Find Academic Supervisors

• Computational Mechanics
• Micromechanics
• Homogenization
• +3 more

Prof. Dr. Matti Schneider serves as Professor of Engineering Mathematics and Head of the Institute of Engineering Mathematics within the Faculty of Civil Engineering at the University of Duisburg-Essen. His academic leadership spans computational mechanics research and teaching core mathematics courses for civil engineering students. His educational background includes: Diploma in Applied Mathematics with distinction from TU Bergakademie Freiberg (2009) PhD (Dr. rer. nat.) from Leipzig University (2013) on "The Leray-Serre spectral sequence in Morse homology on Hilbert manifolds and in Floer homology on cotangent bundles" Professor Schneider's research focuses on advancing computational methods for solid mechanics through FFT-based homogenization techniques, microstructure modeling, and multi-scale material analysis. His work bridges applied mathematics and engineering to solve complex problems in heterogeneous material systems, with particular emphasis on numerical stability, boundary condition implementation, and efficient solver development for industrial applications. His methodologies enable accurate prediction of material behavior across scales from microscopic structures to macroscopic components. Analysis of his 15 most recent publications reveals dominant trends in FFT-based computational homogenization, with significant contributions to thermal problems, porous media, and fiber-reinforced composites. He pioneers the integration of machine learning (particularly deep material networks) with traditional numerical methods to model complex material behaviors like shear-thinning suspensions and 3D-printed materials. His work consistently addresses computational challenges in boundary condition implementation and convergence for stochastic microstructures. Professor Schneider leads the Institute of Engineering Mathematics and directs research within the ERC-funded BeyondRVE project, which focuses on extending representative volume element concepts for advanced material modeling. His collaborative network includes major German research institutions like Fraunhofer ITWM and international partners in materials science.

Abhishek HalderView profile

Associate Professor

• Stochastic Systems
• Control Theory
• Optimal Transport
• +7 more

Abhishek Halder is an Associate Professor in the Department of Aerospace Engineering at Iowa State University and an Associate Adjunct Professor in the Department of Applied Mathematics at the University of California, Santa Cruz. He is also a member of the Translational AI Center at Iowa State University. His academic journey includes joining Iowa State University as an Assistant Professor in July 2023 and previously serving as faculty at UC Santa Cruz starting from October 2017. Dr. Halder's educational background includes studies at IIT Kharagpur and Texas A&M University, where he developed expertise in systems and control theory with applications to matrix analysis, probability, and optimization. His research has been recognized with prestigious awards including the O. Hugo Schuck Best Application Paper Award from the American Automatic Control Council, Applied Mathematics Research Award from UC Santa Cruz, Outstanding Doctoral Student Award from Texas A&M, and Best Dual Degree Thesis Award from IIT Kharagpur. His research focuses on stochastic systems, control and optimization with applications to large scale cyber-physical systems. Dr. Halder has made significant contributions to the fields of optimal transport, Schrödinger Bridge theory, distributional control, and uncertainty propagation in dynamical systems. His work bridges theoretical developments with practical applications in power systems, aerospace engineering, and machine learning. He has secured multiple research grants from NSF, including a CPS Frontier project on Computation-Aware Algorithmic Design for Cyber-Physical Systems. Dr. Halder has demonstrated leadership in the control systems community through editorial roles including Associate Editor for IEEE Transactions on Automatic Control (2025-present), ASME Journal of Dynamic Systems, Measurement, and Control (2025-present), Systems & Control Letters (2022-present), and previously for IEEE Control Systems Society Conference Editorial Board (2019-2025) and IEEE Transactions on Aerospace and Electronic Systems (2019-2022). He is a Senior Member of IEEE and a member of IFAC, SIAM and ASME. His research group has produced numerous publications in top-tier journals and conferences, with recent work focusing on connections between optimal transport theory, stochastic control, and machine learning. The publication trends show increasing integration of Schrödinger Bridge formulations with machine learning techniques for distributional control problems across various domains including power systems, aerospace applications, and resource allocation. O. Hugo Schuck Best Application Paper Award (2024) Applied Mathematics Research Award from UC Santa Cruz (2022) IEEE Senior Member (2021) Outstanding Doctoral Student Award from Texas A&M Best Dual Degree Thesis Award from IIT Kharagpur Dr. Halder has mentored numerous PhD students including Alexis, Georgiy, Iman, Shadi, and Kenneth, many of whom have received prestigious fellowships. His research group maintains strong collaborations with national laboratories including Lawrence Livermore National Lab and Los Alamos National Lab, as well as industry partners. Dr. Halder is also committed to education and outreach, having created and taught the 'Feedback Control' course for high school students in the California State Summer School for Mathematics and Science (COSMOS), introducing complex control theory concepts without calculus or linear algebra.

