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Morgan JonesView profile

Lecturer

• Dynamic Programming
• Reinforcement Learning
• Optimal Control
• +4 more

Dr. Morgan Jones is a Lecturer in Machine Learning and Control Theory at the University of Sheffield's School of Electrical and Electronic Engineering. He holds a B.S. and MMath in Mathematics from the University of Oxford (2016), and a PhD in Aerospace Engineering from Arizona State University (2021). His research focuses on dynamic programming, reinforcement learning, optimal control, and nonlinear systems analysis, with applications to battery scheduling, path planning, and obstacle avoidance. He is affiliated with the Cybernetic Systems and Controls Laboratory (CSCL) at Arizona State University, where he conducted research from 2016 to 2021. Research interests include developing convex optimization tools for solving Hamilton-Jacobi-Bellman (HJB) PDEs, approximating regions of attraction and invariant sets using sum-of-squares (SOS) programming, and advancing methodologies for controller synthesis and system stability analysis. His work bridges theoretical control systems with practical applications in robotics, energy systems, and autonomous navigation. Dr. Jones has published extensively in top-tier journals and conferences, including IEEE Transactions on Automatic Control, Automatica, and the American Control Conference (ACC). His recent contributions address sublevel set approximation, sparse identification of nonlinear dynamics, and bounding controller performance via value function analysis. He teaches advanced systems engineering courses and advises on PhD research in control theory.

Srinivas AluruView profile

Professor

• High Performance Computing
• Data Analytics
• Bioinformatics
• +3 more

Srinivas Aluru is a Regents' Professor and Senior Associate Dean at the Georgia Institute of Technology's College of Computing , within the School of Computational Science and Engineering . His research focuses on High Performance Computing , Bioinformatics , Systems Biology , and Applied Algorithms . He has pioneered parallel methods in computational biology, contributing to plant genome assembly and analysis. Current work includes bioinformatics for high-throughput DNA sequencing and systems biology network inference using Bayesian and mutual information approaches. Aluru holds Fellowships from AAAS and IEEE and has received awards such as the NSF Career Award (1997), IBM Faculty Award (2002), and Swarnajayanti Fellowship (2007). He serves on editorial boards for journals like IEEE Transactions on Parallel and Distributed Systems and International Journal of Data Mining and Bioinformatics . His affiliations include the Institute for Data Engineering and Science (IDEaS) and Machine Learning@GT . Research trends in his articles span genomic data processing, parallel algorithms, and network inference, emphasizing scalability and computational efficiency. He leads efforts in error correction, genome assembly, and large-scale gene regulatory network construction.

Shi-Jie CaoView profile

Professor

• Built Environment
• Ventilation Design and Control
• Indoor Air Quality
• +5 more

Dr. Shi-Jie Cao is a full Professor and Director of the Center for Sustainable Built Environment (CSBE) at Southeast University. He is also a Visiting Professor at the Global Centre for Clean Air Research (GCARE), University of Surrey , and Osaka Prefecture University . Dr. Cao pioneered the multi-disciplinary field of Sustainable Built Environment Systems in China, supported by the National Natural Science Foundation of China (NSFC) . Education: Ph.D., Mechanical Engineering, KU Leuven M.Sc., Mechanical Engineering, Chalmers University of Technology B.E., Xi’an University of Science and Technology Research Focus: Dr. Cao's work integrates Air Quality and Health , Intelligent Ventilation Control , and Urban Environmental Design . His projects address urban heat island mitigation , air pollution control , and low-carbon city development , leveraging CFD modeling , AI-driven ventilation systems , and sensor deployment strategies . Research Trends: His recent publications emphasize urban climate resilience , AI and computer vision in environmental control , and integrated green infrastructure solutions . Key themes include heat-pollution mitigation , rural-urban land use , and health-focused building design . Scientific Recognition: World’s Top 2% Scientists (Built Environment Design, 2020-2024) BEST PAPER AWARD, Energy and Buildings (2021) First Prize, China Award for Science and Technology in Construction (2021) Leadership and Collaborations: Dr. Cao leads the National Key Research and Development Program on urban renewal and has secured over 50M RMB in research grants. He collaborates with institutions such as Harvard University , UCL , and Tampere University , and serves as President of the International Society of Built Environment (ISBE) .

