Find Academic Supervisors

Stephen J. WrightView profile

Professor

• Computational Optimization
• Mathematical Optimization
• Optimization Algorithms
• +3 more

Stephen J. Wright holds the George B. Dantzig Professorship, Sheldon Lubar Chair, and Amar and Balinder Sohi Professorship as a Professor of Computer Sciences at the University of Wisconsin-Madison. His research focuses on computational optimization with broad applications across scientific and engineering disciplines. His expertise spans computational optimization, mathematical optimization, and optimization algorithms, with significant contributions through widely adopted textbooks like Primal Dual Interior-Point Methods and Numerical Optimization . His work bridges theoretical foundations with practical implementations for complex scientific computing challenges. Notable honors include: W.R.G. Baker award from IEEE (2014) SIAM Fellow designation Wright serves as editor-in-chief of the SIAM Journal on Optimization and has held editorial leadership roles for Mathematical Programming (Series A/B), SIAM Review , and SIAM Journal on Scientific Computing . His academic career includes prior positions at North Carolina State University (1986-1990), Argonne National Laboratory (1990-2001), and the University of Chicago (2000-2001), followed by continuous service at UW-Madison since 2001. He has chaired the Mathematical Optimization Society and served as SIAM Trustee, with recent program participation at the Simons Institute including Fall 2021's Geometric Methods in Optimization and Sampling.

Michael FriedlanderView profile

Professor

• Convex Optimization
• Machine Learning
• First-Order Methods
• +3 more

Michael Friedlander is a Professor of Computational Mathematics at the University of British Columbia (UBC), holding joint appointments in the Departments of Computer Science and Mathematics. He serves as Director of the UBC Institute of Applied Mathematics and is a Sauder School Distinguished Scholar. His research focuses on optimization theory, algorithms, and applications, particularly in convex optimization, first-order methods, and machine learning. Friedlander is a SIAM Fellow (2024) and has received multiple teaching awards, including the 2013 UBC CS Teaching Award. Education: PhD (2002) in Operations Research from Stanford University; BA (1993) in Physics from Cornell University. Research Interests: Friedlander’s work spans computational optimization, including first-order methods, gauge duality, federated learning, and applications in signal processing and quantum computing. His contributions include SPGL1, a widely used convex optimization solver, and foundational work on atomic decomposition and polar alignment in structured optimization. Key Contributions: His research bridges optimization with practical applications, such as compressed sensing and data science. Notable areas include dual methods for federated learning, low-rank spectral optimization, and algorithms for sparse recovery. He has advised numerous PhD students, including work on topics like gauge duality and federated learning contribution valuation. Awards & Honors: SIAM Fellow (2024) 2013 UBC CS Department Teaching Award CACS Outstanding Young Researcher Prize AI & Statistics 2009 Best Paper Award Advising & Grants: Supervised over 20 graduate students and postdocs. Active in grant-funded research, including projects on optimization algorithms and their industrial applications. Collaborates with institutions like Pacific Institute for Mathematical Sciences (PIMS) and the Data Science Institute at UBC. Labs & Affiliations: Affiliated with the Institute for Computing, Information and Cognitive Systems (ICICS), Data Science Institute, and Quantum Computing Research Cluster at UBC. Leads interdisciplinary efforts in applied mathematics and computational science.

Silke GlasView profile

Research Fellow

• Reduced basis method
• Variational inequalities
• Wave equations
• +3 more

Silke Glas is a Postdoctoral Researcher at the Institute of Numerical Mathematics, Ulm University, a position she has held since July 2018. Previously, she served as a Research Assistant at the same institute from April 2016 to July 2018 and at the Chair of Energy Trading and Finance, University of Duisburg-Essen (2012-2016), funded by the German Research Foundation's Priority Programme 1324. Her research visits include the Institut Henri-Poincaré in Paris and SISSA in Trieste. Her educational background features: Diploma in Mathematics and Economics from Ulm University (2006-2012), thesis on "Reduced Basis Method for Variational Inequalities" Master of Mathematics from the University of South Florida (2009-2010) Dr. Glas specializes in model reduction for nonlinear problems, with core expertise in reduced basis methods applied to variational inequalities, wave equations, Hamilton-Jacobi-Bellman equations, and space-time formulations. Her work bridges theoretical numerical analysis with practical applications in energy markets, particularly intraday electricity trading. She has developed novel approaches for noncoercive and parabolic systems, addressing challenges in error estimation and computational efficiency. Her publication trajectory reveals evolving sophistication in handling time-dependent nonlinear systems, with increasing emphasis on financial applications. Early work focused on theoretical foundations of variational inequalities, while recent publications integrate model reduction with optimal control for energy trading problems, demonstrating cross-disciplinary impact. Scientific recognition includes: No formal awards documented in source material Research funding has been secured through the German Research Foundation's Priority Programme 1324. No student advising activities are mentioned, though her collaborative work involves prominent researchers like K. Urban and Anthony T. Patera. Her primary research environment at Ulm University's Institute of Numerical Mathematics supports her focus on computational mathematics and real-world applications.

