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Noah Snavely is a Professor of Computer Science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. His research spans computer vision and graphics with a focus on 3D scene understanding from image collections. He also works at Google DeepMind in NYC and leads the Cornell Graphics and Vision Group. His research interests include computer vision and computer graphics , particularly in recovering 3D structure from large photo collections, neural radiance fields, image-based rendering, and scene understanding. His work enables applications in mapping technologies, immersive VR experiences, and synthesizing 3D worlds from text prompts with implications for game design and filmmaking. Recent publications show a strong trend toward generative 3D modeling and neural rendering , with significant contributions in neural radiance fields, view synthesis, and dynamic scene reconstruction. His research group explores unstructured photo collections to develop new technology for 3D world modeling and image analysis. PECASE Microsoft New Faculty Fellowship Alfred P. Sloan Fellowship SIGGRAPH Significant New Researcher Award ACM Fellow (2023) IEEE Fellow NSF CAREER Award Helmholtz Prize Snavely has mentored numerous PhD students and postdocs including Ruojin Cai, Qianqian Wang, and Zhengqi Li. His teaching includes CS5670: Computer Vision across multiple semesters. He has received the Cornell College of Engineering Mr. and Mrs. Richard F. Tucker Teaching Excellence Award (2012). At Cornell Tech, his research group develops technology for modeling the world in 3D from online photo collections and analyzing images for computer graphics applications. His work has been featured in major news outlets including the New York Times, New Scientist, and MIT Technology Review.

• Computer Vision
• Computer Graphics
• 3D Reconstruction
• +5 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.

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

Tomas Palacios is a Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT) , where he directs the Center for Graphene Devices and 2D Systems and leads the Microsystems Technology Laboratories (MTL). His research focuses on pushing the boundaries of microelectronics through novel semiconductor materials and device architectures, including Gallium Nitride (GaN) and 2D materials like graphene and molybdenum disulfide (MoS2). Professor, MIT Electrical Engineering and Computer Science Director, MIT Center for Graphene Devices and 2D Systems Clarence J. LeBel Professor, MIT Faculty Director, Northeast Microelectronics Internship Program (NMIP) Research Interests span multiple cutting-edge domains: High-frequency electronics (>300 GHz) for 6G and quantum applications High-voltage power devices (600V–10kV) for energy conversion Post-silicon logic devices using 2D materials High-temperature electronics (e.g., Venus rover applications) Distributed neural networks on large-area 2D materials Graphene-based biosensors and chemical detection systems Scientific Contributions include: Double recipient of the IEEE George Smith Award for groundbreaking GaN transistor work Co-invented first MoS2 electronic circuits Developed world’s first Wi-Fi-to-electricity conversion antenna Led MIT’s Microsystems Technology Laboratories since 2021 Advising Philosophy emphasizes cross-layer expertise, with students gaining experience from materials synthesis to system-level prototyping. His lab has incubated startups like Vertical Horizons , focused on GaN power devices for AI and EVs.

• Semiconductor Physics
• Gallium Nitride (GaN) Electronics
• Two-Dimensional Materials
• +6 more

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +5 more

Megan Valentine is a Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), affiliated with the College of Engineering. She leads an interdisciplinary research group focused on biological and bioinspired materials, investigating how forces are generated and transmitted in living systems to design responsive synthetic materials. Her work bridges engineering, physics, chemistry, and biology. Education: PhD in Physics from Harvard University, MS in Physics from the University of Pennsylvania, and BS in Physics from Lehigh University. Affiliations include the California NanoSystems Institute (CNSI), Materials Research Laboratory (MRL), Neuroscience Research Institute, and the Center for Stem Cell Biology and Engineering. Research interests span soft material mechanics, bioengineering, and systems biology, with applications in marine-inspired materials, mechanobiology, and soft robotics. Her lab employs advanced experimental techniques to study biophysical and biochemical mechanisms in living systems and translate them into engineered materials capable of self-healing, movement, and environmental responsiveness. Notable awards include the NSF Early CAREER Award, Fulbright Scholarship, and election as Fellow of the American Physical Society and American Institute for Medical and Biological Engineering. Her contributions emphasize creativity, collaboration, and diversity, with a focus on addressing societal challenges through interdisciplinary innovation.

