Find Academic Supervisors

• Multidisciplinary design optimization
• Topology optimization
• Aeroelasticity
• +3 more

Kai A. James is an Associate Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in Computational Science and Engineering. He holds a Ph.D. (2012) and M.A.Sc. (2006) from University of Toronto, and B.A.Sc. (2004) in Engineering Science. His research focuses on multidisciplinary design optimization, topology optimization, aeroelasticity, and nonlinear mechanics, with applications to aerospace structures, additive manufacturing, and smart materials. He teaches courses such as Structural Design Optimization (AE 498), Nonlinear Solid Mechanics (AE 598), and Finite Element Analysis (ME 471). His honors include the NSF CAREER Award (2018), Scott White Aerospace Engineering Fellow (2020), and UIUC Teacher of the Year (2017). His work spans academic publications (over 50 journal/conference articles listed) and innovations in topology optimization frameworks for complex systems. Recent research emphasizes bi-stable structures (e.g., cardiovascular stents, morphing airfoils), thermomechanical design of shape-memory alloys, and spatial packing optimization for engineering systems. His lab, located in Talbot Laboratory, develops computational tools for multiphysics and multiscale design optimization.

• Algebraic Geometry
• Tropical Geometry
• Combinatorics
• +3 more

Kristin Shaw is a Professor in the Department of Mathematics at the University of Oslo, specializing in Algebra, Geometry and Topology. She leads the research group on Algebraic and Topological Cycles in Tropical and Complex Geometries, funded by the BFS. Her office is located in room 1114 of Niels Henrik Abels hus, with contact information including email krisshaw@math.uio.no and phone +47 22855940. Shaw's research focuses on the connections between combinatorics and algebraic geometry over the real and complex numbers, with particular emphasis on tropical geometry. Her work bridges abstract mathematical theory with concrete geometric structures, exploring how combinatorial methods can illuminate deep properties of algebraic varieties. She has made significant contributions to understanding matroids, real algebraic curves, and the topology of hypersurfaces through tropical techniques. Her research demonstrates how combinatorial structures can reveal fundamental insights about algebraic varieties and their topological properties. Analysis of Professor Shaw's recent publications reveals a consistent focus on tropical geometry and its applications to classical algebraic geometry problems. Her work frequently examines the interplay between real and complex geometries, with particular attention to combinatorial structures underlying algebraic varieties. Key themes include matroid theory, enumerative geometry, and the topology of algebraic varieties, demonstrating how tropical methods can provide new insights into longstanding problems in algebraic geometry. Her research shows remarkable depth across multiple subfields while maintaining a coherent theoretical framework that connects combinatorial and geometric approaches. Professor Shaw leads the research group on Algebraic and Topological Cycles in Tropical and Complex Geometries, which is funded by the BFS. Prior to her position at the University of Oslo, she held postdoctoral positions at the Max Planck Institute Leipzig, the Technical University of Berlin, the University of Toronto, and participated in the Fields' Institute semester in Combinatorial Algebraic Geometry. Her collaborative work spans multiple international institutions, reflecting her active engagement in the global mathematical research community.

Xiaocheng ShangView profile

Associate Professor

• Mathematical Optimization
• Data Science
• Numerical Analysis
• +3 more

Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science at the University of Birmingham's School of Mathematics. His research focuses on numerical methods for stochastic differential equations, with applications in computational mathematics, statistics, physics, and data science. He is affiliated with the Optimisation and Numerical Analysis Group, Statistics and Data Science Group, and the Institute for Data and AI. Shang holds a PhD in Applied and Computational Mathematics from the University of Edinburgh (2016) and completed postdocs at the University of Edinburgh, Brown University, and ETH Zurich before joining Birmingham in 2019. His academic achievements include fellowships from The Alan Turing Institute, the LMS Emmy Noether Fellowship, and the EUniWell Leadership Fellowship. He has secured funding from EPSRC, the Royal Society, and the Isaac Newton Institute. Shang is actively involved in supervising PhD students and co-organizing research initiatives such as the Data Science and Computational Statistics Seminar. Research interests include structure-preserving integrators, Bayesian sampling techniques, and machine learning applications in dynamical systems. His work bridges numerical analysis, probability theory, and multiscale modeling in materials science. Recent projects involve neural networks for complex dynamical systems and numerical algorithms for deterministic/stochastic systems.