• Optimization
• Operational Research
• Computational Mathematics
• +3 more

Julian Hall is a Professor in the Department of Mathematics at the University of Edinburgh, specializing in optimization and operational research. He is a key developer of HiGHS, an open-source linear optimization software, and previously developed a solver used in global animal feed formulation and oil reservoir management. His research focuses on enhancing the simplex method for linear programming efficiency. Julian holds a PhD from the University of Dundee under Roger Fletcher after studying at Oxford and working at ICI/AstraZeneca. He has received four best paper awards, including COAP recognitions in 2005, 2013, 2015, and 2018. His work bridges academia and industry, with funding from Google and Huawei. Julian emphasizes applied mathematics’ real-world impact, particularly in agriculture and computational sustainability. Education: PhD in Numerical Analysis and Optimization (Dundee), MSc (Dundee), BA (Oxford), King’s School (Macclesfield). Research Interests: Linear programming algorithms, parallel computing, optimization in agriculture and environmental systems. His software HiGHS is deployed in multi-billion-dollar industries, solving problems like optimal animal feed blends and sustainable livestock production. Julian collaborates with industrial partners and mentors PhD students advancing optimization techniques. His recent work includes parallel simplex methods and GPU-accelerated algorithms, reflecting his commitment to scalable computational solutions.

• Wood Science
• Wood Modification
• Sustainable Materials
• +5 more

Dr. Christian Hansmann is a researcher at the Institut für Holztechnologie und Nachwachsende Rohstoffe , part of the Department für Biotechnologie und Lebensmittelwissenschaften at the University of Natural Resources and Life Sciences, Vienna (BOKU) . With a doctorate in wood technology (2004) and graduation in timber industry (2002), he has focused on wood modification, drying processes, and sustainable material applications. Education Doctorate, BOKU Vienna (2004) Graduation in Timber Industry (2002) His research explores wood science through innovations in wood drying , chemical modification , and bio-based composites . Notable projects include supercritical CO2-assisted impregnation, thermal insulation panels from agricultural byproducts, and eco-efficient hardwood utilization. He has supervised 15 university theses, including studies on wood laminates and bark beetle control. Recent publications highlight his work on wood modification techniques , with 15 articles from 2005–2025 covering topics like hydrophobic wood surfaces , dimensional stability , and flame-retardant treatments . Key methods include UV microscopy, siloxane treatments, and adhesive bonding analysis. Scientific Awards: tecnet Call 2009 'Eco-efficient Products' 1st Prize (2010) Dr. Wolfgang Houska Recognition Award (2009) Schweighofer Innovation Prize (2009) He has delivered 57 lectures across 15 countries, including presentations at SWST International Conventions and European Wood Modification Conferences . As a reviewer for journals like Wood Science and Technology and European Journal of Wood Products , he contributes to quality control in his field.

