جستجوی استاد راهنمای دانشگاهی

James Saundersonمشاهده پروفایل

مدرس ارشد

• Convex Optimization
• Semidefinite Programming
• Polynomial Optimization
• +۴ مورد دیگر

Dr. James Saunderson is a Senior Lecturer and Director of Education in the Department of Electrical and Computer Systems Engineering at Monash University. He holds a PhD in Electrical Engineering and Computer Science from MIT and has held postdoctoral roles at Caltech and the University of Washington. His expertise spans convex optimization, semidefinite programming, and quantum information theory. Education : PhD in EECS, MIT (2015) MS in EECS, MIT (2011) Bachelor of Engineering (Honours) and Bachelor of Science (Honours), University of Melbourne (2008) Research Interests : Convex optimization, quantum information theory, signal processing, and algorithm design. Focuses on algebraic and geometric aspects of optimization, with applications in engineering and quantum systems. Recent Projects : Exploiting duality in quantum relative entropy optimization Hyperbolic programming and conic optimization Applications in nanotechnology and bioinformatics Teaching : Courses include Control System Design, Signals and Systems, and Optimization for Engineers. Awards : SIAM Optimization Best Paper Prize (2020) Grants and Collaborations : Australian Research Council Discovery Early-Career Research Fellow (2020–2024) Leading projects in quantum optimization and bioengineering applications.

• Quantum Information Science
• Quantum Computing
• Quantum Error Correction
• +۶ مورد دیگر

Dr. Thinh Le is a Postdoctoral Research Fellow at the School of Computer Science , University of Technology Sydney , where he works at the Centre for Quantum Software and Information (since 2023). His academic journey includes research fellowships at the Institute for Quantum Optics and Quantum Information Vienna (2020-2023) and Leibniz University Hannover (2019-2020). He earned his PhD in Physics (2015) and BSc in Physics (2011) from the National University of Singapore . Thinh's research focuses on quantum information science , bridging theoretical foundations and experimental implementations. His work addresses critical challenges in quantum computing (error correction, fault tolerance), quantum tomography (device-independent characterization), and quantum software stack development. He investigates quantum randomness generation, Bell nonlocality, and entanglement distillation for quantum networks, with applications in quantum cryptography and secure communication. His recent publications examine advanced topics like trapped-ion lattice surgery (2025), quantum correlation boundaries (2025), and quantum backflow phenomena (2023). Earlier work established foundational limits for real quantum theory (2021) and developed practical entanglement distillation protocols (2018). Scientific awards include the Lise Meitner Postdoctoral Fellowship (2020-2023) and Humboldt Postdoctoral Fellowship (2019-2020). His funded research projects focus on quantum computing error correction and device-independent quantum tomography , aligning with his theoretical expertise guided by experimental applications.

Jeya Jeyakumarمشاهده پروفایل

استاد

• Mathematical Optimization
• Global Optimization
• Optimization under Uncertainty
• +۷ مورد دیگر

Professor Jeya Jeyakumar is a leading applied mathematician at the School of Mathematics and Statistics of the University of New South Wales (UNSW) , internationally recognized for pioneering contributions to mathematical optimization. His work bridges rigorous theoretical analysis with practical computational methods, advancing fields like global optimization, robust decision-making, and machine learning-inspired models. PhD in Optimization, University of Melbourne His research focuses on transforming complex mathematical concepts into robust optimization frameworks for uncertainty quantification, risk minimization, and multi-stage decision-making. Key applications include medical decision support tools (e.g., Alzheimer’s detection via handwriting analysis), radiation therapy planning, and Huntington’s disease characterization. Recent work spans distributionally robust optimization, polynomial optimization, and convexifiable systems. His 15 most recent publications address topics like data-driven optimization over measure spaces, adjustable robustness in medical contexts, and algebraic approaches to fuzzy sets. 2025: Marguerite Frank Award for EURO Journal on Computational Optimization 2019: Joint winner of Journal of Global Optimization Best Paper Prize 2017: Optimization Letters Best Paper Prize Professor Jeyakumar has secured multiple ARC Discovery Project grants (e.g., $471,300 in 2025 for risk-aware optimization) and industry collaborations. He supervises HDR students in areas like two-stage robust optimization and feature selection under uncertainty.

Wynita Griggsمشاهده پروفایل

مدرس

• Mathematical Control Theory
• Intelligent Transportation Systems
• Vehicle-to-Everything Communication
• +۲ مورد دیگر

Wynita Griggs is a Lecturer at Monash University with joint appointments in Civil Engineering and Electrical/Computer Systems Engineering. She earned her Ph.D. from Australian National University on stability results for feedback control systems. Her research bridges mathematical control theory and intelligent transportation applications. Griggs develops theoretical frameworks for robust control, dissipativity, and ensemble agent control, applied to connected vehicles, traffic simulation (SUMO), and pedestrian safety systems. She leads projects including V2X-enabled vehicle-in-the-loop simulation platforms and tram stop renewable energy management with Yarra Trams. She teaches Control System Design and Travel Demand Modelling, while mentoring the Monash Connected Autonomous Vehicles student team. Her work has been implemented in smart city initiatives across Australia and Europe.

