Find Academic Supervisors

• Supply Chain Coordination
• Spreadsheet Modeling
• Decision Making & Optimization
• +3 more

Professor Michelle Cheong Lee Fong is a full-time faculty member at the School of Information Systems, Singapore Management University. She holds leadership roles as Associate Dean for Post-Graduate Professional Education and Director of the Doctor of Engineering program, and is actively involved in designing educational frameworks for technology-driven learning. Current affiliations: Singapore Management University (School of Information Systems) Research focus: Supply chain coordination, spreadsheet modeling, and educational technology Key courses taught: Computer as an Analysis Tool, Financial Modeling, Process Modeling Her research interests in supply chain coordination span strategic hub location optimization, tactical supplier coordination, and operational transportation planning. She also specializes in spreadsheet modeling for business decision-making, having developed textbook resources and case studies for logistics networks and financial planning. Recent work explores AI's role in digital education, including ChatGPT's impact on assessment design and ethical considerations in AI-driven pedagogy. The articles she has written or co-authored demonstrate expertise in operations research, educational technology, and AI applications. Topics include optimization theory, heuristic algorithms for scheduling, and digital assessment frameworks that integrate learning analytics. Her work bridges spreadsheet modeling's pedagogical value with modern challenges like ChatGPT's influence on student learning. Her teaching portfolio includes courses such as IS102 (Computer as an Analysis Tool), IS304 (Process Modeling), and FNCE645 (Financial Modeling). These courses emphasize practical skills in translating business requirements into technology solutions through spreadsheet-based analytical frameworks.

Nicholas WormaldView profile

Professor

• Combinatorics
• Random Graphs
• Graph Theory
• +4 more

Nicholas Wormald is a Professor in the Department of Mathematical Sciences at Monash University, Australia. He joined the university in 2013 as an Australian Laureate Fellow, following a decade as a Canada Research Chair in Combinatorics and Optimization at the University of Waterloo, Canada. His primary research focuses on combinatorics, probabilistic methods, and random structures, with notable contributions to random graph theory, enumeration, and applications in optimization. Wormald holds a PhD from the University of Newcastle. His work spans topics including Hamilton cycles in random regular graphs, k-core emergence, probabilistic combinatorics, and algorithms for graph generation. He has contributed to foundational results such as the sudden emergence of giant components in random graphs and the contiguity of random graph models. He serves on editorial boards of journals like the Electronic Journal of Combinatorics and Random Structures and Algorithms . His research interests also include underground mine optimization and the study of Steiner trees. Notable projects include enumeration and random generation of contingency tables, properties of large discrete structures, and the analysis of random structures' applications. Wormald has authored or co-authored over 250 publications, with recent work focusing on asymptotic enumeration, hypergraphs, and the probabilistic analysis of combinatorial structures.

• Discrete Mathematics
• Combinatorics
• Graph Theory
• +3 more

Jeffry Kahn is a Professor of Mathematics at Rutgers, The State University of New Jersey, within the Department of Mathematics. His research focuses on discrete mathematics and related areas, with particular emphasis on combinatorics, graph theory, and probability. He holds an office in Hill Hall (HLL-728) on the Busch Campus. His work spans foundational contributions to extremal combinatorics, random graph theory, and probabilistic methods. Kahn has co-authored influential papers on threshold phenomena, phase transitions in statistical mechanics models, and structural properties of discrete systems. Notable contributions include resolving Borsuk's conjecture and advancing understanding of random matrix singularity probabilities. He teaches advanced courses such as Combinatorics II (642.583), emphasizing problem-solving and rigorous proofs. His research interests exhibit a cohesive thread in exploring combinatorial structures under probabilistic and extremal frameworks. Recent works address Shamir’s Problem asymptotics, maximal independent sets in hypercubes, and threshold conjectures. His articles often intersect with theoretical computer science, revealing interdisciplinary insights. Despite no explicitly listed awards, his publications in top journals like Annals of Mathematics and Combinatorica underscore his academic impact. His advising focuses on graduate-level combinatorial research, with courses structuring students via problem sets requiring precise, efficient solutions.

