Find Academic Supervisors

Min Kyung LeeView profile

Assistant Professor

• Supply Chain Management
• Service Operations Management
• New Service Innovations
• +2 more

Min Kyung Lee serves as an Assistant Professor of Supply Chain Management at Baylor University's Hankamer School of Business, bringing industry experience from her prior role as a logistic coordinator in the automotive sector before pursuing her doctorate. Her academic credentials include: PhD in Business Administration (2018), Clemson University MS in Marketing (2010), Clemson University BA in Economics (2008), Kangwon National University, South Korea Dr. Lee's research centers on Service Operations Management and Supply Chain Innovation, with particular focus on healthcare logistics, epidemic response systems, and technology-enabled service delivery. Her work bridges theoretical operations frameworks with practical industry applications, especially in crisis-driven supply chain scenarios and service value optimization. Analysis of her publications reveals consistent exploration of data-driven decision-making in volatile environments, including pandemic supply chains, medical crowdfunding dynamics, and cross-cultural procurement platforms. Her research demonstrates increasing emphasis on healthcare operations while maintaining strong connections to broader service innovation principles. She actively contributes to the academic community through membership in the Production and Operations Management Society and Decision Sciences Institute.

Jürgen BielaView profile

Professor

• Power Electronics
• Multi-physics Modeling
• High Power Electronic Systems
• +6 more

Prof. Dr. Jürgen Biela serves as Full Professor at ETH Zurich within the Department of Information Technology and Electrical Engineering, where he leads the Laboratory for High Power Electronic Systems. His academic career at ETH Zurich has progressed from doctoral studies to his current position as head of his research laboratory, with significant contributions to power electronics research and education. Biela earned his diploma with honors from Friedrich-Alexander University in Erlangen, Germany in 2000 and completed his Ph.D. at ETH Zurich in 2005, both in electrical engineering. His educational background includes specialized work on resonant DC-link inverters at Strathclyde University and active control of series connected IGCTs at the Technical University of Munich. His research program focuses on multi-physics modeling, design and optimization of power electronic systems , with particular emphasis on applications for future energy distribution and transmission, pulsed power systems, and advanced medium voltage power electronics based on novel semiconductor technologies like silicon carbide (SiC). He also investigates integrated passive components for ultra-compact and ultra-efficient high-power converter systems, pushing the boundaries of power density and efficiency in electronic power conversion. Analysis of his recent publications reveals strong trends in high-frequency power conversion , with significant work on transformer and inductor design, insulation systems for medium-frequency applications, thermal management of power components, and advanced modeling techniques for electromagnetic phenomena. His research bridges fundamental electromagnetic theory with practical engineering applications, particularly in high-voltage and high-power scenarios where traditional approaches face limitations. As a prolific researcher, Biela has published over 85 journal papers and 210 conference papers while holding more than 35 patents. He serves as an Associate Editor for the IEEE Transactions on Power Electronics and regularly reviews for leading journals and conferences in the field. His work demonstrates consistent contributions to advancing power electronic systems through rigorous theoretical analysis combined with practical implementation. Biela has supervised numerous doctoral and master's students, with recent publications indicating active mentorship of researchers working on advanced power electronic components and systems. His laboratory at ETH Zurich serves as a hub for innovation in high-power electronics, with connections to industry research projects that translate theoretical advances into practical applications. Current research directions include developing cost-effective alternatives to traditional components like Litz wire, improving insulation systems for high-voltage applications, and creating more accurate models for predicting thermal and electromagnetic behavior in power electronic systems.

