Find Academic Supervisors

Ruben JuanesView profile

Professor

• Environmental Engineering
• Geophysics
• Energy Resources
• +3 more

Ruben Juanes is a Professor of Civil and Environmental Engineering and Earth, Atmospheric, and Planetary Sciences at MIT. His research focuses on multiphase flow in porous media, energy resources, and CO₂ sequestration. He holds appointments in both departments and has a strong interdisciplinary focus on geosciences and environmental engineering. His work bridges theory, simulation, and experimentation to address energy and environmental challenges. Education: Ingeniero de Caminos (Civil Engineering), University of La Coruña, Spain (1997) MS in Civil Engineering, UC Berkeley (1999) PhD in Civil Engineering, UC Berkeley (2003) Research interests emphasize fluid dynamics in geologic media, especially CO₂ storage, methane hydrates, and ecohydrology. His group develops computational models to predict large-scale Earth processes, with applications to carbon capture and storage, energy resource management, and subsurface engineering. Notable contributions include advancing understanding of fluid displacement mechanisms, capillary trapping in aquifers, and induced seismicity risks during CO₂ injection. His work has been recognized through awards like the APS Fellowship (2024), DOE Early Career Award (2010), and ARCO Energy Professorship (2008). Advising and Grants: Juanes advises graduate students in CEE and EAPS, focusing on thesis research in multiphase flow and geomechanics. His grants include NSF and DOE funding for projects on subsurface energy systems and induced seismicity. His lab, the Juanes Research Group, collaborates on experimental facilities like the FluidFlower CO₂ storage simulator. Labs/Teams: Active in MIT's Carbon Capture, Utilization, and Storage (CCUS) initiatives and the MIT Energy Initiative (MITEI). Collaborates with industry partners on field-scale CO₂ storage validation and subsurface monitoring technologies.

Nadia FigueroaView profile

Assistant Professor

• Robot Learning from Demonstration
• Human-Robot Collaboration
• Dynamical Systems
• +5 more

Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania. She holds secondary appointments in Computer and Information Science (CIS) and Electrical and Systems Engineering (ESE), and is a core faculty member at the GRASP Lab. Prior to Penn, she was a Postdoctoral Associate at MIT’s CSAIL and earned her PhD in Robotics from EPFL under Prof. Aude Billard. Her research focuses on physical and perceptual adaptive intelligence for robots, enabling fluid collaboration with humans in dynamic environments. Key applications include robot learning from demonstration , human-robot co-manipulation , safe navigation in human-centric spaces , and rehabilitation robotics . Her work integrates machine learning control theory artificial intelligence biomechanics psychology with guarantees of stability, safety, and robustness . Recent publications highlight advancements in reactive collision avoidance dynamical system learning intent estimation EEG-driven assistive control origami-based reconfigurable robots across platforms like autonomous vehicles and humanoid robots. She has authored a 2022 textbook on dynamical systems for robot control and received the Presidential Assistant Professorship at Penn.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +8 more

Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines

Nima FazeliView profile

Assistant Professor

• Robotic Manipulation
• Embodied Intelligence
• Multimodal Representation Learning
• +4 more

Nima Fazeli is an Assistant Professor of Robotics at the University of Michigan (2020–Present), holding courtesy appointments in Computer Science & Engineering (CSE) and Mechanical Engineering. He directs the Manipulation and Machine Intelligence (MMint) Lab, focusing on enabling dexterous robotic manipulation through multimodal representation learning, tactile sensing, and model-based reasoning. His work integrates mechanics, perception, controls, and planning to achieve autonomous interaction with uncertain environments. Education: PhD, MIT (2019); MSc, University of Maryland (2014); BSc, Amirkabir University of Technology (2011) Research interests emphasize embodied intelligence , including visuo-tactile fusion, contact dynamics modeling, and cross-modal learning. Recent work explores tactile shadows, deformable object manipulation, and language-guided robot control. His research is supported by the NSF CAREER grant and National Robotics Initiative, with applications in manufacturing, assistive robotics, and space systems. Publications span topics like tactile sensing hardware (e.g., GelSlim 4.0), visuo-tactile implicit representations (ViTaSCOPE), and failure recovery policies (Racer). His team’s work has been featured in outlets like The New York Times and BBC. Key Awards: NSF CAREER Grant (2024) Teaching includes Introduction to Robotic Manipulation . Collaborations involve cross-disciplinary projects with mechanical, electrical, and biomedical engineering groups.

