Find Academic Supervisors

• Groundwater Ecohydrology
• Hydro-geological Engineering
• Watershed Management
• +7 more

Ali Ameli is an Assistant Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC). He leads the HydroGeoScience for Watershed Management (HGS-WM) Research Group and is affiliated with the Institute of Applied Mathematics. His work focuses on understanding water and solute movement in watersheds, climate impacts, and land-use alterations to inform sustainable water management. Education: PhD in Geological Engineering (University of Waterloo, 2015), followed by postdoctoral research at the University of Saskatchewan and Uppsala University. Research Interests: Groundwater Ecohydrology, Hydro-geological Engineering, Watershed Management, Applied Hydro-geochemistry, Statistical Machine Learning. Recent projects include modeling water table dynamics via machine learning, snowmelt partitioning under climate variability, and interdisciplinary approaches for water security assessments. Supervision & Collaboration: He supervises graduate students in MSc and PhD programs and collaborates with agencies like water conservation organizations. Active in mentoring visiting researchers and co-op students, he emphasizes experiential learning through internships and placements. Affiliations: Institute of Applied Mathematics (UBC), leadership in interdisciplinary water research initiatives, and global collaborations with geochemists and ecologists.

Gabriel STOLTZView profile

Professor

• Computational Statistical Physics
• Molecular Dynamics
• Mathematical Analysis
• +4 more

Professor Gabriel Stoltz is a faculty member at École des Ponts ParisTech, affiliated with the Mathematical and Computer Engineering department. His research focuses on mathematical and numerical analysis of models in molecular simulation, with an emphasis on computational statistical physics. He holds a joint position as a senior researcher at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique). His work integrates techniques from probability theory, stochastic processes, functional analysis, and numerical analysis. He has contributed extensively to free energy computation methods, variance reduction techniques, and the development of machine learning approaches for enhanced sampling in molecular dynamics. Notable collaborations include projects with Tony Lelièvre and Mathias Rousset on free energy calculations and with institutions like Institut Henri Poincaré. Teaching responsibilities include courses on computational statistical physics, machine learning, and scientific computing. His pedagogical approach emphasizes flipped classrooms, supported by publications detailing innovative teaching methods. Key research themes include: Statistical mechanics and stochastic processes Numerical methods for partial differential equations Machine learning applications in molecular dynamics Quantum chemistry and electronic structure calculations His contributions span over 100 peer-reviewed articles, including seminal works on Langevin dynamics, hypocoercivity, and adaptive biasing algorithms. He has served as an editor for Springer Proceedings and co-authored influential textbooks such as Free Energy Computations .

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +3 more

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

Enrico RiccardiView profile

Associate Professor

• Molecular Dynamics
• Machine Learning
• Rare Event Simulation
• +7 more

Enrico Riccardi is an Associate Professor in Computational Engineering at the Department of Energy Resources, Faculty of Science and Technology, University of Stavanger (UIS), Norway. His work bridges computational chemistry, machine learning, and multi-scale modeling, with applications in energy, environmental science, and biophysics. Research Interests: His core expertise lies in molecular dynamics , rare event simulation methods (e.g., reaction kinetics and adsorption), and multi-scale modeling from molecular to continuum levels. He is a key developer of path sampling methodologies and software such as PyRETIS and PyVisA , enabling the study of slow and rare processes in complex systems. His research spans interfacial phenomena in emulsions, membrane permeation, atmospheric chemistry, and data-driven discovery of reaction pathways using machine learning. Recent Publication Trends: Over the past decade, Riccardi has consistently published in high-impact journals such as Journal of Chemical Physics , Physical Chemistry Chemical Physics , and Nature Machine Intelligence . His recent work (2023–2025) shows an expanded scope into educational technology , environmental science , and open-source tool development (e.g., GeoSight), reflecting a growing interdisciplinary impact. The publications reveal a strong focus on algorithmic innovation in simulation methods and their application across chemistry, biology, and engineering. Scientific Contributions: Lead and co-developer of PyRETIS, a widely used open-source library for rare event simulations. Contributor to immuneML, a machine learning ecosystem for immune repertoire analysis published in Nature Machine Intelligence . Active in promoting open science, data sharing, and academic integrity through public commentary and educational initiatives. Advising and Grants: While no formal students are listed in the provided text, Riccardi has mentored or collaborated with numerous early-career researchers and PhD candidates, particularly within the van Erp group. He has contributed to multiple collaborative research projects, likely funded by Norwegian and European research councils, though specific grants are not mentioned. His outreach on postdoctoral challenges suggests engagement with academic policy and mentorship. Labs and Teams: Riccardi is part of a vibrant computational research group at UIS, closely collaborating with Prof. Titus Sebastiaan van Erp and colleagues in the Department of Energy Resources. His work is embedded in a team focused on advanced simulation techniques, with strong ties to international networks in computational chemistry and soft matter physics.

