Find Academic Supervisors

• Biophysics
• Molecular Motors
• Cytoskeletal Dynamics
• +3 more

Janet Sheung is an Assistant Professor of Physics at Scripps College, specializing in biophysical systems and cytoskeletal dynamics. She teaches courses such as Principles of Physics, Electronics Laboratory, and Senior Thesis in Physics/Biophysics. Her research focuses on the interplay between molecular motors, cytoskeletal networks, and active matter, with a particular emphasis on mechanical properties, transport phenomena, and microscopy innovations. Dr. Sheung's work explores how motor proteins like kinesin and myosin drive structural and mechanical changes in cytoskeletal composites, influencing DNA transport, phase separation, and stress propagation. She has pioneered customizable light-sheet microscopy techniques for visualizing these systems in vivo. Her studies integrate experimental and theoretical approaches to understand non-equilibrium dynamics in biological materials. Her articles highlight themes of motor competition, topological effects on DNA transport, and the design of advanced imaging tools. While no awards are explicitly listed, her contributions to biophysics and microscopy instrumentation are evident in her publication record. Advising and grant details are not provided in the available text, but her teaching and research roles suggest active involvement in student mentorship.

Gabor OroszView profile

Professor

• Nonlinear dynamics and control
• Time delay systems
• Connected and automated vehicles
• +6 more

Gabor Orosz is a Professor at the University of Michigan in both the Department of Mechanical Engineering and Department of Civil and Environmental Engineering . His work bridges nonlinear dynamics and control , time delay systems , and connected automated vehicles , with a focus on traffic flow optimization and vehicle safety . Education: PhD in Engineering Mathematics, University of Bristol, UK (2006) MSc in Engineering Physics, Budapest University of Technology and Economics, Hungary (2002) Research Focus : Orosz's research explores the intersection of vehicle automation , connectivity , and nonlinear dynamics . He investigates time delay effects in teleoperation , intent-sharing protocols for cooperative maneuvering , and control barrier functions for safety-critical systems . His work spans theoretical analysis, numerical validation, and real-world experimentation. Article Trends : Recent publications highlight advancements in latency mitigation for remote driving , nonholonomic vehicle control , intent-sharing frameworks , and energy-efficient strategies for connected vehicle systems . Themes include delayed feedback , stochastic communication , and safety-guaranteed control . Awards & Appointments : NSF CAREER Award (2014) Fulbright Scholar at Budapest University of Technology (2023-2024) Editorial roles in Vehicle System Dynamics (2020) and Time Delay Systems (2017) Student Mentorship : Orosz has advised numerous PhD students, including Anil Alan (2024, TU Delft), Chaozhe He (2018, University at Buffalo), and Tamás Molnár (2020, Wichita State University). His alumni work at institutions like Toyota Research Institute , Ford Motor Co. , and Zoox . Labs & Teams : He leads research at the University of Michigan's Mechanical Engineering Department and collaborates with international institutions such as Caltech and Budapest University of Technology . His team focuses on experimental validation of connected vehicle systems and delay-tolerant control .

H. Jane BaeView profile

Assistant Professor

• Turbulence Modeling
• Computational Fluid Dynamics
• Fluid Mechanics
• +3 more

H. Jane Bae is an Assistant Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. Her research focuses on turbulence modeling, particularly developing high-fidelity computational methods to simulate high-Reynolds-number flows for applications in aircraft design, wind farms, and atmospheric predictions. She integrates machine learning, information theory, and numerical techniques to enhance turbulence modeling efficiency. Education: B.S. in Aerospace Engineering from Caltech (2011), Ph.D. in Mechanical Engineering from Stanford University (2018). She joined Caltech in 2021. Research interests include near-wall turbulence dynamics, resolvent analysis, sparse identification of nonlinear dynamics, and reinforcement learning for wall models in LES. Her work addresses computational cost reduction and model accuracy in complex flow simulations. Awards: 2023 Outstanding Referee Award from Physical Review. Teaching includes courses on fluid mechanics (Ae/APh/CE/ME 101 abc) and turbulence (Ae 239 ab). Her lab combines turbulence theory, high-performance computing, and data-driven methods to study unsteady flows over complex surfaces. Notable contributions include machine learning-based wall models and resolvent analysis frameworks for non-stationary flows.

