جستجوی استاد راهنمای دانشگاهی

Dr. Peichen Zhong is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He leads the Applied Machine Learning and Materials Modeling (AM³) Group, focused on advancing computational methods for clean energy technologies. His research integrates machine learning with atomistic simulations to tackle challenges in battery materials, disordered materials, and sustainable energy systems. Education: B.S. in Physics from University of Science and Technology of China (2018); Ph.D. in Materials Science from UC Berkeley (2023, advised by Prof. Gerbrand Ceder); Postdoctoral training at Lawrence Berkeley National Lab and BIDMaP, co-advised by Persson, Cheng, and Krishnapriyan. Research Interests: Computational modeling of battery cathodes/electrolytes, AI-driven interatomic potentials, statistical mechanics in disordered materials, and generative models for scientific discovery. Key areas include Li/Na-ion batteries, solid-state reactions, and sustainable energy materials. Awards: BIDMaP Emerging Scholar Fellowship (UC Berkeley CDSS, 202?), 2023 Rising Stars in Materials Science (CMU/MIT/Stanford). Labs/Teams: The AM³ Group at NUS MSE focuses on interdisciplinary research combining theory, computation, and AI4Science. Current openings include PhD students and postdoctoral researchers.

• Materials Science
• Machine Learning
• Computational Modeling
• +۵ مورد دیگر

Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.

• Reinforcement Learning
• Bandit Algorithms
• Active Learning
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Zhi-Xun Shen is the Paul Pigott Professor in Physical Sciences at Stanford University, holding dual appointments in the Physics and Applied Physics Departments. He is a senior fellow at the Precourt Institute for Energy and serves on advisory boards for the Knight-Hennessy Scholars and Stanford Science Fellows programs. His research focuses on condensed matter and materials physics, particularly the electronic structures of superconductors, topological insulators, and novel materials. Dr. Shen pioneered advanced spectroscopic techniques, including photon-based imaging and scattering methods, and has authored over 600 publications with significant citation impact. His honors include the Kamerlingh Onnes Prize (2000), E.O. Lawrence Award (2010), and Oliver E. Buckley Prize (2011). He co-founded PrimeNano Inc., commercializing technologies from his lab, such as microwave impedance microscopy. His work bridges fundamental physics with energy-related applications, emphasizing the interplay between electronic structure and material properties. Dr. Shen’s research group explores cutting-edge topics like topological surface states, electron-phonon interactions, and superconductivity mechanisms. His inventions, such as non-resonance microwave imaging, have found applications in materials characterization. He remains active in advancing instrumentation and fostering interdisciplinary collaborations through his academic and industry roles.

• Condensed Matter Physics
• Materials Science
• Superconductivity
• +۴ مورد دیگر

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.

• Turbulent Reacting Flow
• Plasma Flow
• Electrostatic Atomization
• +۹ مورد دیگر

Professor Asmita Mukherjee is a faculty member in the Department of Physics at the Indian Institute of Technology Bombay (IIT Bombay), where she holds the position of Professor. She is actively engaged in theoretical particle physics research with a focus on Quantum Chromodynamics (QCD) and related phenomena. Professor Mukherjee's research interests primarily include Theoretical Particle Physics, Quantum Chromodynamics, QCD spin physics, Light-cone wave functions, and collider phenomenology. She teaches several advanced courses including Elementary Particle Physics (PH540), Mathematical Physics-I (PH407), Mathematical Physics-II (PH408), Quantum Physics and Applications (PH107), and various Quantum Mechanics courses (PH422, PH423, PH105). Her publication record shows a strong focus on gravitational form factors, transverse momentum dependent distributions, angular momentum structure of hadrons, and electron-ion collider physics. Recent work has explored gluon contributions to proton structure, dressed quark states, and azimuthal asymmetries in various production processes. Her research demonstrates expertise in light-front quantization techniques and their application to fundamental questions in hadron physics. Professor Mukherjee has received recognition for her work in High Energy Theory, with numerous publications in prestigious journals addressing fundamental questions in hadron structure and QCD dynamics. Her work provides crucial theoretical foundations for upcoming experiments at the Electron Ion Collider. Current PhD students: Sudeep Saha, Amol Pawar Graduated PhD students: Ravi Manohar, Sreeraj Nair, Vikash K. Ojha, Sangem Rajesh, Raj Kishore Postdoctoral researchers: Shaik Khatiza Banu (CFNS), Jai More, Tanmay Maji, Mariyah Siddiqah Research projects: BRNS project with Ravi Singh as Junior Research Fellow Her research group at IIT Bombay continues to explore fundamental questions in hadron physics, with ongoing projects examining the mechanical properties of protons, the decomposition of angular momentum in QCD, and the development of theoretical tools for interpreting data from next-generation colliders.

