Find Academic Supervisors

• Partial Differential Equations
• Interface Evolution Problems
• Fluid Mechanics
• +2 more

Sebastian Hensel is a Researcher at the Hausdorff Center for Mathematics (HCM) at the University of Bonn since September 2021, under the mentorship of Prof. Tim Laux. He holds a PhD in Mathematics from the Institute of Science and Technology Austria (IST Austria), where he studied under Prof. Julian Fischer. His research focuses on partial differential equations, particularly interface evolution problems, fluid mechanics, stochastic homogenization, and stochastic PDEs. He has contributed to topics like mean curvature flow, free boundary problems, and weak-strong uniqueness principles. Education includes a PhD (2021, IST Austria), Master of Science in Mathematics (2017, Humboldt-Universität zu Berlin), and Bachelor degrees in Mathematics (2015, Freie Universität Berlin) and Business Administration (2013, Freie Universität Berlin). His work explores geometric PDEs, including multiphase systems, contact angle dynamics, and stochastic influences. Key contributions include stability analyses of evolving interfaces and convergence studies of phase-field models. His articles often bridge analytical rigor and applied mathematics, addressing challenges in nonlinear dynamics and calculus of variations. He has presented research at international conferences such as FBP 2020, SIAM MS20, and Equadiff 2019. His academic activities include seminars at institutions like TU Chemnitz, University of Regensburg, and Max-Planck Institute for Mathematics in the Sciences, Leipzig.

Gianluigi RozzaView profile

Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Scientific Computing
• +6 more

Gianluigi Rozza is a Full Professor in Numerical Analysis and Scientific Computing at SISSA (International School for Advanced Studies) in Trieste, Italy. He leads the SISSA mathLab research group and serves as Coordinator of the Mathematics Area at SISSA. He holds roles such as SISSA Director's Delegate for Innovation and Knowledge Transfer and member of various committees including the SISSA HPC Committee and SIAM Student Chapter. His research focuses on numerical analysis, computational fluid dynamics, reduced order methods (ROM), and their applications in engineering and biomedical fields. Education: Laurea cum Laude in Aerospace Engineering from Politecnico di Milano (2002), PhD in Applied Mathematics from EPFL (2005). Postdoctoral research at MIT (2006-2008), followed by roles at EPFL and SISSA. He has supervised over 30 PhD and Master’s students, with expertise in reduced order modeling and computational mechanics. Research Interests: Reduced basis methods, parametrized PDEs, fluid-structure interaction, cardiovascular simulations, environmental flows, machine learning integration, and industrial collaborations with companies like Danieli and Fincantieri. He coordinates EU projects such as the ERC Consolidator Grant AROMA-CFD (2016-2021) and the ITN ROMSOC project. Publications: Over 90 scientific papers, two books as editor, and contributions to open-source software like rbMIT and ITHACA-FV. Awards include the ECCOMAS Young Investigator Award (2014), SIAM Fellowship (2025), and the Gili Agonistelli Prize (2025). Grants and Projects: Principal Investigator of ERC AROMA-CFD and ARGOS PoC. Coordinates regional projects like UBE (Under Water Blue Efficiency) and TRIM. Engages in environmental modeling (e.g., SOPHYA, PRELICA) and industrial collaborations under MAR tc FVG. Labs/Teams: SISSA mathLab develops open-source tools for computational science, including ROM libraries and fluid dynamics solvers. Active in organizing workshops, serving on editorial boards (e.g., SIAM Journals), and advisory roles for agencies like ARPA FVG.

Valerio LucariniView profile

Professor

• Climate variability
• Thermodynamics of the climate system
• Tipping points and multistability
• +6 more

Valerio Lucarini is a Professor of Applied Mathematics at the School of Computing and Mathematical Sciences, University of Leicester. His research spans Statistical Mechanics, Climate Dynamics, Dynamical Systems Theory, and Meteorology. He has held leadership roles such as Director of the Centre Mathematics of Planet Earth at the University of Reading (2016-2024) and has been a Visiting Professor at the University of Hamburg. His scientific contributions focus on the thermodynamics of climate systems, tipping points, stochastic processes, and extreme event predictability. Recent work explores Koopman operator theory for response analysis, multiscale dynamical indices, and saddle avoidance in noise-induced transitions. Scientific Awards : 2024 IUGG Keilis-Borok Medal 2022 SIAM Mathematics of Planet Earth Prize 2022 Academia Europaea membership 2021 AGU E. N. Lorenz Lecture 2020 EGU L. F. Richardson Medal His publications demonstrate a multidisciplinary approach combining climate science, statistical physics, and mathematical modeling to address critical questions in climate change, ocean circulation stability, and nonlinear system response to perturbations.