Guanghao QiView profile

Assistant Professor

• Statistical Genetics
• Genomics
• Single-cell RNA Sequencing Analysis
• +3 more

Guanghao Qi is an Assistant Professor in the Department of Biostatistics at the University of Washington. His research focuses on developing statistical and machine learning methods for multi-omics approaches in genetic studies, particularly integrating single-cell RNA-seq, GWAS, and functional genomic data. Key areas include single-cell eQTL analysis, Mendelian randomization, and multi-trait genetic association analyses. Education: PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (2020), BS in Mathematics from Fudan University (2015). Research interests emphasize high-dimensional data analysis, allele-specific expression in single cells, and causal inference using genetic variants. Notable achievements include a 2025 NIH K01 award for developing methods to integrate single-cell eQTL and GWAS data, and the development of the TWiST method for single-cell transcriptome-wide association studies. Recent work highlights advancements in computational tools like SURGE for context-specific genetic regulation analysis, and evaluations of Mendelian randomization methods in studies of type 2 diabetes and cardiovascular disease. His work often bridges computational biology and statistical theory to address challenges in interpreting large-scale genomic datasets. Awards: NIH K01 Award (2025) Key Contributions: TWiST method (2025), SURGE framework (2024), HIPO power optimization (2018) Labs/Teams: Active collaborations in genomic epidemiology and statistical genetics, with a focus on single-cell multi-omics integration and causal inference methodologies.

Sijia GengView profile

Assistant Professor

• Power Systems
• Control Theory
• Nonlinear Dynamical Systems
• +4 more

Sijia Geng is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at Johns Hopkins University (JHU) and a core researcher at the Ralph O’Connor Sustainable Energy Institute (ROSEI). She directs the Power and Energy Network Systems Analysis (PENSA) Laboratory and co-leads the NSF-funded Electric Power Innovation for a Carbon-free Society (EPICS) Center as co-PI. Her research focuses on integrating control theory, mathematical analysis, and optimization to enhance renewable energy utilization and grid resiliency. Education: Ph.D. and M.S. (ECE & Mathematics) from the University of Michigan-Ann Arbor (2016–2022), B.S. in Automation from Harbin Institute of Technology (2016). Postdoctoral work at MIT (2022) and visiting scholar roles at Purdue University (2015) and Pacific Northwest National Lab (2018). Research Interests: Dynamic analysis of inverter-based power systems, nonlinear control theory, data-driven decision-making, and multi-energy systems. Her work emphasizes achieving autonomous, resilient energy systems through advanced computational tools and theoretical frameworks. Awards: Best Paper Award at MIT/Harvard Applied Energy Symposium (2022), MIT Rising Stars in EECS (2021), Barbour Scholarship (2021), Towner Prize (2018), and Gerald and Esther Forrest Fellowship (2016). She is active in IEEE and INFORMS, organizing sessions at PES General Meeting and CISS conferences. Grants & Collaborations: Funded by NSF, DOE, MIT Energy Initiative, and industry. Leads global initiatives through EPICS, collaborating with UK, Australian, and international stakeholders. Co-leads ROSEI’s Grid pillar to advance fossil-free energy systems. Labs & Teams: Directs PENSA Lab, affiliated with JHU’s Data Science and AI Institute, Applied Mathematics & Statistics, and Computer Science departments.

Stefano MarkidisView profile

Professor

• High-Performance Computing
• Quantum Computing
• Computational Fluid Dynamics
• +5 more

Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.

Bayu JayawardhanaView profile

Professor

• Mechatronics
• Nonlinear Control Systems
• Robotics
• +5 more

Prof. Bayu Jayawardhana is a Full Professor in Mechatronics and Control of Nonlinear Systems at the University of Groningen, affiliated with the Faculty of Science and Engineering. He leads the Jayawardhana Group focusing on opto-mechatronics and advanced nonlinear control theories. His roles include Director of Engineering and Scientific Director of the Engineering and Technology Institute Groningen. He holds editorial positions in journals like International Journal of Robust and Nonlinear Control and European Journal of Control . Education: PhD in Control and Power Group from Imperial College London (2006), M.Eng from Nanyang Technological University (2003), and B.Eng from Institut Teknologi Bandung (2000). Research interests span opto-mechatronics for high-tech systems, nonlinear control, and systems biology. Key projects include digital twins for energy optimization, control of ocean energy systems, and modeling of cryogenic actuators for telescopes. His work integrates AI and model-based methods for high-performance systems. Notable awards include the 2016 FSE Faculty Teacher of the Year Award and the Ben Feringa Impact Award (2020). He advises on ventures like Ocean Grazer B.V. and Sencilia B.V. Teaching includes graduate courses on nonlinear control, opto-mechatronics, and fitting dynamical models to data. His research labs include the Groningen Centre for Systems and Control and the Data Science and Systems Complexity Center.

