جست‌وجوی استاد و پژوهشگر

Shantanu Jadhavمشاهده پروفایل

استاد

• Learning and Memory
• Decision Making
• Neurophysiology
• +۵ مورد دیگر

Shantanu Jadhav is a Professor of Psychology at Brandeis University, with affiliations to the Neuroscience Program, the Volen National Center for Complex Systems, and the Sloan-Swartz Center for Theoretical Neurobiology. He earned his B.Tech. from IIT Bombay, Ph.D. from UC San Diego, and completed postdoctoral training at UCSF and UC Berkeley. Research Focus: Neural mechanisms of learning, memory, and decision-making in rodent models Investigation of hippocampal-prefrontal interactions via multielectrode recordings, optogenetics, and computational analysis Role of neural oscillations (theta, gamma, sharp-wave ripples) in memory consolidation and cognitive flexibility Implications for neurological disorders like Alzheimer’s, autism, and schizophrenia Recent work highlights the coordination of dopamine activity with rule representations in the prefrontal cortex and hippocampus, the role of prefrontal ripples in suppressing hippocampal reactivation during sleep, and geometric transformations in cognitive maps enabling cross-environment generalization. His lab has shown that awake sharp-wave ripples are critical for spatial memory and that cross-region neural synchronization underpins memory-guided decisions. Scientific Recognition: Peter and Patricia Gruber International Research Award (2013) Sloan Research Fellow (2015-2017) NARSAD Young Investigator Award (2015-2018) Whitehall Foundation Award (2016-2020) SFARI Core Member (2022-2025) The Jadhav Lab at Brandeis trains postdoctoral fellows, graduate students (via the Neuroscience and Psychology Graduate Programs), and research assistants. Current studies explore hippocampal-prefrontal network dynamics, dopamine signaling in cognitive flexibility, and geometric representations in memory abstraction.

John Altinمشاهده پروفایل

دانشیار

• Adaptive immunity
• Genomics
• Biotechnology
• +۳ مورد دیگر

John Altin is an Associate Professor at TGen's Immunology and Microbial Genomics Division, conducting cross-disciplinary research at the intersection of adaptive immunity, genomics, and biotechnology. His work focuses on high-dimensional immune profiling using advanced platforms like PepSeq and MHC-PepSeq for studying immune responses across $10M+ research programs . Key contributions include understanding T cell regulation pathways and developing serological tools for infection tracking. Immunology and Microbial Genomics Division, TGen Co-developer of PepSeq platform Recipient of >$10M research funding His research spans infectious disease immunology (SARS-CoV-2, tuberculosis, malaria), autoimmune mechanisms (type 1 diabetes), and biotechnological innovations in antibody/TCR analysis. Publications demonstrate cross-reactive immunity patterns and high-dimensional immune profiling across human and animal populations. Laboratory members include Sophia Carvalho (PhD), Heather Mead (PhD), Jorge Soria-Bustos (PhD), and Erin Kelley (MS). Collaborations extend to Prognosys Biosciences, SpaceX research, and Arizona clinical institutions. Current work emphasizes longitudinal infection tracking and vaccine response dynamics in immunocompromised populations.

Olga Kurasovaمشاهده پروفایل

استاد

• Machine Learning
• Data Visualization
• Cybersecurity
• +۳ مورد دیگر

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.

Pasquale Scarlinoمشاهده پروفایل

استادیار

• Quantum Computing
• Hybrid Quantum Circuits
• Semiconductor-Superconductor Interfaces
• +۶ مورد دیگر