• Biophysics
• Mechanical Engineering
• Materials Science
• +5 more

Karen I. Winey serves as the Harold Pender Professor in the Department of Chemical and Biomolecular Engineering and Materials Science and Engineering at the University of Pennsylvania's School of Engineering and Applied Science. Her research group employs experimental and computational tools to investigate advanced polymers for energy applications, particularly focusing on proton and ion conductivity for fuel cells and batteries within the Laboratory for Research on the Structure of Matter (LRSM). Dr. Winey's research encompasses: Designing functional polymers to improve proton, hydroxide, and ion conductivity Studying polymer nanocomposites and nanoparticle dynamics in electrochemical devices Developing polymer-to-polymer upcycling methods to convert waste polyolefins to higher value polymers Revolutionizing understanding of ionomer morphologies beyond traditional spherical aggregate models Her recent publications reveal significant advances in sustainable energy materials, particularly fluorine-free alternatives to conventional polymer electrolytes. The research spans fundamental studies of ion transport mechanisms in precisely engineered polymers to practical applications in energy devices. A critical focus is understanding how nanoscale morphology affects macroscopic properties, with particular attention to structure-property relationships in ion-conducting polymers for fuel cells and batteries. Scientific recognition includes: 2025 Turner J. Alfrey Visiting Professor Harold Pender Professorship (endowed chair) NSF DMR Polymers grant awarded July 2025 Dr. Winey actively mentors a diverse research group including PhD students, postdoctoral researchers, and undergraduates. Her lab recently welcomed six summer undergraduates and has celebrated numerous student achievements including qualifying exam successes and award-winning presentations. Current major projects include the Port5 collaboration and the PolyUp project focused on polymer upcycling through dehydrogenation and functionalization strategies. The Winey Group operates within Penn's state-of-the-art Vagelos Laboratory for Energy Science and Technology (VLEST), maintaining active collaborations with institutions including the University of Konstanz in Germany, Florida State University, and Michigan State University. The group regularly hosts international visitors and participates in major conferences including the APS Global Physics Summit.

• Polymer Science
• Materials Science
• Energy Applications
• +4 more

Kazuhiro Saitou is a Professor of Mechanical Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on computational design synthesis, topology optimization, and manufacturing process integration. He leads the Algorithmic Synthesis Laboratory (ASL), advancing algorithms for automated design and optimization of mechanical systems. Education: Ph.D. (1996), MIT; M.S. (1992), MIT; B.Eng. (1990), University of Tokyo. He has held tenured positions since 1997, including roles as Founding CEO of Comnext, Inc. (2007–2012) and visiting professorships at École Centrale Paris and Donghua University. Research interests include multi-material topology optimization (M^3 TO), AI-driven design, and sustainable manufacturing. Key projects address additive manufacturing, composite structures, and energy-efficient production systems. He has pioneered methods for manufacturability-driven design and assembly optimization. Notable awards include IEEE Fellow (2018), ASME Kos-Ishii Award (2015), and NSF CAREER Award (1999). He serves as Editor-in-Chief for IEEE Transactions on Automation Science and Engineering and holds leadership roles in ASME and IEEE societies. Teaching includes courses on design optimization, CAD, and global product development. His lab has advised over 30 students, with alumni in academia and industry. Current research explores biomechanical modeling, traffic flow optimization, and medical image registration algorithms.

• Computational Design Synthesis
• Topology Optimization
• Manufacturing Process Optimization
• +7 more

Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Computational Design and Fabrication Group and is a member of the Computer Graphics Group. His research spans computer graphics, robotics, and AI-driven manufacturing, with a focus on computational design, tactile sensing, and material science. Matusik holds a PhD in Computer Science from MIT (2003), an MS from MIT (2001), and a BS from UC Berkeley (1997). His work includes groundbreaking projects like differentiable cloth simulation (DiffCloth), AI-enhanced molecular design, and tactile sensing gloves. He has received prestigious awards such as the MIT TR35 (2004), DARPA Young Faculty Award (2012), and Ruth and Joel Spira Teaching Award (2014). Matusik teaches courses on computer graphics, machine learning, and computational fabrication at MIT. Key research themes include: Robotics: Robotic assembly, tactile interaction, and soft robotics Graphics: 3D holography, procedural material generation Manufacturing: Additive fabrication, topology optimization His recent articles explore AI-driven molecular synthesis, holographic displays, and tactile-enabled VR systems. Matusik collaborates on open-source tools like the WiReSens tactile platform and Simit language for sparse systems.

