Find Academic Supervisors

Maya PrzybylskiView profile

Associate Professor

• Architecture
• Computational Design
• Data-Driven Design
• +4 more

Maya Przybylski is O’Donovan Director and Associate Professor at the School of Architecture, University of Waterloo , where she co-founded the DATAlab research group. She holds an M.Arch from the University of Toronto’s Faculty of Architecture, Landscape and Design—earning the Royal Architectural Institute of Canada Medal—and a BSc in Computer Science from the University of Toronto. Research Focus Maya’s scholarship integrates architecture with software engineering to interrogate how data abundance and computational design transform architectural theory, methods, tools, and outcomes. Her SSHRC-funded “Soft Materials” project (2018-) develops architecture-specific approaches that weave sociocultural agency into the technical capacities of data-driven design. Her design work fuses software, data, hardware and physical materiality to envision citizen-oriented, software/data-enabled cities. Awards & Recognition Sandford Fleming Foundation Award for Excellence in Undergraduate Teaching (2018) University of Waterloo Faculty Award (2019) Teaching & Advising Maya has taught core and advanced design studios and research seminars including: ARCH 193 – Design Studio ARCH 484 – Architectural Research ARCH 684 – Special Topics in Architecture ARCH 693 – Thesis Research & Design Studio II She is currently accepting applications from graduate students interested in computational design, data ethics, and experimental material systems. Editorial Leadership Maya is a founding editor of the bracket book series, co-editing bracket [On Farming] (2010) and bracket [At Extremes] (2015), both supported by the Graham Foundation for the Arts.

• Sustainable Process Technology
• Process Optimization
• Carbon Capture and Utilization
• +3 more

Edwin Zondervan is a Full Professor in Sustainable Process Technology, specializing in process optimization, renewable energy systems, and carbon capture technologies. His work contributes to UN Sustainable Development Goals related to affordable and clean energy, climate action, and industry innovation. Research focuses on biofuel production, green hydrogen systems, and sustainable process design through superstructure optimization frameworks. He actively engages in interdisciplinary studies, integrating data-driven methods and digital twin technologies to address energy transition challenges. Recent research highlights include advancing hydrogen-to-power systems, optimizing biodiesel production with heat integration, and stochastic modeling for biorefinery design. His contributions span academic publications, conference presentations (e.g., 2022 talk on PSE in a changing world), and collaborative projects. Zondervan emphasizes agile education strategies to prepare graduates for evolving industry needs, advocating for process systems engineering as a cornerstone of sustainable development. Key themes in his work include decarbonization pathways, energy storage solutions, and circular economy applications. While no awards are explicitly listed, his high citation count (2,895) and h-index (28) reflect significant academic impact. Ongoing efforts focus on integrating renewable energy systems with industrial processes to achieve net-zero goals.

Filippo PaganiView profile

Research Fellow

• Markov Chain Monte Carlo
• Variational Inference
• Tempering
• +7 more

Filippo Pagani is a Postdoctoral Research Fellow at the University of Warwick, working on the OCEAN grant under Professors Gareth Roberts and Adam Johansen. His research focuses on advanced statistical methodologies and their applications in diverse fields such as healthcare and astrophysics. His research interests include: Markov Chain Monte Carlo (MCMC) Variational Inference Tempering and Irreversibility techniques Bayesian Statistics and Machine Learning Applications in disease subtyping, biomarker discovery, and inverse problems Recent publications explore topics such as Bayesian outcome-guided models for precision medicine, numerical methods for MCMC sampling (e.g., ZigZag algorithms), and applications in astrophysical data analysis. Notable works include contributions to disease subtyping via variational mixtures and modeling the impact of the COVID-19 pandemic on healthcare systems. He is affiliated with the OCEAN grant and has contributed to short-term forecasting models during the early stages of the pandemic. No formal advisees are listed.