Farzad MashayekView profile

Professor

• Turbulent Reacting Flow
• Plasma Flow
• Electrostatic Atomization
• +9 more

Farzad Mashayek is a Professor and Department Head of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He is a member of the Graduate Faculty and leads the Computational Multiphase Transport Laboratory. His research integrates high-fidelity simulations, machine learning, and experimental validation across diverse domains in fluid dynamics and energy systems. Educational Background: PhD in Mechanical Engineering, State University of New York at Buffalo, Buffalo, NY MS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran BS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran His research interests include turbulent reacting flows, plasma dynamics, electrostatic atomization, solid-ion and lithium batteries, computational fluid dynamics, and machine learning applications in engineering. He employs high-order spectral element methods, phase-field modeling, and deep neural networks to study complex multiphysics phenomena such as drop impact, battery degradation, and turbulence modeling. The recent publications reflect a strong trend toward integrating machine learning with multiphysics simulations, particularly in battery safety (thermal runaway prediction), materials characterization (STEM image analysis), and fluid dynamics (modal analysis of turbulence). His work often involves collaboration with experimental groups to validate models, especially in dental aerosol suppression and electrohydrodynamics. Scientific Awards: Sustained Service Award, American Institute of Aeronautics and Astronautics (AIAA), Spring 2022 Best Presentation Award, The 20th International Conference on Computational Mathematics, Parallel and Distributed Computing, Summer I 2018 Dr. Mashayek has secured funding from NSF (GOALI program) for controlled coating via charged droplet deposition. He advises graduate students and postdoctoral researchers in computational mechanics and energy systems, fostering interdisciplinary research. He has contributed to engineering education, particularly during the pandemic, with active learning strategies in online instruction. He leads a dynamic research team focused on advancing simulation tools and applying them to real-world challenges in energy, manufacturing, and public health.

Gianluca IaccarinoView profile

Professor

• Scientific Computing
• Uncertainty Quantification
• Turbulence Modeling
• +6 more

Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +4 more

Andrew Zisserman is a Royal Society Research Professor at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). His research focuses on computer vision, artificial intelligence, and neural networks, with significant contributions to multimodal learning, video understanding, and 3D scene analysis. He leads projects exploring visual-language models, audio-visual synchronization, and clinical imaging applications. Key research areas include: Video analysis and temporal modeling Multimodal systems for sign language translation and action recognition 3D shape estimation and physical property inference Foundation models and cross-modal retrieval Recent work highlights: Developed Flamingo and Tapir models for video-language tasks Advancements in spinal MRI analysis and clinical imaging Leadership in EGO4D and VoxCeleb challenges Honors include Fellowship of the Royal Society (FRS) and the ISSLS Prize in Clinical Science 2023 for spinal analysis innovations. His lab collaborates globally, emphasizing real-world applications in healthcare and autonomous systems.

