جستجوی استاد راهنمای دانشگاهی

Yu Meng is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), part of the School of Engineering and Applied Science. He joined UVA in 2024 as a tenure-track faculty member. His research focuses on machine learning, natural language processing (NLP), and data mining, with recent emphasis on large language models (LLMs), alignment, reliability, and ethical AI development. Educated at the University of Illinois Urbana-Champaign (UIUC), Meng earned his Ph.D. in 2023 under advisor Jiawei Han. His doctoral thesis, Efficient and Effective Learning of Text Representations , received the ACM SIGKDD 2024 Dissertation Award. He also held a visiting researcher position at Princeton University under Danqi Chen and was a Google PhD Fellow. His work has been recognized with awards including the Superalignment Fast Grant from OpenAI and notable publications at venues like NeurIPS, ICLR, and ACL. Meng’s research explores topics such as preference optimization (SimPO), retrieval-augmented generation (InstructRAG), and zero-shot learning. He actively serves on program committees for top conferences (ICLR, ICML, NeurIPS) and as an action editor for Transactions of Machine Learning Research (TMLR) . He teaches graduate-level courses on NLP, emphasizing cutting-edge LLM topics like architecture design, instruction tuning, and ethical considerations. Key achievements include contributions to LLM alignment via retrieval optimization, efficient pretraining techniques, and foundational work on weakly supervised learning. His research bridges theory and practice, addressing both technical challenges and societal impacts of AI systems.

• Machine Learning
• Natural Language Processing
• Data Mining
• +۳ مورد دیگر

Diana Tamir is a Professor at Princeton University, where she directs the Princeton Social Neuroscience Lab. She earned her Ph.D. from Harvard University and specializes in the intersection of internal mental processes and external social cognition. Her research investigates: How minds predict others' emotions and mental states The cognitive consequences of self-disclosure and social media use Neural mechanisms of social prediction using fMRI and machine learning Effects of fiction reading on theory of mind Dynamics of spontaneous thought and social bonding Her recent publications (2024-2025) demonstrate strong focus on emotion prediction mechanisms, social interaction dynamics, neural signatures of psychological states, and developmental aspects of social cognition. Methodologically, she employs neuroimaging hyperscanning, ecological momentary assessment, and computational modeling across diverse populations. She currently advises graduate students including Faustine Corbani and Yeaju Diana Kim. Her lab focuses on empirical approaches to understanding how individuals navigate between internal experiences and external social environments.

• Social Neuroscience
• Cognitive Psychology
• Social Cognition
• +۳ مورد دیگر

Hassan Foroosh is a Professor in the Department of Electrical Engineering and Computer Science at the University of Central Florida (UCF), directing the Computational Imaging Laboratory (CIL). He holds a Ph.D. in Computer Science from INRIA-UNSA, France (1996). Prior to UCF, he worked as a Senior Research Scientist at UC Berkeley (2000–2002) and an Assistant Research Professor at the University of Maryland, College Park (1997–2000). Research Interests: His work focuses on Computer Vision, Image Processing, Machine Learning, and Signal Processing. Notable contributions include LiDAR-based perception, adversarial attacks on detectors, medical imaging analysis, and dataset design for action recognition. His research is supported by NASA, NSF, ONR, and industry partners. Publications & Impact: Over 130 peer-reviewed papers, including influential work on super-resolution techniques, transformer networks for 3D object detection, and adversarial machine learning. His recent work explores analytical reasoning in LLMs and multimodal fusion in sports analytics. Awards: Pierro Zamperoni Award (2004), Best ICPR Paper (2004), Sun Microsystems Academic Excellence Award (2004). Labs/Teams: Director of the Computational Imaging Lab (CIL), UCF. Grants: Active funding from NASA, NSF, and industry collaborators.

