Find Academic Supervisors

Jacob Carruth is a Postdoctoral Research Associate in the Department of Mathematics at Princeton University. His academic focus includes adaptive control theory, Whitney extension problems, and Fourier analysis. He earned his PhD in 2019 from the University of Texas at Austin under the advisement of Arie Israel. His research explores theoretical control systems and mathematical analysis, with notable contributions to adaptive control strategies for unknown linear dynamics and extensions of Whitney's theorem in functional spaces. Education: PhD in Mathematics, 2019, University of Texas at Austin (Advisor: Arie Israel) Research Interests: Adaptive control theory with applications to uncertain linear systems Whitney extension problems in Sobolev spaces and geometric analysis Fourier analysis methods in extremal problems and approximation theory His work bridges pure mathematics and applied control theory, addressing fundamental questions in system dynamics and function extensions. Recent Research Trends: His publications emphasize regret minimization strategies, optimal control under parameter uncertainty, and functional analysis techniques. Key themes include developing bounded-regret algorithms for linear systems and advancing understanding of extension operators for function spaces. Lab/Team Affiliations: Currently affiliated with Princeton University Mathematics Department's research groups in analysis and control theory.

• Adaptive Control Theory
• Whitney Extension Problems
• Fourier Analysis
• +3 more

Connor Mooney is an Associate Professor and Chancellor's Fellow in the Department of Mathematics at the University of California, Irvine . His research focuses on Partial Differential Equations , with emphasis on nonlinear elliptic equations, geometric analysis, and the calculus of variations. He explores topics such as Monge-Ampère equations, minimal surfaces, and singularities in solutions to PDEs. Research Interests: Nonlinear elliptic and parabolic PDEs Geometric analysis (minimal surfaces, Lagrangian mean curvature flows) Calculus of variations (singular minimizers, regularity theory) Monge-Ampère equations and optimal transport Bernstein-type theorems and entire solutions Homogenization and bifurcation phenomena Recent Research Trends: His work bridges theoretical analysis with geometric applications, addressing regularity questions in degenerate equations and exploring singular structures in solutions. Recent papers investigate minimal surface systems, anisotropic Bernstein problems, and score-based generative models' mathematical foundations. Grants & Awards: No specific grants or awards listed, though his Chancellor's Fellow title indicates institutional recognition. Research supported by NSF grants (e.g., CAREER: Singular and Global Solutions to Nonlinear Elliptic Equations). Collaborations: Active collaborations with researchers like Ovidiu Savin, Yiming Yang, and Tristan Collins. Maintains the Virtual Analysis and PDE Seminar (VAPS) platform.

• Partial Differential Equations
• Calculus of Variations
• Geometric Analysis
• +3 more

Panagiotis Andrianesis is a Research Associate Professor in Systems Engineering at Boston University's College of Engineering. He specializes in electricity distribution network economics and energy systems optimization. His research focuses on developing robust frameworks for future grid operations, including uncertainty management, renewable integration, and market design. He holds a PhD from the University of Thessaly, Greece, and is affiliated with the Center for Information and Systems Engineering (CISE) and the BU Institute for Sustainable Energy as a Senior Fellow. Education: PhD in Systems Engineering from the University of Thessaly, Greece. Research Interests: His work spans electricity market mechanisms, distributed energy resource coordination, and resilient energy forecasting. Key areas include optimizing power grid economics, designing pricing strategies for EV charging stations, and addressing non-convexities in energy markets. His contributions often blend optimization theory with practical grid applications. Scientific Awards: Senior Fellow at the BU Institute for Sustainable Energy, highlighting his leadership in sustainable energy research. Advising & Grants: While no specific grants or advisees are listed, his research collaborations with industry and academic partners suggest active involvement in funded projects. His work frequently addresses real-world grid challenges. Labs/Teams: Affiliated with the BU Institute for Sustainable Energy and the CISE, where he contributes to interdisciplinary energy systems research.

