Find Academic Supervisors

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).

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

Shubham Tulsiani is an Assistant Professor at Carnegie Mellon University's Robotics Institute, where he leads the Computer Vision group and the Physical Perception Lab. His research focuses on inferring physically and spatially grounded representations from perceptual inputs, with applications in 3D vision, robot manipulation, and neural scene reconstruction. He directs an active research group with multiple PhD and Master's students. Research interests center on 3D scene understanding , robot learning , and generative modeling , with specific emphasis on: self-supervised perception, neural rendering, multi-view geometry, manipulation from visual inputs, and physics-based reasoning. The lab develops methods that leverage physical world constraints as supervisory signals. Recent publications demonstrate strong focus on diffusion models for 3D tasks , sparse-view reconstruction , and robotic manipulation transfer . Key trends include neural inverse rendering, view synthesis from limited observations, and translating human interactions to robot actions. Awards include: Best Student Paper Award at CVPR 2015 Advising includes supervision of 5 PhD students, 4 MS students, and undergraduates. Lab alumni hold positions at Google, Stanford, Meta, and Princeton. The Physical Perception Lab collaborates with FAIR Pittsburgh and the CMU Computer Vision group.

• Computer Vision
• 3D Vision
• Robot Learning
• +4 more

Anthony Rollett is a Professor in the Department of Materials Science and Engineering at Carnegie Mellon University , where he has been a faculty member since 1995. He serves as the Principal Investigator and Co-Director of the NASA-supported Institute for Model-Based Qualification & Certification of Additive Manufacturing (IMQCAM) and co-director of the Next Manufacturing Center . Prior to CMU, he held leadership roles at Los Alamos National Laboratory (1991-1995). Education: Ph.D., Materials Engineering, Drexel University (1987) MA, Metallurgy and Materials Science, Cambridge University (1977) Research Interests: Rollett’s work focuses on microstructural evolution and microstructure-property relationships in 3D using experiments and simulations. His expertise spans additive manufacturing , metal 3D printing , materials for energy systems , grain growth , recrystallization , and stereology , with techniques like high-energy diffraction microscopy (HEDM) and dynamic x-ray radiography (DXR) . Scientific Contributions: He has over 320 peer-reviewed publications and an h-index >80 . His recent articles highlight machine learning for laser processing , fatigue analysis of additively manufactured alloys, and design optimization for heat exchangers in supercritical CO2 and solar thermal applications . Scientific Awards: Fellow of ASM International (1996) Fellow of the Institute of Physics (UK) (2004) Fellow of The Minerals, Metals & Materials Society (TMS) (2011) Cyril Stanley Smith Award (TMS, 2014) Member of Honor, French Metallurgical Society (2015) US Steel Professor (2017) Francqui International Professor (2020-2021) International FAME Award (2023) Leadership & Impact: Rollett co-led the development of a NASA Space Technology Research Institute for additive manufacturing and established a new master’s program in additive manufacturing (2018). His research group is funded by industry , federal agencies , and Pennsylvania state grants . He also serves on the Basic Energy Science Advisory Committee and Defense Programs Advisory Committee for the Department of Energy.

• Additive Manufacturing
• Advanced Materials Processing and Manufacturing
• Digital Twins
• +6 more

Fernando De la Torre is a Courtesy Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with an affiliation to the Robotics Institute where he has been a research faculty member since 2005. He holds a Ph.D. in Electronic Engineering from Ramon Llull University (2002). His research focuses on machine learning and computer vision, with applications in human health, augmented/virtual reality, generative models, and data-centric methodologies. He directs the Human Sensing Laboratory, which explores technologies for human behavior analysis and health monitoring. Notable contributions include founding FacioMetrics LLC (acquired by Meta), advancing facial recognition and 3D human digitization, and developing frameworks for robust visual models. His work bridges theory and practice, with over 225 peer-reviewed publications and editorial roles, including Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. Recent research trends emphasize generative AI applications in satellite imagery analysis, VR/AR rendering optimizations (e.g., Gaussian splatting), and clinical motion recognition for healthcare. His projects often intersect with industry, addressing challenges in wearable health monitoring and immersive technologies. His lab collaborations span academia and industry, focusing on scalable solutions for 3D human modeling, adversarial robustness, and multimodal data fusion. Key achievements include pioneering work on 3D face animation from speech and garment reconstruction from single images.