• Data Science
• Machine Learning
• Harmonic Analysis
• +7 more

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

Diego DonzisView profile

Professor

• Fluid Dynamics
• Turbulence
• Turbulent Mixing
• +3 more

Diego Donzis is a Professor in the Department of Aerospace Engineering at Texas A&M University, affiliated with the College of Engineering. He holds the Presidential Impact Fellow title. His work focuses on high-performance computing for fluid dynamics, particularly compressible turbulence, turbulent mixing, and shock-turbulence interactions. Donzis earned his Ph.D. and M.S. in Aerospace Engineering from the Georgia Institute of Technology. Research interests include large-scale simulations of turbulent flows, thermal boundary condition effects on turbulence, and the development of advanced numerical methods like Selected-Eddy Simulations (SES) for extreme-scale computing. His studies explore universality in turbulence scaling, energy spectra dynamics, and the interplay between compressibility and fluid mixing. Publications emphasize turbulence decay laws, shock-turbulence interactions, and the role of thermal non-equilibrium in turbulent flows. Notable contributions include advancing asynchronous algorithms for exascale CFD and analyzing density gradient statistics in compressible turbulence. Awards include the Presidential Impact Fellow distinction. Donzis collaborates on grants such as the Frontera Travel Grant for compressible turbulence research. His work bridges computational methods with fundamental fluid dynamics, addressing challenges in both numerical accuracy and physical modeling.

Diane GuignardView profile

Assistant Professor

• Numerical Analysis
• Applied Mathematics
• Computational Mathematics
• +6 more

Diane Guignard is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. Her research focuses on numerical analysis, partial differential equations, and computational methods with applications to mechanics and stochastic systems. She holds a position in a leading mathematics department and can be contacted at dguignar@uOttawa.ca . Her research interests include finite element methods, model reduction, uncertainty quantification, and optimal transport-based mesh adaptation. She explores nonlinear approximation theories for high-dimensional anisotropic functions and develops computational frameworks for thin structures and colloidal flow simulations. Her work bridges numerical analysis with practical engineering challenges, emphasizing adaptive algorithms and error estimation techniques. Her recent publications (2021-2024) highlight contributions to goal-oriented mesh adaptation, stochastic field approximations on surfaces, and large deformation analyses of prestrained plates. These studies emphasize interdisciplinary approaches combining mathematical rigor with computational innovation. Dr. Guignard has not been explicitly noted for awards in the provided materials. Her advising record is currently unspecified, though her research group likely engages in advanced numerical methods and computational mechanics projects.

Yan GuoView profile

Professor

• Partial Differential Equations
• Kinetic Theory
• Plasma Physics
• +4 more

Yan Guo is the L. Herbert Ballou University Professor of Applied Mathematics at Brown University. He holds a B.S. from Peking University (1987) and a Ph.D. in Mathematics from Brown University (1993). His research focuses on partial differential equations (PDEs) in kinetic theory, plasma physics, and stellar dynamics, emphasizing nonlinear stability and instability phenomena. He has held roles including Courant Instructor, Manning Assistant Professor, and has been continuously affiliated with Brown since 1995. Education: B.S., Peking University (1987); Ph.D., Brown University (1993). Research interests include rigorous mathematical analysis of PDEs arising in kinetic theory (Boltzmann/Vlasov equations), fluid dynamics (Euler/Navier-Stokes), and stability of galactic models. Notable contributions include nonlinear stability of Maxwellian states, instability of plasma equilibria, and mathematical frameworks for galaxy dynamics. His publications address foundational problems in kinetic theory and fluid dynamics, with recent work on the Euler-Maxwell system and Boltzmann equation regularity. Awards include the Sloan Research Fellowship (1998-2003) and NSF Postdoctoral Fellowship (1995-1998). Guo serves as Managing Editor of the Journal of Partial Differential Equations and Associate Editor for multiple journals, including SIAM Journal of Mathematical Analysis. He has organized conferences on nonlinear wave equations and PDEs since 1998.

Pankaj MehtaView profile

Professor

• Statistical Physics
• Systems Biology
• Ecological Dynamics
• +3 more

Pankaj Mehta is a Professor in the Department of Physics at Boston University, with additional affiliations in the Department of Biomedical Engineering. His research bridges statistical physics, theoretical biology, and interdisciplinary systems approaches. Key areas include ecological dynamics, synthetic biology, and the application of machine learning principles to biological systems. His work focuses on understanding emergent phenomena in biological systems, such as cell fate decisions, ecosystem stability, and signal processing in cellular networks. He has pioneered methods combining physics-based modeling with computational tools to study complex systems, including gene circuits, microbial communities, and cancer dynamics. Recent contributions highlight the use of order parameters for interpreting cellular states, geometric frameworks for ecological niches, and machine learning analogies to ecological principles. His interdisciplinary approach integrates experimental data with theoretical models to address questions in biomedicine, environmental science, and fundamental physics.

