Find Academic Supervisors

• Energy Systems Modeling
• Optimization
• Generation Expansion Planning
• +2 more

Sonja Wogrin is an Associate Professor in the Department of Industrial Organization at the Higher Technical School of Engineering, Pontifical University of Comillas. She holds a Ph.D. in Electricity Markets from the Instituto de Investigación Tecnológica (IIT) at the same university and has been a researcher there since 2009. Her work focuses on decision support systems for energy systems, optimization techniques, and generation capacity expansion planning. Education: B.Sc. in Technical Mathematics (Graz University), M.Sc. in Computation for Design and Optimization (MIT), Ph.D. in Electricity Markets (IIT-Comillas) Affiliations: Researcher at IIT-Comillas, Associate Professor in Industrial Organization Her research interests include energy systems modeling , optimization (particularly bilevel programming ), and electricity storage . She has developed open-source tools like the LEGO model for low-carbon expansion and analyzed market feedback in transmission planning. Her work addresses technical constraints in generation commitment, inertia in low-carbon grids, and policy implications of renewable integration. Recent article trends show a focus on gas pipeline MILP modeling , thermal demand simulations , and convex optimization in imperfect markets . She emphasizes stochasticity , multi-objective frameworks , and GIS-based siting for renewables. Scientific awards include the Iberdrola Spain Research Grant , 4th EASE Student Award (Belgium), and multiple mobility grants from Erasmus and NILS. As an advisor, she has supervised four Ph.D. theses on transmission planning , European gas markets , and co-optimized storage . Her grants include EU-funded projects like STEXEM and hydrogen roadmap development for Colombia with the IDB. She collaborates with institutions such as MIT, Johns Hopkins, and NTNU through research stays and contributes to IEEE Transactions , Applied Energy , and European Journal of Operational Research as a reviewer.

Thanh Thai NguyenView profile

Assistant Professor

• Commutative Algebra
• Algebraic Geometry
• Computational Algebra
• +7 more

Thanh Thai Nguyen is an Assistant Professor in the Department of Mathematics at the University of Dayton, part of the College of Arts and Sciences. He joined the faculty in 2025 after serving as a Britton Postdoctoral Fellow at McMaster University and completing his Ph.D. at Tulane University in 2022 under Dr. Tài Huy Hà. Institution: University of Dayton School: College of Arts and Sciences Department: Department of Mathematics Position: Assistant Professor Education Ph.D., Tulane University, 2022 Thanh Thai Nguyen specializes in commutative algebra with a focus on computational and combinatorial aspects, and its interactions with algebraic geometry and combinatorics. His research centers on asymptotic behavior of algebraic invariants, symbolic powers of ideals, polynomial interpolation (including Chudnovsky’s and Demailly’s conjectures), and the algebra and combinatorics of monomial and binomial ideals. He has made significant contributions to understanding resurgence numbers, Waldschmidt constants, and the structure of Rees algebras. The most recent articles demonstrate a strong trend in linking combinatorial structures (such as simplicial complexes and matroids) with algebraic invariants, exploring duality in asymptotic behavior, and developing computational tools for algebraic geometry. His work often involves deep interplay between convex geometry, commutative algebra, and combinatorics, particularly in the context of graded families of ideals and their symbolic powers. Scientific Awards No scientific awards or honors were mentioned in the provided text. Advising and Grants There is no information available about students advised by Thanh Thai Nguyen or any grants he has received. However, given his active publication record and postdoctoral background, it is likely that he is involved in research mentoring and grant-funded projects, though specifics are not disclosed in the source material. Labs and Research Groups Nguyen was previously affiliated with the Combinatorial Algebraic Geometry group at McMaster University. At the University of Dayton, no specific lab or research group is mentioned, but his research profile suggests he is building a program in computational and combinatorial commutative algebra.