• Theoretical Particle Physics
• Quantum Chromodynamics
• QCD Spin Physics
• +۵ مورد دیگر

David Trend is a Professor and former Chair of the Department of Art at the Claire Trevor School of the Arts, University of California, Irvine. He previously served as Dean of Creative Arts at De Anza College and Graduate Program Coordinator at San Francisco State University's Inter-Arts Center. Trend specializes in visual culture, cultural studies, and critical pedagogy, teaching courses like Language and Vision in Everyday Life and Media, Art, and Technology . His research bridges media analysis, education policy, and radical democracy. He has authored over 200 articles and 16 books, including Elsewhere in America and Reading Digital Culture . As a Getty Scholar, his work explores cultural belonging, digital media impacts, and educational innovation. He advises on state arts councils and the National Endowment for the Arts. His academic contributions span visual arts education, media literacy, and the sociopolitical role of art. Education: - MFA in Photography, Visual Studies Workshop Program, SUNY Buffalo - PhD in Curriculum Theory, Miami University Research Interests: Trend's work examines cultural crises of meaning, digital media's societal impact, and the intersection of art with education and activism. He critiques gentrification's artistic drivers, explores identity formation in digital spaces, and advocates for culturally democratic pedagogical frameworks. Publications Trends: Recent works focus on gentrification's artistic dimensions ( 2022 ), creativity crises ( 2020 ), and belonging in fragmented cultures ( 2016 ). Earlier work addressed media violence myths ( 2007 ), digital literacy ( 2001 ), and radical democracy ( 1996 ). His writing spans academic critiques to practical pedagogical solutions. Awards: Getty Scholar Grants & Consulting: Extensive NEA collaborations, foundation advising, and state arts council consultations. His work emphasizes bridging educational institutions with Silicon Valley corporations through multimedia partnerships. Labs/Teams: Affiliated with UCI's University Art Galleries, Institute and Museum of California Art, and Electronic Art + Design programs.

• Cultural Studies
• Media Analysis
• Critical Pedagogy
• +۳ مورد دیگر

Professor Harry E. Ruda holds the Stanley Meek Chair in Advanced Nanotechnology at the University of Toronto's Faculty of Applied Science and Engineering. He directs the Centre for Advanced Nanotechnology and leads the Electronic-Photonic Materials Group. His research spans semiconductor nanostructures, focusing on nanoelectronics and nanophotonics, with notable contributions to nanowire growth, quantum transport, and plasmonic effects. Education: B.Sc. in Physics, Imperial College London Ph.D. in Physics, MIT His research interests include: Semiconductor nanowire growth via VLS process Quantum transport and spintronics Nonlinear optical effects in quantum dots/nanowires Surface state control through passivation Photonic crystal structures for optical devices Carrier dynamics in low-dimensional systems Recent publications highlight advancements in InAs nanowire sensors, Coulomb impurity effects, photonic crystal design, and hybrid superconducting/semiconductor devices. His work bridges fundamental electron dynamics and applied quantum technologies. Scientific Awards: Fellow, Canadian Academy of Engineering Fellow, Royal Society of Canada Fellow, Institute of Physics (UK) Fellow, Institute of Nanotechnology Fellow, Institution of Engineering and Technology Fellow, Institute of Materials, Minerals and Mining Fellow, Canadian Academy of Engineering Teaching: MSE335: Materials Physics MSE490: Professional Ethics and Practice MSE1023: Quantum Transport

