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Sheng ShenView profile

Professor

• Nanophotonics
• Nanoscale Energy Transport and Conversion
• Advanced Manufacturing
• +7 more

Sheng Shen is a Professor in the Mechanical Engineering Department at Carnegie Mellon University (CMU) , with courtesy appointments in the Departments of Electrical and Computer Engineering and Materials Science and Engineering . He earned his Ph.D. in Mechanical Engineering (Minor in Electrical Engineering) from Massachusetts Institute of Technology (MIT) , and his B.S. and M.S. from Huazhong University of Science and Technology in China. Prior to joining CMU in 2011, he conducted postdoctoral research at UC-Berkeley . Education: Ph.D., Mechanical Engineering, MIT (2010) B.S. & M.S., Power Engineering & Engineering Thermophysics, Huazhong University of Science and Technology (2000 & 2003) Research interests include nanophotonics , nanoscale energy transport and conversion , nanofabrication , and advanced manufacturing , with applications in thermal management , light sources and devices , thermal emission control , solar energy conversion , infrared sensing , and multifunctional materials . His work leverages interdisciplinary expertise in thermal and optical measurements , material synthesis , device fabrication , and theoretical modeling . Recent publications highlight advancements in infrared radiation control , thermal interface materials , metasurface engineering , and graphene-based nanosystems . His scientific awards include: NSF CAREER Award DARPA Director's Fellowship DARPA Young Faculty Award Elsevier/JQSRT Raymond Viskanta Award CMU Dean's Early Career Fellowship Philomathia Foundation Research Fellowship Hewlett-Packard Best Paper Award Best Paper Award, Julius Springer Forum Advising spans Ph.D. and postdoctoral researchers in nanoscale energy systems, with alumni contributing to solar energy conversion , infrared sensing , and flexible electronics . His lab receives funding from ARL, DARPA, DOE, DTRA, NASA, NSF, and ONR , and recently secured a DURIP award for instrumentation.

Omar A. SalehView profile

Professor

• Biophysics
• Soft Matter Physics
• Polymer Physics
• +5 more

Professor Omar A. Saleh is a distinguished physicist and materials scientist at the University of California, Santa Barbara, holding appointments in both the Materials and Physics Departments. Since summer 2023, he has served as Chair of the Materials Department and maintains a minority appointment in the Biomolecular Science and Engineering (BMSE) Program, where he previously served as Director from 2013-2017. His educational background includes a B.S. in Physics from MIT (1997) and a Ph.D. in Physics from Princeton (2003), supported by a Hertz Fellowship. Following postdoctoral work at École Normale Supérieure in Paris developing single-molecule techniques for motor protein/DNA studies, he joined UCSB in 2005. Saleh's research centers on fundamental principles of biomolecular behavior through experimental investigation of biopolymer elasticity and biomimetic organelles. His lab pioneers precision single-molecule stretching experiments to study entropic/energetic contributions in soft systems and creates life-like behaviors using reconstituted nucleic acid/protein assemblies. Key focus areas include DNA nanostar phase separation, liquid-liquid phase behavior, intrinsically disordered proteins, and non-equilibrium biomolecular systems. His publication trends reveal a strong emphasis on biomolecular condensates (2023-2025), with recurring themes in DNA nanotechnology, polyelectrolyte physics, and single-molecule mechanics. Recent work explores tension-mediated control of phase separation, transcriptional regulation of biomolecular liquids, and active matter principles in DNA systems. NSF CAREER Award (2008) Bessel Research Award from Alexander von Humboldt Society (2017) Fellow of the American Physical Society (2019) Saleh actively mentors graduate students and postdocs including Sam Wilken, Gabrielle Abraham, Anna Nguyen, and Aria Chaderjian, whose research spans DNA nanostar liquids, active droplets, and complex coacervation. His lab develops innovative instrumentation including high-speed magnetic tweezers and GPU-based tracking systems, supported by grants such as NSF/MCB-BSF: Direct force measurements of intrinsically disordered proteins. The Saleh Group operates at BioE 3006, focusing on creating quantitative models of biological function through physical reconstitution.