Tom BraeckeveltView profile

Research Fellow

• Materials Science
• Perovskite Solar Cells
• Computational Chemistry
• +3 more

Tom Braeckevelt is a Research Fellow at Ghent University, Belgium, working within the computational materials science group led by Prof. Veronique Van Speybroeck. Based at Tech Lane Ghent Science Park (Technologiepark 46, Zwijnaarde), he collaborates extensively with experimental teams including Prof. Johan Hofkens (photophysics) and Prof. Sara Bals (electron microscopy), bridging theoretical modeling with advanced characterization techniques to solve stability challenges in next-generation photovoltaics. Education: PhD in Materials Science, Ghent University (2018). Dissertation: Designing 2D hybrid organic-inorganic perovskites for game-changing photovoltaics , supervised by Prof. Veronique Van Speybroeck and Dr. Kurt Lejaeghere. His research centers on perovskite stability mechanisms through multiscale computational modeling (DFT, machine learning potentials, molecular dynamics) integrated with experimental validation (TEM, GIWAXS, spectroscopy). Key focus areas include phase transition kinetics, strain engineering, doping strategies, and interfacial design for cesium lead halide perovskites. This work addresses critical barriers to commercial solar cell deployment, particularly ambient-condition stability and efficiency retention. From 2019-2025, Dr. Braeckevelt co-authored 13 high-impact publications including Science (2019), Nature Communications (2022), and ACS Nano (2025), demonstrating progression from fundamental phase transition studies to machine learning-enhanced stability solutions. Recent work expands into covalent organic frameworks and rare-event sampling algorithms, reflecting methodological diversification while maintaining photovoltaic applications as the core driver. No scientific awards or fellowships were documented in the source materials. Supported by institutional research grants at Ghent University, Dr. Braeckevelt has presented findings at 8+ international conferences including PSCO19 (Lausanne), ICAMM (Rennes), and DFT2022 (Brussels). His invited talk at the 2025 Eindhoven Psiflow workshop highlights growing recognition in machine learning for perovskites. No student supervision roles were indicated in current position. He operates within Ghent's integrated materials research ecosystem, contributing to cross-disciplinary projects that combine computational prediction with nanoscale characterization to accelerate renewable energy technology development.

Bruce TidorView profile

Professor

• Computational Biology
• Systems Biology
• Molecular Biophysics
• +3 more

Bruce Tidor is a Professor of Biological Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), leading the Tidor Lab. His research focuses on computational and systems biology, integrating principles from biophysics, artificial intelligence, and applied mathematics to study complex biological systems at molecular and systems levels. Research Interests: Protein and nucleic acid structure-function analysis Computational design of proteins and ligands Gene expression networks and systems biology modeling Drug resistance mechanisms and therapeutic design Lab Activities: The Tidor Lab develops methods for analyzing biomolecular interactions, parameterizing biological networks, and optimizing drug resistance strategies. Notable projects include studying calcium sensor design, oscillatory biological systems, and mechanotransduction in proteins such as FAT domains. Collaborations and Grants: Ongoing work includes MIT-Skoltech collaborations, computational re-engineering of enzymes, and modeling cytokine interactions. The lab has advised numerous PhD and master’s students in biological engineering and related fields.

Mark SherwinView profile

Professor

• Condensed Matter Physics
• Terahertz Science
• Quantum Optics
• +3 more

Mark Sherwin is a Professor of Physics and Director of the Institute for Terahertz Science and Technology at the University of California, Santa Barbara (UCSB) . His research focuses on experimental condensed matter physics using terahertz (THz) free-electron lasers (FELs) for quantum control and spectroscopy. He has mentored over 30 graduate students and holds Fellowships from the American Physical Society and the Alfred P. Sloan Memorial Fellowship . Harvard College BA (1981) UC Berkeley PhD (1988) Research Areas : Quantum coherence in spin systems Terahertz-driven electron-hole recollisions High-field electron paramagnetic resonance (EPR) Quantum dot and nanostructure dynamics Nonlinear optics in semiconductors Charge-density-wave materials Scientific Awards : Fellow, American Physical Society Alfred P. Sloan Memorial Fellowship Collaborations include NASA’s Jet Propulsion Laboratory, small companies for THz modulation, and Prof. Songi Han (UCSB Chemistry/Biochemistry). His group designs custom apparatus with Dr. Nikolay Agladze and develops THz detectors/mixers.

Viresh PatelView profile

Lecturer

• Extremal combinatorics
• Probabilistic combinatorics
• Graph polynomials / partition functions
• +5 more

Dr. Viresh Patel is a Lecturer in Optimisation at the School of Mathematical Sciences, Queen Mary University of London. His research focuses on extremal and probabilistic combinatorics, graph polynomials, phase transitions, and approximation algorithms. University: Queen Mary University of London School: School of Mathematical Sciences Email: viresh.patel@qmul.ac.uk Research Interests: Patel’s work bridges combinatorics, graph theory, and statistical physics. Key areas include Hamiltonian cycles in dense graphs, algorithmic approaches to graph polynomials, and complexity analysis of physical models. Publication Trends: Recent articles address structural graph theory (cycle/path decomposition), computational complexity (Potts/Ising models), and approximation algorithms using probabilistic and Taylor series methods. Collaborations: Regular co-authorship with researchers like Guus Regts, Allan Lo, and Matthew Jenssen indicates strong interdisciplinary engagement.