Bruno SudretView profile

Professor

• Uncertainty Quantification
• Reliability Analysis
• Sensitivity Analysis
• +3 more

Prof. Bruno Sudret is a Full Professor of Risk, Safety, and Uncertainty Quantification at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. His research focuses on computational methods for uncertainty quantification, reliability analysis, and Bayesian approaches for model calibration. He has contributed to the development of the UQLab software framework and the UQWorld community platform. Before joining ETH Zurich in 2012, Sudret held roles at the University of Berkeley (postdoc), EDF R&D (researcher and group head), and Phimeca Engineering (Director of Research and Strategy). Education: Master's in Science from Ecole Polytechnique (1993), Master's and PhD in Civil Engineering from Ecole Nationale des Ponts et Chaussées (1996, 1999). His work emphasizes stochastic simulators, surrogate modeling, and reliability-based design optimization. He serves on editorial boards for journals like Reliability Engineering and Systems Safety and Structural Safety .

• Energy Systems
• Data-Driven Control
• Building Automation
• +3 more

Paul Scharnhorst is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), focusing on energy systems, data-driven control, and building automation. His work bridges theoretical advancements in machine learning and practical applications in renewable energy integration and demand response. He has contributed to the development of uncertainty-aware modeling frameworks and open-source tools for benchmarking building controllers. Education: Completed a doctoral thesis in 2024 titled Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems under supervision of Colin Neil Jones and Baptiste Schubnel at EPFL. Research emphasizes robust control methodologies, kernel-based learning, and energy flexibility coordination in decentralized systems. His tools, such as the Energym library, facilitate standardized testing of building control strategies using simulation models from EnergyPlus and Modelica. Key contributions include uncertainty quantification in battery models for buildings, deterministic error bounds in kernel regression, and learning-based predictive control with safety guarantees. Collaborations involve interdisciplinary teams addressing challenges in smart grid integration and building energy management.

Samik BasuView profile

Professor

• Formal Methods
• Software Engineering
• Computer Security
• +6 more

Samik Basu is a Professor and Director of Graduate Education in the Department of Computer Science at Iowa State University. His research focuses on formal methods, software engineering, and computer security. He holds a Ph.D. from Stony Brook University (2003), an M.S. from Stony Brook (2001), and a B.E. from Jadavpur University (1998). Research Interests: Formal Verification and Model Checking Submodular Optimization and Algorithm Design Qualitative Preference Reasoning Web Services Composition and Security Autonomous Systems and Control Theory His recent work emphasizes submodular maximization algorithms, preference-based decision systems, and formal methods for safety-critical systems. Key grants include NSF awards IIS 225823, CCF 1555780, and others. He advises a diverse group of graduate students in the Formal Methods Group, focusing on projects like intrusion response systems, neural network verification, and multi-stakeholder preference analysis. Students advised include PhD candidates like Erik Rauer and Yanhui Zhu, along with numerous MS/BS researchers. His lab has published extensively in top venues like AAAI, IJCAI, and IEEE conferences.

David D. GamarnikView profile

Professor

• Discrete Probability
• Optimization and Algorithms
• Quantum Computing
• +3 more

David D. Gamarnik is a Professor of Operations Research at the MIT Sloan School of Management, affiliated with the Operations Research and Statistics Group. He holds a BA in Mathematics from New York University (1993) and a PhD in Operations Research from MIT (1998). Prior to joining MIT in 2005, he was a Research Staff Member at IBM T.J. Watson Research Center. His research focuses on discrete probability, optimization, quantum computing, machine learning, and stochastic processes. He serves as an Area Editor for Mathematics of Operations Research and has held editorial roles in journals such as Operations Research and Annals of Applied Probability . Education: Bachelor of Arts in Mathematics, New York University (1993) PhD in Operations Research, MIT (1998) His honors include the Erlang Prize, Best Publication Award from INFORMS’ Applied Probability Society, and recognition as a finalist for the Franz Edelman Prize. He is a Fellow of the Institute for Mathematical Statistics, Institute for Operations Research and Management Science, and the American Mathematical Society. His academic contributions span theoretical computing, algorithm design, and applications in healthcare (e.g., kidney exchange models). Recent work explores algorithmic barriers in optimization, quantum computing, and high-dimensional statistics.