Giacomo BaggioView profile

Assistant Professor

• Control Theory
• Network Science
• Quantum Information
• +3 more

Giacomo Baggio is an Assistant Professor in the Department of Information Engineering at the University of Padova. His research focuses on control theory, network science, and dynamical systems with applications to neuroscience, quantum information, and machine learning. He explores topics such as network controllability, system identification, and the interplay between network structure and functional properties. Key areas of expertise include data-driven control methodologies, analysis of complex networks (both brain networks and engineered systems), and mathematical modeling of nonlinear systems. His work bridges theoretical developments with practical applications in areas like neural networks, quantum state preparation, and distributed control systems. Baggio has contributed to advancements in understanding memory effects in Hopfield networks, optimizing control energy in large-scale systems, and developing frameworks for brain functional connectivity control. His research also addresses fundamental questions in system identification, including finite-sample guarantees and Bayesian approaches to control design. He has been actively involved in editorial and organizational roles within academic communities and has served as a Ph.D. examiner. His projects emphasize interdisciplinary collaboration between engineering, neuroscience, and mathematics to tackle challenges in modern complex systems.

Hendrik WilkaView profile

Researcher

• Numerical Analysis
• Scientific Computing
• Computational Physics
• +2 more

Hendrik Wilka is a researcher at the Department of Mathematics, Technische Universität Darmstadt. His work focuses on numerical analysis and scientific computing, with an emphasis on adaptive algorithms and sparse grid techniques. He has served as a teaching assistant for multiple courses including Numerical Analysis, Mathematical Modelling, and Numerical Linear Algebra under professors such as Jens Lang and Tabea Tscherpel. His recent research includes developing efficient collocation algorithms for piecewise smooth functions with kinks, published in the Journal of Computational Physics (2025). Wilka leads the development of simulation software for pandemic modeling and population dynamics, showcased in projects like the 'Long Night of Mathematics.' His technical contributions include maintaining public resources like PGP keys and academic infrastructure tools. Research interests include advancing computational methods for complex systems, with applications in physics and engineering. His technical activities involve academic software development and educational support through teaching assistance.

Erkki SomersaloView profile

Professor

• Inverse Problems
• Biomedical Imaging
• Mathematical Biology
• +3 more

Erkki Somersalo is a Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. His research focuses on inverse problems with applications in biomedical imaging and mathematical biology. He holds a PhD from the University of Helsinki and has contributed extensively to imaging modalities like Electrical Impedance Tomography (EIT), Magnetoencephalography (MEG), and brain metabolism modeling. Research Interests include: Inverse problems in biomedical imaging Bayesian statistical methods for computational science Mathematical models of brain energetics and neuromuscular systems Development of hierarchical Bayesian models for sparse data Recent work emphasizes interdisciplinary applications, such as modeling pandemic dynamics, muscle recruitment variability, and astrocyte-neuron metabolic interactions. His research often combines advanced numerical methods with biological data analysis. Advising and Grants: While specific student names are not listed, his work involves collaborative projects with researchers in neuroscience, biophysics, and computational science. He has contributed to grant-funded initiatives exploring brain metabolism and pandemic modeling.