James AndersonView profile

Assistant Professor

• Optimal Control Theory
• Robust Control
• Mathematical Programming
• +4 more

James Anderson is an Assistant Professor in the Department of Electrical Engineering at Columbia University, with affiliations to the Data Science Institute (DSI) and multiple research centers including the Computing Systems for Data-Driven Science and Foundations of Data Science. Prior to Columbia, he was a Senior Research Scientist at Caltech’s Computing + Mathematical Sciences division (2016–2019) and held a Junior Research Fellowship at the University of Oxford’s Department of Engineering Science (pre-2012). He earned his DPhil (PhD) in Engineering Science from Oxford in 2012. His research focuses on optimal/robust control theory, mathematical programming, data privacy, and cyber-physical systems, with applications in smart grids, systems biology, and power systems. Recent work emphasizes energy storage strategies, distributed control algorithms, and cybersecurity in critical infrastructure. His publications span advanced control methodologies (e.g., reinforcement learning for LQR problems), energy market dynamics, and resilient system designs. Notable contributions include frameworks for decision-focused energy storage arbitrage and defenses against false data attacks in power grids. He actively collaborates on federated learning approaches for distributed systems and has pioneered techniques for system-level synthesis in cyber-physical architectures. Anderson’s affiliations include the Data Science Institute (DSI) and specialized centers focused on data-driven science and energy systems. His work bridges theoretical control advancements with real-world applications in energy and healthcare.

Ye ZhishengView profile

Associate Professor

• Inventory Control
• Reliability Engineering
• Emergency Response Systems
• +4 more

Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.

Duminda WijesekeraView profile

Professor

• Cybersecurity
• Cyber-Physical Systems
• Digital Forensics
• +5 more

Duminda Wijesekera serves as Professor in the Department of Cyber Security Engineering and Department of Computer Science at George Mason University, where he was inaugural chairman of the Cyber Security Engineering Department until December 2022. He concurrently held the position of visiting research scientist at the National Institute of Standards and Technology (NIST) from 2007-2022 and maintains status as a fellow at the Potomac Institute of Policy Studies. He leads the Mason Innovation Laboratory at Mason Square, driving translational research in cyber-physical security. His educational foundation includes: PhD in Computer Science, University of Minnesota (1997) PhD in Mathematical Logic, Cornell University (1990) BSc in Mathematics, University of Colombo Professor Wijesekera's research centers on cyber-physical system security , with pioneering work in Intelligent Transportation Systems spanning trains, aircraft, and connected vehicles. His digital forensics innovations establish frameworks for evidence-based scenario reconstruction and error management, while his formal methods research provides mathematical guarantees for safety-critical systems. Current projects address Next G-based edge services, digital twin vulnerability detection, and healthcare security architectures, consistently bridging theoretical rigor with real-world infrastructure protection. Analysis of his 2022-2025 publications reveals intense focus on autonomous vehicle security (38% of recent output), including traffic signal control optimization, ramming attack countermeasures, and CARLA-based scenario validation. Digital forensics using AI (20%) and secure manufacturing/edge computing (27%) constitute other major thrusts, demonstrating how formal verification and machine learning converge to solve complex cyber-physical security challenges across transportation, energy, and healthcare domains. His scientific recognition includes: CCI Impact Award (2022) for groundbreaking cyber-physical security contributions Fellowship at the Potomac Institute of Policy Studies for cybersecurity policy leadership Professor Wijesekera has secured substantial research funding through: NIST grants for health record security frameworks (2014-2015) US Department of Transportation projects on wireless frequency mapping for high-speed rail (2013-2014) Cyber Security Research Alliance funding for trust architectures in cyber-physical systems (2014) Commonwealth Cyber Initiative awards for autonomous vehicle security and energy-efficient manufacturing His industry partnerships with Honeywell and NIST ensure practical impact of theoretical research. The Mason Innovation Laboratory under his direction serves as an interdisciplinary hub for cyber-physical security, integrating researchers from computer science, electrical engineering, and policy studies to develop deployable solutions for transportation networks, power grids, and critical infrastructure protection.