Pasquale Scarlino is a Tenure Track Assistant Professor in the Institute of Physics at École Polytechnique Fédérale de Lausanne (EPFL), where he founded and leads the Hybrid Quantum Circuits (HQC) Laboratory. He holds a dual appointment with the School of Basic Sciences (SB) and the Physics Section (SB-SPH), conducting research at the intersection of semiconductor and superconducting quantum technologies. His laboratory develops hybrid quantum hardware for advanced quantum information processing. His educational background includes a Master's degree in Physics from the University of Salento (Italy, 2011), where he was a student of Scuola Superiore ISUFI, followed by a Ph.D. from TU Delft (2016) in the Spin Qubits group of Prof. L.M.K. Vandersypen at the Kavli Institute of Nanoscience-Qutech. His doctoral work focused on Si/SiGe spin qubits in collaboration with the M. Eriksson Group at Wisconsin University. Scarlino's research centers on experimental quantum physics using hybrid superconductor/semiconductor devices with electrostatically defined quantum dots coupled to high-impedance microwave resonators. He investigates light-matter interactions in unconventional regimes, quantum transport in low-dimensional systems, and spin/charge qubit implementations. His work aims to merge semiconductor and superconducting platforms to expand quantum information capabilities, with applications in quantum computing, quantum optics, and analog quantum simulation. Early career achievements include establishing the first coherent interface between superconducting and semiconducting quantum systems using high-impedance resonators. His publication record shows strong focus on microwave photon-mediated interactions between quantum systems, with recent work exploring quantum acoustics, topological band engineering, and criticality-enhanced sensing. The articles demonstrate increasing specialization in hybrid quantum hardware, with a shift toward germanium-based systems and advanced resonator designs in the latest publications. Scarlino has advised eleven Ph.D. students at EPFL and teaches courses including General Physics (Electromagnetism), Solid State Systems for Quantum Information, and Introduction to Quantum Science and Technology. His teaching emphasizes experimental quantum hardware approaches and critical assessment of quantum computing platforms. The Hybrid Quantum Circuits Laboratory operates within EPFL's Institute of Physics, utilizing state-of-the-art nanofabrication facilities and cryogenic measurement setups. The team collaborates extensively with leading quantum research groups worldwide, maintaining strong ties with previous institutions including ETH Zurich, TU Delft, and Microsoft Station Q Copenhagen.

Yonathan Shapirمشاهده پروفایل

استاد

• Theoretical Condensed-Matter Physics
• Statistical Mechanics
• Disordered Systems
• +۶ مورد دیگر

Professor Yonathan Shapir is a full Professor in the Department of Physics at the University of Michigan, with a joint appointment in Chemical Engineering. Since his arrival in 1985, he has advanced from Assistant Professor to full Professor, pursuing theoretical condensed-matter physics and statistical mechanics. Education: B.Sc. in Physics, Tel-Aviv University (1971) Ph.D. in Physics, Tel-Aviv University (1981) Research Interests: Professor Shapir’s work centers on understanding complex disordered systems through statistical mechanics. His investigations encompass critical phenomena in spin-glasses and random-field systems, classical and quantum transport in dirty metals leading to the metal-insulator transition, polymer statistics, fractal properties of percolation clusters, and kinetic models of surface growth and aggregation. These themes are unified by a deep interest in scaling laws, universality, and the emergent geometry of random media. Publication Trends: Between 2001 and 2010, his articles reveal a concentrated effort on diffusion in dynamically disordered environments, scaling behavior of growing surfaces, and the morphology of organic thin films such as pentacene. Earlier work (1996–2000) explored critical dynamics of dendrimers, surface growth on disordered substrates, and transitions in crystalline roughness. The breadth spans from fundamental statistical-physics questions to applications in materials science and organic electronics. Scientific Awards: Fulbright Award (1981) Bourse Joliot-Curie (1982) Visiting Compton Fellowship at the Technion (1989–1990) Advising & Grants: No explicit lists of students or current funding are provided in the material; however, his extensive publication record with numerous co-authors suggests active mentoring and collaboration. Future directions likely continue along the interface of statistical mechanics and nanoscale materials. Labs & Teams: While no specific laboratory names are given, Professor Shapir’s joint appointment implies active participation in both the Physics Department and the Chemical Engineering program, fostering interdisciplinary teams working on materials growth, transport phenomena, and computational statistical physics.