Hassane SadokView profile

Professor

• Numerical Analysis
• Iterative Methods
• Linear Algebra
• +5 more

Hassane Sadok is a Professor and President at the University of the Littoral Côte d'Opale (ULCO), France. He is affiliated with the Department of Mathematics within the College of Science, where he contributes to teaching and research in numerical analysis and scientific computing. Position: Professor Institution: University of the Littoral Côte d'Opale (ULCO) Administrative Role: President since 2016 Department: Mathematics School: College of Science His research focuses on numerical linear algebra, particularly iterative methods for solving large linear systems, Krylov subspace methods (such as GMRES, Arnoldi, and CMRH), and regularization techniques for ill-posed problems. He has made significant contributions to the convergence analysis of minimal residual and orthogonal projection methods. His work bridges theoretical numerical analysis with practical computational algorithms. The recent publications reflect a strong trend in developing and analyzing block and global iterative solvers for multiple linear systems, convergence theory for eigenvalue problems, and regularization strategies like the L-curve and greedy Tikhonov methods. These works are grounded in applied mathematics and computational science, with applications in engineering and data analysis. Although no formal awards are listed in the provided texts, his leadership as university president and sustained research output indicate recognition in both academic and administrative domains. He advises graduate students in numerical analysis and computational mathematics, though specific names are not listed. He has not disclosed information on research grants. His teaching includes Master’s level courses in interpolation, quadrature, and numerical resolution of nonlinear equations. While no specific research lab or team is named, his collaborations with researchers like K. Jbilou, Y. Saad, and L. Reichel suggest active participation in a research group focused on numerical methods, likely within the Laboratoire de Mathématiques Pures et Appliquées (L.M.P.A.) at ULCO.

Robert FischbachView profile

Researcher

• 3D Integration
• Heterogeneous Systems
• Layout Design
• +3 more

Dr. Robert Fischbach is a researcher at the Institute of Micro and Electronic Systems (FET) at Dresden University of Technology. His work focuses on 3D integration, heterogeneous system design, layout verification, and thermal analysis of advanced electronic packaging solutions. Key research areas: 3D Integrated Circuits Thermal and Reliability Engineering Design Automation & Optimization Interposer-Based Systems He has contributed extensively to co-design methodologies, micro-transfer printing assembly, and XML/XSLT-based simulation frameworks. Collaborative work includes automotive mission profiles and standardized formats for electronics reliability. Notable publications span topics in layout verification, thermal modeling, and heterogeneous packaging. His collaborations include researchers like J. Lienig, A. Krinke, and T. Horst.

Peyman AfshaniView profile

Associate Professor

• Geometric algorithms and data structures
• Algorithmics
• Algorithms for hierarchical memory
• +3 more

Peyman Afshani is an Associate Professor in the Department of Computer Science at Aarhus University, Denmark. His research focuses on geometric algorithms, data structures, and algorithms for hierarchical memory systems. He maintains an active research profile with consistent publications since 2009, including 61 research outputs as documented. His primary research interests include: Geometric algorithms and data structures Algorithmics Algorithms for hierarchical memory Range Query optimization Computational Geometry Analysis of his publication trends since 2009 shows sustained output with 3-5 publications annually, peaking at 5 in multiple years (2011, 2012, 2014, 2017, 2019, 2021, 2023, 2024). His recent work (2023-2025) demonstrates continued focus on computational geometry problems including simplex range reporting, semialgebraic range reporting, and convexity in 3D search algorithms. His research shows strong theoretical foundations with practical applications in data structure design and query optimization. Collaboration networks indicate significant engagement with international researchers, particularly in computational geometry and data structure research domains. Professor Afshani supervises PhD students (at least one documented case) and maintains an active research program with consistent output across two decades.

Suyash P. AwateView profile

Professor

• Medical image computing
• Machine learning
• Image analysis
• +2 more

Suyash P. Awate serves as the Asha and Keshav Bhide Chair Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Bombay, Mumbai, India. His research spans medical image computing, machine learning, image analysis, computer vision, and statistical modeling and inference. His work focuses on developing novel computational methods for medical image analysis, reconstruction, and quality enhancement. Prof. Awate's research interests center on medical image computing, where he develops advanced machine learning techniques for medical image reconstruction, segmentation, and quality enhancement. His work addresses critical challenges in medical imaging such as low-dose PET/CT imaging, uncertainty quantification in segmentation, robustness to out-of-distribution data, and accelerated MRI/fMRI acquisition. He has pioneered methods combining variational inference, expectation maximization, and deep learning to improve medical image quality while reducing radiation exposure and scan times. His research has significant clinical applications in neuroimaging, tumor analysis, and brain function mapping. His recent publications demonstrate a strong trend toward integrating deep learning with classical statistical methods, particularly focusing on uncertainty-aware models for medical image analysis. His work consistently addresses the challenge of robustness in medical imaging, developing methods that maintain performance even with degraded input data or distribution shifts. A significant portion of his research targets clinical applications, particularly in neuroimaging and oncology, with emphasis on practical implementation for real-world medical settings. Prof. Awate actively mentors PhD, MTech, and BTech students at IIT Bombay. His current PhD students include Vatsala Sharma (awarded Microsoft Research India PhD Award), Jimut Bahan Pal (CMInDS Fellow, Prime Minister's Research Fellow), Tejomay Padole (TCS Research Fellow), Subhankar Nag, and Koustav Pal. He also guides MTech/MS students Soumya Mukherjee, Jay Gorakhiya, and Russel Abreo, along with BTech student Varshith Anumalasetty. He teaches several courses including CS 736: Medical Image Computing (inaugurated in 2014), CS 663: Fundamentals of Digital Image Processing, CS 215: Data Analysis and Interpretation, CS 740: Mathematics for Visual Computing, and CS 101: Computer Programming and Utilization. His laboratory focuses on developing computational methods for medical image analysis, with particular emphasis on uncertainty quantification, robust deep learning for medical imaging, and accelerated acquisition techniques. The research group maintains strong collaborations with medical institutions to ensure clinical relevance of their computational methods.

