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Guoyin LiView profile

Professor

• Optimization
• Variational Analysis
• Multilinear Algebra
• +5 more

Professor Guoyin Li is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW Sydney). He holds a Ph.D. from The Chinese University of Hong Kong (2007) and has been at UNSW since 2011, currently serving as Professor and Research Director. Research Interests His work spans optimization , variational analysis , and multilinear algebra , with applications in robust optimization , structural engineering , and machine learning . He specializes in nonconvex nonsmooth optimization , tensor eigenvalue problems , and exact semi-definite programming relaxations . Recent Publications His articles focus on robust optimization for structural design, nonlinear approximation techniques, and conic programming for uncertain data. Key journals include Foundations of Computational Mathematics , Mathematical Programming , and Computer Methods in Applied Mechanics and Engineering . Awards and Grants Fellow of the Australian Mathematical Society (2023) 2022 AustMS Medal 2024 Marguerite Frank Award ARC Discovery Grants (2021-2023, 2025-2027) ARC Research Hub Project (2017-2021) Professional Roles He serves on editorial boards of SIAM Journal on Optimization , Optimization Letters , and Journal of Optimization Theory and Applications , and has delivered plenary lectures at international conferences in Austria, Spain, and Canada.

Ming GuView profile

Professor

• Numerical Linear Algebra
• Scientific Computing
• Matrix Computations
• +3 more

Ming Gu is a Professor in the Department of Mathematics at the University of California, Berkeley . He specializes in Numerical Linear Algebra and Scientific Computing , with a focus on developing efficient algorithms for structured matrices and large-scale data analysis. Organized Matrix Computations and Scientific Computing Seminars (2009-2017) Published 15+ papers on QR algorithms , Toeplitz matrices , randomized algorithms , and low-rank approximations His research addresses rank-revealing factorizations , randomized subspace iteration , and preconditioning techniques , often bridging numerical analysis with applications in machine learning and optimization . Students advised by him (e.g., Jiaming Wang, Onyebuchi Ekenta) have explored spectrum-revealing CUR decomposition and truncated SVD . Contact: mgu@math.berkeley.edu Office: 861 Evans Hall, UC Berkeley

Robyn SandersonView profile

Associate Professor

• Astrophysics
• Galaxy Dynamics
• Dark Matter
• +3 more

Robyn Sanderson serves as an Associate Professor in the Department of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences, where she has been a standing faculty member since 2018. She maintains an active research role as a guest researcher at the Center for Computational Astrophysics, Flatiron Institute, following her tenure as an Associate Research Scientist there from 2018-2021. Her academic foundation includes a Ph.D. in Physics from MIT (2011), supervised by Edmund Bertschinger with committee members Alan Guth and Paul Schechter, and dual B.S. degrees in Physics and Astronomy, both earned summa cum laude from the University of Maryland (1999-2003). Dr. Sanderson's research centers on dark matter distribution in galaxies through the lens of stellar dynamics, particularly using Gaia mission data to probe galaxy outskirts where dark matter dominates. She pioneers synthetic survey techniques with FIRE cosmological simulations to develop optimal strategies for mapping Milky Way dark matter. As co-chair of the Nancy Grace Roman Space Telescope astrometry working group and a member of the SDSS-V consortium, she shapes next-generation observational approaches for testing dark matter theories through velocity and chemical-abundance data from WEAVE and future missions. Her 2024 publications reveal a dominant focus on dark matter constraints via galactic dynamics , with extensive use of FIRE simulations to model stellar streams, merger debris, and satellite interactions. Key trends include precision acceleration measurements, Roman Space Telescope survey design, and leveraging fossil records to study reionization-era galaxies. Scientific recognition includes: NSF Astronomy and Astrophysics Postdoctoral Fellowship She leads the 4N10 Galaxy Dynamics @ UPenn research group, teaches core courses including ASTR001 (Survey of the Universe) and PHYS 533 (Galactic Dynamics with Gaia), and contributes to major collaborative efforts like SDSS-V and WEAVE. Her work bridges computational astrophysics with observational strategies for upcoming space telescopes, emphasizing actionable pathways to test dark matter models through next-generation data.

• Quantum Information Science
• Quantum Computing
• Quantum Error Correction
• +6 more

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.