• Computational stochastic mechanics
• Machine learning in engineering
• Engineering safety assessment
• +۷ مورد دیگر

Dr Dee Wu serves as a Senior Lecturer at the School of Civil and Environmental Engineering at the University of Technology Sydney (UTS), specializing in the integration of computational mechanics, machine learning, and engineering design. With a strong research profile focused on structural reliability and safety assessment, Dr Wu develops innovative frameworks that bridge theoretical mechanics with practical engineering applications, particularly in the realm of composite materials and uncertain structural behavior. Dr Wu's research interests center on computational stochastic and non-stochastic mechanics, with particular emphasis on machine-learning-aided engineering safety assessment, nondeterministic methods for isogeometric analysis with polymorphic uncertainties, and AI techniques for composite material design. Their work addresses critical challenges in structural engineering where uncertainty quantification becomes essential for safety evaluation. The research output reveals a clear trajectory toward developing virtual modeling techniques that significantly enhance computational efficiency while maintaining accuracy in structural analysis. Dr Wu's publications demonstrate expertise in phase-field methods, support vector regression variants (including Extended SVR, Capped SVR, and Twin SVR), and uncertainty quantification frameworks that handle both aleatoric and epistemic uncertainties. These techniques have been successfully applied to fracture mechanics, buckling analysis, vibration analysis, and impact assessment problems. Dr Wu actively pursues funded research in three main areas: Digital twin applications in Civil Engineering, Machine learning aided engineering analysis and design, and Safety assessment for Smart City initiatives. Currently, they are a key participant in the ARC Discovery Project 'Assessment of Dynamic Pile Driving Using Machine Learning' (DP230102781), running from June 2023 to May 2026, working alongside researchers Khabbaz M, Fatahi B, and Zhang X. In teaching, Dr Wu delivers courses including Introduction to Civil and Environmental Engineering (48310), Advanced Engineering Computing (48371), and Finite Element Analysis (49047), demonstrating commitment to both foundational and advanced engineering education. Their ORCID identifier is 0000-0002-7284-5024, and they maintain an active Google Scholar profile reflecting their substantial research contributions in computational structural engineering.

Tony Stillfjordمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +۴ مورد دیگر

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net

Scott Anthony Sissonمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Statistics
• Statistical Extreme Value Theory
• +۳ مورد دیگر

Professor Scott Anthony Sisson is a leading academic at the University of New South Wales (UNSW) , holding the position of Professor of Statistics and Data Science in the School of Mathematics and Statistics . He serves as Director of the UNSW Data Science Hub (uDASH) and Deputy Director of the UNSW AI Institute (UNSW.ai) . Previously, he was Deputy Director of the Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and held leadership roles in the Australasian Society of Bayesian Analysis and Statistical Society of Australia . PhD in Statistics (Bristol University, 2002) MSc in Environmental Statistics and Systems (Lancaster University, 1997) BSc in Mathematics and Statistics (Lancaster University, 1996) His research focuses on computational statistics and Bayesian inference , with expertise in machine learning , extreme value theory , and high-dimensional data analysis . He develops simulation-based algorithms for complex statistical problems and applies these to diverse scientific challenges like seagrass decline, urban flood modeling, and drug delivery systems. His recent work spans quantum computing for statistics, graphon modeling, and synthetic likelihood methods. Scientific awards include: 2024 Fellow of the International Society of Bayesian Analysis 2023 Fellow of the Institute of Mathematical Statistics 2017 ARC Future Fellowship 2010 Queen Elizabeth II Research Fellowship 2006 John Yu Fellowship His advising team has mentored students in statistical modeling, Bayesian computation, and applied data science. Grants from the Australian Research Council and industry collaborations support his research in government and scientific applications. He contributes as Associate Editor for Journal of Computational and Graphical Statistics and Statistics and Computing .