Merel SchuringView profile

Assistant Professor

• Occupational Health
• Public Health
• Socioeconomic Health Inequalities
• +13 more

Dr. Merel Schuring is an Assistant Professor in Occupational Health at the Department of Public Health, Erasmus University Medical Center. Her research focuses on reducing socioeconomic health inequalities by promoting sustainable employment, particularly among vulnerable populations such as individuals with chronic diseases, mental health challenges, disabilities, and youth at risk of school dropout. Institution: Erasmus University Medical Center Department: Department of Public Health Position: Assistant Professor in Occupational Health Her research interests lie at the intersection of public health, employment, and social inequality. She investigates how health conditions and working environments influence labor market participation across the life course. Key themes include inclusive hiring practices for people with disabilities, supporting employees with health challenges, and preventing youth unemployment through improved education-to-work transitions. Methodologically, she specializes in advanced quantitative techniques—including difference-in-differences, multilevel modeling, and propensity score matching—and integrates these with qualitative methods such as interviews and focus groups. Her work often employs mixed-methods and realist evaluation approaches to assess interventions like Bewegen Werkt and Werk als Beste Zorg . The recent article trends reflect a consistent focus on mental health, employment trajectories, socioeconomic disparities, and policy-relevant outcomes. Her work spans epidemiology, occupational health, psychology, and social policy, with strong emphasis on longitudinal and register-based studies, as well as qualitative insights into barriers and facilitators in real-world contexts. Dr. Schuring collaborates with multidisciplinary teams across public health, psychology, and economics, co-producing knowledge with policymakers and employers to ensure actionable impact. Her research contributes to UN Sustainable Development Goals on good health, reduced inequalities, and decent work. Supervised doctoral students and research projects, including studies on school dropout and mental health Involved in evaluating national and regional public health programs Active in knowledge translation and stakeholder engagement She leads and contributes to research groups focused on sustainable employment and health equity, working closely with institutional and community partners to develop evidence-based strategies for inclusive labor markets.

Kyle G. WebberView profile

Professor

• Ceramics
• Ferroelectricity
• Piezoelectric Materials
• +4 more

Kyle G. Webber is a Professor at the Institute of Glass and Ceramics , Department of Materials Science and Engineering , Friedrich-Alexander University Erlangen-Nürnberg. His research focuses on advanced functional ceramics, particularly lead-free piezoelectrics and ferroelectrics, with emphasis on structural, dielectric, and electromechanical properties under stress and electric fields. Key research areas include high-throughput ceramics processing, domain wall dynamics, and thermal/mechanical stability of ferroelectric materials. He employs techniques like x-ray fluorescence holography, in-situ diffraction, and digital image correlation for structural analysis. Recent publications highlight trends in lead-free piezoceramics, stress/temperature-induced phase transitions, and defect engineering. Notable work includes optimizing grain size effects and exploring polymorphic phase boundaries in (K,Na)NbO3-based materials.

• Forest Ecology
• Plant Physiology
• Tree Hydraulics
• +4 more

Assoc. Prof. Sabine Rosner is a faculty member at the Institute of Botany , part of the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). Her research bridges forest ecology , plant physiology , and wood hydraulics , focusing on drought stress impacts on tree health and xylem vulnerability. Education : Diploma (1994) in Agricultural Sciences, Vienna Doctoral Degree (1998) in Natural Sciences Venia Docendi (2013) in Botany Her work explores hydraulic conductivity modeling , embolism resistance gradients , and acoustic emission testing for non-invasive drought stress monitoring. Recent projects like Smart Bark and TOPWOOD highlight her focus on tree health under climate extremes. Her publications span journals such as Frontiers in Plant Science and New Phytologist , with keywords like conifer wood anatomy , water transport , and forest pest interactions . Notable scientific awards include the BOKU Teaching Award (2021), Elise Richter Stipendium (2009), and Hertha Firnberg Stipendium (2006). She has supervised 11 theses across master’s and doctoral levels, mentoring students in topics like plant stress reactions and wood variation . Her community services include editorial roles in journals like Frontiers in Plant Science and membership in societies such as the International Academy of Wood Science (IAWS).