Tsun-Ming TsengView profile

Professor

• Microfluidic Large-Scale Integration
• Optical Network-on-Chip
• Microfabrication
• +3 more

Tsun-Ming Tseng is a Professor and principal investigator at the Chair of Electronic Design Automation at the Technical University of Munich (TUM). He leads the Emerging Technology Group and oversees multiple DFG/BMBF-funded research projects in the areas of microfluidic large-scale integration, optical network-on-chip design, and novel microfabrication techniques. Dr. Tseng's research focuses on design automation for emerging technologies, with particular expertise in three main areas: microfluidic large-scale integration, optical network-on-chip systems, and novel microfabrication processes. His work bridges the gap between electronic design automation and cutting-edge applications in bioengineering, photonics, and advanced manufacturing. His research group develops sophisticated algorithms and tools for optimizing design, reliability, and performance in these emerging domains. Analysis of Dr. Tseng's recent publications reveals a strong focus on practical implementation challenges in emerging technologies. His work spans both theoretical algorithm development and practical system implementation, with particular emphasis on reliability, performance optimization, and manufacturing considerations. The research shows increasing integration between different technology domains, particularly the convergence of microfluidics, optical networking, and electronic design automation. Dr. Tseng has been awarded multiple significant research grants including: "DE-TW-CloudWRONoC" (BMBF-NSTC project, PI, 2025-2028, EUR 797.7K) "DE-TW-PI3D" (BMBF-NSTC project, PI, 2024-2027, EUR 391.6K) "Physical Design for Microfluidic Large-Scale Integration" (DFG research grant, PI, 2024-2026, EUR 331.9K) Multiple other DFG and industrial projects totaling over EUR 3 million in funding He has successfully supervised numerous doctoral researchers and postdoctoral fellows, with current group members including Jiahui Peng, Debraj Kundu, Liaoyuan Cheng, and several others. Dr. Tseng leads the Emerging Technology Group at TUM, which focuses on developing design automation methodologies for next-generation technologies. The group maintains strong collaborations with international institutions, including partnerships with researchers in Taiwan and Hong Kong. The team operates state-of-the-art facilities for research in microfluidics, optical networking, and advanced microfabrication techniques.

Peter J. ThomasView profile

Professor

• Systems Neuroscience
• Computational Neuroscience
• Theoretical Biophysics
• +7 more

Peter J. Thomas is a Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University's College of Arts and Sciences, with secondary appointments in Electrical Engineering and Computer Science, Cognitive Science, and Biology. He serves as Co-Editor-in-Chief of Biological Cybernetics and leads the Computational Biomathematics Laboratory. Primary Affiliation: Department of Mathematics, Applied Mathematics, and Statistics Secondary Affiliations: Department of Electrical Engineering and Computer Science, Department of Cognitive Science, Department of Biology Leadership: Co-Editor-in-Chief of Biological Cybernetics Thomas earned his B.A. in Physics and Philosophy from Yale University (1990), M.S. in Mathematics from the University of Chicago (1994), and both M.A. in Conceptual Foundations of Science and Ph.D. in Mathematics from the University of Chicago (2000). His research spans mathematical neuroscience, theoretical biophysics, and information theory applications to biological systems. Thomas specializes in understanding how noise and stochasticity affect neural coding, developing mathematical frameworks for gradient sensing in cells, and applying graph theory to biological networks. His work on stochastic shielding has provided novel approaches to simplifying complex stochastic models while preserving essential dynamics. His research bridges theoretical mathematics with experimental neuroscience through collaborations with the Chiel laboratory and others. Thomas's recent publications demonstrate a strong focus on stochastic oscillators, sensory feedback mechanisms, and information theory applications to biological systems. His work consistently develops novel mathematical frameworks to address specific biological questions, with significant contributions to understanding phase dynamics in neural oscillators and information processing in biochemical signaling. Core Fulbright Scholar Program (2013) Simons Fellow in Mathematics Program (2014) Multiple NSF grants as Principal Investigator Co-Editor-in-Chief of Biological Cybernetics Thomas has mentored numerous students at all levels, from undergraduates to postdoctoral researchers. His laboratory has produced successful scholars who have gone on to faculty positions at institutions like New Jersey Institute of Technology and the University of Nevada, Reno. He has actively organized workshops at the Banff International Research Station and served on editorial boards for leading journals in computational neuroscience. The Computational Biomathematics Laboratory focuses on developing mathematical frameworks to understand neural dynamics, cellular signaling, and pattern formation. The lab maintains strong collaborations with experimental neuroscience groups and has made significant contributions to understanding rhythmic neural systems, respiratory control mechanisms, and information processing in biological systems.

