boards for Applied Geography , Progress in Economic Geography , and Canadian Geographies . Academic partnerships include collaborations with researchers like Tara Vinodrai, Ben Donald, and international scholars. Her current projects examine post-pandemic urban recovery, mobility-as-a-service business models, and equitable transportation technology implementation across Canadian cities.

Dennis SäringView profile

Professor

• Deep Learning
• Medical Image Analysis
• Artificial Intelligence
• +5 more

Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.

Wei XiangView profile

Assistant Professor

• Economics
• International Economics
• Macroeconomics
• +3 more

Wei Xiang is an Assistant Professor of Economics at the University of Michigan's Department of Economics, housed within the College of Literature, Science, and the Arts. He earned his Ph.D. in Economics from Yale University in 2024, focusing on international trade, macroeconomics, and environmental economics. His research bridges economic theory with applied analyses, addressing global trade dynamics, macroeconomic policies, and environmental sustainability. Education: Ph.D. in Economics, Yale University (2024). Research Interests: His work explores the intersection of international trade policies, macroeconomic stability, and environmental regulations. He examines how trade agreements influence economic growth and environmental outcomes, with a focus on empirical methodologies to assess policy impacts. Additionally, he investigates macroeconomic models to understand global economic fluctuations and their implications for sustainable development. Publications: His recent articles highlight contributions to vehicle communication systems, signal processing, and wireless technologies, reflecting interdisciplinary engagement with engineering applications. These include real-time prediction models for GPS errors and beamforming optimization in vehicular networks. Labs/Teams: Not explicitly stated, but his research collaborations likely involve interdisciplinary teams in economics, engineering, and environmental science.