Anirban PatraView profile

Associate Professor

• Computational Mechanics
• Crystal Plasticity
• Constitutive Modeling
• +7 more

Anirban Patra is an Associate Professor in the Department of Metallurgical Engineering and Materials Science at the Indian Institute of Technology Bombay (IIT Bombay) , where he has been a faculty member since October 2017. He holds a Ph.D. from Georgia Institute of Technology (USA) and a B.Tech. from IIT Kharagpur. His research is primarily in computational mechanics, crystal plasticity, and constitutive modeling of structural materials. Education Ph.D. in Materials Science and Engineering, Georgia Institute of Technology, USA (2009–2013) B.Tech. in Metallurgical and Materials Engineering, IIT Kharagpur (2005–2009) His research interests span computational mechanics , crystal plasticity , strain gradient plasticity , finite element modeling , and microstructure-property correlations in structural materials. He focuses on modeling deformation behavior in nuclear, aerospace, automotive, and additively manufactured materials, with an emphasis on irradiation effects, creep, fatigue, and failure mechanisms. He has developed ρ-CP , an open-source dislocation density-based crystal plasticity framework, enabling predictive simulations across scales. His recent publications indicate strong trends in multiscale modeling , residual strain prediction , non-Schmid effects , and machine learning integration for accelerated materials design. His work frequently combines simulations with experimental validation. Scientific Awards and Recognition Serves on the Editorial Advisory Board of the International Journal of Plasticity . Supervised students awarded Prime Minister's Research Fellowship . Advising and Grants Prof. Patra has mentored numerous Ph.D., M.Tech., and dual-degree students, many of whom have pursued postdoctoral and industry positions at institutions like Purdue University and Ansys. His research is supported through institutional and project-based funding, though specific grants are not listed. He actively recruits researchers with backgrounds in mechanics, materials, and computational methods. Current advisees: Gulivindala Gopi, Girish Kulkarni, Lopamudra Singh, Alanka Naga Satya Sai Mahesh, Suryakant Priyadarshan, Mukul. Alumni include Namit Pai, Suketa Chaudhary, Manikandan Muruganandam, Preet Shah, and others. Labs and Teams He leads the Computational Mechanics and Materials Research Group at IIT Bombay, which develops and applies advanced modeling tools like ρ-CP. The group focuses on high-performance computing, open-source software development, and interdisciplinary collaboration with experimentalists.

Sam OttoView profile

Assistant Professor

• Scientific Machine Learning
• Dynamical Systems
• Model Reduction
• +7 more

Sam Otto is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, joining the faculty in July 2024. His research lies at the intersection of machine learning and continuum mechanics, with a focus on developing rigorous, data-driven methods for modeling, forecasting, and controlling high-dimensional nonlinear systems such as fluid flows. He leads the Otto Lab, which advances scientific machine learning through principled algorithms and theoretical understanding. Research Interests: Dr. Otto’s work centers on scientific machine learning, particularly in developing algorithms that integrate physical knowledge—such as symmetry, scale hierarchy, and smoothness—into data-driven models. His research addresses fundamental questions about what can be learned from limited data and how to ensure reliability in engineering applications. Key areas include model reduction, operator learning, sensor placement, and symmetry enforcement in neural networks. He applies these methods to fluid dynamics and other continuum mechanics problems. Publication Trends: His recent publications (2019–2024) show a strong trajectory in developing theory and algorithms for learning in nonlinear dynamical systems. There is a clear emphasis on incorporating physical structure into machine learning models, with recurring themes of symmetry, dimensionality reduction, and operator-based modeling. The work spans theoretical advances and practical computational methods, often leveraging Koopman theory, autoencoders, and covariance-based reductions. Education: Ph.D. in Mechanical and Aerospace Engineering, Princeton University, 2022 B.S. in Aeronautics and Astronautics, Purdue University, 2016 Scientific Awards: Advising and Grants: While no current students or grants are listed, Dr. Otto is expected to advise graduate students through the Mechanical and Aerospace Engineering graduate program at Cornell. As a new faculty member, he is likely pursuing funding for his research in scientific machine learning and dynamical systems. His lab is actively publishing in top journals and preprint servers, indicating ongoing research support, possibly through postdoctoral fellowships or early-career grants. Labs and Teams: Dr. Otto leads the Otto Lab at Cornell, which focuses on machine learning for high-dimensional dynamics. The lab develops computational tools that combine data and physical principles to model complex systems. It is part of the broader research ecosystem in the Sibley School, collaborating with experts in fluid mechanics, control theory, and applied mathematics.