• Wireless Communications
• 5G/6G Networks
• Mobile Communications
• +6 more

Marina Petrova is a Professor at RWTH Aachen University, holding positions in both the Teaching and Research Area of Mobile Communications and Computing and the Chair and Institute for Networked Systems. She is also a member of the Steering Committee for the Mobility & Transport Engineering (MTE) profile area at the university. Her office is located at Kackertstraße 9, 52072 Aachen, Germany. Professor Petrova's research focuses on cutting-edge wireless communication technologies, with particular emphasis on next-generation mobile networks. Her work spans multiple dimensions of wireless systems including: 5G and 6G network architectures and protocols Cell-Free Massive MIMO systems Millimeter-wave communications Resource allocation and scheduling in wireless networks Wi-Fi sensing and coexistence analysis Integration of distributed learning services in wireless networks Beamforming and beam management techniques Ultra-Reliable Low-Latency Communications (URLLC) Her recent publications demonstrate a strong trend toward the integration of artificial intelligence and machine learning techniques in wireless network design and optimization. She has been particularly active in exploring the convergence of communication and sensing functionalities (ISAC - Integrated Sensing and Communication), which is considered a key enabler for future 6G networks. Professor Petrova's research also addresses practical implementation challenges in next-generation wireless systems, with several publications focusing on ns-3 implementations and experimental validations. Professor Petrova has received recognition for her contributions to the field through numerous publications in top-tier venues, though specific awards are not mentioned in the available information. Her work shows strong industry relevance with applications in smart industries, autonomous systems, and future communication networks.

• High-energy transient phenomena
• Fast radio bursts
• Tidal disruption events
• +11 more

Wenbin Lu is an Assistant Professor in the Department of Astronomy at the University of California Berkeley, where he conducts theoretical research on high-energy transient phenomena. He is also affiliated with the Theoretical Astrophysics Center at UC Berkeley. PhD in Astronomy, University of Texas at Austin (2018) Bachelor in Physics, Peking University (2013) Professor Lu specializes in extreme astrophysical events that serve as natural laboratories for studying physics under conditions of high energy density, strong gravity, and intense magnetic fields. His work integrates multiple physical domains including plasma physics, relativistic hydrodynamics, radiative transfer, and stellar dynamics. He maintains active collaborations with researchers worldwide and encourages student involvement in his projects. Analysis of his recent publications reveals a strong focus on tidal disruption events and fast radio bursts, with increasing emphasis on multi-messenger approaches and theoretical modeling of observational data from facilities like JWST, Chandra, and radio telescopes. His work demonstrates consistent theoretical innovation in explaining complex transient phenomena. Burke Fellow at Caltech (2018-2021) Lyman Spitzer Fellow at Princeton University (2021-2022) Professor Lu actively mentors students and postdocs, with many projects originating from discussions with junior researchers. He teaches courses in Radiation and Stars at UC Berkeley. His research is supported by multiple grants that enable computational modeling and observational collaborations across various wavelengths. His theoretical work often involves complex numerical simulations of astrophysical phenomena, particularly focusing on the hydrodynamic evolution of stellar debris in tidal disruption events and plasma processes in fast radio burst emission mechanisms.