William NordhausView profile

Professor

• Environmental Economics
• Climate Change Economics
• Macroeconomics
• +6 more

William Nordhaus is Professor of Economics at Yale University and Professor at the Yale School of the Environment . His work bridges economic theory, environmental policy, and macroeconomic analysis, with a focus on climate change, productivity measurement, and non-market accounting systems. 1963 BA, Yale University 1967 PhD, Massachusetts Institute of Technology 1962 Certificat, Institut D'Etudes Politiques (Paris) Nordhaus has pioneered economic modeling of climate change through his DICE/RICE frameworks, developed the G-Econ project mapping geophysical economic activity, and advocated for integrating environmental costs into national accounts. His recent DICE-2023 model emphasizes risk mitigation, carbon pricing, and updated climate projections. Notable awards include the American Economic Association Distinguished Fellow Prize (2004), membership in the National Academy of Sciences , and fellowship in the American Academy of Arts and Sciences . He has held leadership roles at the National Bureau of Economic Research, Congressional Budget Office, and Brookings Panel on Economic Activity. Nordhaus directs the Yale Project on Non-Market Accounting and has contributed to U.S. federal regulation reform, energy efficiency studies, and nuclear energy policy analysis. His affiliations include the G-Econ project and advisory roles on climate committees.

Laurent DemanetView profile

Professor

• Inverse Problems
• Wave Propagation
• Geophysical Imaging
• +11 more

Laurent Demanet is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT), with a primary appointment in the Department of Mathematics and a joint appointment in Earth, Atmospheric, and Planetary Sciences (EAPS). He served as Director of MIT’s Earth Resources Laboratory from 2018 to 2024, focusing on seismic imaging and wave propagation to visualize Earth's subsurface structures. Undergraduate: Mathematical Engineering and Theoretical Physics, Université de Louvain, Belgium PhD: Applied and Computational Mathematics, California Institute of Technology (Caltech), 2006 (advisor: Emmanuel Candès, William P. Carey Prize for best dissertation in mathematical sciences) Postdoctoral: Szegö Assistant Professorship, Stanford University (2006-2009) His research centers on applied mathematics and scientific computing for geophysics, particularly inverse problems, wave propagation, and signal processing. He develops numerical algorithms and machine learning tools to analyze indirect, incomplete, and noisy data, enabling accurate subsurface modeling for environmental monitoring, carbon sequestration, hydrogen storage, and geothermal energy. His work also addresses seismic hazards induced by resource exploration and storage activities. Scientific awards and honors include: National Science Foundation CAREER Award (2012) Alfred P. Sloan Research Fellowship (2011) Air Force Young Investigator Award (2011) MIT Class of 1954 Career Development Professorship (2013-2016) As Director of MIT’s Earth Resources Laboratory and through his research group, Demanet has advanced computational methods for geophysical imaging, mentored students, and secured grants for interdisciplinary projects. His work bridges applied mathematics, computational science, and geophysics to solve pressing environmental and energy challenges. He leads research on subsurface visualization, integrating machine learning and numerical algorithms to improve seismic imaging and quantify predictive uncertainty. His lab's applications span natural hazard assessment, resource management, and sustainable energy solutions.

Vineeth N BalasubramanianView profile

Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +8 more

Vineeth N Balasubramanian is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad, with affiliate faculty status in the Department of Artificial Intelligence. His research focuses on the intersection of deep learning, machine learning, and computer vision, emphasizing explainability, robustness, and real-world applications. He leads Lab 1055, which investigates problems such as Explainable and robust AI/ML systems Lifelong learning in evolving environments Multimodal vision-language models Applications in agriculture, autonomous navigation, and human behavior analysis His recent work includes causal reasoning in transformers, vision-language model capabilities, and drone-based object detection. Funded by organizations like Google, Microsoft, Intel, and DST, he has received multiple awards including the World's Top 2% Scientists (2022-23), INSA/INAE Fellowships, and Best Paper recognitions. Lab 1055 collaborates with institutions like CMU, UBC, and Monash University, contributing to cutting-edge advancements in AI.