• Electricity Distribution Networks
• Energy Economics
• Power Systems Optimization
• +3 more

Roles and Affiliations: Dennice F. Gayme is an Associate Professor in the Department of Mechanical Engineering at Johns Hopkins University's Whiting School of Engineering. She holds secondary appointments in Electrical and Computer Engineering and Environmental Health Engineering. She serves as a member of the Ralph O’Connor Sustainable Energy Institute (ROSEI) leadership council and is the faculty advisor for the Society of Women Engineers (SWE) and Hopkins Students Wind Energy Team (HSWET). Education: Bachelor’s in Mechanical Engineering and Society, McMaster University (1997) MS in Mechanical Engineering, UC Berkeley (1998) PhD in Control and Dynamical Systems, Caltech (2010) Postdoctoral Fellow in Computing and Mathematical Sciences, Caltech (2010–2012) Research Interests: Her work focuses on modeling, analysis, and control of large-scale networked systems in applications such as wall-bounded turbulent flows, wind farms, and power grids. Key areas include: Control systems for renewable energy integration Wake dynamics and wind farm optimization Fluid-structure interactions and turbulence Networked dynamical systems and distributed control Her lab employs computational methods from fluid mechanics, applied mathematics, and optimization. Recent Research Trends: Articles emphasize interdisciplinary approaches combining turbulence modeling, wind energy systems, and control theory. Key themes include wake steering optimization, offshore wind farm dynamics, and turbulence control using riblet surfaces. Recent work also explores climate resilience in energy systems and market mechanisms for renewable integration. Awards and Recognition: NSF CAREER Award (2017) ONR Young Investigator Program Award (2017) JHU Discovery Awards (2019, 2022, 2024) IEEE Senior Member and SIAM/APS/ASME Fellow Advising and Grants: Advised over 10 PhD students, including Carl Shapiro, Ismail Hameduddin, and Genevieve Starke. Secured grants from NSF, ONR, and JHU. Her team developed the JHTDB-wind database for wind farm research. Labs and Collaborations: Leads the Networked and Spatially Distributed Systems Research Group. Collaborates with institutions like Portland State University on offshore wind potential. Serves on editorial boards for Physical Review Fluids and Annual Review of Fluid Mechanics .

• Wall-Bounded Turbulent Flow
• Wind Farm Modeling and Control
• Grid Integration of Renewable Energy
• +3 more

Xiong Wang is a J.J. Sylvester Assistant Professor in the Department of Mathematics at Johns Hopkins University (non-tenure track). His research focuses on probability theory, stochastic analysis, and their applications to machine learning and complex systems. He holds a Ph.D. from the University of Alberta (2022), where he received the Faculty of Science Dissertation Award. Education: Ph.D. in Mathematics, University of Alberta (2022) Advisor: Professor Yaozhong Hu Research Interests: Probability Theory & Stochastic Processes Machine Learning Algorithms Partial Differential Equations (PDEs) Interacting Particle Systems Inverse Problems in Stochastic Dynamics Mathematical Foundations of Data Science Teaching: Instructor for advanced courses at Johns Hopkins, including Stochastic Differential Equations, Partial Differential Equations, and Real Analysis Prior teaching roles at University of Alberta (Calculus, Linear Algebra) Recent Activities: Speaker at over 30 international conferences/seminars since 2021 Active in SIAM, CMS, and ICERM events Collaborations with institutions globally (e.g., Peking University, Brown University)

• Probability Theory
• Stochastic Analysis
• Machine Learning
• +5 more

Tenio Popmintchev is an Assistant Professor at the University of California, San Diego (UCSD), previously affiliated with the JILA institute at the University of Colorado Boulder. His research focuses on extreme nonlinear optics and attosecond science, particularly in developing tabletop X-ray lasers and high-harmonic generation techniques. He holds a Ph.D. in Physics from the University of Colorado Boulder (2010). Key research areas include phase-matched generation of coherent X-rays, ultrafast imaging of quantum materials, and applications in magnetic circular dichroism spectroscopy. His work has led to breakthroughs in generating high-brightness X-ray pulses using mid-infrared and ultraviolet lasers, enabling compact and cost-effective X-ray sources. Notable achievements include the Science News Young Scientist award and multiple patents on X-ray generation methods. His research has been published in top journals like Science and Nature Photonics , with contributions to ultrafast laser technology, coherent X-ray sources, and quantum design of light. Awards: Science News Young Scientist award (2016), U.S. Patents 8,462,824 (2013) and 61873794 (2015). Labs/Teams: JILA (Boulder), UCSD Physics Department labs. Future Work: Expanding tabletop X-ray laser applications, improving laser efficiency, and exploring quantum materials dynamics.