Rameshwar Pratapمشاهده پروفایل

دانشیار

• Algorithms in Machine Learning
• Theoretical Computer Science
• Sketching and Dimensionality Reduction
• +۵ مورد دیگر

Rameshwar Pratap is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). Previously, he served as an Assistant Professor at the School of Computing and Electrical Engineering, IIT Mandi for three years. Education: Ph.D. in Theoretical Computer Science, Chennai Mathematical Institute Research Interests: His research lies at the intersection of theory and practice, focusing on extremely simple yet practical approximation algorithms with provable guarantees . Key themes include: Sketching and dimensionality reduction algorithms for tensors and similarity measures Improving speed, scalability, and accuracy of existing sketching methods Applications in machine learning: node embedding in large-scale networks, itemset mining, model compression He extensively employs techniques from matrix and tensor algebra, sampling, random projection, and randomized hashing . Publications Overview: Across 2021–2025 his work has appeared in top venues such as IEEE Globecom, Theoretical Computer Science, Acta Informatica, Information Processing Letters, Algorithmica, UAI, ICALP, Machine Learning, TKDE, and ACML. Recurring themes are randomized sketching, locality-sensitive hashing, compressed matrix multiplication, variance reduction, and subspace approximation , demonstrating both theoretical depth and practical impact. Awards & Honors: Early Career Research Grant (PM-ECRG) 2025, Anusandhan National Research Foundation (ANRF) Best Paper Award, COCOON 2020 Students & Funding: First Ph.D. student: Bhisham Dev Verma (co-advised with Prof. Manoj Thakur) graduated June 2025 MS by Research student: Punit Pankaj Dubey graduated October 2022 Currently hiring 1 Junior Research Fellow for the PM-ECRG project “Improving Similarity Search in Practice” Labs & Teams: Works within the Algorithms & Theory group at IIT Hyderabad, collaborating with national and international researchers. Erdös number is 3.

• Biostatistics
• Cancer Genomics
• Statistical Methods for High-Dimensional Data
• +۴ مورد دیگر

Yuan Huang is an Assistant Professor in the Department of Biostatistics at the Yale School of Public Health . Her research focuses on statistical methods for high-dimensional data, motivated by challenges in cancer genomics and neurodegenerative diseases. She develops approaches for biomarker identification, network structure estimation, and gene-environment interaction analysis, with applications in Alzheimer’s, Huntington’s, and Parkinson’s diseases. Key Affiliations: Yale Cancer Center, Center for Brain & Mind Health, Yale Center for Analytical Sciences (YCAS) Her methodological work emphasizes integrative analysis across multiple datasets to improve reproducibility and discovery. Recent collaborations span clinical trials, genetics, and epidemiology, with a focus on addressing heterogeneity and nonlinearity in complex biomedical data. Her publications include Bayesian finite mixture models, precision matrix estimation, and advanced techniques for high-dimensional causal mediation analysis. She actively engages in translational research, linking statistical innovation to clinical and public health challenges.

• Quantum Monte Carlo methods
• Quantum many-body systems
• Electronic structure of condensed matter
• +۵ مورد دیگر

David M. Ceperley is a Founder Professor in Physics and Research Professor at the University of Illinois at Urbana-Champaign, where he has been a faculty member since 1987. He maintains his office in the Engineering Sciences Building and is affiliated with the Department of Physics within the College of Engineering. His distinguished career has established him as a leading authority in computational quantum physics. Professor Ceperley received his BS in physics from the University of Michigan in 1971 and his Ph.D. in theoretical physics from Cornell University in 1976. Following postdoctoral appointments at the University of Paris and Rutgers University, he worked as a staff scientist at both Lawrence Berkeley and Lawrence Livermore National Laboratories before joining the UIUC faculty. From 1987 until 2012, he also served as a staff scientist at the National Center for Supercomputing Applications. Ceperley's research focuses on developing and applying quantum Monte Carlo methods to study quantum many-body systems. His most significant contribution is his calculation of the energy of the electron gas, which provides fundamental input for electronic structure calculations. He pioneered path integral Monte Carlo methods for quantum systems at finite temperature, particularly for superfluid helium and hydrogen under extreme conditions. His current research encompasses electron fluids, metalization of hydrogen at high pressure, temperature-dependent simulations of solids and liquids, and cold atom systems. Analysis of Ceperley's publication record reveals a consistent trajectory from foundational method development to increasingly complex applications in condensed matter physics. His recent work demonstrates a strong emphasis on high-pressure physics, particularly the behavior of hydrogen and related systems under extreme conditions. The integration of quantum Monte Carlo with other computational approaches represents a significant evolution in his research methodology. B. J. Alder CECAM Prize (2016) Member International Academy of Quantum Molecular Sciences (2013) Blue Waters Professor (2014) Center for Advanced Studies Professor (2009) Founder Professor of Engineering (2006) National Academy of Sciences (2005) Fellow, American Academy of Arts & Sciences (1999) Rahman Prize in Computational Physics (1998) Feenberg Medal (1994) Professor Ceperley has mentored numerous graduate students and postdoctoral researchers throughout his career, contributing significantly to the training of computational physicists. His research has been consistently supported by major funding agencies including the National Science Foundation and Department of Energy. He has taught courses including MSE 485 (Atomic Scale Simulations) and PHYS 460 (Condensed Matter Physics), demonstrating his commitment to education alongside research. His work has positioned him as a leader in computational quantum physics, developing methods that can find exact properties of many-body systems and apply them to diverse materials. His research group continues to advance computational techniques for studying materials under extreme conditions, with particular emphasis on high-pressure hydrogen physics and quantum phase transitions.