Yin Tat LeeView profile

Associate Professor

• Convex Optimization
• Convex Geometry
• Graph Algorithms
• +4 more

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, and a Senior Principal Researcher at Microsoft AI. His research spans Convex Optimization , Convex Geometry , Graph Algorithms , Online Algorithms , and Differential Privacy , with a focus on designing theoretically optimal algorithms for combinatorial and convex problems. Lee earned a Ph.D. in Mathematics (2016) and B.S. in Mathematics (2012) from MIT and The Chinese University of Hong Kong, respectively. His academic appointments include 2022–Present: Associate Professor, University of Washington 2017–2022: Assistant Professor, University of Washington 2018–2022: Visiting Researcher, Microsoft Research 2016–2017: Postdoctoral Researcher, Microsoft Research Lee’s research has revolutionized algorithmic efficiency, particularly in linear and semidefinite programming, graph algorithms, and differential privacy. Key contributions include the first nearly-linear-time algorithm for linear programs with small treewidth (STOC 2021), solving linear programs as fast as linear systems (STOC 2019), and optimal distributed non-smooth optimization (NeurIPS 2018). His work integrates techniques from convex geometry, spectral graph theory, and stochastic processes. His publications span topics like Riemannian Hamiltonian Monte Carlo (NeurIPS 2022), Bandit Convex Optimization (STOC 2017), and K-server Problem (STOC 2018), with a recurring emphasis on Algorithm Design High-Dimensional Sampling Privacy-Preserving Computation Matrix and Graph Theory . Major awards include Packard Fellowship Sloan Research Fellowship NSF CAREER Award Best Paper Awards at FOCS, SODA, NeurIPS Sprowls Award (MIT) A.W. Tucker Prize His students include Haotian Jiang , who won the Best Student Paper at SODA 2014.

Amin KargarianView profile

Associate Professor

• Power Systems Resilience
• Quantum Computing Applications
• Grid Optimization
• +2 more

Amin Kargarian is ISC Constructors Associate Professor at LSU, leading the RAISE Lab focused on cyber-physical infrastructure resilience. His research integrates quantum computing, machine learning, and optimization for power systems applications. Current projects include NSF-funded work on quantum-enhanced grid optimization and flood resilience planning for Entergy. Kargarian develops computational frameworks addressing climate hazards in energy infrastructure, with emphasis on equitable community outcomes. His lab pioneers quantum-classical hybrid algorithms for large-scale power system optimization challenges. Research areas span resilient grid design, stochastic optimization under uncertainty, quantum algorithm development, and community-aware energy planning. Kargarian mentors graduate students in power systems engineering and quantum computing applications.

Selin AhipasaogluView profile

Professor

• Operational Research
• Convex Optimization
• Robust Optimization
• +5 more

Prof. Selin Damla Ahipasaoglu is a Professor in Operational Research at the University of Southampton's School of Mathematical Sciences . She serves on the management team of the UKRI CDT SustAI (Artificial Intelligence for Sustainability) as Senior Tutor and Co-Lead for the Transportation and Logistics Theme . Her work bridges mathematical optimization with practical applications in sustainability, finance, and transportation systems. Research Interests : Convex Optimization Robust Optimization Discrete Choice Theory Experimental Design Machine Learning Current Research : Focused on robust optimization and its applications in discrete choice modeling, portfolio optimization, and transportation systems. She explores theoretical frameworks alongside real-world implementations, particularly through interdisciplinary projects like the UKRI CDT SustAI. Teaching : In the 2025/2026 academic year, she teaches MATH3017: Mathematical Programming and MATH2013: Operational Research II . She supervises PhD students in Mathematical Sciences, including Kexin Lai, Samuel Jericho Ward, and others.

• Data Visualization
• Visual Analytics
• Big Data Analysis
• +12 more

Professor Jang Yoon is a faculty member in the Department of Computer Engineering at Sejong University, South Korea. He currently holds the position of Daeyang Distinguished Professor and leads the Data Visualization Lab. His academic journey includes postdoctoral research at the Swiss National Supercomputing Center (2007-2009), ETH Zurich (2009-2011), and Purdue University (2011-2012). His educational background includes a Bachelor's degree from Seoul National University in Electrical Engineering (2000), and Master's and Doctoral degrees from Purdue University in Electrical and Computer Engineering (2002 and 2007). His academic progression at Sejong University shows his appointment as Assistant Professor (2012-2016), Associate Professor (2016-2022), and Professor (2022-present). Professor Jang's research spans multiple domains within data science and visualization, with primary focus on data visualization, visual analytics, and their applications in various domains. His work bridges theoretical computer science with practical applications in traffic analysis, healthcare, and smart city infrastructure. He has developed innovative techniques for spatiotemporal data visualization, volume rendering, and causal analysis in complex datasets. His recent publications (2023-2025) demonstrate a strong focus on integrating deep learning with visualization techniques, particularly in traffic analysis, volume rendering, and large language model interpretability. His work shows a clear trajectory toward combining causal inference with visual analytics, applying these methods to urban traffic systems, structural health monitoring, and public relations analysis. Professor Jang has served in numerous leadership roles in major visualization conferences including IEEE VIS, IEEE PacificVis (as General Chair in 2023), EuroVis, and HCI Korea conferences. His service contributions include program committee memberships and chair positions across multiple prestigious conferences in the visualization field. His laboratory work focuses on practical applications of visualization techniques with numerous patents registered in Korea. His research has resulted in multiple practical systems for traffic analysis, VR sickness detection, data quality improvement, and eye-tracking applications. The lab maintains strong industry connections through applied research projects addressing real-world problems.