Kenneth S. HenryView profile

Associate Professor

• Auditory Neuroscience
• Cochlear Synaptopathy
• Neural Mechanisms of Hearing
• +3 more

Dr. Kenneth S. Henry is an Associate Professor at the University of Rochester Medical Center (SMD) with joint appointments in the Departments of Otolaryngology, Biomedical Engineering, and Neuroscience. His research focuses on auditory neuroscience, cochlear synaptopathy, and neural mechanisms of hearing, using behavioral, neurophysiological, and histological approaches. Department of Otolaryngology Department of Biomedical Engineering Department of Neuroscience Research Interests: His lab investigates auditory nerve loss, hidden hearing loss, and neural bases of masked amplitude-modulation perception. Using avian and mammalian models, his work explores how cochlear neurodegeneration impacts behavioral and neural sensitivity to complex sounds in noise. Publications Trends: Recent research examines auditory nerve encoding, envelope processing in hearing loss, cochlear fluid dynamics, and neural mechanisms of speech-in-noise perception using avian models. Lab Members: Current researchers include graduate students Leslie Gonzales (Neuroscience) and Yingxuan Wang (Biomedical Engineering). Alumni include John Wilson (PhD student) and Steph Wong (ENT resident). Contact: Email kenneth_henry@urmc.rochester.edu or call (585) 275-4851 for collaboration/postdoc opportunities.

Katherine J. FranzView profile

Professor

• Bioinorganic Chemistry
• Metal Homeostasis
• Antimicrobial Agents
• +4 more

Katherine J. Franz is a Professor of Chemistry at Duke University, affiliated with the Trinity College of Arts & Sciences and the Duke Cancer Institute. She holds a Ph.D. from MIT (2000) and a B.A. from Wellesley College (1995). Her research focuses on bioinorganic chemistry, particularly metal ion coordination in biological systems, with applications in antimicrobial therapies, cancer metallomics, and neurodegenerative diseases. Key projects include developing prochelators targeting fungal and bacterial pathogens, studying copper's role in antifungal drug efficacy, and designing light-activated metal complexes for controlled drug release. Dr. Franz has received numerous awards including the Camille Dreyfus Teacher-Scholar Award (2009), Sloan Research Fellowship (2008), and the NSF CAREER Award (2005). She leads a lab with 6 current students/mentees and has secured grants from NIH, NSF, and the US-Israel Binational Science Foundation. Her lab's work spans from fundamental metalloprotein studies to translational drug development, emphasizing interdisciplinary approaches in chemistry and biology. Education: Ph.D. in Chemistry, MIT, 2000 B.A. in Chemistry, Wellesley College, 1995 Research Interests: Metal homeostasis in pathogens, copper's role in antifungal resistance, prodrug design for targeted therapy, and mechanistic studies of metalloproteins. Grants: Tri-Institutional Molecular Mycology Training Program (NIH, 2024–2029) Duke PREP Biomedical Sciences Program (NIGMS, 2022–2027) Copper-Mucin Interaction Study (BSF, 2022–2026) Her lab's publications (n=15+ since 2020) highlight breakthroughs in prodrug selectivity, copper-induced protein toxicity, and histatin antifungal mechanisms. The Franz Lab actively collaborates with clinicians and computational scientists to advance therapeutic strategies addressing unmet medical needs in infectious diseases and cancer.