Kenny S BreuerView profile

Professor

• Fluid Mechanics
• Animal Flight
• Bacterial Motility
• +4 more

Kenneth S. Breuer is a Professor of Engineering at Brown University, serving as Director of the Center for Fluid Mechanics. He holds appointments in the School of Engineering and collaborates across disciplines, including Biology and Physics. His research focuses on fluid mechanics, animal flight mechanics (particularly bats), bacterial motility, renewable energy, and turbulence. Breuer earned his Sc.B. from Brown University and M.Sc./Ph.D. from MIT, returning to Brown in 1999 after faculty service at MIT. Education: Sc.B. (Brown), M.Sc./Ph.D. (MIT). Awards include Fellowships from the American Physical Society and American Society of Mechanical Engineers, and the Harold and Esther Edgerton Chair at MIT. He has authored over 100 publications and edited books such as *Microscale Diagnostic Techniques*. Research Interests: Fluid mechanics at micro/nanoscales, bio-inspired flight mechanisms, energy harvesting, and vortex dynamics. Collaborations include Professors Sharon Swartz (Biology) and Thomas Powers (Engineering). Current projects explore bat wing aerodynamics, membrane hydrofoils, and aerosol transmission in vehicles. Awards: APS Division of Fluid Dynamics Chair (2012), Midwest Mechanics Lecturer, and multiple fellowships. Teaching includes courses in Fluid Mechanics, Transport Phenomena, and Renewable Energy Systems. His lab develops bio-inspired robotic systems and studies flow interactions in animal and engineered systems.

Kyros KutulakosView profile

Professor

• Computational Imaging
• Computer Vision
• 3D Sensing
• +9 more

Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.

Avi LoebView profile

Professor

• Cosmology
• Gravity
• High Energy Astrophysics
• +5 more

Avi Loeb , the Frank B. Baird Jr. Professor of Science at Harvard University, is a leading astrophysicist and former Chair of Harvard's Department of Astronomy (2011-2020). He directs the Institute for Theory and Computation (ITC) and the Galileo Project (2021-present), while holding the Sackler Senior Professorship at Tel Aviv University. His research spans cosmology, black holes, interstellar objects, and the search for extraterrestrial life. Education: PhD in Physics from Hebrew University of Jerusalem (1986) Former Positions: Member of the Institute for Advanced Study, Princeton (1988-1993) Loeb's research interests include: Gravitational wave detection and interstellar object analysis Simulation hypothesis and cosmic reality Technological signatures beyond the Standard Model His recent publications focus on 3I/ATLAS , gravitational SETI, and AI alignment in space exploration. Awards include being named among TIME's 25 Most Influential People in Space (2012), 14 Most Inspiring Israelis (2020), and ranking #3 in global astronomical impact (2024). He chairs the Breakthrough Starshot Initiative and advises the National Academies' Board on Physics and Astronomy.

William ChuehView profile

Professor

• Energy Storage
• Renewable Energy
• Materials for Carbon-Neutral Energy
• +3 more

William Chueh is a Professor in the Departments of Materials Science and Engineering and Energy Science & Engineering at Stanford University. He serves as Director of the Precourt Institute for Energy and Faculty Director of the Energy Innovation and Emerging Technologies Program. His research focuses on redox-active materials for energy storage, conversion, and carbon-neutral energy cycles. Education: PhD, Materials Science, Caltech (2010) BS, Applied Physics, Caltech (2005) Research Interests: Energy storage and conversion systems (batteries, fuel cells, electrolyzers) Multi-scale electrochemical and chemical reaction dynamics Materials design rules for redox-active solids Thermodynamic frameworks for sustainable energy Publication Trends: His work spans fundamental materials synthesis, electrochemical characterization, and modeling of redox reactions. Key themes include solar thermochemical cycles, ceria-based systems for CO2/H2O conversion, and advanced battery technologies. Scientific Honors: Outstanding Young Investigator Award (MRS, 2018) Camille Dreyfus Teacher-Scholar Award (2016) Sloan Research Fellowship (2016) CAREER Award (NSF, 2015) Advising: He advises students in energy technologies, materials science, and electrochemistry, including doctoral and master’s candidates. Contact: wchueh@stanford.edu

Sumanta AcharyaView profile

Professor

• Thermal and Fluid Sciences
• Computational Fluid Dynamics
• Turbulence Modeling
• +5 more

Sumanta Acharya is a Professor in the Department of Mechanical Engineering at Illinois Tech's Armour College of Engineering. His career spans computational methods, experimental fluid mechanics, and combustion, with affiliations including ASME, AIAA, and ASTFE. Ph.D. in Mechanical Engineering, University of Minnesota (1982) M.S. in Mechanical Engineering, University of Minnesota (1980) B.S. in Mechanical Engineering, Indian Institute of Technology (1978) A leading expert in thermal and fluid sciences, Acharya focuses on gas turbine heat transfer, turbulence modeling, and advanced cooling systems. His work integrates Computational Fluid Dynamics (CFD) with experimental validation for applications in biofuels , hydrogen combustion , and phase change materials . Recent publications highlight innovations in Brayton cycle integration, impingement cooling, and aerothermal performance optimization. Awarded by ASME, AIAA, and LSU, his honors include the ASME Heat Transfer Memorial Award (2011) and ASME Fellow (1999). He has contributed to key committees, including the ASME Heat Transfer Division Executive Committee and the Department of Energy's University Turbine Systems Research program. Researcher to Know, Illinois Science & Technology Coalition (2022) ASME Dedicated Service Award (2019) AIAA Thermophysics Award (2015) Contact: sacharya1@illinoistech.edu | Phone: 312.567.3701