• Extreme Nonlinear Optics
• Attosecond Science
• X-ray Lasers
• +3 more

Hongbo Zhao is an Assistant Professor jointly appointed in the Department of Physics and Department of Chemistry & Biochemistry at the University of California, San Diego (UCSD), effective September 2024. His research bridges biophysics, soft matter, active matter, physical chemistry, and applied mathematics, focusing on uncovering fundamental principles of soft and living matter through interdisciplinary approaches. Prior to UCSD, he was a Princeton Bioengineering Initiative (PBI2) Distinguished Postdoctoral Fellow at Princeton University, working with Andrej Košmrlj, Clifford P. Brangwynne, and Sujit Datta. He earned his Ph.D. in Chemical Engineering from MIT under Martin Z. Bazant, focusing on energy materials and data-driven discovery in chemical physics. Key research interests include biological phase separation and phase transitions, physics of soft and active matter, and data-driven discovery from image-based datasets. His work explores how biomolecular condensates organize cellular components, the interplay between nonequilibrium activities and phase separation, and emergent phenomena in active matter systems. He develops computational tools and statistical mechanics frameworks to analyze complex systems across scales. Zhao's recent publications highlight contributions to understanding condensate-driven DNA repositioning, chemotactic motility-induced phase separation, and learning reaction kinetics from X-ray imaging. His research has been supported by grants including the PBI2 Fellowship and collaborations with institutions like Stanford University and the SLAC National Accelerator Laboratory. He leads the Zhao Research Group at UCSD, fostering an interdisciplinary environment for graduate and undergraduate students in theoretical and computational biophysics.

• Biophysics
• Soft Matter
• Active Matter
• +5 more

Wentao Tang is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at North Carolina State University. His research focuses on data-driven control algorithms integrating nonlinear control theory with machine learning, optimization of complex systems, and network topology-based decomposition for distributed control. He received his B.S. in Chemical Engineering and Mathematics from Tsinghua University (2015) and his Ph.D. in Chemical Engineering from the University of Minnesota (2020), followed by work as a process control engineer at Shell Global Solutions. His research group, DISCO (Data-Informed Systems Control & Optimization), explores topics such as distributed optimization, dissipativity learning control, and network structure analysis for industrial processes. Notable contributions include methods for optimal decomposition of large-scale systems and development of Lyapunov envelope algorithms for distributed MPC. Recent group additions include Ph.D. student Jarod Morris and postdoctoral researcher Dr. Xiuzhen Ye. His work emphasizes bridging theoretical control advancements with practical applications, particularly in plantwide control systems and resilient industrial automation. The group actively collaborates on projects funded by startup grants and industry partnerships, with publications spanning control theory, optimization, and machine learning.

• Data-Driven Control
• Nonlinear Control Theory
• Machine Learning
• +3 more

Ivana Komunjer is a Professor of Economics at Georgetown University's Department of Economics. Her research focuses on econometric theory, financial economics, and macroeconomics, with a particular emphasis on asymmetric power distribution (APD) models and their applications to risk measurement. She has developed widely used MATLAB tools for APD density functions, including pdf, cdf, quantile, and random number generators. Her work also addresses dynamic stochastic general equilibrium (DSGE) models, nonlinear filtering, and quantile methods in economic forecasting. Komunjer teaches advanced courses such as Macroeconometrics and Theory of Financial Markets. Her contributions span theoretical econometrics, financial modeling, and policy analysis, with a strong emphasis on methodological innovation.

• Econometrics
• Financial Economics
• Macroeconomics
• +4 more

Suhan Zhong is a Visiting Assistant Professor in the Department of Mathematics at Texas A&M University, mentored by Prof. Jianxin Zhou. He holds a Ph.D. in Mathematics from UCSD (2022), supervised by Prof. Jiawang Nie. His research focuses on optimization theory, with expertise in operations research, mathematical programming, and their applications to data science and machine learning. He employs techniques from real algebraic geometry, moment problems, and stochastic optimization to tackle complex problems like multilevel optimization, equilibrium models, and tensor analysis. His work includes advancements in generalized Nash equilibrium problems, distributionally robust optimization, and global solution methods for nonconvex stochastic programs. He teaches MATH 311 and contributes to the TAMU Math Circle as an instructor. Current research trends include polynomial optimization under uncertainty, robust constraint handling, and algorithmic development for bilevel systems. His articles reflect contributions to stochastic programming, equilibrium analysis, and tensor decomposition. Presently seeking tenure-track or postdoctoral positions, Zhong emphasizes interdisciplinary approaches to optimization challenges. His work bridges theoretical foundations with practical applications in finance, data science, and engineering systems.

• Optimization
• Operations Research
• Mathematical Programming
• +3 more

Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).