Enrique MalladaView profile

Associate Professor

• Control of Networked Systems
• Power Systems
• Optimization
• +5 more

Enrique Mallada is an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Mechanical Engineering, Applied Mathematics and Statistics, and Computer Science. He leads the Networks, Dynamics, and Learning Laboratory (NetDL2ab) and is a core member of MINDS (Mathematical Institute for Data Science) and ROSEI (Ralph O’Conner Sustainable Energy Institute). His research focuses on control systems, optimization, power systems, and machine learning, with applications in networked systems, energy grids, and distributed coordination. Education: B.S. in Telecommunications Engineering from Universidad ORT (2005), Ph.D. in Electrical and Computer Engineering with a minor in Applied Mathematics from Cornell University (2014). Postdoctoral research at Caltech’s Center for the Mathematics of Information (2013–2015). Research Interests : Networked systems (synchronization, distributed coordination), power systems (frequency control, grid resilience), optimization (time-varying algorithms, reinforcement learning), and machine learning (safety-critical applications, sparse recovery). Key Projects : Real-time optimization for infrastructure networks, voltage collapse stabilization in power grids, control of distributed energy resources, and safety-aware reinforcement learning. His work integrates tools from control theory, optimization, and machine learning to address challenges in large-scale systems. Awards : NSF CAREER Award, Caltech CMI Fellowship, Cornell Jacobs Fellowship, JHU Discovery/Catalyst Awards, and Excellence in Teaching Award. Advising & Grants : Mentors graduate students in ECE and related fields. Active in grants focused on energy systems, control theory, and AI safety. Organizes conferences like CISS 2019 and serves on technical committees for IEEE Smart-GridComm and ACC. Labs & Affiliations : NetDL2ab Lab (directed), MINDS (core member), ROSEI (core member), LCSR (affiliate), and Data Science & AI Institute (member). His research bridges academia and industry, addressing real-world challenges in energy and automation.

Josh SheltonView profile

Lecturer

• Fluid Dynamics
• Asymptotic Analysis
• Heat Transfer
• +5 more

Dr. Josh Shelton is a Lecturer in Fluid Dynamics at the University of St Andrews, affiliated with the School of Mathematics and Statistics. His research focuses on asymptotic analysis, fluid dynamics, and numerical methods, with applications to heat transfer, wave phenomena, and geophysical flows. His work explores complex systems such as capillary waves, Rossby waves, and ice-crystal icing physics, employing advanced mathematical techniques like exponential asymptotics and singular perturbation theory. Collaborations with researchers like Prof. Trinh and Dr. Peters have yielded impactful contributions to computational fluid dynamics and boundary layer modeling. Recent publications (2022–2025) emphasize the interplay between theoretical analysis and numerical simulations, addressing topics such as enthalpy-based models for phase change, viscous dissipation effects in surface waves, and trapped equatorial Rossby waves. These studies highlight advancements in understanding nonlinear phenomena and free-surface dynamics. No scientific awards are listed, but his active research program includes collaborations with international teams and contributions to peer-reviewed journals like Journal of Fluid Mechanics and SIAM Journal on Applied Mathematics . Dr. Shelton advises no listed students but collaborates extensively on grant-funded projects involving asymptotic methods and fluid mechanics applications.

Xiaochuan TianView profile

Associate Professor

• Mathematical Modeling
• Applied Analysis
• Numerical Analysis
• +4 more

Dr. Xiaochuan Tian is an Associate Professor in the Department of Mathematics at the University of California, San Diego (UCSD). She holds a PhD in Applied Mathematics from Columbia University (2017) and has held academic positions including Assistant Professor at UCSD (2020–2024), R.H. Bing Instructor at the University of Texas at Austin (2017–2020), and Postdoctoral Researcher at Columbia (2017). Her research focuses on numerical analysis, nonlocal models, fractional PDEs, and meshfree methods. She has secured grants including an NSF CAREER Award (2023) and Alfred P. Sloan Fellowship (2023). Research interests include nonlocal modeling, calculus of variations, asymptotic compatibility, and multiscale methods. Notable contributions span nonlocal function spaces, asymptotically compatible schemes, and numerical methods for integro-differential equations. She advises three PhD students and teaches advanced courses such as Numerical PDEs and Computational Mathematics. Her work has been recognized with awards like the SIAM Review SIGEST Award (2019) and AWM Dissertation Prize (2018). Grants: NSF DMS-2240180 (2023–2028) NSF DMS-2111608 (2021–2024) Awards: ICBS Frontiers of Science Award (2024) SIAM Outstanding Paper Prize (2016) Labs/Teams: Center for Computational Mathematics (CCoM) at UCSD Research Group on Nonlocal Modeling