Yaniv PlanView profile

Associate Professor

• Applied Probability
• Compressive Sensing
• Matrix Completion
• +3 more

Yaniv Plan is an Associate Professor of Mathematics at the University of British Columbia. His research focuses on applied probability, compressive sensing, matrix completion, and mathematical foundations of machine learning. He co-organizes the interdisciplinary group Mathematics of Information, Learning, and Data (MILD). His work bridges high-dimensional probability with applications in signal processing and data science. He teaches advanced courses such as Probability in High Dimensions and Compressed Sensing , emphasizing theoretical foundations like concentration inequalities, random matrix theory, and optimization. His research has led to advancements in 1-bit compressed sensing, matrix completion, and robust recovery algorithms. Notably, he received the NIPS 2018 Best Paper Award for contributions to learning Gaussian mixtures via sample compression. Plan’s publications address topics like sub-Gaussian matrices, weighted matrix completion, and non-smooth stochastic gradient descent. His work often combines rigorous mathematical analysis with practical applications in compressed sensing and sparse recovery. He collaborates widely, with co-authors including Roman Vershynin, Emmanuel Candès, and Mary Wootters. His research has been supported by grants exploring compressed sensing, high-dimensional data, and algorithmic robustness.

• Computer Architecture
• Systems & Networking
• Approximate Computing
• +6 more

Weng Fai WONG is an Associate Professor and Deputy Head of the Department of Computer Science at the School of Computing, National University of Singapore (NUS). With over three decades of academic experience at NUS, he has established himself as a leading researcher in computer systems, with particular expertise in the interface between hardware and software stacks. Dr. Wong received his B.Sc. (First Class Honors) and M.Sc. from the National University of Singapore in 1989 and 1991 respectively, followed by a Dr.Eng.Sc. from the University of Tsukuba in 1993. His academic journey began at NUS (then DISCS) in 1985, where he progressed from student to Senior Tutor in 1989, and later returned from Japan as a Lecturer in 1993. Dr. Wong's research focuses on systems and networking, with special emphasis on hardware-software co-optimization. His current research interests include approximate computing , neuromorphic computing , and hardware acceleration for deep learning. His work spans computer architecture , embedded systems , compilers and runtime systems , and programming languages . He has made significant contributions to optimizing software for novel hardware including FPGAs, GPUs, and non-volatile memory technologies. His recent publications (2023-2025) demonstrate a strong focus on energy-efficient AI computing, with particular emphasis on spiking neural networks, large language model acceleration, and FPGA-based solutions for graph processing and machine learning workloads. His research shows a clear trajectory toward green AI through hardware-software co-design that minimizes energy consumption while maintaining computational effectiveness. Dr. Wong is a Member of ACM and a Senior Member of IEEE. His paper "Exploiting half precision arithmetic in Nvidia GPUs" was a Best Paper Finalist at the IEEE High Performance Extreme Computing Conference (HPEC 2017). As Deputy Head of the Department of Computer Science at NUS, Dr. Wong plays a key leadership role in academic administration while maintaining an active research program. His work has been supported by numerous research grants, though specific details are not provided in the available information. Dr. Wong leads research in the Systems & Networking area at NUS, with particular focus on the Hardware-Software Interface Laboratory. His team explores innovative approaches to bridge the gap between theoretical computer science and practical hardware implementation, with applications spanning from edge computing to large-scale data centers.