Laura GrigoriView profile

Professor

• Numerical Linear Algebra
• High Performance Computing
• Deterministic and Randomized Algorithms
• +4 more

Laura Grigori is a Full Professor and Chair of High Performance Numerical Algorithms and Simulations at EPFL's School of Basic Sciences (SB) Department of Mathematics (MATH). Her research focuses on numerical linear algebra, high performance computing, and tensor computations, with applications in astrophysics and molecular simulations. She leads the HPNalgs lab and teaches courses in numerical analysis and HPC. Her awards include the SIAM Supercomputing Career Prize (2024) and SIAM Fellow distinction (2020). She advises four PhD students and has authored numerous papers on communication-avoiding algorithms, randomized methods, and parallel linear algebra techniques. Her work addresses scalability challenges in scientific computing and large-scale data analysis. Labs/Teams: HPNalgs Lab (https://www.epfl.ch/labs/hpnalgs/) Grants: ERC Synergy Grant (2019) for Extreme-scale Computational Chemistry

Matthias KatzfussView profile

Professor

• Computational Spatial Statistics
• Spatio-Temporal Statistics
• Gaussian Processes
• +5 more

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

• Calculus of Variations
• Nonlinear Partial Differential Equations
• Optimal Transport
• +4 more

Emanuela Radici is an Assistant Professor at the Università degli Studi dell'Aquila, specializing in advanced mathematical analysis with a focus on Calculus of Variations, Nonlinear PDEs, and Deterministic Particle Approximation. Her research explores topics such as optimal transport, aggregation-diffusion equations, and traffic models. She has organized workshops on Calculus of Variations and PDEs, demonstrating leadership in academic collaboration. Her work bridges theoretical analysis with applied problems, including opinion dynamics and congestion modeling. Key contributions include particle approximation methods for conservation laws and studies on BV homeomorphisms. She actively participates in international conferences and research events, contributing to the advancement of geometric methods in calculus of variations. Dr. Radici’s research output includes 22 published papers, with recent works addressing nonlinear mobilities, degenerate traffic models, and minimal extensions in geometric norms. Her interdisciplinary approach integrates functional analysis, differential geometry, and numerical methods.

Dustin MixonView profile

Associate Professor

• Applied Harmonic Analysis
• Mathematical Signal Processing
• Compressed Sensing

Dustin Mixon is Associate Professor of Mathematics at Ohio State University, specializing in applied harmonic analysis, compressed sensing, and frame theory. His research develops mathematical foundations for signal processing, including equiangular tight frames, optimal line packings, and phase retrieval guarantees. Recent work addresses max filtering injectivity, Euclidean distortion of orbit spaces, and clustering algorithms via semidefinite programming. Mixon has contributed to the theory of numerically erasure-robust frames (NERFs) and deterministic matrix constructions with the restricted isometry property. He co-authored papers on group-invariant packings and phase transitions in phase retrieval, solving open problems in combinatorial geometry. His blog 'Short, Fat Matrices' disseminates research on frame theory and compressed sensing.

• Bayesian Statistics
• Markov Chain Monte Carlo
• Probability Theory
• +3 more

Georgios Vasdekis is a Research Fellow at the Department of Statistics, University of Warwick. He completed his PhD in 2020 under the supervision of Prof. Gareth Roberts and Dr. Jon Warren, focusing on Markov Chain Monte Carlo (MCMC) techniques and their Bayesian applications. His work includes advancing Piecewise Deterministic Markov Processes (PDMPs) for Big Data sampling, notably the Zig-Zag process. He holds a B.Sc. in Mathematics from the University of Athens (2015) and a MASt from Homerton College, University of Cambridge (2016). Education: B.Sc. Mathematics, University of Athens, 2015 MASt, University of Cambridge, 2016 Research Interests: Vasdekis specializes in theoretical and applied probability, particularly in MCMC methods and PDMPs. His current projects explore the Zig-Zag process for efficient posterior sampling in high-dimensional Bayesian models. He also investigates stochastic integrable systems and broader applications of probability theory. Talks/Posters: Key presentations include discussions on the Zig-Zag process’s geometric ergodicity (IPSA Colloquium 2019), its generalization (Warwick Seminar 2019), and applications in Bayesian tools at the Greek Stochastics Meeting (2018). He actively participates in Probability and Simulation reading groups at Warwick. Awards: EPSRC-funded PhD studentship (2016–2020) Onassis Foundation Scholarship (2015–2016) Teaching: From 2017–2019, Vasdekis taught Probability, Analysis, and Mathematical Statistics courses as a Teaching Assistant. He supervised first-year non-mathematics students in multiple terms, fostering foundational understanding in quantitative disciplines. Events & Collaborations: He attended key events like the LMS Summer School in Computational Statistics (2018), the Greek Stochastics Meeting (2018), and the MASDOC retreat. His work aligns with interdisciplinary teams at Warwick, including the Probability group and MASDOC Centre.