John O. MillerView profile

Associate Professor

• Operations Research
• Military Simulation Modeling
• Defense Logistics
• +3 more

Dr. John O. Miller is an Associate Professor of Operations Research in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT), where he has served since 1997 in roles including Military and Civilian Deputy Department Head and Director of the Center for Operational Analysis. A retired U.S. Air Force Lieutenant Colonel, he combines more than three decades of military experience with scholarly expertise in simulation modeling, defense logistics, and operations research. Education: Ph.D. in Industrial Engineering, The Ohio State University, 1997 M.S. in Operations Research, Air Force Institute of Technology, 1987 M.B.A., University of Missouri at Columbia, 1983 B.S. in Biology, United States Air Force Academy, 1980 Dr. Miller’s research focuses on the development and application of simulation methodologies—especially agent-based and discrete-event modeling—to military logistics, weapon system evaluation, and combat readiness. His work often integrates multivariate statistics, experimental design, and optimization techniques to address Air Force and Department of Defense challenges such as sortie generation, munitions supply chains, and directed-energy weapon assessment. Across more than 40 refereed articles, recent publications demonstrate a sustained emphasis on: Metamodeling of large-scale simulations using dynamic Bayesian networks and bootstrapping Agent-based exploration of air-to-air missile concepts and aircraft maintenance manpower Statistical evaluation of pattern-recognition and automatic-target-recognition algorithms Logistics degradation modeling for bomber fleets and brigade combat teams These contributions underscore his leadership in military simulation and defense-focused operations research. Scientific & Teaching Honors: AFIT Instructor of the Quarter, 2005 Tau Beta Pi Engineering Honor Society (Alumnus Member), 2001 AFIT Student Chapter ORSA Outstanding OR Educator, 1999 MORS Barchi Prize Nominee, 1998 Alpha Pi Mu & Omega Rho Honor Societies USAFA Department Instructor of the Year, 1993 Dr. Miller has advised numerous M.S. and Ph.D. students whose dissertations and theses advance simulation optimization, military logistics, and combat modeling. His teaching interests span simulation modeling and analysis, design of experiments, probability and statistics, and operations research methods for defense applications. He maintains active professional memberships in INFORMS, the Military Operations Research Society, and the Air Force Association, and he frequently presents at both invited and organized conferences, fostering collaboration among military, academic, and industry analysts.

Richard AxelView profile

Professor

• Olfaction
• Neural representations
• Molecular neuroscience
• +3 more

Richard Axel is a University Professor at Columbia University, holding appointments in the Vagelos College of Physicians and Surgeons as Professor of Neuroscience, Professor of Biochemistry and Molecular Biophysics, and Professor of Pathology and Cellular Biology. He serves as Codirector of Columbia's Mortimer B. Zuckerman Mind Brain Behavior Institute and is an Investigator at the Howard Hughes Medical Institute since 1984. Dr. Axel earned his AB from Columbia College and MD from Johns Hopkins Medical School. His Nobel Prize-winning research identified over 1,000 odorant receptors in the nose that transmit olfactory information to the brain, revolutionizing our understanding of the sense of smell. His early work with colleagues developed groundbreaking gene transfer techniques that enabled the introduction of virtually any gene into any cell, leading to novel approaches for gene isolation and analysis of gene function. Dr. Axel's research focuses on understanding how olfactory information is processed in the brain to create internal representations of the external world. His work spans molecular neuroscience, neural circuitry, and the relationship between sensory input and behavioral output. He has pioneered techniques that have advanced our understanding of neural function and sensory processing mechanisms. Analysis of Dr. Axel's recent publications reveals a continued focus on olfactory processing systems, with increasing integration of computational approaches and machine learning to understand neural representations. His work spans from molecular mechanisms to circuit-level analyses, with particular emphasis on how odor information is encoded and transformed in the brain to produce meaningful perceptions and behaviors across multiple model systems. Nobel Prize in Physiology or Medicine (2004) Howard Hughes Medical Institute Investigator (1984-Present) The Royal Society Foreign Member (2014) Gairdner Foundation International Award (2003) American Philosophical Society Member (2003) National Academy of Sciences Member (1983) Richard Lounsbery Award (1989) American Association for the Advancement of Science Fellow (2018) American Academy of Arts and Sciences Fellow As Codirector of the Zuckerman Institute, Dr. Axel oversees one of the world's leading neuroscience research centers, fostering interdisciplinary collaboration across multiple departments. His lab continues to train the next generation of neuroscientists, investigating how sensory information is transformed into meaningful perceptions and behaviors. Dr. Axel's early work on gene transfer techniques laid the foundation for numerous advances in molecular biology and neuroscience, including the isolation and analysis of the CD4 gene, the cellular receptor for HIV. Dr. Axel leads the Axel Lab at Columbia University, which is part of the Zuckerman Institute. His research team investigates how organisms recognize olfactory information in the environment and transmit it to the brain, where it is processed to create internal representations of the external world. The lab employs multidisciplinary approaches combining molecular, cellular, and systems neuroscience to unravel the neural circuits underlying sensory processing and behavior, with particular focus on understanding how these representations translate stimulus features into appropriate innate and learned behaviors.