• Computer Vision
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Pengfei Wang is an Assistant Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU). He holds a Ph.D. in Geotechnical Engineering and an M.S. in Statistics from UCLA, alongside a B.S. in Transportation Engineering from Tongji University. Prior to ODU, he conducted postdoctoral research at UCLA. His expertise focuses on Geotechnical Engineering , Engineering Seismology , and Applied Statistics , with emphasis on regional geo-hazard modeling, multi-hazards risk assessment, and statistical learning applications. Key research interests include seismic site response analysis, liquefaction susceptibility, and probabilistic risk frameworks for infrastructure resilience. Dr. Wang’s work integrates geospatial analysis and statistical methodologies to address challenges in earthquake engineering. He has developed frameworks for regional landslide and liquefaction risk assessments, particularly in vulnerable regions like California’s Sacramento-San Joaquin Delta. His contributions include advancing HVSR (Horizontal-to-Vertical Spectral Ratio) methodologies and ergodic site response modeling. He maintains active collaborations with institutions globally and contributes to open-source databases for seismic data, promoting transparency and reproducibility in geotechnical research. His educational background in transportation engineering enriches interdisciplinary approaches to civil infrastructure resilience.

• Geotechnical Engineering
• Engineering Seismology
• Applied Statistics
• +۴ مورد دیگر

Stuart Kurtz is a Professor in Computer Science and the College at the University of Chicago, and serves as Master of the Physical Sciences Collegiate Division. He holds the endowed position of George and Elizabeth Yovovich Professor. His research focuses on theoretical computer science, including computational complexity theory, randomness in computation, type theory, and formal logic. He has contributed to foundational areas such as the Berman-Hartmanis Isomorphism Conjecture and the computational properties of random sets. Kurtz is affiliated with the Theoretical Computer Science and Programming Languages Groups at the University of Chicago. He has been recognized with the 2009 Quantrell Award for teaching excellence. His service roles include Director of Undergraduate Studies and Department Chair in Computer Science. He actively mentors Ph.D. students and has advised multiple graduates in complexity theory and related fields. His academic background includes a Mathematics Ph.D. from the University of Illinois, supervised by Carl Jockusch. He approaches type theory as an intersection of formal logic and functional programming. Research interests span measure-theoretic randomness, computational logic, and complexity class separations. His recent work explores connections between theoretical computer science and interdisciplinary fields like physics and statistics. In teaching, Kurtz has instructed courses such as Formal Language Theory, Discrete Mathematics, and Honors Intro Programming. His service contributions include roles in the Computation Institute and Toyota Technological Institute at Chicago. His lab affiliations and collaborative work reflect a strong commitment to advancing theoretical foundations in computer science.

• Theoretical Computer Science
• Computational Complexity Theory
• Type Theory
• +۴ مورد دیگر

Debbie Senesky is an Associate Professor at Stanford University in both the Aeronautics and Astronautics Department and the Electrical Engineering Department, as well as a Senior Fellow at the Precourt Institute for Energy. She serves as the Principal Investigator of the EXtreme Environment Microsystems Laboratory (XLab) and Site Director of nano@stanford. Dr. Senesky received her B.S. in mechanical engineering from the University of Southern California (2001), followed by M.S. (2004) and Ph.D. (2007) degrees in mechanical engineering from the University of California, Berkeley. Prior to joining Stanford, she held positions at GE Sensing (formerly NovaSensor), GE Global Research Center, and Hewlett Packard. Her research focuses on developing nanomaterials and electronic systems capable of operating in extreme environments, including high-temperature conditions for Venus exploration, microgravity synthesis of nanomaterials, and harsh environment electronics. Dr. Senesky's work bridges multiple disciplines, connecting aerospace engineering, electrical engineering, materials science, and space technology to solve challenges in extreme environment applications. Dr. Senesky has made significant contributions to the field of high-temperature electronics, GaN-based sensors, graphene aerogel synthesis in microgravity, and materials for space applications. Her recent publications demonstrate a strong focus on practical applications of these technologies, particularly for space exploration and extreme environment sensing. Presidential Early Career Award for Scientists and Engineers (PECASE), NASA (2025) Emerging Leader Abie Award from AnitaB.org (2018) Early Faculty Career Award from NASA (2012) Gabilan Faculty Fellowship Award (2012) Sloan Ph.D. Fellowship (2004-2006) Dr. Senesky actively advises students at all levels, from undergraduate to postdoctoral researchers, and has established herself as a leader in promoting diversity in STEM through her role as Faculty Advisor for the Stanford Chapter of the National Society of Women Engineers. Her collaborative approach is evident in her numerous interdisciplinary projects and partnerships with NASA, industry, and other research institutions. She directs the EXtreme Environment Microsystems Laboratory (XLab), which focuses on developing technologies for operation in extreme environments including high temperature, radiation, and microgravity conditions. The lab's work has applications for space exploration, particularly for Venus missions, as well as terrestrial applications requiring robust electronics.