Ted RalphsView profile

Professor

• Mathematical Optimization
• Operations Research
• Integer Programming
• +7 more

Ted Ralphs is a Professor of Industrial and Systems Engineering at Lehigh University’s Rossin College of Engineering. He serves as co-founder and director of the Computational Optimization Research at Lehigh (COR@L) Laboratory, and chairs the INFORMS Computing Society. His research focuses on large-scale computation and optimization, bridging theoretical and practical applications through high-performance computing and mathematical techniques. Ralphs holds a Ph.D. in Operations Research from Cornell University and advanced degrees in Mathematics and Applied Mathematics from Carnegie Mellon University. His expertise spans Supply Chain Management, Grid Computing, Mathematical Optimization, Financial Engineering, and Algorithm Development. He teaches courses in computational methods, discrete optimization, financial optimization, and algorithms in systems engineering. Ralphs has received notable honors including the 2021 Rossin College Outstanding Doctoral Student Advising Award and election as an INFORMS Fellow in 2023. His research contributions include advances in bilevel optimization, decomposition methods, and open-source optimization software (e.g., COIN-OR’s Cbc solver). He has led collaborative projects such as a Naval grant-funded initiative with the University of Pittsburgh on bilevel optimization. Ralphs’ work emphasizes scalable algorithms and their real-world applicability in energy markets, logistics, and combinatorial problems.

Luis BarbaView profile

Research Fellow

• Computational Geometry
• Algorithms
• Graph Theory
• +4 more

Luis Barba is a Research Fellow in the Machine Learning and Optimization group at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, working under Professor Martin Jaggi. He completed his PhD through a cotutelle program between Carleton University, Ottawa and Université Libre de Bruxelles, Brussels, supervised by Professors Stefan Langerman, Jit Bose, Pat Morin and Vida Dujmović. Prior to that, he earned his master's degree at Universidad Nacional Autónoma de México (UNAM) under Professor Jorge Urrutia. Dr. Barba's research spans computational geometry, algorithms, graph theory, and more recently, machine learning and optimization. His work addresses fundamental problems in geometric data structures, Voronoi diagrams, graph coloring, and distributed learning. He has made significant contributions to understanding time-space trade-offs in geometric algorithms and developing efficient methods for problems like geodesic Voronoi diagrams and dynamic graph coloring. Dr. Barba's publication record demonstrates a clear evolution from theoretical computational geometry to practical applications in machine learning. Early in his career, he focused on fundamental geometric problems including linear-time algorithms for geodesic Voronoi diagrams and efficient convex hull computation in polygonal domains with obstacles. More recently, his work has shifted toward machine learning, where he has developed novel optimization techniques for distributed and federated learning settings, including implicit gradient alignment methods and multilayer lookahead approaches. Dr. Barba has published extensively in top-tier conferences and journals including Symposium on Computational Geometry (SoCG), Canadian Conference on Computational Geometry (CCCG), Algorithmica, and Discrete and Computational Geometry. His collaborative work demonstrates strong connections across the computational geometry and algorithms communities, with frequent co-authorship with leading researchers in these fields. Throughout his career, Dr. Barba has maintained a consistent focus on algorithmic efficiency and computational complexity, whether addressing theoretical geometric problems or practical machine learning challenges. His work exemplifies how deep theoretical insights can inform practical computational approaches across different domains of computer science.

Simon WeberView profile

Researcher

• Unique Sink Orientations
• Computational Complexity
• Combinatorial Algorithms
• +7 more

Simon Weber is a researcher affiliated with the ETH Zurich (Department of Computer Science). His work focuses on Unique Sink Orientations (USOs) , a combinatorial abstraction of optimization problems like Linear and Quadratic Programming. Simon's research spans three areas: (1) Structure of USOs and their links to Oriented Matroids; (2) Constructions of high-dimensional USOs to analyze algorithm complexity; and (3) Algorithmic improvements for sink-finding. He also explores topics in graph compression, neural networks, and ∃R-complete problems. Key Publications: PhD thesis on USO reductions, ∃R-completeness in neural training, and USO phase analysis. Scientific Contributions: Advances in USO complexity, FPT algorithms for MaxCut, and recognition of geometric hypergraphs and nerves of convex sets. He has supervised multiple theses at ETH Zurich, including topics on USO visualization, MaxCut algorithms, and necklace splitting. His teaching experience includes being a Head Assistant for courses like Geometry: Combinatorics & Algorithms and Topological Data Analysis . Simon's work has been recognized with Best Paper and Best Student Paper Finalist awards at SC19.

