Semi-Definite Optimization Professors and Supervisors

Vassalos ParaskevasView profile

Assistant Professor

Athens University of Economics and BusinessGreece

Vassalos Paraskevas is an Assistant Professor at the Department of Informatics , Athens University of Economics and Business . He holds a Degree in Mathematics from the University of Athens (1998), a Postgraduate Diploma in Computational Mathematics and Informatics , and a Doctorate in Computational Mathematics from the University of Ioannina (2003). His office is located at the Antoniadou Wing, 5th floor, and he can be reached at pvassal@aueb.gr or +30 210 8203 187. Education BSc in Mathematics, University of Athens MSc in Computational Mathematics and Informatics, University of Ioannina PhD in Computational Mathematics, University of Ioannina (2003) His research focuses on Numerical Linear Algebra , particularly on solving linear systems with structured matrices (e.g., Toeplitz and block Toeplitz systems), signal/image reconstruction , and numerical solutions of differential equations . He has developed preconditioning techniques for iterative solvers and studied stochastic matrices with Toeplitz-like structures. His publications (2002-2008) analyze preconditioning strategies for Toeplitz systems, covering spectral analysis of matrices from PDE discretization, superlinear convergence in PCG methods, and approximation techniques for multilevel Toeplitz matrices. These works span Numerical Analysis , Applied Mathematics , and Computational Science , with applications in linear systems , differential equations , and matrix theory .

Official University Profile

James SheeView profile

Assistant Professor

Rice UniversityUnited States

James Shee is the Norman Hackerman-Welch Assistant Professor of Chemistry at Rice University, joining in 2023. His research focuses on developing ab initio computational methods to model weak and strong correlations in transition metal and f-block compounds, with applications in catalysis, magnetism, drug design, and energy storage. He specializes in quantum Monte Carlo techniques, quantum algorithms, and nuclear quantum effects. Prior to Rice, he was a postdoctoral researcher at UC Berkeley under Martin Head-Gordon. He holds a Ph.D. in Chemical Physics from Columbia University (2019) and an A.B. in Chemistry from Princeton University (2014). His research interests include quantum Monte Carlo (QMC) method development, electronic structure theory, and the intersection of quantum computation with chemistry. The Shee Lab emphasizes practical applications such as metal organic frameworks, drug design, and quantum information processing. He has been recognized with the Ruth L. Kirschstein National Research Service Award (2021) and the Louis Hammett Award (2019). Education: Ph.D. in Chemical Physics, Columbia University, 2019 A.B. in Chemistry, Princeton University, 2014 Awards: Ruth L. Kirschstein National Research Service Award (NIH, 2021) Louis Hammett Award for Excellence in Graduate Research (Columbia, 2019) His recent work bridges theoretical innovation with hardware-efficient algorithms for quantum computation, targeting challenges in transition metal chemistry. The lab collaborates on nuclear quantum effects and semi-classical potentials for simulating metal cofactors. Ongoing projects include quantum simulations of strongly correlated molecules using Rydberg atom arrays and optimizing AFQMC for scalable quantum hardware.