Laurent Bellaicheمشاهده پروفایل

استاد

• Computational Condensed Matter Physics
• Ferroelectrics
• Multiferroics
• +۷ مورد دیگر

Laurent Bellaiche is a Distinguished Professor in the Department of Physics within the College of Arts and Sciences at the University of Arkansas. His research focuses on computational condensed matter physics with emphasis on ferroelectrics, multiferroics, and semiconductor materials. He leads the Computational Condensed Matter Physics (CCMP) Group and serves as a founding member of the Smart Ferroic Materials Center. His primary research interests include: Developing first-principles methods for predicting properties of ferroelectrics and multiferroics Investigating topological defects, spin liquids, and magnetic skyrmions Studying non-equilibrium effects for neuromorphic computing applications Optimizing electro-optic, electrocaloric, and piezoelectric effects Designing antiferroelectrics for high-energy-density applications Professor Bellaiche's recent publications (2024-2025) demonstrate significant activity in topological polar structures, skyrmion engineering, strain-induced phenomena, and computational design of functional materials. His work shows strong interdisciplinary connections between condensed matter theory, materials science, and device physics with particular emphasis on emergent topological phenomena in low-dimensional systems. Scientific awards include: Twenty-First Century Professorship in Nanotechnology and Science Education NSF CAREER Awardee Bellaiche maintains active collaborations with experimental groups internationally, particularly with CentraleSupélec in France. He is involved in innovative educational initiatives including a course titled "Thinking Outside the Box: Physics, Soccer and much more" and contributes to the Soccernostalgia podcast. His research group emphasizes both fundamental theoretical advances and practical applications in next-generation electronic and energy materials.

Yao Liمشاهده پروفایل

دانشیار

• Applied Probability
• Applied Dynamical Systems
• Numerical Analysis
• +۴ مورد دیگر

Yao Li is an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He holds a Ph.D. in Mathematics from Georgia Institute of Technology (2012) and previously served as a Courant Instructor at New York University’s Courant Institute. His research focuses on applied probability, dynamical systems, numerical analysis, and their intersections with machine learning, neuroscience, and statistical mechanics. He has contributed to understanding stochastic dynamics, invariant measures, and the mathematical foundations of machine learning. His work bridges theoretical mathematics with interdisciplinary applications in biology, physics, and engineering. Education : Ph.D. in Mathematics, Georgia Institute of Technology, 2012 Courant Instructor, Courant Institute of Mathematical Sciences, New York University Research Interests : Dr. Li’s research spans applied probability, dynamical systems, and numerical analysis. Key areas include: Machine learning and data-driven computational methods Neuroscience modeling (spiking neural networks, cortical processing) Nonequilibrium statistical mechanics (thermodynamic laws, energy exchange models) Stochastic processes and their ergodic properties His work emphasizes rigorous mathematical analysis combined with numerical simulations, addressing challenges in high-dimensional stochastic systems. Teaching & Advising : He teaches advanced courses such as Numerical Analysis, Applied Math Project Seminar, and Statistics. While specific student advisees are not listed, his research group likely engages graduate students in interdisciplinary projects. His courses integrate theoretical concepts with real-world applications, reflecting his research focus on computational and applied methods. Labs/Teams : Dr. Li’s research is conducted within the Department of Mathematics and Statistics at UMass Amherst, collaborating with institutions like the Courant Institute and Georgia Tech. His work often involves interdisciplinary teams addressing problems at the intersection of mathematics, physics, and biology.