Torsten UeckerdtView profile

Professor

• Graph Theory
• Geometric Graph Theory
• Discrete Mathematics
• +8 more

Torsten Ueckerdt is an Interim Professor in the Algorithmics I group at the Institute of Theoretical Informatics, Karlsruher Institut für Technologie (KIT). He has held academic positions since 2012, including postdoctoral roles and habilitation in Discrete Mathematics. His research focuses on combinatorial objects like graphs, posets, and hypergraphs in geometric settings, exploring structural properties, colorings, and geometric representations. Holds a PhD from TU Berlin (2011) and habilitation from KIT (2017). Professional service includes editorial roles (Annals of Combinatorics) and program committees for conferences like Graph Drawing, SoCG, and EuroCG. Supervised numerous students across Bachelor's, Master's, and diploma theses. Research interests span structural graph theory, geometric graph theory, Ramsey theory, discrete geometry, and combinatorial games. Active in graph drawing and algorithm design, with contributions to queue layouts, graph representations, and optimization problems like cartograms and wind farm cabling.

Santosh AryalView profile

Associate Professor

• Nanomedicine
• Drug Delivery Systems
• Cancer Therapy
• +4 more

Dr. Santosh Aryal is an Associate Professor in the Department of Pharmaceutical Science and Health Outcomes at the University of Texas at Tyler (UT-Tyler). He holds a Ph.D. in Bionanosystem Engineering from Chonbuk National University, South Korea, and has held prior appointments at institutions including the University of California, San Diego, and Houston Methodist Research Institute. His primary research focuses on nanomedicine, particularly in designing nanoparticle-based systems for targeted drug delivery, photothermal therapy, and imaging applications in cancer and vascular diseases. Dr. Aryal's work emphasizes biomimetic approaches, leveraging extracellular vesicles and bioengineered membranes to enhance therapeutic efficacy and reduce off-target effects. Dr. Aryal's academic journey includes postdoctoral training in nanoengineering and cancer research, followed by six years as an Assistant Professor at Kansas State University. At UT-Tyler, his Aryal Lab develops innovative nanomedicines to address challenges in drug delivery, such as immune system compatibility and tumor targeting. His research is supported by grants from the National Science Foundation (NSF) and National Institutes of Health (NIH). Key research themes include nanoparticle-biomaterial interface studies, paramagnetic imaging agents, and combinatorial drug delivery systems. He has published extensively on topics like liposomal formulations, graphene-based nanocomposites, and enzyme-responsive drug release mechanisms. His work bridges engineering and medicine, aiming to translate nanotechnology into clinical solutions for unmet medical needs. Dr. Aryal's laboratory also explores physiologically based pharmacokinetic modeling to predict nanoparticle behavior in vivo, ensuring safe and effective clinical translation. His contributions to biomimetic nanoparticles and targeted therapies have advanced understanding of nanoparticle-cell interactions and imaging-guided cancer treatments.

David Bernal NeiraView profile

Assistant Professor

• Quantum Computing
• Optimization Algorithms
• Chemical Engineering
• +3 more

David Bernal Neira is an Assistant Professor in the Davidson School of Chemical Engineering at Purdue University, joined in August 2023. His research focuses on optimization algorithms, quantum computing, and computational methods applied to chemical and energy systems. He holds a PhD in Chemical Engineering from Carnegie Mellon University and degrees from Universidad de Los Andes, Colombia. Education: PhD in Chemical Engineering, Carnegie Mellon University (2017–2021) M.Sc. in Chemical Engineering, Universidad de Los Andes (2014–2016) B.A.Sc. in Chemical Engineering, Universidad de Los Andes (2010–2014) B.A.Sc. in Physics, Universidad de Los Andes (2011–2018) Research Interests: His work bridges classical and quantum optimization, with applications in process systems, energy, and chemical engineering. Key areas include mathematical modeling, quantum annealing, and federated learning. He develops algorithms and software tools, such as GDP and QUBO frameworks, and explores quantum computing for chemistry and combinatorial problems. Publications: Over 30 peer-reviewed articles since 2020, focusing on quantum optimization, federated learning, and algorithm design. Recent work emphasizes benchmarking quantum hardware and hybrid quantum-classical methods. Awards: Fellow, National Academies (2025, 2023) Best Talk Award (2022) Outstanding Teaching Assistant (2019) Advising & Grants: Supervises graduate students in quantum computing and optimization. Collaborates with NASA, USRA, and industry on quantum projects. Formerly an Associate Scientist at NASA QuAIL and Adjunct Professor at Carnegie Mellon. Labs & Teams: Leads the SECQUOIA Research Group at Purdue, focusing on systems engineering via quantum and classical optimization. Active in federally funded initiatives and industry partnerships.