• Aerosols, Air Quality and Climate Change
• Renewable Energy Integration
• Solar Farm Designs
• +11 more

Dr. K. Max Zhang is a Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. He is the director of the Energy and the Environment Research Laboratory (EERL) and a fellow at the Atkinson Center for a Sustainable Future. His research is deeply interdisciplinary, focusing on sustainable energy systems, air quality, and environmental justice, with significant impacts on policy and community development in New York and beyond. Ph.D., Mechanical Engineering, University of California-Davis, 2004 B.S., Thermal Engineering, Tianjin University, 1998 B.A., English Language, Tianjin University, 1998 Dr. Zhang’s research centers on the integration of energy and environmental systems. He investigates air pollution dynamics using advanced numerical models like CTAG, with applications in near-source pollution, indoor air quality, and environmental justice. His work on renewable energy systems includes designing sustainable solar farms and managing distributed energy resources such as heat pumps to enhance grid flexibility. He also leads a pioneering initiative to create the first statewide public IoT network in the U.S., enabling hyperlocal weather forecasting and microclimate monitoring. His recent publications reflect a strong trend toward agrivoltaics, peer-to-peer energy markets, and IoT-based environmental monitoring. These works demonstrate a consistent focus on data-driven modeling, community-scale energy solutions, and the integration of social considerations into technical systems. The keywords across his articles highlight expertise in sustainability, machine learning, air quality, and energy transition. Cornell Town-Gown Achievement Award (2022) Engaged Scholar Prize, Cornell University (2017) People's Choice Sign of Sustainability Award, Sustainable Tompkins (2016) Scientific and Technological Achievement Award, Environmental Protection Agency (2015) Fellow of the American Society of Mechanical Engineers Dr. Zhang is actively involved in mentoring students and securing research grants from agencies such as the National Science Foundation (NSF) and the New York State Energy Research and Development Authority (NYSERDA). His projects often involve interdisciplinary collaboration across eight Cornell colleges and 16 academic departments. He has led initiatives such as the Cornell Atkinson Academic Venture Fund projects and the development of a county-level energy roadmap for Tompkins County. He also teaches courses in engineering thermodynamics, future energy systems, and air quality, emphasizing experiential and community-based learning. Dr. Zhang leads the Energy and the Environment Research Laboratory (EERL) and collaborates with the Atkinson Center for a Sustainable Future. His lab functions as a hub for innovation in sustainable communities, combining advanced modeling with real-world applications. Through partnerships with community organizations, government agencies, and industry, his team develops science-driven solutions to urban and rural sustainability challenges.

Tapio SchneiderView profile

Professor

• Atmospheric Dynamics
• Climate Modeling
• Geophysical Turbulence
• +4 more

Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at the California Institute of Technology. His research focuses on atmospheric dynamics across Earth and other planets, climate modeling innovations, and geophysical turbulence analysis. He contributes to the Climate Modeling Alliance (CliMA) and develops advanced computational tools for climate prediction. Albert-Ludwigs-Universität Freiburg (Vordiplom, 1993) Princeton University (M.Sc. 1997, Ph.D. 2001) University of Washington, Seattle (Visiting Graduate Student, 1994-1995) His research spans climate dynamics , atmospheric turbulence , and AI-enhanced climate modeling , addressing challenges in cloud dynamics, extreme weather patterns, and planetary climate systems. Current work emphasizes hybrid machine learning-physical models and computational acceleration for high-resolution simulations. Recent publications highlight trends in AI integration for climate science, with applications in hydrology , cloud microphysics , ocean circulation , snowpack modeling , and climate tipping points . His team develops open-source tools like ClimateMachine for GPU-accelerated simulations. Scientific Recognition: Fellow, American Geophysical Union (2022) Rosenstiel Award (2019) World Economic Forum Young Scientist (2012) David and Lucile Packard Fellow (2005-2010) Alfred P. Sloan Research Fellow (2004-2006) Tapio leads climate dynamics research at Caltech, directs the Linde Center for Global Environmental Science (2011-2012), and serves as Editor for the Journal of Advances in Modeling Earth Systems . His group collaborates with NASA Jet Propulsion Laboratory (2016-2024) and Google Research (2022-present).