Paola FalugiView profile

Senior Lecturer

• Predictive Control Systems
• Data-Driven Modelling
• System Modelling and Optimization
• +4 more

Dr. Paola Falugi is a Senior Lecturer in Electro-Mechanical Engineering at the University of East London and holds an honorary visiting researcher position at Imperial College London. Her expertise spans predictive control systems, data-driven modeling, and energy network optimization under uncertainty. Senior Lecturer, Department of Engineering & Construction, School of Architecture, Computing and Engineering, University of East London Honorary Visiting Researcher, Imperial College London Research focuses on: Predictive control strategies for uncertain systems Data-driven modeling for control applications Optimization methods in energy network expansion Energy management under stochastic conditions Control systems for robotics and mechatronics Recent publications highlight her contributions to: Robust co-design frameworks for building energy systems Machine learning integration in transmission expansion planning Automated scenario generation for optimal control Control strategies for residential buildings with energy storage Her work bridges theoretical advancements in control theory with practical applications in energy systems and building automation.

Daniel BoleyView profile

Professor

• Numerical Linear Algebra
• Data Mining
• Machine Learning
• +5 more

Daniel Boley is a Professor and Distinguished University Teaching Professor at the University of Minnesota, within the College of Science and Engineering, Department of Computer Science and Engineering. He serves as the Director of Graduate Studies for the Graduate Program in Data Science, which offers a Master's of Science and a Post-Baccalaureate Certificate. His office is located in Kenneth H. Keller Hall at 4-225C. Professor Boley's research spans computational methods in linear algebra, scalable data mining algorithms, and applications in systems biology and bioinformatics. His work focuses on scalable algorithms for convex optimization in machine learning, analysis of networks and graphs from metabolic biochemical networks, and wireless device networks. He has made significant contributions to numerical linear algebra methods for control problems, parallel algorithms, and iterative methods for matrix eigenproblems. His research interests also include algebraic models in systems and evolutionary biology, and biochemical metabolic networks. His recent publications demonstrate a strong focus on applying graph theory and network analysis to diverse domains including robot swarms, medical imaging (particularly for glioblastoma and COVID-19 diagnosis), and metabolic network analysis. His work bridges theoretical computer science with practical applications in biology and medicine, with a consistent emphasis on developing scalable computational methods. The trend shows increasing interdisciplinary collaboration, particularly with medical researchers. Distinguished Member by the ACM Top university award for post baccalaureate, graduate and professional education Distinguished University Teaching Professor title Professor Boley has advised numerous PhD students including Tatiana Lenskaia (2021), Shaozhe Tao (2018), Ham Ching Lam (2014), and others dating back to 1994. His research has been supported by various grants enabling work on scalable computation of elementary pathways through metabolic networks, Markov models of viral evolution, and scalable data mining algorithms for text analysis. He has developed software tools for clustering, dot plot visualization, and educational graphics. Professor Boley directs the Graduate Program in Data Science and has been involved in projects such as the Principal Direction Divisive Partitioning (PDDP) Project. His research group develops practical implementations of theoretical advances, including the PDDP clustering algorithm, Dot.py genome viewer, and various educational graphics tools for introductory programming courses. He maintains active collaborations across disciplines, particularly in bioinformatics and medical imaging applications.

Laurent PfeifferView profile

Researcher

• Optimal Control
• Mean Field Games
• Stochastic Optimization
• +3 more

Laurent Pfeiffer is a researcher at the Signals and Systems Laboratory , focusing on Optimization and Control Theory . His work bridges theoretical advancements in mean field games, stochastic optimization, and numerical methods with practical applications in energy systems and fluid dynamics. Research Interests: Optimal Control, Mean Field Games, Stochastic Optimization, Nonlinear Programming Recent Publications: Explore mean field games, nonconvex optimization, and control theory applications in nuclear energy, gas portfolios, and fluid dynamics. Labs: Signals and Systems Laboratory, specializing in control systems and mathematical modeling.