• Semiconductor Nanostructures
• Nanoelectronics
• Nanophotonics
• +۵ مورد دیگر

Fei Fang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University (CMU) , where she explores the intersection of artificial intelligence and multi-agent systems . Her work integrates machine learning with game theory to address challenges in security , sustainability , and mobility , aligning with the AI for Social Good mission. Ph.D. in Computer Science, University of Southern California (2016) B.Eng. in Electronic Engineering, Tsinghua University (2011) Recent research focuses on reinforcement learning , large language models (LLMs) , and human-AI collaboration . Her team’s work has been recognized with 15+ awards across prestigious venues like IAAI, AAAI, and IJCAI. Notable accolades include the 2023 Allen Newell Award , 2022 Sloan Fellowship , and NSF CAREER Award (2021) . She actively contributes to educational initiatives , including teaching "Demystifying AI for Everyone" at CMU, and has sought part-time teaching assistants for course development. Her research spans 15+ domains , including AI ethics , cyber defense , traffic optimization , and public health .

• Artificial Intelligence
• Multi-Agent Systems
• Machine Learning
• +۵ مورد دیگر

Chris G. Van de Walle is the Herbert Kroemer Distinguished Professor in the Department of Materials at the University of California, Santa Barbara's College of Engineering. As a member of the National Academy of Engineering and fellow of multiple prestigious scientific societies including the American Physical Society and Materials Research Society, he leads the Computational Materials Group which is part of UCSB's strong computational science cluster within the Materials Department. His research interests focus on novel electronic materials, particularly wide-band-gap semiconductors (III-V nitrides, II-VI compounds), transparent conductors, complex oxides, loss mechanisms in light emitters, two-dimensional conductors, quantum information science, and the physics and chemistry of hydrogen interactions with solids. His group uses first-principles computational techniques to study atomic and electronic structure of crystalline, polycrystalline and amorphous materials, interfaces, surfaces, defects, and heterojunctions. Analysis of his recent publications reveals a strong focus on semiconductor materials for quantum technologies, with particular emphasis on GaN, Ga 2 O 3 , and related compounds. His work combines computational materials science with applications in optoelectronics, quantum information, and energy technologies, demonstrating consistent innovation in understanding defects and their role in material properties. Major Awards and Recognitions: Aneesur Rahman Prize for Computational Physics (APS) Materials Theory Award (MRS) Vannevar Bush Faculty Fellowship (DoD) John Bardeen Award (TMS) Medard W. Welch Award (AVS) Highly Cited Researcher (Clarivate Analytics) Professor Van de Walle has mentored numerous successful researchers, including Dr. Fangzhou Zhao (Corbett Prize winner) and Dr. Mark Turiansky (APS Nicholas Metropolis Award recipient). His group maintains strong connections with the UCSB Quantum Foundry and the Solid State Lighting and Energy Electronics Center, demonstrating collaborative research efforts across multiple disciplines. The group actively investigates defects for quantum information science, loss mechanisms in light emitters, nitride semiconductors, halide perovskites, oxides, and hydrogen interactions with materials.

• Computational Materials Science
• Semiconductors
• Nitride Materials
• +۷ مورد دیگر

Yochai Benkler is the Berkman Professor of Entrepreneurial Legal Studies at Harvard Law School and faculty co-director of the Berkman Klein Center for Internet and Society at Harvard University. He specializes in Law and Political Economy, Technology Law and Policy, Communications Law, and Access to Knowledge. His work bridges academia and activism, focusing on the intersection of technology, collaboration, and societal structures. Benkler's research emphasizes decentralized collaboration, information commons, and the impact of technology on democracy. He has advised governments and international organizations on innovation policy and telecommunications. His notable works include The Wealth of Networks (2006), which explores social production in the digital age, and Reconstructing Class Analysis (2024), co-authored with Talha Syed, which rethinks class dynamics within capitalism. He has received numerous awards, including the Oxford Internet Institute Lifetime Achievement Award (2012), Ford Foundation Visionaries Award (2011), and Electronic Frontier Foundation Pioneer Award (2007). His work is publicly accessible at benkler.org . Benkler serves on the boards of the Open Society Foundations and the Freedombox Foundation. His research spans law, political economy, and technology, addressing issues like fake news, network propaganda, and the role of the internet in governance.