Jay Sui TungView profile

Assistant Professor

• Remote Sensing
• Computer Modeling
• Machine Learning
• +7 more

Dr. Jay Sui Tung is an Assistant Professor in the Department of Geosciences at Texas Tech University, affiliated with the College of Arts and Sciences. His work focuses on geophysical modeling of natural and anthropogenic surface deformation processes, including earthquakes, volcanic eruptions, and energy-related activities. He holds a Ph.D., M.Phil., and B.Sc. in Earth Sciences and Physics from the University of Hong Kong. Education: 2013: Ph.D. Earth Sciences, University of Hong Kong 2009: M.Phil. Physics, University of Hong Kong 2007: B.S. Physics, University of Hong Kong Research Interests: Dr. Tung uses remote sensing, numerical models, and machine learning to study crustal processes linked to natural hazards and climate change. His work addresses induced seismicity from wastewater injection, energy systems, and coastal impacts of sea-level rise. Recent work includes a 2020 study on the Mentone earthquake linked to wastewater disposal. Labs/Teams: He leads the GeoModeling for Earth Sustainability (GES) Lab, which investigates geomechanical and hydrogeological processes to mitigate risks from human-induced and natural hazards. Recruitment: Actively hiring a Postdoc Research Associate and PhD students (Fall 2024).

• Experimental Gravity
• Astrophysical Signals
• Nuclear Physics
• +8 more

Reed Essick is an Assistant Professor at the Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto. His research focuses on experimental gravity, astrophysical signals, and nuclear physics, with particular emphasis on neutron stars, black holes, and gravitational waves. He develops advanced statistical methods like hierarchical Bayesian inference and nonparametric analysis for interpreting observational data from pulsars and gravitational wave detectors. Dr. Essick collaborates extensively with international observatories such as LIGO, Virgo, and KAGRA, contributing to cutting-edge projects like multimessenger astronomy and precision cosmology. His work bridges computational astrophysics with observational techniques, addressing fundamental questions about dense matter and strong-field gravity. Key contributions include studies on gravitational wave equation-of-state constraints, pulsar timing analysis, and the application of machine learning to detector data. His research leverages both ground-based interferometers and space-based observations to explore extreme astrophysical environments.

Beth Anne BennettView profile

Senior Lecturer

• Combustion Phenomena
• Fluid Dynamics
• Heat Transfer
• +4 more

Beth Anne Bennett is a Senior Lecturer in the Department of Mechanical Engineering at Yale University. Her research focuses on computational methods for solving complex fluid dynamics and combustion problems, particularly involving adaptive grid refinement techniques for nonlinear PDEs. She holds a Ph.D. from Yale University, where her doctoral work centered on developing efficient numerical algorithms for multidimensional combustion phenomena. Her research interests include laminar combustion, fluid dynamics, heat transfer, and solidification processes. She has pioneered solution-adaptive gridding techniques like Local Rectangular Refinement (LRR) for both nonreacting and reacting flows, with applications to steady and unsteady multidimensional systems. Bennett has been recognized with the National Science Foundation ADVANCE Fellows Award (2002-2006). Her publications span computational studies of ethanol/dimethyl ether blending effects in flames, oxygen-enhanced methane flames, and axisymmetric coflow flames. She actively contributes to professional societies including The Combustion Institute, ASME, SIAM, ASEE, and SWE. Her work integrates computational innovation with experimental validation, addressing challenges in parallelization, sparse matrix treatments, and algorithm optimization for convection-diffusion problems. Bennett's research bridges fundamental numerical methods and applied combustion engineering, advancing both theoretical frameworks and practical applications in energy systems.