Alois Christian KnollView profile

Professor

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +6 more

Prof. Alois Christian Knoll is a full professor at the Technical University of Munich (TUM) in the School of Computation, Information and Technology. His academic career includes roles at Bielefeld University and leadership in major EU initiatives like the Human Brain Project and ECHORD++. He specializes in robotics, AI, and autonomous systems, with a focus on medical robotics, sensor-based systems, and neuromorphic engineering. Knoll has supervised over 100 doctoral theses and authored/co-authored over 1,000 publications. Education: Diploma in Electrical Engineering (University of Stuttgart, 1985); PhD in Computer Science (Technical University of Berlin, 1988); Habilitation (TU Berlin, 1993). He has been at TUM since 2001, leading the Robotics, AI, and Real-Time Systems department. Research interests span autonomous systems, neuro-IT integration, and traffic simulation. Key projects include fortiss (Bavarian State Institute for Computer Science) and TUM-CREATE (Singapore collaboration). Awards include IEEE Fellow, University of Tokyo Fellow, and the Carl-Ramsauer-Prize (1990). Current roles include editorships in robotics journals, leadership in EU flagship projects, and teaching across multiple programs. His work bridges computer science, neuroscience, and engineering, with applications in healthcare, automotive systems, and urban mobility.

Srijan KumarView profile

Assistant Professor

• Data Science
• Applied Machine Learning
• Network Analysis
• +7 more

Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. His research focuses on data science, AI for security, and online safety, addressing challenges in detecting malicious users, misinformation, and enhancing AI robustness. His work has been deployed in platforms like Flipkart and Wikipedia, and recognized through awards such as the NSF CAREER and Forbes 30 Under 30. Education: B.Tech from Indian Institute of Technology, Kharagpur Ph.D. in Computer Science from University of Maryland, College Park Postdoctoral training at Stanford University Research Interests: Multi-modal/multi-lingual detection of harmful content and users Adversarial robustness of AI models Graph and network analysis for early detection Responsible recommender systems Awards & Grants: NSF CAREER Award (2023) Kavli Fellow (2022) Facebook/Adobe Faculty Awards NSF Convergence Accelerator Phase II grant ($5M) Advising & Labs: Leads the CLAWS Lab, advising over 20 students. Active in mentoring through conferences and NSF-funded projects.

Petros KoumoutsakosView profile

Professor

• Machine Learning
• Computational Fluid Dynamics
• Turbulence Modeling
• +3 more

Petros Koumoutsakos is the Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he also serves as Area Chair for Applied Mathematics. His research integrates machine learning with computational science to advance understanding of complex systems, including fluid dynamics, turbulence modeling, and biomedical applications. He leads the CSE Lab, focusing on high-performance computing and interdisciplinary collaborations such as a recent study with Citadel Securities and Google Cloud to simulate heart disease in cloud environments. Key research interests include reinforcement learning for turbulence closures, generative models for PDE solutions, and physics-informed AI for biomedical imaging and wildfire prediction. He was awarded the PRACE HPC Excellence Award (2023) for contributions to high-performance computing. His work bridges computational methods with real-world applications, emphasizing interpretability and scalability in multiscale systems. Grants & Collaborations: Leadership in multi-institutional projects, including turbulence modeling via reinforcement learning and cloud-based HPC studies. Labs/Teams: Director of the CSE Lab, advancing AI, computational fluid dynamics, and biomedical simulations.

Jiyuan TuView profile

Professor

• Computational Fluid Dynamics (CFD)
• Multiphase Flows
• Thermal Sciences
• +4 more