Sapna SarupriaView profile

Associate Professor

• Molecular Simulations
• Statistical Mechanics
• Computational Chemistry
• +5 more

Sapna Sarupria is an Associate Professor in the Department of Chemistry at the University of Minnesota with adjunct appointments at Michigan Technological University (Physics) and Clemson University (Chemical and Biomolecular Engineering). Her research develops cutting-edge computational methods to solve complex problems in condensed matter systems, with applications spanning sustainable energy, biotechnology, and public health. Her academic background includes: PhD in Chemical Engineering from Rensselaer Polytechnic Institute (2009) MS in Chemical Engineering from Texas A&M University (2004) Professor Sarupria's work centers on surface-driven phenomena using molecular modeling and statistical mechanics. The SAMPEL Lab (Simulations and Advanced Methods for Probing Energy Landscapes) pioneers rare event sampling techniques to study previously intractable processes like ice nucleation and protein-surface interactions. Current projects address critical challenges in water purification membranes, enzymatic reactions, and vaccine stabilization, combining method development with real-world applications in energy and biotechnology. Her research bridges computational chemistry with materials science and biophysics through innovative simulation approaches. Recent 2025 publications demonstrate strong integration of machine learning with molecular simulation, particularly in polymorph prediction (LeaPP framework) and solvent-inclusive hybrid methods. Key themes include nucleation kinetics, virus-excipient interactions for biologics, and thermodynamic stabilization mechanisms. These works span computational chemistry, biophysics, and materials science, with consistent focus on developing transferable methodologies for condensed phase phenomena. Professor Sarupria actively mentors students through the SAMPEL Lab's inclusive environment, currently advising 8 PhD candidates with diverse backgrounds. Her leadership extends to departmental DEI initiatives and national roles in AIChE and ACS. The lab maintains robust collaborations with experimental groups at multiple institutions, translating computational insights into practical solutions for membrane technology and biologic formulation. Students gain expertise in high-performance computing while contributing to projects with direct societal impact in water sustainability and vaccine development.

Tyler EngstromView profile

Assistant Professor

• Soft Matter Physics
• Biological Physics
• Biomechanics
• +7 more

Tyler Engstrom is an Assistant Professor in the Department of Physics and Astronomy within the College of Natural and Health Sciences at the University of Northern Colorado, where he also serves as Faculty Associate to the Dean for Internships. His academic journey spans industry and academia with roles at MiTeGen, Hobart and William Smith Colleges, and Syracuse University. His educational background: Ph.D. in Physics, Penn State University (2015) B.S. in Metallurgical Engineering, South Dakota School of Mines and Technology (2005) Dr. Engstrom's research centers on mechanical instabilities in biological systems, investigating how differential growth creates structures like brain folds and retinal foveae. His work uniquely bridges theoretical physics and biological phenomena , exploring quantum-classical analogies in elasticity while collaborating with biologists. Recent publications reveal strong focus on morphogenesis mechanisms and biomechanical modeling , with applications in developmental biology and tissue engineering. His publication record shows increasing specialization in biological physics since 2016, with significant contributions to understanding compression stiffening in tissues and quantum-inspired mechanical models. The 2023 Physical Review E paper exemplifies his innovative approach to connecting classical elasticity with quantum systems. Research funding achievements: National Eye Institute grant R15EY035473 (2024) as co-PI College of Natural and Health Sciences GRIP Awards (2023, 2024) UNC New Project Proposal grant (2021) Dr. Engstrom maintains active collaborations with biologists while mentoring students through research projects. His editorial work for American Journal of Physics demonstrates commitment to physics education. The combination of industry experience (MiTeGen), academic research, and teaching creates a dynamic environment for student involvement in cutting-edge biophysics. Though no formal lab name is specified, his research group employs interdisciplinary methods to study mechanical instabilities in biological contexts, utilizing computational modeling, theoretical frameworks, and experimental collaborations to advance understanding of tissue morphogenesis.

• Molecular Dynamics
• Theoretical Chemistry
• QM/MM Models
• +3 more

Prof. Dr. Bettina G. Keller is a Professor of Theoretical Chemistry at Freie Universität Berlin, leading the Keller Lab in the Department of Physical and Theoretical Chemistry. Her research integrates molecular dynamics simulations, QM/MM models, and kinetic analyses to study biomolecular systems, chemical reactions, and allosteric mechanisms. She collaborates extensively within SFB consortia and GRK research training groups. Her work spans: Development of grid-based models for molecular association Allosteric regulation in pathogen toxins Fluorination effects on protein-ligand interactions Glycopeptide hydrogel design Advanced sampling algorithms like Girsanov reweighting She has received multiple honors including the Hellmann-Preis (2018) and memberships in elite academies. Current projects involve: SFB 1449: Dynamic hydrogels at biointerfaces GRK 2662: Charging in the future VW Momentum: Molecular mobility Her lab maintains strong computational resources and open-source tools for the scientific community.