• Machine Learning
• Computer Vision
• Augmented Reality
• +4 more

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +5 more

Daniel Cardoso Llach is an Associate Professor at Carnegie Mellon University's School of Architecture , where he chairs the Master of Science in Computational Design program and co-directs the CoDe Lab . His scholarship merges history, science and technology studies (STS), and computational design , focusing on the cultural and socio-technical dimensions of design automation. Education: PhD and MS in Architecture: Design and Computation from MIT , BArch from Universidad de los Andes Research Grants: Supported by the Graham Foundation for historical CAD exhibitions and by the Alexander Von Humboldt Foundation for postwar computational design research in Germany His work interrogates the politics of software, the materiality of computational systems , and the ethical implications of AI/robotics in architectural practice. Recent projects include reconstructing early CAD systems and analyzing data-driven urban technologies. Scientific awards include: Alexander Von Humboldt Fellowship (2024–2025) ACM CSCW Methods Mention for emulation-based software research (2021)

• Architectural Automation
• Human-Machine Interaction
• Digital Technology History
• +3 more

Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +2 more

Yisong Guo is a Professor in the Department of Chemistry at Carnegie Mellon University, specializing in bioinorganic spectroscopy and iron-containing metalloproteins . His work bridges biochemistry , spectroscopy , and Density Functional Theory to uncover mechanisms of metalloenzyme catalysis in processes like N₂ , H₂ , and O₂ activation. Guo earned a Ph.D. in Applied Science from the University of California, Davis (2003–2009). He has held academic positions at Carnegie Mellon since 2014, advancing from Assistant to Professor. Research Interests focus on iron enzymes such as nitrogenases , hydrogenases , and oxygenases , using Mössbauer , EPR , and Nuclear Resonance Vibrational Spectroscopy (NRVS) . His group integrates synchrotron radiation and DFT calculations to resolve geometric and electronic structures of iron cofactors during catalytic cycles, with applications in alternative energy and human health . Key Publications highlight C-H bond activation , iron hydride identification , nonheme iron catalysis , and ligand design for enhanced reactivity. His work also explores imaging techniques for iron cofactors in whole cells using synchrotron Mössbauer. Awards NSF CAREER Award (2017) Scientific Leadership includes developing synchrotron-based spectroscopic methods and interdisciplinary collaborations with groups at synchrotron facilities . He teaches graduate and undergraduate courses in physical chemistry and bioinorganic spectroscopy .

• Bioinorganic Chemistry
• Iron-Containing Metalloproteins
• Spectroscopy
• +5 more

Jonas August is a Project Scientist at the Robotics Institute of Carnegie Mellon University , focusing on geometry and uncertainty in computer vision and medical imaging. He employs probabilistic techniques like Markov processes and random fields to develop algorithms for medical image enhancement, including streak artifact removal in X-ray CT scans and vascular structure inference. Education: Ph.D., Yale University (2001) M.Phil., Yale University (1999) M.Sc., Yale University (1999) M.Eng., McGill University (1996) B.Eng., McGill University (1993) His research spans medical imaging , image regularization , and mathematical modeling , with applications in robotic perception and neural architecture. His work includes scalable regularized tomography algorithms and the development of DOC , a GNU/Linux cluster for medical imaging. He is supported by the National Science Foundation (Grant No. 0305719). August explores image statistics , curve inference , and object representation stability , contributing to contour fragment grouping and perceptual organization theories.