• Financial Mathematics
• Insurance Mathematics
• Model Uncertainty
• +4 more

Prof. Dr. Katharina Oberpriller is a faculty member at the Department of Mathematics, University of Munich, working in the Financial and Insurance Mathematics research group. Her research focuses on model uncertainty, insurance risk markets, and credit risk modeling. She collaborates extensively on publications related to stochastic processes, affine models, and financial risk management.

• Shape Optimization
• Numerical Analysis
• Integro-Partial Differential Equations
• +3 more

Peter Philip is a Senior Lecturer at the Department of Mathematics, Faculty of Mathematics, Computer Science, and Statistics of Ludwig-Maximilians University Munich. His career since 2008 at LMU includes research in shape optimization and numerical analysis of integro-partial differential equations, with prior roles at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and the Institute for Mathematics and its Applications (IMA). He has taught extensively across mathematics disciplines and led software development for crystal growth simulations. Positions: Academic Senior Counselor (2013–present), Academic Counselor (2008–2013), Industrial Postdoctoral Fellow (2004–2006), Research Associate (1997–2004) Education: Habilitation (2012) and Ph.D. (2003) in Mathematics from LMU Munich and Humboldt University Berlin, respectively; Diplom in Mathematics (1997, with excellence) from Free University Berlin Research Interests: His work focuses on Shape optimization via control of integro-partial differential equations Conductive-radiative heat transfer analysis and control Crack propagation modeling using energy functional minimization Numerical simulation of semiconductor crystal growth processes Finite volume methods for anisotropic and complex geometries Optimal control of electromagnetic heating systems Publications: He has published extensively in applied mathematics journals, with key contributions to conductive-radiative heat transfer, sublimation growth of SiC crystals, and crack propagation models. His work bridges theoretical analysis, numerical implementation, and industrial applications. Teaching: Since 2008, he has taught courses such as Numerical Mathematics, Analysis, Linear Algebra, and Axiomatic Set Theory, with a teaching load of 13 hours per week. His lectures include both foundational and advanced topics, supported by freely downloadable lecture notes.

• Harmonic Analysis
• Fourier Restriction Theory
• Sharp Inequalities
• +4 more

Diogo Oliveira e Silva is an Associate Professor in the Department of Mathematics at Instituto Superior Técnico, Lisbon, and holds an Honorary Senior Research Fellow position at the University of Birmingham. His academic journey includes a PhD from UC Berkeley under Michael Christ (2012), a Habilitation at Universität Bonn (2017), and prior roles at Bonn and Berkeley. His research focuses on harmonic analysis, particularly sharp inequalities, oscillatory integrals, Fourier restriction theory, uncertainty principles, and nonlinear Fourier analysis. Education: PhD in Mathematics (2012, UC Berkeley), Habilitation (2017, Universität Bonn) Affiliations: Instituto Superior Técnico (current), University of Birmingham (honorary), former positions at Bonn and Berkeley His work explores Fourier restriction theory , symmetry breaking phenomena , spherical packings , and sharp inequalities across geometric and analytic contexts. Recent publications address dimension-free estimates , stability of extremizers , and sign uncertainty principles , often leveraging collaborations with researchers like R. Mandel, G. Negro, and R. Quilodrán. Grants from EPSRC , DFG , NSF , AIM , and FCT have supported his work. He has been actively involved in international conferences and lecture series , including events in Rwanda, Germany, Brazil, and Japan. Teaching roles span complex analysis, Fourier analysis, and PDE courses at IST and Birmingham.

Laurence Likforman-SulemView profile

Associate Professor

• Document Analysis
• Handwriting Recognition
• Historical Document Processing
• +7 more

Laurence Likforman-Sulem is an Associate Professor at Institut Polytechnique de Paris , affiliated with the Signal, Statistics and Learning (S2A) team in the Image, Data, Signal (IDS) department . She has been at Télécom Paris since 1991, where she teaches Pattern Recognition , Signal Processing , and Document Analysis . PhD from ENST-Paris (1989) HDR from Sorbonne University (2008) Her research integrates Markovian methods (HMMs, Bayesian Networks) and deep learning (BLSTMs, CNNs) for: Handwriting recognition in historical documents Character analysis in Byzantine seals Parkinson’s disease detection through multimodal signals Biometric authentication using hand shape Recent work focuses on Byzantine seal character recognition (BHAi project) and multimodal group cohesion analysis (IEEE ICMI 2021 Best Paper). She has supervised 10 PhD students and numerous Master internships. Scientific Awards Winning system at ICDAR 05 Arabic Hand-Written Word Recognition Competition Fondation Telecom Thesis Award 2014 (2nd prize for Olivier Morillot) Best Paper Award, ICMI 2021 Active in conference leadership, she chaired ICDAR 2015 and ICPR 2022 document analysis tracks. Her 15 most recent publications span Byzantine document analysis, Parkinson’s detection, and low-energy neural architectures.