• High-Dimensional Statistics
• Nonparametric Statistics
• Statistical Learning
• +9 more

Moody T. Chu is a Professor in the Department of Mathematics at North Carolina State University since 1982, holding a PhD from Michigan State University. His research focuses on numerical linear algebra, dynamical systems, inverse problems, and quantum computing. He has received prestigious teaching awards including the Alumni Distinguished Undergraduate Professorship (2006) and multiple Board of Governors Awards (2010, 2013, 2014). His work bridges computational mathematics with applications in physics, engineering, and data science. Research interests include numerical methods for differential equations, tensor approximation, and quantum simulation. His articles explore topics like Cartan decomposition for quantum Hamiltonians, Lax dynamics, and low-rank tensor approximations. Over 200 publications span numerical analysis, inverse eigenvalue problems, and optimization techniques. Chu’s contributions also address algorithm design for matrix completion and structured low-rank approximations. He has advised numerous graduate students (details not listed here) and led projects on adaptive optics and stochastic processes. His work on nonnegative matrix factorization and Markov chain dynamics has influenced data mining and machine learning applications. Chu’s lab focuses on advancing computational frameworks for complex systems, emphasizing interdisciplinary collaboration.

• Numerical Ordinary Differential Equations
• Numerical Linear Algebra
• Dynamical Systems
• +5 more

Pavel Kurilovich is a Postdoctoral Fellow at Harvard University. His research focuses on experimental condensed matter physics, particularly in the realm of superconducting qubits and their operational challenges. He holds a Ph.D. from Yale University (2025), advised by Michel Devoret, with a dissertation addressing unwanted transitions in superconducting qubits induced by microwave drives. His work systematically categorizes decoherence mechanisms, including spurious resonances, photon scattering, and AC Stark effects. Key research interests include quantum computing hardware development, superconductivity, and experimental approaches to mitigate qubit decoherence. Kurilovich's experimental studies aim to enhance qubit operation fidelity by suppressing microwave-induced transitions, a critical step toward fault-tolerant quantum computation. Recent articles highlight advancements in characterizing qubit transitions, quasiparticle dynamics, and readout optimization. His contributions include developing methodologies to stabilize qubit states and improve measurement precision through high-frequency techniques and gap-engineered transmons. No scientific awards are listed, though his work is foundational for quantum hardware advancements. His research aligns with Harvard's efforts in quantum information science and superconducting systems.

• Condensed Matter Physics
• Quantum Computing
• Superconductivity
• +3 more

Nikhil Karanjgaokar is an Associate Professor of Aerospace Engineering at Worcester Polytechnic Institute (WPI), where he has been since August 2015. His research focuses on experimental and computational mechanics of novel materials and structures, particularly in granular media dynamics, nanostructured materials, and optical measurement techniques. He leads the Structures and Materials Laboratory (HL 028) , equipped with advanced tools such as Digital Image Correlation (DIC), high-speed imaging systems, and laser Doppler vibrometers. Education: B.Tech. in Mechanical Engineering, National Institute of Technology Calicut, India (2006) M.S. in Mechanical Engineering, Carnegie Mellon University (2007) Ph.D. in Mechanical Engineering, University of Illinois at Urbana-Champaign (2013) Research Interests: Micro/nano-scale experimental mechanics Temperature and strain rate effects on nanostructured materials Dynamic response of granular media and inhomogeneous materials Optical measurement techniques for material characterization His lab investigates granular systems under impact, adaptive metamaterials, and thin-film mechanics. Notable projects include developing bulletproof vests with self-optimizing materials and exploring interfacial adhesion in polymer-based composites. His work has been featured in media such as WBUR and the Worcester Business Journal .

• Experimental Mechanics
• Nanostructured Materials
• Granular Media Dynamics
• +3 more

Francesca Parise is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Her research focuses on multi-agent systems, particularly analyzing large networks of autonomous decision-makers in transportation, finance, and social systems, employing tools from control theory, game theory, and optimization. She holds a PhD from ETH Zurich and was a Postdoctoral Research Fellow at MIT's Laboratory for Information and Decision Systems under Prof. Asuman Ozdaglar. Her work bridges theoretical foundations with practical applications in networked systems and interventions. Education: Bachelor's and Master's from University of Padua, Italy PhD in Control Engineering from ETH Zurich (2016) Her research interests span game-theoretic models for network analysis, learning dynamics in time-varying systems, and optimal intervention strategies. Key themes include synchronization in power grids, epidemic control via testing, and dynamic resource allocation. Recent work emphasizes graphon-based frameworks for large-scale network games and computational methods for equilibrium analysis. She explores both theoretical guarantees and real-world applications in transportation and energy systems. Publications highlight contributions to networked game theory, stochastic systems, and control applications, with a focus on scalable solutions for complex systems. Her work often intersects with interdisciplinary challenges in systems biology and socio-technical networks. She currently holds an office at Frank H. T. Rhodes Hall, Cornell University.

• Multi-Agent Systems
• Network Theory
• Control Systems
• +6 more