Mehdi YazdiView profile

Lecturer

• Low-dimensional topology
• Geometry
• Dynamics
• +9 more

Mehdi Yazdi is a Lecturer in Pure Mathematics at King's College London, part of the Department of Mathematics within the Faculty of Natural, Mathematical & Engineering Sciences. His research focuses on low-dimensional topology, geometry, and dynamics, with particular emphasis on 3-dimensional manifolds, foliations, mapping class groups, and algorithmic aspects of topology. He holds a PhD from Princeton University (2017) and has held postdoctoral fellowships at the University of Oxford, including the Glasstone Research Fellowship in Science, Titchmarsh Research Fellowship, and a UKRI Postdoctoral Research Fellowship. Education: PhD in Pure Mathematics from Princeton University (2017). Prior to King's College, he was at Oxford from 2017–2021. Research interests include the study of geometric structures on manifolds, algorithmic problems in topology, and the interplay between dynamics and geometry. His work has contributed to understanding foliations, knot theory, and computational aspects of topological invariants. Publications span topics like the stability of foliations, Thurston norms, and computational complexity of knot genus, reflecting his expertise in both theoretical and algorithmic dimensions of geometry and topology. He is a member of the Geometry Group at King's, which explores areas such as algebraic geometry, cohomology theories, and symplectic geometry. His research also engages with broader questions in geometric analysis and low-dimensional dynamics.

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +3 more

Günter Rote is a Professor in the Department of Computer Science at Freie Universität Berlin, specifically within the Theoretical Computer Science group (Arbeitsgruppe Theoretische Informatik). He holds a formal academic title of Professor Dr. and is affiliated with the Faculty of Mathematics and Computer Science. His research focuses on theoretical computer science, computational geometry, algorithms, and discrete mathematics. Key research interests include geometric algorithms, optimization problems (e.g., shortest paths, traveling salesman problems), and algorithm design for parallel computing systems. His work spans topics such as systolic arrays, convex hulls, and combinatorial optimization. Rote’s contributions include foundational studies on computational geometry problems, algorithmic complexity, and practical applications in energy equity and infrastructure design. Publications highlight contributions to solving extremal equations, polygon transformations, and the quadratic assignment problem. He has been active in academic leadership, mentoring students, and contributing to computational science communities. His email is rote@inf.fu-berlin.de, and his office is located at Takustraße 9 in Berlin.

Ellen ZeguraView profile

Professor

• Computer Networking
• Computing for Development
• Social Responsibility in STEM Education
• +6 more

Ellen Zegura is the Stephen Fleming Chair and Professor in the School of Computer Science at Georgia Tech's College of Computing. She holds multiple degrees from Washington University in St. Louis: BS in Computer Science, BS in Electrical Engineering, MS in Computer Science, and DSc in Computer Science. Her research focuses on computer networking, social responsibility in STEM education, and computing for development. She co-founded the Computing for Good initiative, emphasizing project-based learning to address societal challenges. Zegura is an IEEE and ACM Fellow, and serves on the Computing Research Association (CRA) Executive Board. Her education spans interdisciplinary fields at Washington University, combining computer science and electrical engineering. She has held leadership roles at NSF and CRA, advocating for equitable technology policies. Notable contributions include advancing QoE metrics for video conferencing, analyzing mobile broadband infrastructure disparities, and developing ethics education frameworks for computing curricula. Research interests include network measurement, community-empowered data practices, and bridging technical innovation with social impact. Recent work examines tribal mobility during pandemics, sensor co-design with Indigenous communities, and ethical pedagogy for teaching assistants. Her labs and collaborations, such as CERCS, emphasize interdisciplinary problem-solving. Zegura’s awards reflect her dual impact in technical innovation and societal engagement.

Dmitri KrioukovView profile

Associate Professor

• Network Science
• Complex Networks
• Mathematical Physics
• +7 more

Prof. Dmitri Krioukov is an Associate Professor in the Department of Physics at Northeastern University and holds an affiliated faculty position in Electrical and Computer Engineering. He directs the DK-Lab at the Network Science Institute, focusing on theoretical aspects of complex networks, including latent network geometry, random geometric graphs, and navigation in networks. His work bridges mathematical physics and applied network science, with applications to real-world data such as the Internet's structure. Research interests revolve around the interplay between network topology and geometry, including studies of causal sets, graph curvature, and dynamics in complex systems. He has pioneered frameworks linking network growth to hyperbolic geometry, enabling efficient routing algorithms. Notable contributions include the discovery of latent geometric structures underlying real-world networks and their implications for navigation and scalability. He has been recognized for high-impact publications, including multiple Stanford University Annual Assessments placing him among the top 2% most-cited scientists in his field (2024, 2023, 2022). His lab's interdisciplinary approach integrates principles from physics, mathematics, and computer science to address fundamental questions in network science.