• Computer Vision
• Medical Imaging
• Image Regularization
• +3 more

Jessica K. Hodgins is the Allen Newell University Professor of Computer Science and Robotics at Carnegie Mellon University (CMU), affiliated with the Robotics Institute and Computer Science Department. She previously held leadership roles at Disney Research (2008–2016) as VP of Research and managed faculty affairs in the Robotics Institute (2005–2015). She earned her Ph.D. in Computer Science from CMU in 1989. Her research spans computer graphics , humanoid robotics , and human-robot interaction , with a focus on generating and analyzing human motion for animation and robotics. Key areas include motion capture, physics-based simulation, and assistive technologies for Parkinson’s disease using wearable sensors. Her work has been recognized through awards like the ACM SIGGRAPH Achievement Award (2010) and the Steven Anson Coons Award (2017). She has advised numerous students and contributed to initiatives like ACM SIGGRAPH editorial leadership and mentoring programs. Labs: Graphics Lab and Human Sensing Lab . Current advisees include Emily Kim, Yuyao Shi, and Jiashun Wang. Past students have contributed to projects in animation, robotics, and biomedical engineering.

• Computer Graphics
• Robotics
• Humanoid Robotics
• +4 more

Dejan Slepčev is a Professor and Associate Dean for Faculty and Graduate Affairs at the Mellon College of Science, Department of Mathematical Sciences, Carnegie Mellon University. His research focuses on applied analysis, with applications to data science, collective behavior of particle systems, and energy-driven systems. He holds a Ph.D. from the University of Texas at Austin. His research interests include partial differential equations, calculus of variations, optimal transportation, and their applications to machine learning tasks such as clustering and classification. He develops mathematical frameworks for analyzing variational and PDE-based problems on random data samples, improving algorithms for data science. He also studies systems of interacting particles, particularly those modeling collective behavior in biological and physical systems, and energy-driven systems involving pattern formation and interface evolution. His work bridges theoretical analysis and computational methods, addressing challenges in data analysis, image processing, and the dynamics of complex systems. Key contributions include studies on spectral clustering, graph-based semi-supervised learning, and the continuum limits of discrete variational problems. Education: Ph.D., University of Texas at Austin.

• Applied Mathematics
• Data Science
• Partial Differential Equations
• +7 more

Frederike Dümbgen is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, starting in Spring 2026. She is currently a researcher with the Willow team at Inria Paris, focusing on optimization for robotics, and previously served as a postdoctoral fellow at the University of Toronto's Robotics Institute. Education: Ph.D. in Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2021) M.Sc. in Mechanical Engineering, EPFL (2016) B.Sc. in Mechanical Engineering, EPFL (2013) Her research centers on improving the efficiency and safety of robots operating in the physical world through principled optimization and machine learning methods. She emphasizes certifiable and globally optimal algorithms to build reliable foundations for next-generation robotics in domains such as autonomous vehicles, assistive technology, and manufacturing. Her work bridges robotics, control systems, and artificial intelligence, with a strong focus on mathematical rigor and scalability. The 15 most recent publications highlight a consistent trajectory in robotics-focused optimization, particularly in state estimation, SLAM, pose estimation, and data-driven methods. These works frequently employ semidefinite programming, convex relaxations, and Koopman-based linearization, demonstrating a deep integration of theoretical optimization with practical robotic applications. Keywords across these papers include robotics, optimization, machine learning, and estimation, with subfields like certifiable algorithms, global optimality, and sensor fusion recurring throughout. Dr. Dümbgen has not yet had scientific awards listed in the provided text. She has not yet advised any named students in the provided materials, and there is no mention of grants or funding sources. However, her research trajectory and publication record suggest active involvement in competitive research environments. Her experience includes internships at Disney Research and ABB, and her master’s thesis was completed at ETH Zürich’s Autonomous Systems Lab. She is currently affiliated with the Willow research team at Inria Paris, a group known for foundational work in computer vision, machine learning, and robotics. This team emphasizes mathematical rigor in algorithm design, aligning closely with her focus on certifiable and globally optimal methods.

• Robotics
• Optimization
• Machine Learning
• +12 more

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

• Artificial Intelligence
• Computational Neuroscience
• Computer Vision
• +5 more

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

• Engineering Design Automation
• Artificial Intelligence in Design
• Computational Modeling
• +5 more