Giampaolo LiuzziView profile

Associate Professor

• Nonlinear Optimization
• Global Optimization
• Mixed Integer Nonlinear Programming
• +3 more

Giampaolo Liuzzi is an Associate Professor at the Department of Computer, Automation, and Management Engineering 'Antonio Ruberti' (DIAG) at Sapienza University of Rome since July 2023. Previously, he was a fixed-term researcher at the same department (July 2020-June 2023) and a Senior Researcher at the Institute of Systems Analysis and Computer Science 'A. Ruberti' of the CNR until July 2020. He teaches Mathematical Programming, Complements of Mathematics, and Mathematical Analysis 2 for various engineering programs at Sapienza University. Dr. Liuzzi's research focuses on Nonlinear Optimization , particularly derivative-free methods for constrained and unconstrained optimization, global optimization, mixed integer nonlinear programming, and applications in operations research and machine learning. His work spans theoretical developments in optimization algorithms as well as practical applications in engineering design, simulation-based optimization, and biomedical systems. He has made significant contributions to derivative-free optimization techniques that don't require gradient information, which is particularly valuable for black-box optimization problems where derivatives are unavailable or expensive to compute. His recent publications (2022-2025) demonstrate a strong focus on advancing derivative-free optimization methods, with particular attention to complexity analysis, convergence properties, and practical implementations for challenging problem classes including nonsmooth, constrained, multi-objective, and mixed-integer optimization problems. His work bridges theoretical computer science with practical engineering applications, with publications appearing in top optimization journals like Optimization Methods & Software, Computational Optimization and Applications, and Journal of Optimization Theory and Applications. Dr. Liuzzi has received significant professional recognition through National Scientific Habilitations for both Associate Professor (2014) and Full Professor (2018) positions in Italy. He is actively involved in the academic community as an administrator of the Derivative-Free Library (DFL), a collection of algorithms and methods for derivative-free optimization developed through collaboration among several prestigious Italian research institutions. As an educator, Dr. Liuzzi has developed comprehensive teaching materials for courses in Mathematical Programming, Complements of Mathematics, and Mathematical Analysis. He is also engaged in academic entrepreneurship as a co-founder of DEIX s.r.l., a Sapienza startup focused on algorithms and industrial software for planning and control of complex systems. Additionally, he organized the 2nd Derivative-Free Optimization Symposium (DFOS'24) in June 2024 in Padua, highlighting his leadership role in this specialized optimization community.

Nikolaos SamarasView profile

Professor

• Algorithm Analysis
• Computational Complexity
• Algorithmic Operations Research
• +9 more