Raed Al KontarView profile

Associate Professor

• Federated Learning
• Personalized Data Science
• Uncertainty Quantification
• +5 more

Raed Al Kontar serves as an Associate Professor with tenure in the Industrial & Operations Engineering (IOE) department at the University of Michigan's College of Engineering. He leads the Data Science Lab and holds affiliate appointments with the Michigan Institute for Data Science and Computational Discovery and Engineering. His research bridges probabilistic modeling with engineering applications, focusing on personalized, collaborative, and distributed data analytics where knowledge from diverse sources is integrated while preserving privacy. His research program addresses three core questions across descriptive, predictive, and prescriptive analytics: extracting shared/unique patterns across datasets, enabling collaborative model improvement while maintaining data privacy, and optimizing distributed trial-and-error processes. Key research areas include Federated Learning, Uncertainty Quantification, Bayesian Optimization, and Digital Twins, with applications spanning healthcare, manufacturing, and materials science. Current funding includes NSF (including a 2022 CAREER award), NIH, NLM, and industry partnerships. Research Trends: His recent publications demonstrate a strong focus on heterogeneous data integration through novel matrix/tensor factorization techniques, personalized PCA frameworks, and consensus-based distributed optimization. The work consistently bridges theoretical guarantees (e.g., identifiability conditions) with practical applications in hydrogel development, 3D printing, and pharmaceutical safety. Award Highlights: His group has won 12 best paper awards since 2022 across INFORMS, ASA, and IISE, including the 2024 Wilson Prize and INFORMS Data Mining Section's Best General Track Paper. Dr. Al Kontar has successfully placed multiple PhD students in tenure-track positions including Naichen Shi (Northwestern), Xubo Yue (Northeastern), and Seokhyun Chung (University of Virginia). His lab maintains active collaborations with medical researchers (NIH/NLM projects), materials scientists (autonomous experimentation), and pharmaceutical safety experts. The lab combines theoretical innovation with real-world impact, exemplified by their NSF CAREER-funded work on the Internet of Federated Things.

CHUA Chek BengView profile

Associate Professor

• Convex Optimization
• Interior-Point Methods
• Symmetric Cones
• +3 more

Associate Professor CHUA Chek Beng serves in the Division of Mathematical Sciences within the School of Physical & Mathematical Sciences at Nanyang Technological University (NTU), Singapore. His academic profile centers on theoretical optimization with strong emphasis on mathematical structures underlying convex programming. His educational credentials include: Ph.D. in Operations Research, Cornell University (2003) M.Sc. in Operations Research, Cornell University (2002) B.Sc. First Class Honours in Mathematics, National University of Singapore (1999) Professor Chua's research critically examines primal-dual interior-point methods for convex optimization , the algebraic structure of convex cones , representability of convex cones , and complementarity problems over convex cones . His work bridges abstract algebra and computational optimization, particularly through T-algebra frameworks for symmetric cones. This theoretical foundation enables novel algorithmic approaches to semidefinite programming and variational inequalities, with consistent contributions to top-tier journals like SIAM Journal on Optimization and Mathematical Programming . His publication trajectory reveals deepening specialization in cone-based optimization—from foundational T-algebra theory (2003–2009) to advanced smoothing Newton methods (2013–2015)—with recurring themes in central path analysis, error bounds, and algorithmic convergence. All works demonstrate rigorous mathematical treatment of symmetric cone programming and complementarity problems. His accolades feature: SIAM Student Paper Prize (2003) Olin Fellowship from Cornell University (2001–2002) Cornell Sage Fellowship (1999–2001) Professor Chua actively disseminates findings through major international forums including SIAM Conferences and International Symposia on Mathematical Programming, though no student supervision details or grant activities appear in the source documentation. Similarly, laboratory affiliations or research team structures remain unspecified.