• Nanomaterials for Extreme Environments
• High-Temperature Electronics
• Venus Exploration Technology
• +۵ مورد دیگر

Alex Chortos is an Assistant Professor of Mechanical Engineering at Purdue University's School of Mechanical Engineering. His research focuses on bio-inspired electronics, mechanically adaptive materials, and advanced manufacturing techniques. He leads the Chortos Lab, which explores innovations in soft actuators, wearable haptics, and polymer design. Chortos holds a B.A.Sc. from the University of Waterloo (2011), a Ph.D. from Stanford University (2017), and completed a postdoctoral fellowship at Harvard University (2020). His academic work bridges fundamental material science with practical applications in robotics, biomedical devices, and human-machine interfaces. Key research areas include: Multimaterial additive fabrication for soft robotics Stretchable sensors and transistors for e-skin applications Design of durable and adaptive polymer systems His publications emphasize advancements in 3D printing techniques, bioinspired sensor systems, and the development of mechanically robust electronic components. Recent work explores photodynamic polymers and machine learning-driven optimization of soft actuators.

• Bio-inspired electronics
• Mechanically adaptive electronics
• Multimaterial additive fabrication
• +۹ مورد دیگر

Bo Lu is a Professor of Biostatistics and Statistics at The Ohio State University. His research focuses on advancing early detection technologies for forest pathogens and pests using spectroscopy, machine learning, and metabolomics. He specializes in understanding plant defense mechanisms against invasive species and climate-driven disease dynamics. Key research areas include: Non-invasive disease diagnostics via FT-IR/Raman spectroscopy Machine learning applications in plant health monitoring Molecular basis of plant-pathogen interactions Environmental risk modeling for emerging forest diseases Notable projects include developing early detection systems for ash dieback, beech leaf disease, and soybean cyst nematode infestations. His work integrates statistical modeling with biochemical analysis to improve forest biosecurity strategies globally. He advocates for international collaboration in addressing the global forest health crisis through improved diagnostic frameworks and resistance breeding programs.

• Plant Pathology
• Forest Health Management
• Environmental Risk Modeling
• +۳ مورد دیگر

Professor Michael Keidar holds the A. James Clark Professorship at the George Washington University (GW) , School of Engineering and Applied Science, within the Mechanical and Aerospace Engineering department. He leads the Micropropulsion and Nanotechnology Lab , pioneering research in plasma medicine, micropropulsion systems, and plasma nanoscience. His lab collaborates with industry partners like Vector (licensed plasma thruster technology) and US Patent Innovations, LLC (a $5.3M grant for cold plasma cancer therapy). Key research areas include: Cold plasma applications in biomedical treatment Microthrusters for nanosatellites Synthesis of graphene and carbon nanotubes Multi-scale plasma simulations Scientific accolades include the 2017 Ronald C. Davidson Award and AIAA Engineer of the Year (2016-2017), alongside leadership in interdisciplinary projects with GW’s Global Food Institute .