Ali Yousefiمشاهده پروفایل

دانشیار

• Neuroscience Data Analysis
• Biomedical Engineering
• Brain-Computer Interfaces (BCI)
• +۶ مورد دیگر

Dr. Ali Yousefi is an Associate Professor in the Department of Biomedical Engineering at the University of Houston's Cullen College of Engineering. His research focuses on developing statistical and computational methods for analyzing neuroscience data, particularly in linking neural activity to biological/behavioral signals. Key areas include model identification, Bayesian analysis, and real-time neural decoding for applications like brain-computer interfaces and closed-loop stimulation systems. Education: B.S. (Electrical Engineering, Iran University of Science & Technology, 1998), M.S. (Electrical Engineering, Sharif University of Technology, 2000), Ph.D. (Electrical Engineering, University of Southern California, 2014). Postdoctoral training at Harvard Medical School (2019) and Boston University (2019). Research Interests: Neural data analysis frameworks Dynamic neural ensemble modeling Closed-loop brain stimulation systems Bayesian statistical inference High-dimensional data decoding Labs/Teams: Principal Investigator of Yousefilab, focused on neurotechnology and BCI development. Active in interdisciplinary collaborations combining engineering, neuroscience, and machine learning. Key Contributions: Developed methodologies for neural signal decoding, including Bayesian Gaussian process models and latent variable techniques. Pioneered real-time cognitive state prediction and closed-loop systems for enhancing cognitive control in humans.

Deyu Liمشاهده پروفایل

استاد

• Micro/Nano Engineering
• Thermal Sciences
• Nanofabrication Techniques
• +۷ مورد دیگر

Professor Deyu Li is a faculty member in the Department of Mechanical Engineering at Vanderbilt University's School of Engineering. His research focuses on advancing energy and biomedical technologies through the study of micro/nano-scale thermal and fluid phenomena, nanofabrication techniques, and computational simulations using molecular dynamics and Monte Carlo methods. He leads the Micro/Nanofluidics Lab (MNTFL), which develops novel devices for energy conversion and medical applications. Education background includes: Ph.D., Mechanical Engineering, University of California M.E., Thermal Science, Tsinghua University B.E., Engineering Thermophysics, University of Science & Technology of China His research interests span several key areas, with a strong emphasis on nanoscale thermal and fluid transport, nanomaterials engineering, and the development of advanced microfluidic platforms. He explores these topics through experimental and computational approaches, including molecular dynamics simulations and nanofabrication. Specific areas of focus include: Micro/Nano energy systems and their applications in sustainable technologies Optimization of thermoelectric materials via phonon engineering Design of lab-on-a-chip devices for biomedical research Electrochemical flow capacitors and their performance in energy storage Characterization of nanowire and nanotube thermal properties Development of biocompatible microfluidic platforms Recent publications (2021–2025) highlight innovations in nanoscale thermal and fluid transport, including breakthroughs in phonon-mediated heat conduction, advanced thermoelectric materials, and biomedical applications of microfluidics. His work addresses challenges in energy efficiency, material interface optimization, and scalable fabrication of nanodevices. No scientific awards are explicitly mentioned in the provided text. Professor Li's advising and grants narrative remains unspecified, as no formal advisees or grants are listed. His research activities are centered on the Micro/Nanoscale Thermal-Fluids Lab (MNTFL), which bridges fundamental physics with practical applications in renewable energy and healthcare.