• Operations Research
• Stochastic Optimization
• Multi-objective Optimization
• +2 more

Susan Hunter is an Associate Professor in the Edwardson School of Industrial Engineering at Purdue University, West Lafayette, with her office located in GRIS 272. She can be contacted via email at susanhunter@purdue.edu. Her research centers on Operations Research with deep specialization in Stochastic Optimization and Multi-objective Optimization . She develops theoretical frameworks and practical algorithms for simulation-based optimization under uncertainty, focusing on Pareto front approximation, adaptive sampling techniques, and parallel computing implementations. Key contributions include error quantification methods, confidence region constructions, and performance indicator analyses for stochastic multi-objective problems. Analysis of her 2020-2025 publications reveals dominant trends in multi-objective stochastic simulation optimization, particularly addressing computationally expensive functions and integer-variable constraints. Her work bridges theoretical rigor (e.g., Central Limit Theorems for confidence regions) with practical tools like the SCORE algorithm for sampling allocation and bi-PASS for parallel optimization. She also contributes methodological guidance through LaTeX submission templates for academic publishing.

• Machine Learning
• Optimization
• Data Mining
• +2 more

Dr. Annabella Astorino is a researcher at ICAR-CNR (Italian National Research Council) in Rende, Italy, with expertise in machine learning, optimization, and computational biology. Her work focuses on classification algorithms using nonsmooth optimization, spherical separation, and multiple instance learning (MIL) approaches. Key Research Themes Geometric separation techniques (polyhedral, spherical) Multiple instance learning for biomedical applications Nonsmooth optimization in semisupervised learning Clustering methods for edge detection in images Recent publications highlight her development of Lagrangian relaxation for MIL, kernel-enhanced spherical separation algorithms, and DC optimization for computer vision tasks. Her collaborations include researchers like A. Fuduli, M. Gaudioso, and P. Veltri.

• Riemann Surfaces
• Teichmüller Theory
• Weil-Petersson Geometry
• +5 more

Scott A. Wolpert is a Professor in the Mathematics Department at the University of Maryland, where he holds the distinguished title of Distinguished Scholar-Teacher. His academic career has been primarily focused on geometric analysis and complex geometry, with significant contributions to the understanding of Riemann surfaces and their moduli spaces. Wolpert's research centers on the intricate geometry of Riemann surfaces, particularly through the lens of Fuchsian groups, hyperbolic geometry, and Teichmüller theory. His work has significantly advanced our understanding of the Weil-Petersson geometry for Teichmüller space, including curvature properties, convexity, and the behavior of geodesics near the completion locus. He has also made important contributions to spectral theory of Riemann surfaces, exploring questions related to isospectrality ("Can you hear the shape of the drum?") and the nature of high-energy eigenfunctions. Analysis of his publication record reveals a consistent focus on the geometry of moduli spaces of Riemann surfaces, with particular emphasis on Weil-Petersson geometry. His work spans both theoretical developments and concrete calculations, often connecting differential geometry with complex analysis, topology, and mathematical physics. Over time, his research has evolved from foundational work on the symplectic geometry of deformations to more recent investigations of curvature properties and asymptotic behavior in degenerating families of Riemann surfaces. Among his notable achievements is being named a Distinguished Scholar-Teacher at the University of Maryland, recognizing his excellence in both research and teaching. Professor Wolpert has been actively involved in the mathematical community through organizing workshops and conferences, including the NSF CBMS Conference on Families of Riemann Surfaces and Weil-Petersson Geometry at Central Connecticut State University in 2009, and the Workshop on Dynamics of the Weil-Petersson geodesic flow at the American Institute of Mathematics in 2012. His collaborations include work with Carolyn Gordon and David Webb on isospectral domains, and his research has influenced prominent mathematicians including Maryam Mirzakhani and Jeff Brock.

• Control Systems Engineering
• Optimization
• System Identification
• +4 more

Constantino Manuel Lagoa is a Professor in Electrical Engineering, specializing in control systems engineering, optimization, and behavioral informatics. His work spans energy systems stability, computational modeling, and health interventions. Current projects include Controlling Sub- and Supersynchronous Oscillations (NSF-funded) and Precision Text Messaging for Physical Activity (NHLBI). Research fingerprint highlights expertise in Computational Complexity , Convex Relaxation , and System Identification . Key contributions include adaptive control frameworks for energy systems and innovative applications of control theory in behavioral health promotion. His work intersects engineering rigor with societal impact through SDG-aligned research on health and sustainable ecosystems. Recent publications focus on physics-based system identification, nonlinear dynamics recovery, and ecosystem service trade-offs. Collaborations span multi-institutional teams in electrical engineering and public health domains.