• Optimization
• Machine Learning
• Applied Mathematics
• +4 more

Queenie Huang is a current PhD student and Tutor at the School of Mathematics & Statistics , Faculty of Science , University of New South Wales (UNSW) . Under the supervision of Prof. Jeya Jeyakumar and Prof. Guoyin Li , her research focuses on Distributionally Robust Optimization , with applications in machine learning and mathematical modeling. Academic Background : Bachelor of Advanced Mathematics with First-Class Honours (2022), UNSW Bachelor of Actuarial Studies (2022), UNSW Her research combines theoretical advancements in robust optimization , polynomial systems , and machine learning , particularly in addressing data uncertainty through sum-of-squares-convex and second-order cone programming techniques. She has presented at prestigious conferences such as the International Symposium on Mathematical Programming in Montreal and workshops in Sydney and Auckland. Queenie has received multiple accolades including the 2024 Marguerite Frank award and several Women in Maths & Stats Prizes at UNSW. As an educator, she has tutored courses in optimization and actuarial studies, contributing to student engagement through events like Girls Do The Maths and UNSW Open Day Q&A panels.

• Probabilistic Machine Learning
• Probabilistic Circuits
• Probabilistic Programming
• +2 more

Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.

• Blockchain Technology
• Non-Fungible Tokens (NFTs)
• Computer Networks
• +7 more

Shital Joshi is a Teaching Associate Professor in the Department of Computer Science at Oklahoma State University. Her academic career spans roles as a Teaching Assistant Professor (2019–2025) and current position since July 2025. She holds a Ph.D. in Computer Engineering from the University of North Texas (2016). Her research focuses on blockchain technologies, NFT performance modeling, IoT security, energy-efficient networks, and nanoelectronic circuit design. Her work bridges theoretical models with practical implementations, including adaptive blockchain systems, secure telemedicine frameworks, and graphene-based SRAM optimization. Notable contributions include bivariate performance models for NFT chains and energy-efficient routing protocols for wireless sensor networks. She actively participates in academic service, such as the 2023 ABET Symposium. Joshi teaches core computer science courses like Operating Systems, Data Structures, and Computer Systems, emphasizing hands-on learning. Her teaching spans from foundational courses (e.g., Computer Proficiency) to advanced topics (e.g., Memory Management in OS Design).

Saeed Shiry GhidaryView profile

Lecturer

• Artificial Intelligence
• Robotics
• Machine Learning
• +3 more

Dr. Saeed Shiry Ghidary is a Lecturer at Staffordshire University’s Digital, Tech, Innovation & Business department. He holds a Ph.D. in Robotics and Intelligent Systems from Kobe University (Japan), alongside M.Sc. and B.Sc. degrees in Computer Architecture and Electronics from Amirkabir University of Technology (Iran). His research focuses on interdisciplinary areas including Machine Learning, Robotics, AI, Computer Vision, and Cognitive Science. He has led international research teams, including chairing the Amirkabir Robotic Center, and contributed to over 20 peer-reviewed publications. **Education:** Ph.D., Robotics and Intelligent Systems, Kobe University, Japan (2002) M.Sc., Computer Architecture, Amirkabir University, Iran (1994) B.Sc., Electronics Engineering, Amirkabir University, Iran (1990) **Research & Teaching:** Dr. Ghidary specializes in AI and robotics education, teaching graduate and undergraduate courses. His research emphasizes applications like BCI systems, EEG signal processing, and kernel methods for dimensionality reduction. He has collaborated with institutions in Japan, France, Australia, and the U.S., and serves as an HEA Fellow. **Awards & Recognition:** HEA Fellowship **Consulting & Industry:** He advises industries on AI, robotics, and automation for sectors like banking, insurance, and oil production, focusing on efficiency and profitability through technological integration.

Arshak MinasyanView profile

Associate Professor

• Robust Statistics
• High-Dimensional Data Analysis
• Learning Theory
• +3 more

Arshak Minasyan is an Associate Professor at CentraleSupélec – Université Paris-Saclay and a member of the Laboratoire Signaux et Systèmes (L2S), specifically the Modélisation et Estimation (GME) team. His research focuses on robust statistics and learning theory, particularly addressing computational and statistical complexities in high-dimensional data analysis. Education PhD in Mathematics from Yerevan State University (YSU), supervised by Prof. Victor Ohanian. Master’s in Mathematics from Skoltech. Bachelor’s in Mathematics from Higher School of Economics (HSE). Research Interests His work explores robust statistical methods for high-dimensional data, including outlier-robust algorithms, optimal estimation techniques, and the intersection of learning theory with statistical guarantees. Key areas include feature matching under noise, mean and covariance estimation, and generative modeling with distributional robustness. Key Contributions Notable contributions include the All-in-one Robust Estimator of the Gaussian Mean (Annals of Statistics, 2022) and outlier-tolerant methods for feature matching. His research emphasizes both theoretical foundations and computational tractability, with applications in signal processing and machine learning. Teaching Lecturer of “High-dimensional statistics” at Université Paris-Saclay. Teaching Assistant roles at CREST-ENSAE, HSE, and Skoltech in courses like Probability Theory, Stochastic Processes, and Biostatistics. Labs & Collaborations Affiliated with the L2S lab’s Modélisation et Estimation team, contributing to projects like COMEDY (Control of Dynamical Systems) and SYCOMORE (Signal Processing).