Professor Jiyuan Tu is a Professor in the Department of Mechanical and Automotive Engineering at RMIT University's School of Engineering. He specializes in computational fluid dynamics (CFD), multiphase flows, and their applications in renewable/nuclear energy, biomedical engineering, and built environment systems. His research has led to over 500 peer-reviewed articles, 9 books, and $10M+ in ARC grants. He has supervised over 50 postgraduate students and received prestigious awards such as the RMIT Research Excellence Award (2012) and Fulbright Senior Scholar Award (2008). Research interests include CFD modelling of bioaerosol transport, drug delivery systems, and thermal energy storage. He pioneered numerical models for multiphase flows, contributing to software implementations in industries. Notable works include books on CFD and multiphase flow analysis, and leadership in international conferences like COBEE 2018. He holds honorary professorships at Tsinghua University and is Editor-in-Chief of the Experimental and Computational of Multiphase Flow journal. Industry experience includes roles at ANSTO (1996-2001). Awards span fellowships from JSPS, KOSEF, and Fulbright programs. Grants include ARC Discovery, Linkage, and LIEF projects. His work ranks him among the world’s top researchers in pebble bed reactors and airborne infection studies (SciVal 2016-2025).

Xiaowei JiaView profile

Assistant Professor

• Machine Learning
• Data Mining
• Physics-Guided Neural Networks
• +5 more

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China (USTC) and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal and environmental challenges, such as climate modeling, hydrology, and fairness in AI. Education: Ph.D., University of Minnesota (2020) M.S., State University of New York at Buffalo B.S., University of Science and Technology of China (USTC) Research Interests: Knowledge-Guided Machine Learning Spatiotemporal Data Mining Fairness in AI for Social Good Applications in Environmental Science and Healthcare Publications showcase his work on physics-integrated neural networks, spatiotemporal modeling (e.g., water temperature prediction), and fairness-aware algorithms. His work has been recognized with Best Paper awards at SIAM SDM (2022, 2023). Awards include the Best Applied Data Science Paper Award at SIAM SDM in 2022 and 2023. He teaches advanced machine learning courses, emphasizing theory integration with real-world applications.

Changxi ZhengView profile

Associate Professor

• Computer Graphics
• Scientific Computing
• Physics-Based Sound Synthesis
• +7 more

Changxi Zheng is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science (SEAS). He directs Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC). After receiving his PhD from Cornell University, he joined the faculty of Computer Science Department at Columbia, where he has established himself as a leading researcher in computer graphics and scientific computing. Dr. Zheng's research spans multiple areas of applied computer science with a particular focus on computer graphics and scientific computing. His work centers around developing numerical models for simulating physical phenomena involving complex motions such as fluids, bubbles, and thin rods, along with their resulting acoustic waves. Leveraging computational insights from these models, he devises methods for improving tangible object creation, enabling novel human-computer interactions, and developing software tools for acoustic and photonic devices. His research has attracted significant public interest and media coverage, including projects like FontCode, AirCode, and Computational Metallophone Design. His recent publications reveal a strong interdisciplinary approach, bridging computer graphics, physics simulation, machine learning, and hardware design. His work demonstrates consistent innovation in computational methods for simulating physical phenomena and applying these techniques to practical problems in 3D printing, acoustic modeling, and interactive systems. The breadth of his research spans from fundamental physics-based simulations to practical applications in industry. Columbia SEAS Dean's Fellow (for advised students) NSF Graduate Research Fellow (for Ruilin Xu) Snap Research Fellow (for Rundi Wu) CKGSB Fellow (for Yun Fei) Adobe Research Fellow (for Gabriel Cirio) Marie Sklodowska-Curie Individual Fellow (for Rundi Wu) Best Paper Award at ACM International Conference on Multimedia (ACMMM), 2019 Dr. Zheng actively mentors a diverse group of students, including current PhD candidates and postdoctoral researchers. His research group has received support from various sources that enable their innovative work in computational graphics and physics-based simulation. He has supervised numerous successful students who have gone on to positions at leading technology companies including Adobe, Tencent, Facebook, and academic institutions. As director of Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC), Dr. Zheng leads a vibrant research team focused on advancing the state of the art in computer graphics, physics-based simulation, and their applications. The group maintains strong collaborations with industry partners and academic institutions worldwide, fostering an environment of innovation and practical application of theoretical concepts.