Jayadev S. AthreyaView profile

Professor

• Ergodic Theory
• Dynamical Systems
• Geometry
• +3 more

Jayadev S. Athreya is a Professor of Mathematics and Comparative History of Ideas at the University of Washington. He also serves as co-Director of the Pacific Institute for the Mathematical Sciences (PIMS) and founder of the Washington Experimental Mathematics Lab. His research focuses on ergodic theory, dynamical systems, geometry, and number theory, with a particular emphasis on translation surfaces, billiards dynamics, and moduli spaces. He holds a PhD from the University of Chicago (2006) and has been actively involved in promoting mathematical education and outreach. Axioms emphasizing equitable and joyful mathematics experiences guide his work. Education: PhD in Mathematics, University of Chicago, 2006 Research Interests: Athreya’s interdisciplinary work bridges geometry, dynamics, and number theory. Key areas include the study of billiards in polygons, Teichmüller theory, and the statistical properties of geodesics on hyperbolic surfaces. His contributions to translation surfaces and counting problems in geometry have advanced understanding of moduli spaces and extremal cocycles. Labs/Teams: He leads the Washington Experimental Mathematics Lab, fostering collaborative research through hands-on projects in geometry and combinatorics. The lab emphasizes creativity and inclusivity in mathematical exploration. Grants/Advising: While specific grants are not detailed, his leadership roles and active research output indicate sustained funding and mentorship in experimental mathematics and education initiatives.

Sara BilleyView profile

Professor

• Algebraic Combinatorics
• Algebraic Geometry
• Combinatorics
• +7 more

Professor Sara Billey is a faculty member in the Department of Mathematics at the University of Washington. She holds a PhD from the University of California, San Diego (1994) and an SB from MIT (1990). Her research focuses on algebraic combinatorics, including Schubert polynomials, flag manifolds, and representation theory of Lie groups. She advocates for computational methods in mathematics and explores undecidable problems. Notable awards include the PECASE Award. Her work bridges theoretical mathematics with machine learning applications, as seen in recent collaborations on AI-driven conjecturing tools. Education: PhD (UCSD, 1994), SB (MIT, 1990). Research emphasizes algebraic structures and their combinatorial interpretations, with contributions to Schubert varieties, Bruhat orders, and Kazhdan-Lusztig polynomials. She has pioneered computational frameworks for conjecture generation in pure mathematics. Grants include PECASE-funded projects on combinatorial structures in algebra and geometry. Active in teaching and advising, though no specific advisees are listed.

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

Angelica Cueto is an Associate Professor in the Department of Mathematics at The Ohio State University, affiliated with the College of Arts and Sciences. Her research focuses on Tropical Geometry, Algebraic Geometry, Combinatorics, and Non-Archimedean Geometry. She explores topics such as moduli spaces, surface singularities, and degenerations of classical varieties, with a particular emphasis on tropical methods and their applications. Her work bridges algebraic geometry with combinatorial structures, addressing problems like the Milnor fiber conjecture, tropicalizations of curves, and faithful tropicalizations of Grassmannians. Recent publications (2023–2024) highlight advancements in splice-type surface singularities and real lifts of bitangents. She maintains a professional website detailing her research and collaborations. No scientific awards or grants are explicitly listed in the provided text. Her academic contributions are centered on foundational research in tropical and algebraic geometry, with a focus on geometric combinatorics and moduli theory.