• Micropropulsion
• Nanotechnology
• Plasma Medicine
• +۸ مورد دیگر

Babak Moaveni is a Professor in the Department of Civil and Environmental Engineering at Tufts University, serving as the Associate Chair since September 2024. He also holds a joint appointment as a Professor in Electrical and Computer Engineering. His research focuses on structural health monitoring, Bayesian inference, earthquake engineering, and offshore wind energy systems. Moaveni earned his Ph.D. in Structural Engineering from the University of California San Diego (2007), following an M.S. (2001) and B.S. (1999) from Sharif University of Technology in Tehran, Iran. His research interests span probabilistic system identification, signal processing, uncertainty quantification, and verification/validation of computational models. Notable grants include leadership in the PIRE project on offshore wind energy digital twins and the Coastal Virginia Offshore Wind Pilot Project. He has supervised multiple Ph.D. and M.S. students, with current advisees including Mehdi Akhlaghi and Nasim Partovi-Mehr. Moaveni has received the Best Presentation Award at the 2022 EDGE Symposium and serves on editorial boards for journals like Structural Health Monitoring and Frontiers in Built Environment . His lab, the Structural Health Monitoring Lab, specializes in infrastructure management and offshore wind energy systems. Key professional activities include membership in the American Society of Civil Engineers (ASCE) and roles on Tufts' Tenure and Promotion Committee. His teaching includes courses on structural health monitoring, numerical methods, and structural reliability.

• Structural Health Monitoring
• Bayesian Inference
• Earthquake Engineering
• +۷ مورد دیگر

Clifford Cheung is a Professor of Theoretical Physics at the California Institute of Technology (Caltech). He is affiliated with the Department of Theoretical Physics within the Division of Physics, Mathematics and Astronomy. His research focuses on fundamental questions in quantum gravity, scattering amplitudes, string theory, and effective field theories, with particular emphasis on bootstrap principles, black hole dynamics, and gravitational wave physics. Cheung's work bridges high-energy physics, astrophysics, and mathematical physics. He has pioneered methods to reconstruct scattering amplitudes using symmetry principles and positivity bounds, contributing significantly to our understanding of string theory's uniqueness and the interplay between gravitational systems and gauge theories. His research also explores the implications of effective field theories for extreme mass ratio binaries and gravitational wave phenomena. Key themes in his work include the development of novel symmetry-based approaches to quantum gravity, the application of bootstrap methods to constrain fundamental theories, and the exploration of connections between scattering amplitudes and black hole thermodynamics. His findings have advanced our knowledge of gravitational interactions, string universality, and the foundational structure of spacetime. Cheung has been featured in Caltech news for his contributions to theoretical physics, including insights into the 'miracle and beauty of physics' and his work on the Sloan Research Fellowships. His research outputs consistently address cutting-edge topics in theoretical physics, with a focus on unifying principles and rigorous mathematical frameworks.

• Theoretical Physics
• Quantum Gravity
• Scattering Amplitudes
• +۵ مورد دیگر

Helen Suh is a Professor at Tufts University, jointly appointed in the departments of Civil and Environmental Engineering and Community Health . As an internationally-recognized expert in air pollution health effects, she combines environmental epidemiology , exposure science , and data analytics to investigate how pollutants impact human health. Sc.D. , Harvard University (1993) M.S. , Harvard University (1990) S.B. , Massachusetts Institute of Technology (1985) Her research focuses on three areas: air pollutant impacts on cognitive performance and child development , multi-pollutant health effects , and GIS-based spatio-temporal modeling for epidemiological studies. Recent publications highlight her work on PM2.5 measurement error correction , hormonal disruptions in pregnancy , and machine learning applications in environmental health analysis. Current trends include: Advanced statistical methods for exposure assessment Multi-omics approaches to cardiometabolic health International comparative studies (e.g., Tehran, Puerto Rico) Long-term mortality analysis in Medicare populations Pollution-immune system interactions in vulnerable groups Policy-relevant modeling for air quality standards Helen Suh has served as an Associate Editor for the Journal of Exposure Science and Environmental Epidemiology and advised major U.S. and international health organizations. Her work spans over 150 publications and integrates multidisciplinary team leadership in environmental health science. Her laboratory develops large-scale data analytics tools and spatio-temporal exposure models to support population-level health research. Current projects include air pollution and aging cohorts , urban environmental noise measurement , and epigenetic responses to pollutants .