Hassan Qudrat-UllahView profile

Professor

• Sustainability
• Global/Climate Change
• System Dynamics
• +3 more

Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.

• 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

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +3 more

Geoffrey E. Hinton is a distinguished Professor in the Department of Computer Science at the University of Toronto. He is renowned for his foundational contributions to machine learning, particularly in the development of deep learning and neural networks. His research focuses on understanding learning processes in both artificial and biological systems, with key contributions including Boltzmann machines, backpropagation, and deep belief networks. He teaches advanced machine learning courses such as CSC2535, emphasizing topics like graphical models, variational inference, and deep learning architectures. His work has been published extensively in top journals and conferences, with recent papers exploring forward-forward algorithms, analog diffusion models, and scalable neural network training methods. Hinton has advised numerous PhD and master's students and collaborates with institutions like Vector Institute. He is a central figure in the global AI community, regularly presenting at conferences (e.g., 2023 talks on CBS 60 Minutes, BBC, and PBS). His lab focuses on advancing machine learning theory and applications, addressing challenges in vision, language, and generative models.

Kai S. ExnerView profile

Professor

• Electrocatalysis
• Inorganic Chemistry
• Materials Science
• +7 more

Prof. Kai S. Exner is a Professor of Theoretical Inorganic Chemistry at the University of Duisburg-Essen's Faculty of Chemistry and a member of the Zentrum für Medizinische Biotechnologie (ZMB). His research focuses on electrocatalysis , particularly optimizing electrode materials for energy applications such as batteries, electrolyzers, and fuel cells. He employs computational methods like ab initio calculations and microkinetic modeling to bridge theoretical insights with experimental and industrial applications, emphasizing sustainable catalyst design without relying on noble metals. His work integrates basic research with applied medical biotechnology through ZMB collaborations, though his primary affiliation lies in the Faculty of Chemistry. Exner has pioneered concepts like the electrochemical-step asymmetry index and free-energy span model, which are critical for screening electrocatalysts. His research group, 'Theoretical Inorganic Chemistry,' actively explores mechanisms behind oxygen and chlorine evolution reactions, emphasizing selectivity control and reaction pathway complexity. Exner received the Gottschalk-Diederich-Baedeker-Preis (2024) for advancing the energy transition through foundational electrocatalysis research. He leads Aktuelles Projekt funded by third-party grants and collaborates with facilities like the Analytics Core Facility Essen (ACE) and Imaging Center Campus Essen (ICCE). His advisory role focuses on guiding theoretical and computational approaches in electrochemistry, and he has established a reputation for interdisciplinary research connecting computational chemistry with practical energy solutions.

Michael CarbinView profile

Assistant Professor

• Programming Systems
• Approximate Computing
• Probabilistic Programming
• +4 more

Michael Carbin is the Jamieson Career Development Assistant Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) and leads the MIT Programming Systems Group. His research focuses on programming systems that address system uncertainty to enhance performance, energy efficiency, and resilience, particularly in environments involving neural networks , approximate computing , and unreliable hardware . His work spans probabilistic programming , quantum computing , and machine learning systems . Articles highlight contributions in pruning neural networks , quantum data structures , and compiler optimization , reflecting trends in deep learning , formal verification , and language-driven systems . Scientific Awards : MIT Frank E. Perkins Award (2020) Sloan Research Fellowship (2020) Facebook Research Award (2019) NSF CAREER Award (2018) Best Paper Awards at OOPSLA (2013, 2014) He has advised numerous graduate students and postdocs including Eric Atkinson, Cambridge Yang, and Charles Yuan, and served on program committees for conferences like POPL, OOPSLA, and ICLR. His group collaborates with institutions such as MIT CSAIL and explores applications in quantum algorithms and probabilistic inference .