• Computer Graphics
• Computational Design and Fabrication
• Computer Vision
• +7 more

Subhransu Maji is an Associate Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, and the co-director of the Computer Vision Lab. He is also affiliated with the Center for Data Science and holds a part-time role as an Amazon Scholar. His research focuses on high-level visual recognition algorithms and interdisciplinary applications in ecology and astronomy. He has received prestigious awards including the NSF CAREER Award (2018), Best Paper at WACV 2015, and the Google Graduate Fellowship (2008). Education: PhD in Computer Science from UC Berkeley (2011), BTech from IIT Kanpur (2006). Prior roles include Research Assistant Professor at Toyota Technological Institute at Chicago (2012-2014). Research Interests: Computer Vision Machine Learning AI Applications in Ecology and Astronomy 3D Shape Understanding Climate Science Grants and Funding: Supported by NSF, NASA, Climate Change AI, and industry grants from Facebook, NVIDIA, Adobe, and Dolby. Current projects include satellite imagery analysis for ecology and material science applications using deep learning. Labs and Teams: Leads the Computer Vision Lab, collaborates with interdisciplinary teams on ecological monitoring (e.g., bird migration tracking via radar data) and material property prediction (e.g., zeolite adsorption modeling).

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

Robert MacCurdy is an Assistant Professor at the Department of Mechanical Engineering, University of Colorado Boulder . He leads the Matter Assembly Computation Lab (MACLab) focused on automating robot design and fabrication. His research bridges computational design and advanced manufacturing to create "robots that walk out of the printer." The lab develops tools like OpenVCAD , an open-source volumetric multi-material geometry compiler.

• Automated robot design
• Additive manufacturing
• Soft robotics
• +6 more

Tzu-Mao Li is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD), affiliated with the Center for Visual Computing. His research focuses on differentiable graphics algorithms, combining classical visual computing with modern machine learning techniques. He holds a Ph.D. from MIT CSAIL under Frédo Durand and a postdoc at MIT and UC Berkeley with Jonathan Ragan-Kelley. His work spans rendering, programming languages for graphics, Monte Carlo methods, and inverse problems. Education: B.S. and M.S. from National Taiwan University (2011-2013), advised by Yung-Yu Chuang. Ph.D. from MIT CSAIL (Computer Graphics Group), advised by Frédo Durand. Postdoctoral research at MIT and UC Berkeley with Jonathan Ragan-Kelley. Research Interests: Differentiable rendering, Monte Carlo integration, programming language design for visual computing, physical simulation, adversarial machine learning, and applications in computer vision and robotics. Key areas include rendering algorithms (path tracing, bidirectional methods), optimization techniques (MCMC, gradient-based), and neural representations (SDFs, neural fields). Publications focus on advancing rendering algorithms, differentiable systems, and applications in inverse problems. Notable contributions include edge sampling for differentiable rendering, warped-area sampling, and diffusion models for BSDF sampling. Awards: ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award, multiple Best Paper Awards at SIGGRAPH, and oral presentations at ICCV. Teaching: Courses include CSE 167 (Computer Graphics), CSE 168 (Rendering), and CSE 272 (Advanced Image Synthesis), emphasizing physically-based methods and programming.

• Computer Graphics
• Rendering Algorithms
• Differentiable Programming
• +5 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.

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

Michael J. Aziz is the Gene and Tracy Sykes Professor of Materials and Energy Technologies at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He serves as Area Chair for Materials Science and Mechanical Engineering and is a Faculty Associate at the Harvard University Center for the Environment. His research focuses on electrochemical engineering for energy and environmental applications, including redox flow batteries, carbon capture, and sustainable energy technologies. Aziz leads the Aziz Group, which develops grid-scale energy storage solutions and innovative methods for CO₂ removal. He holds equity in Quino Energy, a startup commercializing his battery research, and serves as Chief Scientist and Board Member. His work bridges fundamental materials science with practical engineering, emphasizing ClimateTech solutions. Key contributions include aqueous organic redox flow batteries, quinone-based carbon capture systems, and wearable energy storage devices. Education & Affiliations: Affiliated with SEAS since joining Harvard, his academic roles include coordinating the Graduate Consortium for Energy and Environment (2009–2018). His lab (Materials Science Group) is located at McKay 504, with administrative support from Sabrina Azinheira. Research Interests: Aziz's group investigates electrochemical energy storage, CO₂ capture via electrochemical systems, and novel materials for sustainable technologies. They employ advanced techniques like operando electrochemical fluorescence microscopy to study porous electrode dynamics and battery degradation mechanisms. Their work emphasizes scalability and real-world applicability, such as grid-scale battery infrastructure and decarbonization strategies. Recent Trends in Publications: Aziz's recent work emphasizes carbon capture innovations (e.g., acid-base concentration swing methods), hydrogen storage under ambient conditions, and electrochemical synthesis of industrial chemicals like hydrogen peroxide. His group also develops open-source tools like RFBzero for battery modeling and explores bioinspired materials (e.g., self-gelling hydrogel batteries). Awards & Recognition: While no personal awards are explicitly listed in the text, his team members (e.g., Dawei Xi) have received accolades such as the 2025 Carbon Future Young Investigator Award. Aziz's contributions have been recognized through industry partnerships and startup ventures. Advising & Industry Impact: Aziz advises PhD students focusing on electrochemical systems (e.g., Jordan Sosa, Tommy George). His industry engagement includes licensing intellectual property to Quino Energy, which achieved a manufacturing milestone in 2024 for grid-scale battery systems. His research bridges academia and industry, addressing climate challenges through technological innovation. Labs & Teams: The Aziz Group includes interdisciplinary researchers from electrochemistry, chemical engineering, and materials science. Collaborators include institutions like MIT and industry partners. Current projects target next-gen batteries, CO₂ removal systems, and scalable energy storage solutions.