Shixun HuangView profile

Lecturer

• Data Mining
• Machine Learning
• Algorithm Design
• +3 more

Dr. Shixun Huang is a Lecturer in the School of Computing and Information Technology at the University of Wollongong, Australia. He holds a PhD from RMIT University and specializes in data mining, machine learning, and optimization algorithms for high-dimensional data problems. His research develops efficient algorithms for data discovery, similarity search, and network analysis. Current projects focus on optimized data acquisition strategies for machine learning, cost-effective labeling for graph neural networks, and cardinality estimation in high-dimensional databases. Methodologically, he combines combinatorial optimization with machine learning techniques. Dr. Huang supervises graduate research on diffusion models for medical imaging, graph prompt learning, and image captioning systems. His honors include multiple best paper awards at top database conferences and recognition for teaching excellence (College Top Course Award at RMIT). Recent publications address dataset distinctiveness maximization (WWW 2025), high-dimensional similarity search (VLDB 2025), and edge computing optimization (2024). Earlier foundational work established new approaches for influence maximization in social networks and temporal graph representation learning.

Zhongyuan ZhaoView profile

Academic

• Wireless Communications and Networking
• Machine Learning
• Signal Processing
• +7 more

Zhongyuan Zhao is a Research Assistant Professor at Rice University's Department of Electrical and Computer Engineering, affiliated with the George R. Brown School of Engineering. He holds a Ph.D. in Computer Engineering from the University of Nebraska-Lincoln and completed his postdoctoral studies at Rice under Prof. Santiago Segarra. His research integrates graph-based neuro-symbolic approaches with domain-specific models to address challenges in wireless communications, edge computing, and networked systems. Dr. Zhao has over a decade of industry experience in 4G base-station development at Ericsson and ArrayComm, followed by academic research roles focusing on distributed algorithms and machine learning applications. His education includes a B.Sc. and M.S. in Electronic Engineering from the University of Electronic Science and Technology of China, where he also worked as a teaching assistant for national design contests. He earned a minor in Finance (15 credits) during his Ph.D. and is completing the CFA Level III program in 2024. Zhao has contributed to large-scale wireless testbeds like NEXTT and authored/co-authored 25+ peer-reviewed publications, 2 patents, and delivered academic service roles including conference session chair and journal reviewer. Research interests span autonomous networking, graph-based machine learning, stochastic optimization, and their applications in infrastructureless wireless systems. His work emphasizes distributed solutions for resource allocation, scheduling, and edge intelligence. Awards include the Future Faculty Fellowship (2023) and IEEE travel grants. He has mentored over 40 students through research internships, hackathons, and undergraduate projects, showcasing his commitment to education. Recent projects focus on scalable computation offloading, generalized backpressure routing in tactical networks, and neuro-symbolic AI frameworks for networked systems. His lab work involves open-source contributions to tools like the Actor-Twin Framework and SelR algorithm, promoting reproducibility and collaboration in the field.

Roberto Solis-ObaView profile

Professor

• Algorithm Design
• Approximation Algorithms
• Data Structures
• +9 more

Overview Roberto Solis-Oba is a Professor in the Department of Computer Science at the University of Western Ontario. He holds a Ph.D. from Purdue University (supervised by Prof. Greg Frederickson) and completed a postdoctoral fellowship at the Max Planck Institute for Informatics under Prof. Kurt Mehlhorn. His research focuses on algorithm design, particularly approximation algorithms for NP-hard optimization problems, with applications in networks, scheduling, packing, and computational biology. Education & Background Ph.D., Computer Science, Purdue University (1990s) Postdoctoral Fellowship, Max Planck Institute for Informatics Research Interests His work spans algorithm design, including approximation algorithms, data structures, parallel/distributed/online/randomized algorithms. Key application areas include networks, scheduling, packing optimization, computational biology, and data mining. He co-founded the Workshop on Approximation and Online Algorithms (WAOA), held annually since 2003. Awards & Recognition Faculty Scholar Award, University of Western Ontario USC Teaching Award, University of Western Ontario Professional Contributions Prof. Solis-Oba has served on program committees for major algorithm-focused conferences and contributed to research in scheduling, packing, and graph theory. His work emphasizes practical and theoretical advancements in algorithmic efficiency and optimization.