Official University Profile

İbrahim Halil GÜMÜŞView profile

Professor

Adiyaman UniversityTurkey

Prof. Dr. İbrahim Halil GÜMÜŞ is a distinguished faculty member at Adıyaman University, Faculty of Arts and Sciences, Department of Mathematics, where he currently holds the position of Professor (since 2023). Previously, he served as Associate Professor (2017-2023) and Assistant Professor (2011-2017) at the same institution. Before his academic career, he worked as a Teacher at Public Schools under the Ministry of National Education from 2002 to 2011. He earned his educational qualifications from Selçuk University: B.Sc. in Mathematics (1998-2002), M.Sc. in Mathematics (2002-2005), and PhD in Mathematics (2005-2011). His Master's Thesis focused on 'On the Hadamard Product of Matrices' (2005), while his PhD Thesis examined 'Bounds on arithmetic, geometric and Heinz means of positive definite matrices' (2011). Prof. GÜMÜŞ's research primarily centers on Matrix Theory and Operator Inequalities, with significant contributions to Positive Operators, Matrix Analysis, and Optimization. His work demonstrates a strong theoretical foundation in mathematical inequalities with increasing applications in data science and medical informatics. He has published extensively in high-impact journals such as Linear and Multilinear Algebra, Journal of Mathematical Analysis and Applications, and Operators and Matrices. His publication record shows a clear evolution from theoretical matrix inequalities toward practical applications in data analysis, particularly evident in his recent work on synthetic data generation for imbalanced datasets using geometric means and Heinz averages. This interdisciplinary approach bridges pure mathematics with machine learning applications, especially in medical data analysis. TÜBİTAK Publication Incentive Award, 2012 TÜBİTAK Publication Incentive Award, 2015 TÜBİTAK Publication Incentive Award, 2017 TÜBİTAK Publication Incentive Award, 2018 TÜBİTAK Publication Incentive Award, 2019 TÜBİTAK Publication Incentive Award, 2021 Prof. GÜMÜŞ has successfully supervised multiple Master's theses on topics including inequalities for positive multilinear mappings, geometric inequalities via majorization methods, determinants of positive semi-definite matrices, and mathematical approaches to synthetic data sampling. He serves as a referee for prestigious journals including Journal of Inequalities and Applications and Mathematical Reviews/MathSciNet. His administrative roles include Farabi Coordinator, Erasmus Coordinator, Bologna Coordinator for both the Faculty of Arts and Sciences and Institute of Science, and Mevlana Exchange Program Institutional Coordinator. His work bridges theoretical mathematics with practical applications in data science, particularly in addressing challenges related to imbalanced datasets in medical informatics through innovative mathematical approaches.

Official University Profile

Azadeh AlaviView profile

Lecturer

RMIT UniversityAustralia

Dr. Azadeh Alavi is a Lecturer at RMIT University's School of Computing Technologies in Melbourne, Australia. Her research focuses on interdisciplinary applications of artificial intelligence, particularly in medical imaging, structural engineering, quantum computing, and sustainability. She supervises PhD and master's projects in areas such as AI-driven medical image analysis, digital twin technology for infrastructure monitoring, and psychedelics-inspired therapeutics design. Her work spans diverse domains including healthcare analytics (e.g., diabetic foot ulcer classification, dementia prediction), civil infrastructure monitoring (e.g., bridge integrity assessment, real-time structural health systems), and theoretical AI (e.g., analyzing AI systems against consciousness definitions). Key contributions include developing PRIMITI for miRNA-mRNA interaction prediction and SellaMorph-Net for sella turcica segmentation in cephalometric imaging. Dr. Alavi’s recent publications highlight advancements in multimodal fusion learning for medical imaging, quantum-classical hybrid models for structural monitoring, and AI applications in fashion sustainability. She is an active collaborator in international research networks and maintains an ORCID profile (0000-0002-9565-217X). Her supervision portfolio includes projects on AI ethics, digital twin implementations, and health data mining. She teaches advanced computing modules and advises students on cutting-edge AI methodologies for real-world challenges across engineering and healthcare sectors.

Official University Profile

Spyridon PougkakiotisView profile

Lecturer

King’s College LondonUnited Kingdom

Spyridon Pougkakiotis is a Lecturer in Optimization at King’s College London’s Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He previously held positions as Lecturer in Mathematics at the University of Dundee (2023–2024) and as a postdoctoral research associate at Yale University (2022–2023). His research focuses on continuous optimization, stochastic optimization, and risk-aware decision-making, with applications in operational research, engineering, and data science. He has developed efficient algorithms for large-scale optimization problems and contributed to preconditioning techniques in interior point methods. Education: BSc in Informatics and Telecommunications, National and Kapodistrian University of Athens (2012–2016) MSc in Operational Research with Computational Optimisation, University of Edinburgh (2016–2017) PhD in Optimization, University of Edinburgh (2017–2022) His research interests emphasize nonlinear optimization (including non-smooth, stochastic, and black-box problems) and algorithm development. He has been recognized with the 2022 OR Society Doctoral Award and was a finalist for the 2024 Euro Doctoral Dissertation Award. Teaching includes undergraduate courses on numerical methods with Python and an MSc module on machine learning. He is involved in King’s Financial Mathematics group and organized events like the “Afternoon of Stochastic Optimisation” workshop in 2025. Awards: 2022 OR Society Doctoral Award (Most Distinguished Doctoral Research in Operational Research) 2024 Euro Doctoral Dissertation Award Finalist Current research projects include a 50/50 Haleon Industry Studentship (2025–2028), focusing on optimization applications in industry.

Official University Profile