• Environmental Health
• Environmental Epidemiology
• Air Pollution
• +۲ مورد دیگر

Margaret Burnett is a Distinguished Professor at Oregon State University's School of Electrical Engineering and Computer Science (EECS). She specializes in software engineering, human-computer interaction (HCI), and inclusive design. Her research focuses on end-user programming, gender-inclusive software (via the GenderMag method), and improving accessibility in AI systems. She leads the EUSES Consortium and the AgAID Institute, fostering collaboration between academia and industry. Education: Ph.D., Computer Science (with honors), University of Kansas (1987–1991) M.S., Computer Science, University of Kansas (1979–1981) B.A. Mathematics, Cum Laude, Phi Beta Kappa, Miami University (1967–1970) Research & Awards: Recipient of the 2023 AnitaB.org Technical Leadership Abie Award, ACM Fellow, IEEE Fellow, and numerous university awards. Her work has been recognized for advancing inclusive design methodologies and mentoring students in computing. Teaching & Mentorship: Teaches courses like Inclusive Design with Personas (CS 468/568). Mentored over 50 graduate students, many of whom became professors, researchers, or UX professionals. Current students include Sadia Afroz, Alec Busteed, and Fatima Moussaoui. Labs & Collaborations: Leads the EUSES Consortium (multi-institution collaboration on end-user software engineering) and the AgAID Institute (AI for agriculture). Active in developing the GenderMag and InclusiveMag methodologies to address gender and socioeconomic biases in software.

• Software Engineering
• Human-Computer Interaction (HCI)
• End-User Programming
• +۵ مورد دیگر

Hubert Wagner is an Assistant Professor in Data Science at the University of Florida's Department of Mathematics, part of the College of Liberal Arts and Sciences. He teaches courses such as Computational Applied Topology and Linear Algebra for Data Science. Prior to joining UF, he completed a postdoctoral fellowship at IST Austria under Herbert Edelsbrunner and earned his PhD from Jagiellonian University under Marian Mrozek. His research focuses on developing topological algorithms and tools for practical applications in fields like astrophysics and biomedicine. Notably, he received the 2022 Google Research Scholar Award in Algorithms & Optimization for his work on Bregman divergences and topological methods in high-dimensional data analysis. His research interests span computational geometry, topological data analysis, machine learning, and algorithm engineering. Recent projects include optimizing topological computations for large-scale imaging data (e.g., cosmic microwave background analysis) and detecting adversarial attacks on neural networks using persistent homology. He emphasizes practical applications through collaborations with industry and interdisciplinary research. Hubert is actively involved in academic service, including course development and mentoring. His work has been published in leading venues such as SoCG and NeurIPS, with a focus on bridging theoretical foundations and real-world computational challenges.

• Computational Geometry
• Topological Data Analysis
• Machine Learning
• +۳ مورد دیگر

Phiala E. Shanahan is the Class of 1957 Career Development Associate Professor of Physics at the Massachusetts Institute of Technology (MIT). Her research focuses on theoretical nuclear and particle physics, particularly the structure of hadrons and nuclei from QCD. She integrates machine learning to overcome computational challenges in QCD studies, pioneering techniques for lattice gauge theory simulations. Affiliated with MIT's Center for Theoretical Physics, Laboratory for Nuclear Science, and the NSF AI Institute for Fundamental Interactions (IAIFI), she also collaborates with Jefferson Lab and the Electron-Ion Collider project. Education: BSc (2012) and PhD (2015) from the University of Adelaide. Career: Postdoctoral Associate at MIT (2015–2017), then joint position as Assistant Professor at College of William & Mary and Senior Staff Scientist at Jefferson Lab (2017–2018) before joining MIT in 2018. Research Interests: Gluon structure in nuclei, strange quarks in protons/nuclei, and machine learning applications. Her work predicts gluon distributions testable at Jefferson Lab and the Electron-Ion Collider, with implications for dark matter detection via precision calculations. She also explores nuclear forces and symmetry-breaking effects in QCD. Awards: 2023 South Australian Woman of the Year, 2022 Ruby Payne-Scott Medal, 2021 Maria Goeppert Mayer Award (APS), 2020 Kenneth G. Wilson Award, 2018 NSF CAREER Award, 2016 Bragg Gold Medal. Grants & Labs: DOE Early Career Award (2020), IAIFI affiliate, MIT Center for Theoretical Physics. Active in public engagement, including a Perimeter Institute lecture on 'The Building Blocks of the Universe.'

• Particle Physics
• Nuclear Theory
• Quantum Chromodynamics (QCD)
• +۳ مورد دیگر