Nikolaos Samaras is a Full Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia in Thessaloniki, Greece. He has been serving as director of the Computational Methodologies & Operations Research (CMOR) Laboratory since April 2016. His academic career includes positions as Assistant Professor (2007-2012) and Lecturer (2003-2007) at the same institution, and earlier as an adjacent Lecturer at the Technological Institute of Western Macedonia (1998-2000). Dr. Samaras earned his Diploma in Applied Informatics from the University of Macedonia in 1996 and his Ph.D. in Applied Informatics from the same university in 2001. His educational background forms the foundation for his extensive research in computational optimization and operations research. Professor Samaras's research focuses on the interface between computer science and operations research, with particular expertise in linear and nonlinear optimization, network optimization, integer optimization, and scientific computing including HPC and GPU programming. His work has resulted in the development of new algorithmic families for optimization problems, efficient GPU implementations of the revised simplex algorithm, and novel algorithms and software for operations research. His research spans theoretical algorithm development, practical implementation, and real-world applications across various engineering and scientific domains. His extensive publication record includes over 35 journal papers in prestigious venues such as Computers and Operations Research, European Journal of Operational Research, and Journal of Artificial Intelligence Research, more than 85 conference papers, and four textbooks (two in English and two in Greek). His work has been recognized through citations and the Thomson ISI/ASIS&T Citation Analysis Research Grant in 2005. ACM Senior Member (2016) Thomson ISI/ASIS&T Citation Analysis Research Grant (2005) Editorial board member of Operations Research: An International Journal Reviewer for numerous top journals including Mathematical Programming Computation and European Journal of Operational Research Professor Samaras has supervised four current Ph.D. students working on hybrid simplex algorithms, large-scale optimization using Apache Hadoop, algorithmic procedures in matrix theory, and smoothed complexity analysis. He has successfully guided five Ph.D. students to completion, including Nikolaos Ploskas who won the 2014 HELORS Doctoral Dissertation Award. Additionally, he has supervised 45 master's theses and 84 bachelor's theses. His research group has secured funding from diverse sources including the European Union, Greek Secretariat of Research and Technology, and industry partners like Veltio Greece LTD. The Computational Methodologies & Operations Research (CMOR) Laboratory, which he directs, focuses on developing and implementing optimization algorithms with applications in transportation, energy systems, and business process design. The lab has produced notable software tools including Euclides and Visual LinProg, which have educational applications in linear programming.

Sophie HuibertsView profile

Researcher

• Linear Programming
• Mixed Integer Programming
• Algorithm Analysis
• +4 more

Sophie Huiberts is a CNRS researcher at LIMOS, Clermont Auvergne University in Clermont-Ferrand since fall 2023. Previously, she was a Simons Junior Fellow at Columbia University in New York City, hosted by Tim Roughgarden. She completed her PhD research at Centrum Wiskunde & Informatica in Amsterdam under Daniel Dadush and received her doctorate in 2022 from Utrecht University. Dr. Huiberts specializes in theoretical aspects of mathematical optimization, particularly focusing on the gap between practical performance and theoretical predictions of linear programming algorithms. Her research examines software implementations like Gurobi, CPLEX, SCIP, and HiGHS to understand why these algorithms perform better in practice than worst-case analysis would suggest. She has made significant contributions to smoothed analysis of the simplex method, establishing both upper and lower bounds on its complexity under perturbations of worst-case inputs. Analysis of her publication record shows consistent focus on bridging theoretical computer science with practical optimization methods. Her work spans linear programming theory, integer programming, combinatorial optimization, and computational geometry, with particular emphasis on understanding the geometric properties of optimization problems and the behavior of algorithms on real-world instances. Simons Junior Fellowship Dr. Huiberts maintains active engagement with the research community through social media platforms including Mastodon and Bluesky, and produces high-quality recordings of her research talks available on YouTube. She has made a conscious decision to stop air travel since 2023 due to climate concerns, demonstrating commitment to sustainable research practices while maintaining scientific connections through digital means. She is affiliated with LIMOS (Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes), a research laboratory at Clermont Auvergne University focused on computer science, modeling, and optimization systems, where she continues her investigations into the theoretical foundations of practical optimization algorithms.

Daniel P. RobinsonView profile

Professor

• Computational Optimization
• Machine Learning
• Data Science
• +3 more

Professor Daniel P. Robinson holds the position of Professor and MS Program Director in the Department of Industrial and Systems Engineering at Lehigh University. Previously, he served as a Postdoctoral Researcher at the University of Oxford and Northwestern University, and as an Assistant Professor at Johns Hopkins University. His research focuses on computational optimization and its applications in data science, machine learning, and computer vision, with a particular emphasis on healthcare and algorithm design. Education: Ph.D. in Mathematics from the University of California, San Diego; postdoctoral training at Oxford University and Northwestern University. Research Interests: Dr. Robinson’s work bridges mathematical optimization and data science, emphasizing algorithm design for continuous optimization problems. His areas include computational optimization, machine learning, data science, and computer vision applications. He has contributed to fair machine learning frameworks, stochastic optimization algorithms, and neural network compression techniques. His publications span top-tier journals and conferences such as Mathematical Programming, SIAM Journal on Optimization, and ICML. Notable grants include NSF funding for optimization research. He co-founded Johns Hopkins’ Mathematical Institute for Data Science (MINDS) and helped establish the JHU Master of Science in Data Science program. Scientific Awards: Twice recipient of the Professor Joel Dean Award for Excellence in Teaching. Advising & Grants: As MS Program Director, he oversees academic programs. His grants include over $1M from the Office of Naval Research and NSF support. He collaborates on projects like scalable subspace clustering and privacy-preserving machine learning. Labs/Teams: Leadership roles in Lehigh ISE’s optimization programs, fostering interdisciplinary research in data science and optimization.