Sayan MukherjeeView profile

Professor

• Geometry and topology in probabilistic modeling
• Stochastic topology
• Statistical and computational biology
• +7 more

Sayan Mukherjee is a Professor of Statistical Science, Computer Science, and Mathematics at Duke University, with affiliations at the Information Initiative at Duke. His academic work spans multiple departments, reflecting his interdisciplinary research approach. His research focuses on three main areas: Geometry and topology in probabilistic modeling , where he develops methods for modeling geometric and topological properties of objects/data with uncertainty; Modeling massive data , building statistical models and algorithms for dimension reduction, classification, and matrix completion that scale to huge datasets; and Applied Bayesian models in computational biology , collaborating with biologists on projects ranging from functional genomics to population biology. Mukherjee has mentored numerous graduate students and postdoctoral fellows who have gone on to positions at institutions including Harvard, U Chicago, Duke, UNC, and industry positions at companies like Google, Novartis, and SAS. His collaborative network spans across multiple disciplines including geometry, topology, learning theory, cancer biology, computational biology, and evolutionary ecology. He has developed significant software tools including Bayesian Sparse Factor Analysis of Genetic Covariance Matrices (BSFG), Automated 3D Geometric Morphometrics (auto3dgm), and Analysis of Sample Set Enrichment Scores (ASSESS), demonstrating his commitment to creating practical computational methods for complex biological and statistical problems.

Anna LukinaView profile

Assistant Professor

• Artificial Intelligence Safety
• Formal Methods
• AI Interpretability
• +4 more

Anna Lukina is an Assistant Professor in the Department of Intelligent Systems at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. She leads the Sequential Uncertainty Monitoring and Interpretability (SUMI) Lab, focusing on improving safety and interpretability of artificial intelligence through formal methods with applications in engineering, transportation, health, and finance. Her research spans the critical intersection of formal verification and machine learning, particularly in developing techniques for runtime monitoring of neural networks, safety verification of decision-tree policies, and creating verifiable reinforcement learning systems. She has established strong international collaborations with researchers across the US, Europe, Japan, and Australia. Lukina's recent publications (2021-2025) demonstrate consistent output in top AI venues including AAAI, NeurIPS, and IJCAI, with a clear trajectory toward increasingly sophisticated verification techniques for complex AI systems. Her work shows strong emphasis on practical applications while maintaining theoretical rigor, particularly in creating methods that provide formal guarantees for black-box AI systems. As part of her service commitment, she leads initiatives promoting junior computer scientists from underrepresented communities, reflecting her dedication to diversity in the field as highlighted in her DerStandard interview "Warum so wenige Frauen Den Code knacken wollen" and university magazine Delta. She currently supervises multiple PhD researchers including Sterre Lutz, Daniël Vos, Aaron Berger, and Johannes Koch, along with numerous successful MSc graduates who have completed theses on topics ranging from anomaly detection to genetic programming for explainable AI.

• Dynamical Systems
• Partial Differential Equations
• Pharmacology
• +4 more

Vivi Rottschäfer is an Associate Professor in the Analysis and Dynamical Systems group at the Mathematical Institute, Leiden University , and holds a Professor position by special appointment in Industrial Mathematics at the Korteweg-de Vries Institute for Mathematics, University of Amsterdam. Her research focuses on Dynamical Systems , Partial Differential Equations , and their applications in Pharmacology , Health , and Ecology . She develops mathematical models for drug distribution in the brain, ecological pattern formation, and industrial problems, with a particular interest in blow-up phenomena , pattern dynamics , and multi-scale modeling . Recent publications (2025-2020) span cellular communication optimization , nanomaterial toxicokinetics , neural feedback dynamics , and ecosystem resilience . Her work often bridges pure mathematical analysis with real-world challenges, including collaborations in receptor theory , drug development , and environmental toxicology . She organizes major conferences like the International Congress on Industrial and Applied Mathematics (ICIAM 2027) and the Study Group Mathematics with Industry , and has taught advanced courses on Bifurcation Theory , Perturbation Methods , and Dynamical Systems since 2010.