Robert J. WebberView profile

Assistant Professor

• Mathematics of Information
• Data Science
• Scientific Computing
• +5 more

Robert J. Webber is an Assistant Professor in the Department of Mathematics at the University of California, San Diego (UCSD). He holds a Ph.D. in Mathematics from New York University (2021) and previously served as a postdoctoral scholar at the California Institute of Technology (Caltech) from 2021 to 2024, hosted by Joel A. Tropp. Research Focus: Webber specializes in randomized numerical methods for data science and scientific computing, including interdisciplinary applications in chemistry, astronomy, and geophysics. His work emphasizes randomized algorithms for matrix approximation, extreme-scale linear algebra, and rare event sampling. Notable projects include the randomly pivoted Cholesky method for kernel matrices and randomly sparsified Richardson iteration for large-scale linear systems. Teaching: He teaches probability and data science courses at the upper-undergraduate and graduate levels at UCSD. His collaborative projects include studying Mercury’s orbital instability and extreme weather patterns using rare event sampling techniques. Publications: Over 30 peer-reviewed articles since 2019, with notable contributions to journals like Communications on Pure & Applied Mathematics and SIAM Journal on Scientific Computing . His work has been featured in Forbes , SIAM News , and the California Business Journal .

Paul BourgadeView profile

Professor

• Probability Theory
• Random Matrices
• Statistical Physics
• +3 more

Paul Bourgade is a Professor of Mathematics at New York University's Courant Institute, affiliated with the Faculty of Arts and Science. He holds a Ph.D. from Université Paris 6 (2009), and prior degrees in Mathematics, Physics, and Computer Science from top-tier French institutions. His research focuses on probability theory, particularly random matrices and their connections to statistical physics, partial differential equations, and analytic number theory. He has authored numerous high-impact publications on topics like the random matrix universality class and the Riemann zeta function. Education: Ph.D., Mathematics, Université Paris 6, 2009 M.S., Probability and Computer Science, 2007 B.S., Mathematics and Physics, École Polytechnique, 2006 Research Interests: Bourgade explores the probabilistic structures underlying complex systems. His work bridges random matrix theory with statistical physics, addressing questions about eigenvalue distributions, universality phenomena, and connections to number theory. Recent projects include studies on non-Hermitian dynamics, zeta function zeros, and the universality of β-ensembles. Professional Roles: Editor, Annals of Probability Associate Editor, Communications on Pure and Applied Mathematics Organizer of the Courant Institute Probability Seminar Advising: Guided undergraduate/master’s research on zeta function extrema, Dirichlet L-functions, and Brownian behavior analogies.

Yufeng LiuView profile

Professor

• High-dimensional Data Analysis
• Machine Learning
• Nonparametric Statistics
• +3 more

Dr. Yufeng Liu is a Professor at the University of North Carolina at Chapel Hill, holding positions in the Department of Statistics and Operations Research, Department of Genetics, and Department of Biostatistics. He is affiliated with the Carolina Center for Genome Sciences and the UNC Lineberger Comprehensive Cancer Center. His research focuses on statistical machine learning, high-dimensional data analysis, and bioinformatics, with applications in cancer genomics and neuroimaging. Dr. Liu earned his Ph.D. in Statistics from The Ohio State University (2004), following an M.S. (2001) and B.S. (1999) from the same institution and Nankai University, respectively. He has received prestigious awards including the NSF CAREER Award (2008–2013) and election as a Fellow of the American Statistical Association (2013) and the Institute of Mathematical Statistics (2017). Research Interests: Machine Learning, High-dimensional Data Analysis, Nonparametric Statistics, Bioinformatics, Cancer Genomics Professional Service: Associate Editor roles in journals like the Journal of the American Statistical Association, and leadership in statistical societies His work spans methodological development for classification, regression, and clustering, with applications in genomics and medical data. Notable contributions include the development of robust classification methods and algorithms for high-dimensional datasets. Dr. Liu has been involved in significant collaborative projects, such as The Cancer Genome Atlas (TCGA), and holds grants focused on statistical methods for genomic data. His research has led to advancements in understanding cancer genetics and personalized medicine strategies.