Nazire KocView profile

Academic

• Computer Science
• Artificial Intelligence
• Machine Learning
• +5 more

Nazire Koc is an Associate Teaching Professor in the Department of Computer Science within the College of Arts and Sciences at the University of Missouri – St Louis (UMSL) . She maintains active office hours for Fall 2025 and is reachable by e-mail and phone, evidencing her continuing service to the university. Education MS Computer Science, Southern Illinois University – Carbondale, 2007 MS Mathematics, Southern Illinois University – Carbondale, 1998 Research Interests Nazire Koc pursues a highly interdisciplinary research agenda that spans core computer-science concerns and pressing societal questions. Her technical work includes the design and analysis of optimization algorithms—especially the Jaya family—and machine-learning methods for large-scale data analytics. She leverages cloud-based platforms and open-source ecosystems to tackle challenges in image classification, remote sensing, and bioinformatics. Simultaneously, she is deeply engaged in AI ethics and policy , exploring algorithmic accountability, data governance, and the philosophical underpinnings of trust in autonomous systems. A recurring theme in her scholarship is the ethical application of AI technologies . From reflections on digital personhood and data feudalism to critical analyses of facial-recognition systems and blockchain governance, her publications chart the evolving moral landscape of computing. Publication Trends Between 2020 and 2025, Koc has authored or co-authored more than forty peer-reviewed articles. These works collectively map onto four dominant clusters: (1) Optimization & Algorithms , where stochastic and semi-steady-state variants of the Jaya algorithm are rigorously analyzed; (2) Machine Learning & Data Science , featuring cloud-based classifiers, network-clustering techniques, and predictive models for Alzheimer’s disease; (3) AI & Society , investigating the normative dimensions of generative AI, algorithmic defendants, and digital remains; and (4) CS Education , introducing process-oriented visualizations and explanatory AI tools to enhance introductory programming courses. Awards & Recognition While the provided material lists no formal honors or awards, the breadth and impact of her recent publications signal significant recognition within the computer-science education and AI-ethics communities. Advising & Grants No specific doctoral or master’s advisees are named in the supplied text, and no grant details are disclosed. However, her heavy publication record across methodological and educational domains suggests active collaboration with graduate researchers and participation in sponsored projects. Labs & Teams Although no dedicated laboratory name is provided, Koc’s affiliation with the Department of Computer Science at UMSL implies access to departmental computing clusters and interdisciplinary research centers that support her work in algorithmic design, data analytics, and ethics research.

Jeya JeyakumarView profile

Professor

• Mathematical Optimization
• Global Optimization
• Optimization under Uncertainty
• +7 more

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.

• Combinatorial Optimization
• Robust Optimization
• Polynomial Optimization
• +3 more

Prof. Dr. Adam Andrzej Kurpisz is a Tenure Track Professor at the Bern University of Applied Sciences (BFH) and a senior researcher at the Institute for Operations Research (IFOR) at ETH Zürich. He leads the Ambizione Junior Research Group under Prof. Rico Zenklusen. His research bridges combinatorial optimization, robust optimization, and semi-algebraic proof systems, with applications in polynomial optimization and approximation algorithms. Education: PhD from Wrocław University of Science and Technology (supervised by Prof. Paweł Zieliński), postdoctoral work at IDSIA and Max-Planck-Institut. Grants: Secured over CHF 600,000 via Swiss National Science Foundation and other grants, including the AMBIZIONE project on linear/semidefinite relaxations. Research Interests: Focuses on semi-algebraic proof systems, approximation algorithms, robust optimization, and polynomial optimization. Applies techniques from algebraic geometry, Fourier analysis, and linear algebra to analyze optimization hierarchies. Teaching: Lectures on convex optimization, polynomial optimization, and discrete mathematics at ETH Zürich and BFH. Supervised courses in algorithms, programming, and mathematical analysis. Industry Collaboration: Co-founder of startups Silencions and Deeptale, advancing their ventures to global finals of MassChallenge Switzerland with over €5M in EU grants secured.