Kil-Chan HaView profile

Professor

• Quantum Entanglement
• Matrix Theory
• Deep Learning
• +3 more

Kil-Chan Ha is a Professor in the Department of Mathematics and Statistics at Sejong University, South Korea. With 36 research outputs since 1998 and publications continuing through 2022, he maintains an active research program in mathematical physics and quantum information theory. His work bridges theoretical mathematics with quantum computing applications. His educational background includes: Ph.D. in Mathematics from Seoul National University (2000) M.S. in Mathematics from Seoul National University (1993) B.S. in Mathematics from Seoul National University (1991) Professor Ha's research focuses on the mathematical foundations of quantum information processing. His work on quantum entanglement has established important criteria for determining separability of quantum states based on diagonal and antidiagonal entries. He has made significant contributions to matrix theory applications in quantum systems and has explored the intersection of deep learning with quantum information processing. His cryptographic research includes the construction of public key systems based on non-abelian groups, showing the practical applications of abstract algebra. Analysis of his publication record reveals a consistent research trajectory focusing on quantum entanglement (45% of research fingerprint), matrix algebra (61%), and convex geometry (30%). His recent work shows increasing integration of deep learning techniques with quantum information theory, reflecting emerging interdisciplinary trends in the field. According to Scopus metrics, Professor Ha has accumulated 535 citations with an h-index of 13, demonstrating the impact of his research in the mathematical physics community. His work on positive linear maps between matrix algebras has been particularly influential, with one 2016 paper receiving 9 Scopus citations. His professional experience includes positions as Senior Researcher at NSRI (2000-2002) and ETRI (1999-2000) before joining Sejong University. His major research achievements include the construction of separability criteria for multi-qubit states, characterization of facial structures of separable states, and development of non-abelian group-based cryptographic systems.

Mathieu BlondelView profile

Researcher

• Differentiable Programming
• Machine Learning
• Deep Learning
• +9 more

Mathieu Blondel is a Researcher at Google DeepMind in Paris, France. He obtained his PhD in Machine Learning from Kobe University in 2013. From 2013 to 2019, he worked at NTT's Communication Science Laboratories in Kyoto, Japan. Research Interests: Differentiable programming Language models and deep learning Loss functions for structured prediction Optimization algorithms Optimal transport theory Polynomial networks Machine learning software Scientific Awards: Open-science honorable mention at ECML PKDD 2013 Software Contributions: Mathieu is a core contributor to scikit-learn , a foundational Python library for machine learning. He also maintains open-source projects on differentiable sorting, optimal transport, and Fenchel-Young losses.

Jie NingView profile

Associate Professor

• Operations Management
• Supply Chain Management
• Operations-Finance Interface
• +3 more

Jie Ning is an Associate Professor in the Department of Operations at the Weatherhead School of Management, Case Western Reserve University. She joined the faculty in 2013 after completing her PhD in Industrial and Operations Engineering at the University of Michigan. PhD in Industrial and Operations Engineering, University of Michigan (2013) MS in Physics, University of Michigan (2008) BSE in Industrial and Operations Engineering, University of Michigan (2008) BS in Physics, University of Science and Technology of China (2006) Her research focuses on the intersection of operations management with finance and economics, particularly examining how financial constraints affect operational decisions and interfirm interactions. Key areas include trade credit strategies, risk-averse contracting, stochastic optimization, and empirical analysis of supply chain dynamics. Recent publications (2023–2025) demonstrate her emphasis on financial instruments in supply chains (e.g., expedited payments, blockchain-based debt monitoring), asymmetric innovation capabilities in sourcing decisions, and trade credit mechanisms under competition. Earlier works (2017–2021) explore theoretical foundations of affine Markov decision processes and practical applications in capacity management. Weatherhead Emerging Impact Award (2021) Weatherhead Faculty Scholar (2021) Lewis-Progressive Fellow (2020) She teaches courses in Operations Research and Supply Chain Management, including Operations Management I/II, Strategic Sourcing, and Supply Chain Risk Management. Her work with Fortune 500 companies combines empirical and theoretical research to address real-world operational challenges.