Tyler H. SummersView profile

Assistant Professor

• Feedback Control
• Optimization in Complex Dynamical Networks
• Electric Power Networks
• +4 more

Dr. Tyler H. Summers is an Assistant Professor of Mechanical Engineering at the University of Texas at Dallas (UTD), with an affiliate appointment in Electrical Engineering. He holds a PhD in Aerospace Engineering from the University of Texas at Austin (2010) and completed a postdoctoral fellowship at ETH Zurich (2011–2015). His research focuses on feedback control and optimization in complex dynamical networks, including electric power grids and distributed robotics. Key contributions include stochastic optimal power flow methods, distributed formation control algorithms, and robust control design under uncertainty. Education: PhD in Aerospace Engineering (University of Texas at Austin, 2010) M.S. in Aerospace Engineering (University of Texas at Austin, 2007) B.S. in Mechanical Engineering (Texas Christian University, 2004) His research interests emphasize theoretical and computational tools for cyber-physical systems, including power networks and robotic teams. Notable achievements include a NSF CAREER Award ($500K) and an Army YIP grant ($350K). He leads the Control, Optimization, and Networks (COIN) Lab, which develops algorithms for robust control and distributed optimization. Recent work addresses challenges in integrating renewable energy into power systems and enabling safe autonomous robotics in uncertain environments. Grants and projects involve collaborations with institutions like the University of Melbourne and the Australian National University. Grants & Awards: NSF CAREER Award (2021) Army Research Office YIP (2017) Air Force Office of Scientific Research (2019) Lab & Team: The COIN Lab focuses on interdisciplinary projects involving students and postdocs in control theory, robotics, and optimization.

• Numerical Analysis
• Computational Electromagnetism
• Finite Element Methods
• +3 more

Ana Maria Alonso Rodriguez is a Full Professor of Numerical Analysis at the Department of Mathematics, University of Trento. She holds a PhD in Applied Mathematics from Universidad Complutense de Madrid (1993) and has held academic positions across Italy and Spain since 1990. Her research focuses on numerical methods for partial differential equations, computational electromagnetism, finite element methods, and domain decomposition techniques. She has organized international workshops and minisymposia, including the 2022 Oberwolfach workshop on Hilbert Complexes and the 2018 ICOSAHOM conference session on high-order methods. Her work bridges numerical analysis, topology, and applied electromagnetism, with recent contributions to Whitney finite elements and discrete potential theory. Education: PhD in Applied Mathematics, Universidad Complutense de Madrid (1988-1993) Licenciatura en Ciencias Matematicas, same institution (1982-1987) Research emphasizes high-order discretizations for electromagnetic problems, leveraging finite element exterior calculus and graph-based decomposition techniques. Recent work (2024) advances tree-cotree methods for curl operator spectra and Whitney form interpolation. She actively collaborates with international institutions like the CI2MA in Chile and the Laboratoire J. A. Dieudonné in France. Teaching includes courses on numerical PDEs, finite elements, computational electromagnetism, and MATLAB-based numerical analysis at both undergraduate and PhD levels. She has supervised numerous courses in Italy and Spain since 2000, integrating practical software tools like FreeFem and MODULEF into instruction.

• Sports Analytics
• Predictive Analytics
• Complex Networks
• +3 more

Dr. Otto Koppius is an Assistant Professor at the Rotterdam School of Management , Department of Technology and Operations Management. His research focuses on sports analytics , predictive analytics , and complex networks , emphasizing data-driven decision-making in organizations. He explores applications ranging from talent identification in sports using sensor data to sustainability in supply chains. Key research interests include: Methodological advances in predictive analytics, including feature engineering and algorithmic bias detection. Integration of predictive analytics into organizational practices. Smart cargo sensor data for optimizing supply chain networks. Past work includes studies on social influence in networks, closed-loop supply chains, and knowledge transfer within firms. His articles analyze topics like network interventions, innovation dynamics, and digital ecosystem orchestration. No scientific awards were explicitly mentioned in the provided text. He has supervised 6 academic works, though specific student names are not listed. Research extends to themes like sustainability, business strategy, and organizational behavior, with a focus on translating technical methods to real-world business challenges.