• Industrial Organization
• Game Theory
• Econometrics
• +5 more

C. Lanier Benkard is the Gregor G Peterson Professor of Economics at the Graduate School of Business, Stanford University. He is a prominent researcher in industrial organization, game theory, and econometrics, focusing on dynamic models of market competition and structural estimation. Research Interests: His work spans Dynamic games and equilibrium modeling Hedonic pricing and demand estimation Econometric tools for imperfect competition Computational methods for large-scale industries Publication Trends: His research emphasizes oblivious equilibrium approximations, strategic interactions in concentrated industries, and empirical analysis of markets with heterogeneous consumers. He frequently collaborates with scholars like Gabriel Weintraub and Patrick Bajari. Tools & Extensions: He has developed computational resources, including C++ and Matlab code, to analyze oblivious equilibrium. Current work includes extensions to Markov Perfect Industry Dynamics and aggregate shock modeling.

• Offshore Renewable Energy
• Marine Engineering
• Coastal Structural Design
• +3 more

Dr. Rameeza Moideen is a Researcher at the University of Edinburgh's School of Engineering, affiliated with the Energy Systems Research Institute. Her work focuses on offshore renewable energy infrastructure, coastal structural resilience, and fluid-structure interaction dynamics. Research Interests Her research spans vortex-induced vibrations in marine power cables, extreme wave impacts on coastal decks, and climate change adaptation for port infrastructure. She applies advanced numerical simulations to analyze hydrodynamic forces, structural stresses, and material degradation mechanisms. Key Research Trends Recent work emphasizes lazy wave dynamic cables under varying currents (2025), focused wave impacts on bridge decks (2023-2021), and marine growth effects on tubular structures (2021). These studies combine computational modeling with real-world climate scenarios to improve offshore energy systems and coastal infrastructure durability. Awards & Grants No specific awards or grants mentioned in available texts. Research is likely funded through institutional and collaborative projects within the Energy Systems Institute. Labs & Teams Active within the Energy Systems Research Institute at Edinburgh, collaborating on offshore renewable energy projects and coastal engineering initiatives.

Yongjie Jessica ZhangView profile

Professor

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +5 more

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

Matthias IhmeView profile

Professor

• Combustion Science
• Fluid Dynamics
• Computational Fluid Dynamics
• +5 more

Matthias Ihme is a Professor in the Department of Mechanical Engineering and Photon Science Directorate at Stanford University. His research focuses on large-eddy simulation (LES) of turbulent reacting flows, aeroacoustics, combustion-generated noise, numerical methods, and high-order schemes. He holds a Ph.D. from Stanford University (2008), an M.Sc. in Computational Engineering from the University of Erlangen (Germany, 2002), and a Dipl.-Ing. in Mechanical Engineering from Munich University of Applied Sciences (Germany, 2000). His work bridges computational fluid dynamics, combustion science, and photon science, with notable contributions to supercritical fluid dynamics, machine learning integration in fluid simulations, and high-fidelity atmospheric transport modeling. Recent research emphasizes ultrafast cluster dynamics, shock-induced interface behavior, and stochastic ignition mechanisms in advanced fuel systems. Publications highlight interdisciplinary advancements, including physics-informed ML frameworks for reacting flows and experimental studies using X-ray photon correlation spectroscopy. His projects often involve high-performance computing and collaboration with national labs like SLAC.