• Law and Political Economy
• Technology Law and Policy
• Communications Law
• +۳ مورد دیگر

Liping Liu is a Professor in the Department of Management at The University of Akron's College of Business. He holds a Ph.D. in Business from the University of Kansas (1995), Master of Engineering in Systems Engineering (1991), and dual bachelor's degrees in Applied Mathematics (1986) and River Dynamics (1987). Ph.D., University of Kansas MS, Huazhong University of Science and Technology B.E., Wuhan University BS, Huazhong University of Science and Technology His research spans Artificial Intelligence , Electronic Business , Systems Analysis , Data Quality , and Belief Function Theory . He pioneered coarse utility theory and linear belief functions , now taught in top Ph.D. programs across multiple disciplines. Key trends in his publications include Belief Function Applications (2012-2024), Medical Data Systems (2003-2015), and Decision Theory (2004-2014). Recent works focus on Gamma Belief Functions (2024) and computational improvements in linear belief function operations (2019-2016). Scientific contributions recognized via: Microsoft Azure Educator Grant (2014-2016) Inclusion in Who's Who in America (2010-2013) and Who's Who in the World (2011-2013) As an editor and committee member for major conferences (INFORMS, AMCIS, Belief Functions conferences), he bridges academic research with practical systems implementation in e-business and healthcare domains.

• Uncertainty Reasoning and Decision Making in Artificial Intelligence
• Electronic Business
• Systems Analysis and Design
• +۷ مورد دیگر

Patrick Rinke serves as an Adjunct Professor in the Department of Applied Physics at Aalto University, Finland. His research bridges theoretical physics, materials science, and computational methodologies with a strong focus on machine learning applications. His computational work spans electronic structure theory, materials design, and atmospheric chemistry. Rinke's research integrates Bayesian optimization, active learning, and high-throughput computational screening to accelerate materials discovery, particularly in hybrid perovskites, catalysts, and biomaterials. Recent work demonstrates machine learning's transformative potential in predicting molecular properties, optimizing materials functionality, and solving complex physical chemistry problems. His scientific contributions have been recognized with multiple awards: Thesis Prize from the Institute of Physics (2003) DFG Research Scholarship (2007-2009) Outstanding Postdoctoral Achievement Award (2009) Outstanding Referee of Physical Review Letters (2014) August-Wilhelm Scheer Visiting Professorship (2017)

• Theoretical Physics
• Material Science
• Computational Science
• +۱ مورد دیگر

Matteo Cagnoni is a Researcher at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino . His research focuses on Density Functional Theory , Quantum Chemistry , and Thermoelectric Materials for Solar Cells . He is actively involved in the European Union’s MIRACLE project , developing photonic meta-concrete for radiative cooling solutions. Research Interests: Development of cement-based radiative coolers for solar cell thermal management Computational discovery of intermediate-band solar cell materials Electronic properties of semiconductors and insulators Teaching: Electronic transport in crystalline and organic semiconductors Advanced experimental physics Scientific Contributions: Matteo has published extensively on radiative cooling, perovskite/silicon tandem solar cells, and thermoelectric materials. His work spans journals like Nature Communications , Advanced Functional Materials , and Progress in Photovoltaics , with a focus on simulation engineering , photonic devices , and energy-efficient materials . Labs & Collaborations: He works within the Microwave and Optoelectronics Group (MOG) at DET, collaborating with international institutions on EU-funded projects.