Philip Loldrup FosbølView profile

Associate Professor

• Carbon Dioxide Capture and Storage
• Thermodynamics and Phase Equilibrium
• Process Design, Simulation, and Optimization
• +12 more

Philip Loldrup Fosbøl is an Associate Professor in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), College of Engineering. He is actively affiliated with CERE – Center for Energy Resources Engineering, where he conducts research on CO 2 capture, storage, transport, and utilization. His work integrates thermodynamic modeling, process simulation, and pilot-scale experimentation to address challenges in carbon management and sustainable energy systems. His research interests include: Carbon Dioxide Capture and Storage (CCS) Thermodynamics and Phase Equilibrium of Electrolyte Solutions Process Design, Simulation, and Optimization CO 2 Corrosion in Energy Systems Biogas Upgrading and Cleaning CO 2 Utilization and Conversion Development of Predictive Thermodynamic Models Mobile and Large-Scale Pilot Facilities for CO 2 Capture His recent publications (2025) highlight a strong focus on biogas upgrading, solvent degradation in industrial CO 2 capture, thermophysical property measurements, and novel electrochemical separation methods. These works reflect a consistent trend toward energy-efficient, scalable, and industrially applicable solutions for decarbonization, particularly in flue gas and biogas treatment. Scientific awards received: Top PhD Thesis of the Year (2008) He actively supervises multiple PhD students and leads research projects funded by industrial partners such as Ørsted, Shell, Equinor, and Novozymes, as well as EU initiatives including CASTOR, iCap, and OCTAVIUS. His work contributes to UN Sustainable Development Goals related to climate action and affordable, clean energy. He is involved in laboratory research on thermodynamic equilibrium (VLE, SLE), heat capacity, corrosion mechanisms, and core flooding for CO 2 storage. His team develops experimental methods and operates pilot facilities for CO 2 capture and biogas cleaning, often in collaboration with key researchers like Kaj Thomsen, Nicolas von Solms, and Georgios Kontogeorgis.

Oskar HallatschekView profile

Associate Professor

• Biological Physics
• Evolutionary Dynamics
• Population Genetics
• +3 more

Oskar Hallatschek is an Associate Professor and McAdams Chair in the Department of Physics and Integrative Biology at the University of California, Berkeley. His research focuses on biological physics and evolutionary dynamics, particularly how collective patterns emerge from heterogeneous microbial populations. He leads the Hallatschek Lab, which maintains collaborations with institutions like the University of Leipzig. Research Emphasis: Microbial systems, population genetics, non-equilibrium statistical physics, evolutionary adaptation, and mechanical interactions in cellular populations. Publications span journals like Nature , PNAS , and Science , with recent work on allele surfing, jamming in microbial colonies, and long-range dispersal effects. Grants: Supported by NIH and NSF, as indicated by institutional logos in the lab web presence.

• Atomic Force Microscopy
• Nanotechnology
• Mechatronics
• +4 more

Amir Farokh Payam is a Senior Lecturer in Electronics and Software at the School of Engineering, Ulster University, UK since 2019. He holds a PhD in Electronics Engineering-Nanotechnology and has previously worked at Instituto de Ciencia de Materiales de Madrid (2012–2015), Durham University (2016–2018), and University of Bristol (2018–2019). Education: B.Sc. and M.Sc. in Electrical and Electronics Engineering, PhD in Electronics Engineering-Nanotechnology His research focuses on dynamic Atomic Force Microscopy (AFM) , NEMS/MEMS , Surface Science , and Applied Nonlinear Control . Recent work explores nonlinear harmonics in AFM, solid-liquid interfacial dynamics, and single-cell biomechanical profiling. He leads projects on quantum sensor fabrication and cancer cell viscoelasticity analysis. Key trends in his 15 most recent publications (2025–2023) include advancements in nanoscale imaging , multifrequency AFM , viscoelastic material analysis , and biomedical applications such as viral protein sensing and corneal cell profiling. Collaborative projects span institutions in Ireland, Spain, and the UK. Scientific Awards: Editor's Highlights in Journal of Applied Physics (2018) and Nanotechnology (2015) Best Innovative Idea, Second International R&D Award of Iran (2012) Distinct Graduate Student, University of Tehran Visiting Study Scholarship, Instituto de Microelectronica de Madrid (2010) M.Sc. First Class Student (top among 15) He serves as Unit Director for Mechatronics II and Electronics II modules and supervises BEng/MEng research projects. Active research grants include Royal Society funding (2024–2026) for cancer cell biomechanics and an ongoing 2022–2026 project on solid-liquid interface dynamics.