• Materials Science
• Energy Technologies
• Electrochemical Engineering
• +4 more

Emilia Oddo is an Associate Professor of Greek Archaeology in the Department of Classical Studies at Tulane University. She holds a Ph.D. in Classics from the University of Cincinnati (2016), an M.A. in Classics from the University of Cincinnati (2010), and an M.A. from Katholieke Universiteit Leuven, Belgium (2007). Her research focuses on the archaeology of Bronze Age Crete, particularly the Late Bronze Age (Neopalatial period), with a specialization in pottery styles and their social implications. She leads excavations at Knossos, analyzing pottery from Sir Arthur Evans’ early 20th-century excavations to understand ritual practices and architectural functions. In 2024, she will lead a student field trip through the Greek Islands organized by the American School of Classical Studies at Athens. Her publications include Knossos: The House of the Frescoes (2022) and South by Southeast: The History and Archaeology of Southeast Crete (2022), which explore Minoan social dynamics and regional identity. Her work highlights how pottery styles reflect communal identity and ritual behaviors. Recent research reinterprets Evans’ archival materials to reveal new insights into Knossos’ urban context and ritual spaces. Teaching courses like CLAS-2320 (Greek Religion) and CLAS-3170 (Greek Art and Archaeology), Oddo bridges academic scholarship and pedagogy. Her field projects emphasize interdisciplinary methods, such as ceramic regionalism and contextual analysis, to reconstruct Bronze Age Cretan society. She actively contributes to archaeological discourse through edited volumes and peer-reviewed articles.

• Greek Archaeology
• Bronze Age Crete
• Late Bronze Age Pottery
• +4 more

Supratik Guha is a Professor at the Pritzker School of Molecular Engineering and Senior Advisor to Argonne National Laboratory's Physical Sciences and Engineering directorate. His work bridges industrial R&D with academic and national lab research, focusing on quantum computing , semiconductor materials , and sensor networks for water and soil monitoring. Guha leads Argonne’s quantum information science strategy and serves as Faculty Director for the University of Chicago Center in Delhi. Education: PhD in Materials Science (USC, 1991), BTech in Engineering Physics (IIT Kharagpur, 1985) Research interests span multiple domains: Quantum technologies focusing on erbium-doped oxides for quantum memory and quantum interconnects Sensor networks for soil and water quality monitoring using cyberphysical systems Nanofabrication techniques including controlled spalling for heterogeneous material integration Advanced memory technologies exploring ferroelectric and optically addressable memory at atomic scales Scientific awards include: Election to National Academy of Engineering (2015) APS Prize for Industrial Applications of Physics (2015) Vannevar Bush Faculty Fellow (2018) Fellow of Materials Research Society and American Physical Society IBM Corporate Award (2013) Advising notable students like Manish Kumar Singh (co-founder memQ ), Cheng Ji (now at Intel), and Vamsi Nittala (now at Micron Technology). His group contributes to major DOE , NSF , and USDA funded projects including: Q-NEXT - DOE National Quantum Information Center AIFARMS - NSF/USDA AI for Agriculture Institute Thoreau Project - Geospatial sensor networks Labs and teams operate across University of Chicago and Argonne National Lab , with facilities for molecular beam epitaxy , nanofabrication , and optical/electrical characterization . The group has spawned startups like memQ (quantum networking) and K1 Semiconductors (wide-bandgap material transfer).

• Quantum Computing
• Semiconductor Materials
• Sensor Networks
• +4 more