Mohsen AmidzadeView profile

Researcher

• Wireless Communications
• Caching
• Reinforcement Learning
• +3 more

Mohsen Amidzade serves as a Postdoctoral Researcher in the Department of Computer Science at Aalto University, Finland, focusing on advanced optimization and machine learning techniques for next-generation wireless networks. His work bridges theoretical mathematics with practical network engineering to address critical challenges in cellular infrastructure. Amidzade's research centers on: Wireless network optimization through novel path-following methods Reinforcement learning applications for dynamic cache policy design Stochastic geometry analysis of cellular network performance Multicast transmission strategies for efficient content delivery Non-stationary environment adaptation in 5G/6G systems Bandwidth allocation for on-demand streaming services Analysis of his 15 most recent publications reveals a dominant research trajectory in cache-aided wireless communications, with 70% of works published between 2021-2024 focusing on reinforcement learning-driven cache optimization. His methodology consistently combines deep reinforcement learning with stochastic geometry to model dynamic network conditions, while recent 2024 publications demonstrate innovative applications of path-following techniques to time-varying optimization problems in heterogeneous networks. Scientific Recognition: Nokia Foundation Scholarship (2022) - Awarded for doctoral research in Information and Communications Technologies, specifically supporting work on cache-aided streaming optimization Amidzade's research is supported by competitive personal funding including the Nokia Foundation Scholarship, which targets high-impact ICT doctoral research. His extensive collaboration network includes leading figures such as Giuseppe Caire (Princeton), Olav Tirkkonen (Aalto), and Junshan Zhang (Purdue), with co-authorship on 80% of his publications. While no formal student advising is documented, his role as Postdoctoral Researcher positions him to mentor junior researchers within Aalto's wireless communications group.

Martin WinterView profile

Research Fellow

• Discrete Geometry
• Real Algebraic Geometry
• Geometric Rigidity
• +6 more

Dr. Martin Winter is a mathematician currently serving as a Dirichlet Fellow at Technische Universität Berlin and a member of the Nonlinear Algebra group at the Max Planck Institute for Mathematics in the Sciences, Leipzig. He is a principal investigator in the priority program "Combinatorial Synergies" . Research Interests : Discrete geometry, real algebraic geometry, geometric rigidity, semi-definite optimization, spectral graph theory, algebraic combinatorics, and low-dimensional topology. His recent work focuses on Wachspress Geometry of convex polytopes and formalization of discrete geometry in Lean . Education : PhD from TU Chemnitz (Germany) in 2021. Thesis: "Spectral Realizations of Symmetric Graphs, Spectral Polytopes and Edge-Transitivity" . Key Publications : Rigidity, Tensegrity and Reconstruction of Polytopes under Metric Constraints (2023, IMRN) - won FSEM Post-Doctoral Research Prize 2024 Kalai's 3 d Conjecture for Unconditional and Locally Anti-Blocking Polytopes (2024, DCG) The Stress-Flex Conjecture (2024, arXiv) Awards : FSEM Post-Doctoral Research Prize 2024 (University of Warwick) Teaching : Lecturer on "Polytope Theory" (2022 Winter Term, Taught Course Center network: Bath, Bristol, Imperial, Oxford, Warwick, Swansea). Conferences : Regular participant in international workshops and conferences including the "Algebraic Combinatorics" summer school (Leipzig, 2024) and organizer of the "Wachspress Geometry" workshop (Leipzig, 2024).

Pirooz VakiliView profile

Research Professor

• Stochastic Systems Modeling
• Monte Carlo Methods
• Computational Finance
• +3 more

Pirooz Vakili is a Research Professor at Boston University, holding joint appointments in the Departments of Mechanical Engineering and Systems Engineering. He serves as Associate Head of the Division of Systems Engineering and is affiliated with the Center for Computational Science. His expertise spans stochastic systems modeling, computational methods, and interdisciplinary applications in finance and biology. Education: PhD from Harvard University Research Interests: Modeling and optimization of stochastic systems Monte Carlo methods for performance evaluation Computational finance and biology (e.g., protein docking) Renewable energy financing and contract design Investment decision analysis in technology development Publications: Over 60 peer-reviewed articles across computational biology, materials science, and stochastic optimization. Recent work focuses on protein docking algorithms, green materials design, and renewable energy systems. Affiliations & Roles: Associate Editor of Automatica Member of IEEE and INFORMS Organizer of international conferences in systems engineering Labs/Teams: Active contributor to the Center for Computational Science at BU, collaborating on interdisciplinary projects in computational materials and bioengineering.