Vera Roshchinaمشاهده پروفایل

دانشیار

• Convex Geometry
• Mathematical Optimization
• Nonsmooth Analysis
• +۳ مورد دیگر

Associate Professor Vera Roshchina is a mathematician at the University of New South Wales (UNSW) Sydney , affiliated with the College of Science and School of Mathematics & Statistics . Her research focuses on convex geometry , mathematical optimization , and nonsmooth analysis , with significant contributions to projection methods, hyperbolicity cones, and Chebyshev approximation. Her recent work explores constructing spectrahedra with prescribed facial dimensions (2025) optimal camouflaging in billiards (2025) convergence of projection algorithms (2024) amenability of hyperbolicity cones (2024) topological properties of convex sets (2023) She is supported by multiple ARC Discovery grants and received the Christopher Heyde Medal (2021) . Her teaching includes courses in Mathematical Optimization and Calculus . She organized and participated in international research programs like MoCaO lectures (2024) and the WODCA2025 workshop , collaborating with researchers globally. Her publications span journals in optimization, nonlinear analysis, and dynamical systems.

Hoa Buiمشاهده پروفایل

پژوهشگر ارشد

• Operations Research
• Optimisation
• Discrete Optimization
• +۴ مورد دیگر

Dr. Hoa Bui is a Research Fellow at Curtin University's Centre for Optimisation and Decision Science, affiliated with the School of Electrical Engineering, Computing and Mathematical Sciences. Her research focuses on Operations Research, Optimization, Combinatorics and Discrete Mathematics, and Variational Analysis. She holds a PhD in Mathematics from Federation University, Australia (2020) and a BSc (Hons) in Mathematics from the University of Education, Ho Chi Minh City, Vietnam (2016). Research Interests: Dr. Bui's work bridges theoretical and applied aspects of optimisation, including discrete optimisation, convex optimisation, and their applications in scheduling, facility location, and combinatorial problems. She also explores variational analysis and graph theory, contributing to both foundational and industry-driven solutions. Awards: Trailblazer Prize Curtinnovation Award (2024) AustMS WIMSIG Cheryl E. Praeger Travel Award (2021) Maryam Mirzakhani Award (2020) Lift-Off Fellowship (2020) Advising & Grants: While her profile does not list formal advisees, her collaborative work indicates involvement in interdisciplinary projects. She is active in grant-funded research, particularly in optimisation for mining and industrial applications. Labs/Teams: Dr. Bui contributes to the Centre for Optimisation and Decision Science, focusing on cutting-edge optimisation methodologies and their real-world implementation.

Mareike Dresslerمشاهده پروفایل

مدرس

• Real and Computational Algebraic Geometry
• Polynomial and Convex Optimization
• Matrix and Tensor Computation
• +۳ مورد دیگر

Dr. Mareike Dressler is a Senior Lecturer (Assistant Professor, tenure-track) at the School of Mathematics and Statistics, UNSW Sydney. She holds a PhD in Mathematics from Goethe University Frankfurt (2018) and has held postdoctoral positions at Max Planck Institute for Mathematics in the Sciences (Leipzig) and University of California, San Diego. Education: PhD (Mathematics, 2018), M.Sc. (2013), B.Sc. (2010) from Goethe University Frankfurt Prior Affiliations: Postdoctoral researcher at MPI MiS (Leipzig), Stefan E. Warschawski Assistant Professor at UCSD, Semester Postdoctoral Fellow at Brown University (ICERM) Her research focuses on Real and Computational Algebraic Geometry, Polynomial and Convex Optimization, Matrix and Tensor Computation, and their applications in Data Science and Machine Learning. She has developed optimization techniques using sums of nonnegative circuit polynomials and explored connections between convex geometry and algebraic structures. Her recent publications span topics including multivariate Chebyshev polynomials, matrix completion ranks, SONC cone duality, and applications of semidefinite programming. Collaborations include work with Venkat Chandrasekaran, Bernd Sturmfels, and Roland Lasserre, with methodological contributions to polynomial optimization and tensor analysis. At UNSW Sydney, she teaches undergraduate and postgraduate courses including MATH 1131 (Mathematics 1A, Algebra) and MATH 5185 (Special Topic: Nonnegativity and Polynomial Optimization). Her research group engages with both theoretical and applied problems in optimization and algebraic methods.