• Density Functional Theory
• Quantum Chemistry
• Solar Cells
• +۴ مورد دیگر

William H. Matthaeus is the Martin A. Pomerantz Chair of Physics & Astronomy at the University of Delaware, where he has been a faculty member since 1983. He is affiliated with the Bartol Research Institute, housed in the H. Rodney Sharp Laboratory on the University of Delaware campus. His work focuses on space physics, plasma physics, turbulence theory, and computational physics. Dr. Matthaeus received his B.A. degree in physics and philosophy from the University of Pennsylvania in 1973, followed by a Ph.D. from William and Mary in 1979. His academic journey included a National Academy of Sciences Research Associate position from 1980-1982 before joining the University of Delaware. Matthaeus's research spans solar wind physics, space plasmas, kinetic microinstabilities, and space mission development. His work involves theoretical, computational, and observational approaches to understanding plasma turbulence and magnetic reconnection in space environments. He has made significant contributions to understanding energy transfer in magnetohydrodynamic turbulence, solar wind dynamics, and particle acceleration mechanisms. His recent publications demonstrate a continued focus on multiscale plasma phenomena, with particular attention to turbulence in the solar wind, magnetosheath, and near-Sun environments. The research utilizes data from multiple spacecraft missions including Parker Solar Probe, MMS, and HelioSwarm, combined with sophisticated numerical simulations. James Clerk Maxwell Prize in Plasma Physics (2019) from the American Physical Society James B. Macelwane Award from the American Physical Society University of Delaware College of Arts & Sciences Scholarship Award Fellow of the American Physical Society Fellow of the American Geophysical Union Fellow of the American Association for the Advancement of Science Fellow of The Institute of Physics Dr. Matthaeus serves as director of the Delaware NASA Space Grant Consortium and the Delaware NASA EPSCoR program. He is a co-investigator on several major spacecraft missions including Cluster/PEACE, the Magnetospheric Multiscale mission, the Parker Solar Probe ISOIS instruments, the Interstellar Mapping and Acceleration Probe, PUNCH and Helioswarm. For over a decade, he has organized the Arcetri Workshop on Plasma Astrophysics in Florence, Italy, fostering international collaboration in the field. His research group at the Bartol Research Institute maintains active collaborations with scientists worldwide and contributes to advancing our understanding of fundamental plasma processes that govern space weather and astrophysical phenomena.

• Space Physics
• Plasma Physics
• Turbulence Theory
• +۷ مورد دیگر

Erik B. Sudderth is a Professor of Computer Science and Statistics and Chancellor's Fellow at the University of California, Irvine (UCI). He leads the Learning, Inference, & Vision Group and directs multiple research centers, including the UCI Center for Machine Learning and Intelligent Systems and the HPI Research Center in Machine Learning and Data Science. He previously served as an Associate Professor at Brown University. Education: B.S. (summa cum laude) in Electrical Engineering from UC San Diego (1999), M.S. and Ph.D. in EECS from MIT (2002, 2006). His research focuses on statistical methods for scalable machine learning, Bayesian nonparametrics, probabilistic graphical models, and applications in computer vision, AI, and environmental science. Key areas include nonparametric clustering, deep generative models, and particle-based inference algorithms. Research interests span diverse topics: advancing Bayesian nonparametric models for medical time series, scalable variational inference, and AI ethics. Notable contributions include the NET-VISA seismic monitoring system (ISBA Mitchell Prize, 2014), the BNPy toolbox (NSF CAREER Award), and work on diverse particle max-product algorithms for continuous inference. Scientific awards include the NSF CAREER Award, ISBA Mitchell Prize, and recognition as one of "AI's 10 to Watch" (IEEE). He has served as editor for top journals (JMLR, IEEE PAMI) and conference chairs (NeurIPS, CVPR). His work bridges theory and practice, with applications in robotics, climate science, and healthcare. Labs/Teams: UCI Learning, Inference, & Vision Group; UCI Center for Machine Learning; CREATE Technology Center. Grants include NSF funding for visually impaired collaboration tools and soil biogeochemical modeling.

• Machine Learning
• Computer Vision
• Statistical Inference
• +۳ مورد دیگر