Mahdiar TaheriView profile

Research Fellow

• Material Science
• Thermal Engineering
• Nanotechnology
• +5 more

Mahdiar Taheri is a Research Fellow in the School of Engineering at the Australian National University (ANU). His research focuses on advanced materials and energy systems, including solar thermal applications, nanotechnology, and sustainable energy storage solutions. He leads and collaborates on projects addressing challenges in thermal energy storage, hydrogen storage, and carbon sequestration. Key collaborations involve institutions globally, emphasizing interdisciplinary approaches to renewable energy and environmental engineering. His work spans experimental, computational, and applied methodologies, with notable contributions to nanofluids, metal-organic frameworks (MOFs), and battery technologies. Research interests include: Thermal energy conversion and storage Solar-driven chemical processes Nanomaterial synthesis and applications Hydrogen and lithium-sulfur battery systems Antimicrobial coatings and environmental remediation Projects include development of packed-bed solar reactors, coral-inspired hierarchical coatings, and scalable hydrogen storage solutions. He has secured grants for initiatives like solar thermal desalination and low-cost industrial heat supply systems. Notable contributions include: Pioneering thermodiffusive desalination techniques Advancing MOF-based hydrogen storage Designing stable high-capacity Li-S batteries His lab focuses on bridging fundamental material science with applied engineering solutions for sustainability challenges.

• Environmental Analytical Chemistry
• Per- and Polyfluoroalkyl Substances (PFAS)
• Groundwater Contamination
• +3 more

Christian Zwiener is a Professor (W3) for Environmental Analytical Chemistry at the Center for Applied Geoscience, University of Tübingen since 2009. He holds a Habilitation and Venia Legendi in Water Chemistry (KIT, 2004) and a Ph.D. in Water Chemistry (Technical University of Munich, 1995). His research focuses on PFAS analysis , non-target screening , and environmental fate of contaminants using high-resolution mass spectrometry . Education: Habilitation in Water Chemistry, KIT (2004) Ph.D. in Water Chemistry, Technical University of Munich (1995) Diploma in Analytical and Environmental Chemistry, University of Ulm (1989) Research Trends: PFAS contamination in soils , human hair , and textiles Non-target screening workflows for novel contaminants and transformation products Environmental fate of pharmaceuticals and biocides Development of HRMS data mining tools (e.g., FindPFΔS, PFΔScreen) Scientific Awards: Level II Scientific and Technological Achievement Award (STAA) , EPA (2008) Research Award , Water Chemical Society, GDCh (2001) Professional Service: Chairman, Journal Vom Wasser (2016–present) Delegate, EUChemS Division of Chemistry & Environment (2020–present) Member of Water Research Perspectives Commission (2018–present) Advisory Board, VEGAS, University of Stuttgart (2014–present) His work bridges analytical chemistry , environmental toxicology , and public health , with key contributions to PFAS contamination and CKDu groundwater links . He has developed standardized methods for PFAS analysis and electrochemical oxidation protocols for contaminant degradation.

Mauricio PongaView profile

Associate Professor

• Mechanics of Materials
• Computational Modeling
• Multiscale Modeling
• +4 more

Mauricio Ponga is an Associate Professor at the Department of Mechanical Engineering, University of British Columbia (UBC), Faculty of Applied Science. His research focuses on computational mechanics, multiscale modeling, and material failure analysis. He holds a Ph.D. and M.Sc. from the University of Seville, a B.S. from the University of La Plata, and completed postdoctoral work at Caltech. Education: B.S. (University of La Plata), M.Sc. (University of Seville), Ph.D. (University of Seville), Postdoctoral Fellowship (Caltech) Research Interests Mechanics of materials under extreme conditions Multiscale modeling (atomic to continuum) Thermal and electronic transport in materials Material failure mechanisms (spallation, twinning) Industrial applications (batteries, superconductors) Recent Publications highlight his work on high-entropy alloys, polymer brushes, 2D materials, and novel simulation methods like local two-temperature molecular dynamics (l2T-MD) and accelerated mesodynamics (aMD). Projects include collaborations with TRIUMF, NSERC, and FP Innovations. Labs & Collaborations Principal Investigator of the Modeling and Simulation Research Group at UBC Collaborations with S2SES (Coanda-effect screens), DND (gradient nano-grained alloys), and Caltech (postdoctoral work)