Lorenzo GarlappiView profile

Professor

• Asset Pricing
• Credit Risk
• Real Options
• +4 more

Lorenzo Garlappi is the TSX Venture Exchange Professor of Finance at the Sauder School of Business, University of British Columbia. He holds a PhD from UBC, a Doctorate from the University of Trieste, and a BS in Economics from Bocconi University, Italy. His research focuses on asset pricing, credit risk, real options, and portfolio choice, with applications to corporate finance and investment strategies. Education: PhD in Finance, University of British Columbia (UBC) Doctorate in Economics, University of Trieste, Italy BS in Economics, Bocconi University, Milan, Italy Research Interests: Garlappi explores theoretical and empirical aspects of financial markets, including: Credit risk dynamics and debt issuance strategies Monetary policy impacts on investment behavior Climate change effects on real estate pricing Optimal portfolio diversification under uncertainty Real options in corporate decision-making Awards & Recognition: Dimensional Fund Advisors Distinguished Paper Award (2021) Best Paper Award at ASU Sonoram Winter Conference (2019) Summer Haven Investment Management Prize (2019) Crowell Memorial Prize (2010) Teaching & Contributions: Teaches Risk Management and Financial Engineering at Sauder. His work bridges academic rigor and practical finance, offering frameworks for addressing market inefficiencies and uncertainty. He collaborates with industry practitioners to refine asset allocation strategies and corporate finance policies. Labs & Teams: Leads research initiatives at Sauder’s Finance Division, focusing on computational finance, corporate governance, and sustainable investing. Engages in interdisciplinary projects with economics and environmental science departments.

Daan CrommelinView profile

Professor

• Uncertainty Quantification
• Stochastic Modeling
• Data-Driven Modeling
• +6 more

Daan Crommelin holds a part-time professorship in Numerical Analysis and Dynamical Systems at the KdV Institute for Mathematics, University of Amsterdam, and is a senior researcher at CWI Amsterdam's Scientific Computing group. He serves on CWI's management team and previously led its Scientific Computing group (2013–2021). His research focuses on stochastic modeling of multiscale systems, uncertainty quantification, and rare event analysis, with applications in climate science, renewable energy, and fluid dynamics. Crommelin combines methods from scientific computing, applied probability, and dynamical systems to address challenges in atmosphere-ocean-climate modeling. He has contributed to projects like the EU-funded VECMA initiative for exascale computing and collaborated on superparameterization techniques for climate models. His work also extends to epidemic modeling and computational chemistry. Crommelin earned his PhD in 2003 from Utrecht University, with a thesis co-supervised by KNMI, and holds an MSc in theoretical physics and an MA in philosophy from the University of Amsterdam.

Subhash C. SarinView profile

Professor

• Operations Research
• Production Scheduling
• Applied Mathematical Programming
• +5 more

Subhash C. Sarin is the Paul T. Norton Endowed Professor at Virginia Tech's Grado Department of Industrial and Systems Engineering within the College of Engineering. His expertise spans operations research, production scheduling, applied mathematical programming, and manufacturing systems design. He holds a Ph.D. from North Carolina State University (1978), an M.S. from Kansas State University (1973), and a BSc from Delhi College of Engineering (1970). Research focuses on scheduling optimization, semiconductor manufacturing, and biomass logistics. Notable contributions include stochastic scheduling models, disassembly optimization, and primary pharmaceutical manufacturing scheduling. He has led over 40 funded projects, including USDA/DOE initiatives for biomass logistics. Recipient of numerous awards, including the IIE David F. Baker Distinguished Research Award (2015) and Virginia Tech's Alumni Award for Excellence in Graduate Advising (2013). He directs the Electronics Manufacturing Research Laboratory and serves as associate director of the Center for High Performance Manufacturing. His teaching excellence is recognized through awards like the Sporn Award (1997) and Holzman Educator Award (2000). Professional service includes editorial roles for journals like Computers and Industrial Engineering. Key projects include optimizing nuclear power plant outages and semiconductor wafer fabrication. His work integrates mathematical modeling with real-world applications in manufacturing, logistics, and sustainability.