Yawen WangView profile

Assistant Professor

• Gear Dynamics
• Structural Vibration and Acoustics
• Active Noise Control
• +3 more

Dr. Yawen Wang is an Assistant Professor of Research in the Mechanical and Aerospace Engineering Department at The University of Texas at Arlington (UTA). He directs the Vibro-Acoustics and Sound Quality Research Laboratory and leads the Hypoid and Bevel Gear Mesh and Dynamic Modeling Consortium. His academic career includes adjunct roles at the University of Cincinnati and teaching positions since 2017. Dr. Wang holds a PhD in Mechanical Engineering from the University of Cincinnati (2017), an MS from the same institution (2013), and a BS from Chongqing University (2010). Educational Background PhD in Mechanical Engineering, University of Cincinnati, 2017 MS in Mechanical Engineering, University of Cincinnati, 2013 Bachelor of Science in Mechanical Engineering, Chongqing University, 2010 Research Expertise Dr. Wang specializes in precision machine elements (gears, bearings), gear noise/vibration, structural acoustics, active noise control, automotive NVH, and data-driven design. His work integrates computational modeling, experimental techniques, and analytical approaches to solve engineering challenges in powertrain systems and vehicle dynamics. Recent projects include optimizing hypoid gear designs, improving drivetrain prognostics via Vibration Resonance Spectrometry (VRS), and reducing electric vehicle noise through advanced materials and control systems. Professional Contributions He has authored/co-authored over 70 journal/conference papers and collaborates with industry leaders like Ford, GM, Daimler, and Toyota. His awards include the InterNoise Hallberg Foundation Award (2015) and NoiseCon Michiko Finegold Award (2014). Dr. Wang serves on ASME committees and chairs sessions at the International Power Transmission and Gearing Conference. Academic Leadership He advises graduate students, including Chia Ching Lin for a dissertation on axle system dynamics. Teaching responsibilities span mechanical vibrations, structural dynamics, and powertrain design. Dr. Wang also oversees grants from sponsors such as Jian’an Industrial Co., advancing gear mesh modeling and dynamic analysis. Labs & Consortia As director of the Vibro-Acoustics Lab, he focuses on sound quality design, active control systems, and vehicle interior acoustics. The Gear Consortium he leads develops tools like HGSim for gear mesh simulation, with recent updates to version 7.8.3 (2024).

• Computational Fluid Dynamics
• Aerodynamic Shape Optimization
• Simulation-Based Design
• +3 more

Dr. Jason Hicken is an Associate Professor in the Mechanical, Aerospace, and Nuclear Engineering Department at Rensselaer Polytechnic Institute (RPI), and co-director of the Rensselaer Augmented and Virtual Environment. His research focuses on computational fluid dynamics, aerodynamic shape optimization, and simulation-based design. He holds a Ph.D. in Aerospace Engineering from the University of Toronto Institute for Aerospace Studies (2009), with postdoctoral training at Stanford University. Notable awards include the AFOSR Young Investigator Award (2015) and NSF CAREER Award (2016). His research interests span high-order numerical methods, entropy-stable discretizations, and optimization techniques for complex systems. He has contributed to advancing finite-difference operators, summation-by-parts schemes, and multi-disciplinary design optimization. His work bridges computational engineering and applied mathematics, addressing challenges in fluid dynamics, thermal systems, and aerospace applications. His lab, the Optimal Design Lab (http://www.optimaldesignlab.com/), develops innovative tools for engineering design optimization. Key achievements include developing entropy-stable numerical frameworks and integrating machine learning techniques for preconditioning in advection-dominated flows. His research has been applied to electric motor design, thermal management systems, and additive manufacturing. Awards recognize his contributions to foundational methods in computational engineering and aerospace optimization.