• Optimization
• Control Theory
• Partial Differential Equations (PDEs)
• +3 more

Prof. Daniel Walter is a Junior Professor at the Institute of Mathematics, Humboldt University of Berlin, within the Faculty of Mathematics and Natural Sciences. His research focuses on non-smooth optimization, optimal control, and numerical analysis of partial differential equations (PDEs). He specializes in developing advanced methods for sensor placement in inverse problems and stabilizing control systems using computational techniques. His work integrates theoretical analysis with practical applications, addressing challenges in feedback stabilization, convergence of optimization algorithms, and data-driven approaches. Notable contributions include studies on extremal points in optimization norms, linear convergence rates of conditional gradient methods, and semiglobal stabilization using neural networks. Key Research Areas: Non-smooth optimization and sparse methods PDE-constrained optimization and control Inverse problems and sensor placement strategies Numerical methods for parabolic and elliptic equations Recent publications emphasize algorithmic advancements in optimization, with a focus on acceleration and convergence guarantees. His interdisciplinary approach bridges mathematical theory with engineering applications, particularly in stabilization and parameter estimation. Advising & Grants: While no specific grants or advisees are listed, his research indicates active involvement in training through cutting-edge projects in optimization and control.

Damian KozburView profile

Associate Professor

• Econometrics
• Machine Learning
• High-Dimensional Data Analysis
• +3 more

Prof. Damian Kozbur is an Associate Professor of Econometrics at the University of Zurich's Department of Economics, affiliated with the Digital Society Initiative. He holds a PhD from the University of Chicago (2014) and has been at UZH since 2016. His research focuses on integrating economic theory with machine learning tools, particularly in high-dimensional econometric models. Key areas include estimation techniques for weak instruments, panel data analysis, and clustering methods for statistical inference. He has contributed to journals like Econometrica and the Journal of Business and Economic Statistics. His work addresses applications such as market dynamics, causal inference in complex data, and policy analysis. Education: PhD in Econometrics from the University of Chicago Booth School of Business (2014), BA in Mathematics (2008). Previous roles include ETH Zurich Fellow (2014–2016). Teaching includes advanced econometrics and machine learning courses for doctoral students. Research Interests: High-dimensional econometrics, model selection, statistical inference, and applications of machine learning to economic problems. His recent articles tackle spatial dependence robustness, debiased machine learning, and forecast hedging with random forests. Professional Service: Associate Editor of the Journal of Business and Economic Statistics since 2022. Active in reviewing for top econometric outlets like Journal of Econometrics and Econometric Theory.

• Financial Econometrics
• Quantitative Finance
• Risk Management
• +7 more

Olivier Scaillet is a Research Fellow at the Swiss Finance Institute, affiliated with the University of Geneva. His work spans financial econometrics, quantitative finance, and risk management, with a focus on stochastic volatility models, copulas, and high-frequency data analysis. Research Interests : Financial econometrics and nonparametric estimation Stochastic volatility and jump-diffusion models Asset pricing and factor models in large panels Systemic risk and recovery rate density estimation Machine learning applications in finance Market microstructure and high-frequency data dynamics Scientific Contributions : Developed methodologies for nonstandard error analysis in multi-analyst studies Advanced techniques for testing stochastic dominance efficiency and latent factor models Explored copula-based goodness-of-fit tests and threshold effects in time series Innovated in American option pricing under complex market conditions