Ting-Kam Leonard WongView profile

Associate Professor

• Mathematical Finance
• Probability
• Optimal Transport
• +2 more

Ting-Kam Leonard Wong is an Associate Professor jointly appointed at the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences at the University of Toronto and University of Toronto Scarborough , respectively. He also holds a non-budgetary cross-appointment in the Department of Mathematics since May 2025. Education: PhD in Mathematics, University of Washington (2016) MPhil in Mathematics, The Chinese University of Hong Kong (2011) Research Interests: His research lies at the intersection of mathematical finance, probability theory, optimal transport, and information geometry . He explores deep theoretical questions in stochastic processes and their applications in financial modeling, market analysis, and data science. His work often involves developing geometric frameworks to understand complex probabilistic systems, including the study of Wasserstein distances, Bregman divergences, and their roles in optimization and inference. Research Trends: Across his recent publications, a clear trend emerges toward integrating geometric methods with stochastic modeling and financial applications . He has contributed to the development of new divergence measures, optimization algorithms, and portfolio frameworks. His work often bridges theoretical probability with practical tools in finance and machine learning, such as portfolio optimization, PCA under transport metrics, and scalable inference methods. Awards & Funding: NSERC Discovery Grants (2019, 2025) Connaught New Researcher Award (2020) Data Science Institute Seed Funding for Methodologists (2022) Best Paper Award, Geometric Science of Information Conference (2019) Advising & Grants: He currently supervises several PhD students including Elijah French , Madhu Gunasingam , and Amanjit Kainth . He previously co-supervised Steven Campbell , who successfully defended in 2023. His research is supported by multiple federal and institutional grants, reflecting his active role in advancing methodological research in statistics and finance. Editorial & Service Roles: He serves as an Associate Editor for the journal Information Geometry (2022–present), actively contributing to the dissemination of research in geometric methods for data science and inference.

Katarzyna Dziubalska-KołaczykView profile

Professor

• Phonology
• Phonetics
• Natural Phonology
• +9 more

Professor Katarzyna Dziubalska-Kołaczyk is a leading figure in phonology, phonetics, and natural linguistics at Adam Mickiewicz University (AMU) in Poznań, Poland. She has served as Deputy Dean, Dean, and currently as Deputy Rector for Science and Humanities at AMU, while leading the Interdisciplinary Center for Speech and Language Processing and co-heading Bilingualism Matters@Poznań. Education: M.A. (1983), Ph.D. (1988), D.Litt. (1995) in English Linguistics from AMU; Senior Fulbright Grant (2001/2002) at University of Hawai'i at Manoa. Her research spans Natural Phonology , Phonotactic and Morphonotactic Theory , and the NAD (Net Auditory Distance) Model , with applications in speech processing, literacy training, and language revitalization. She has pioneered the Natural Growth Theory of Acquisition and challenged syllable necessity in her Beats-and-Binding Phonology framework. Recent publications focus on phonotactic complexity , cross-linguistic cluster analysis , and multilingual acquisition , reflecting her integration of theoretical and applied linguistics. Her editorial leadership of Poznań Studies in Contemporary Linguistics since 1999 underscores her influence in the field. Major Awards: Knight's Cross of Polonia Restituta, Senior Fulbright Grant, AMU Rector's Prizes (20+), Gold Cross of Merit, Minister of Science and Higher Education Prize. Professional Leadership: President of Societas Linguistica Europaea (2012-2014), member of Polish Academy of Sciences Linguistics Committee, Academia Europaea, and Austrian Academy of Sciences. As an educator, she has taught phonetics, phonology, and language acquisition at AMU and Universität Wien, mentoring a generation of linguists in Natural Phonology and phonotactic research . Her lab work and conference organization (e.g., New Sounds, PLM meetings) highlight her commitment to interdisciplinary collaboration.