Andreas R. BlassView profile

Professor

• Set Theory
• Category Theory
• Finite Combinatorics
• +5 more

Andreas R. Blass is a Professor of Mathematics at the University of Michigan, affiliated with the Department of Mathematics. His primary research focuses on mathematical logic, set theory, category theory, finite combinatorics, and theoretical computer science. He serves as associate chairman for regular faculty appointments, reflecting his administrative contributions. His work bridges foundational mathematics with applications in computer science, such as linear logic, complexity theory, and algorithmic analysis. Blass's research explores topics like infinitary combinatorics, cardinal characteristics of the continuum, topos theory, and geometric morphisms. He has contributed significantly to understanding connections between category theory and set theory, as well as the theoretical underpinnings of computation. His publications span journals in logic, computer science, and mathematics, addressing foundational questions in these interdisciplinary areas.

• Mechanical Engineering
• Additive Manufacturing
• Smart Materials
• +4 more

Pascal LAFON is a Full Professor at the University of Technology of Troyes (UTT), affiliated with the Laboratory of Mechanical & Material Engineering (LASMIS) and the Department of Physics, Mechanics, Materials and Nanotechnology . He serves as the Director of Corporate Relations at UTT since 2023 and previously led LASMIS lab from 2012-2016. His academic duties include coordinating the 'Mechanics, Material and Advanced Manufacturing' master’s program (2018-2023) and the UTT doctoral program's 'Material, Mechanics, Optics and Nanotechnology' training (2012-2023). Director of Corporate Relations, UTT (2023-present) Head, LASMIS Lab (2012-2016) Master’s Program Coordinator (2018-2023) Doctoral Training Supervisor (2012-2023) His research focuses on Mechanical Optimization (mixed/random variables, nonlinear objectives), Additive Manufacturing (design, material behavior), and Smart Material Applications . Current projects integrate metamodeling (RBF, Kriging) with multi-objective optimization for manufacturing processes and structural systems. Publications and projects demonstrate expertise in: metal forming , composite materials , springback analysis , and breakwater design . He has supervised 13 PhD students since 1996, including international collaborations with Lebanese University and Northwestern Polytechnical University .

• Mechanical Engineering
• Aerospace Engineering
• Systems Engineering
• +3 more

Gerhard Stefan Székely is a Lecturer and Head of the Bachelor's Program in Mechanical Engineering at the Lucerne School of Engineering and Architecture (HSLU). He specializes in mechanical systems for space research, systems engineering, and innovative teaching methods like flipped classrooms and blended learning. His industry experience includes 16 years at RUAG Space Zurich, leading R&D in FEM analysis for satellite mechanisms and systems engineering for projects like GAIA BRM and ALADIN instruments. Education: TU München: Mechanical Engineering (Aerospace focus) PhD from Universität Innsbruck: Stochastic structural mechanics for ESA projects Research Focus: Space mechanical systems, systems engineering methodologies, and educational innovation in STEM. Notable projects include the VenSpec-H spectrometer for ESA's EnVision mission and the COW experiment studying cartilage in microgravity. Professional Contributions: Authored over 20 peer-reviewed publications and technical reports on space mechanisms, structural reliability, and educational methodologies. Leads HSLU's contributions to national MINT education networks.