جست‌وجوی استاد و پژوهشگر

Giles Richardsonمشاهده پروفایل

استاد

• Renewable Energy Storage
• Biomedical Modelling
• Mathematical Physics
• +۴ مورد دیگر

Professor Giles Richardson is a leading academic at the University of Southampton , holding the rank of Professor in Applied Mathematics. His research activities are deeply integrated with the Institute for Life Sciences and Applied Mathematics and Theoretical Physics research groups. Education: BSc Mathematics (Bristol), MSc Mathematical Modelling (Oxford), DPhil Applied Mathematics (Oxford) Richardson's research spans renewable energy systems and biomedical applications , with specific focus on: Mathematical modelling of lithium-ion battery performance and degradation Perovskite solar cell physics and stability challenges Photo-electrochemical water splitting for hydrogen fuel Redox flow battery optimisation Magnetically targeted drug delivery mechanisms Alzheimer's disease and cerebral drainage failures His recent publications demonstrate expertise in: Advanced electrochemical modelling techniques Mathematical physics applications to energy systems Materials science diagnostics for photovoltaic technologies Computational methods for battery simulation Scientific achievements include: John Ockendon Prize 2018 John Ockendon Prize 2020 Richardson has developed the IonMonger 2.0 simulation tool and co-founded DandeLiion , a software company providing physics-based battery simulations. His teaching portfolio includes modules in: Numerical Methods (MATH3018/6141) Modelling with Differential Equations (MATH6149) Mathematics for Electronics (MATH2047)

Leonid Chindelevitchمشاهده پروفایل

استادیار

• Genetics
• Microbiology
• Numerical and Computational Mathematics
• +۶ مورد دیگر

Leonid Chindelevitch is an Assistant Professor in Infectious Disease Epidemiology at the School of Public Health, part of the Faculty of Medicine at Imperial College London. He is affiliated with the Artificial Intelligence Network, the Centre for eXplainable Artificial Intelligence (XAI), and the MRC Centre for Global Infectious Disease Analysis. His research focuses on mathematical and computational modeling of antimicrobial resistance in infectious diseases, combining molecular-level analyses (computational biology, systems biology) with population-level approaches (epidemiology, population genetics). He also applies science to policy to improve healthcare outcomes, particularly in low-resource settings. Education: PhD in Applied Mathematics from MIT (supervised by Bonnie Berger) BSc in Mathematics and Computer Science from McGill University Research Interests: Dr. Chindelevitch’s work spans computational biology, algorithm development, discrete optimization, machine learning, and artificial intelligence. He investigates genomic determinants of drug resistance in pathogens like M. tuberculosis and B. burgdorferi , leveraging methods such as exact optimization and deep neural networks. His team integrates MLST typing and tandem repeat copy number analysis to understand resistance mechanisms. Awards: Alfred P. Sloan Research Fellowship (2015) Advising & Grants: Previously a faculty member at Simon Fraser University (2015–2020), he transitioned to Imperial College in 2020. He consults for the Foundation for Innovative New Diagnostics on M. tuberculosis drug resistance and co-led a global genomics catalog project. His industry experience includes computational roles at Pfizer and the Massachusetts General Hospital. Labs & Teams: Active in the MRC Centre for Global Infectious Disease Analysis and collaborates with the Imperial College’s AI Network to advance explainable AI in healthcare.

• Operational Research
• Facility Location Optimization
• Prescriptive Analytics
• +۴ مورد دیگر

Thomas Byrne is a University Teacher in Operational Research at the School of Mathematics, University of Edinburgh. He holds a PhD in Optimisation and Operational Research from the University of Edinburgh following an MSc in Statistics and Operational Research (2015). His research focuses on prescriptive analytics for optimal facility location, addressing critical applications in healthcare, supply chains, 5G infrastructure, and pandemic response. Byrne has taught courses including Fundamentals of Operational Research and Topics in Applied OR, incorporating cutting-edge research into his lectures. Education: PhD in Optimisation/OR, University of Edinburgh MSc in Statistics/OR, University of Edinburgh BA/MMath Mathematics, University of Cambridge Research Interests: Byrne's work addresses facility location problems across domains like emergency services, healthcare systems, and logistics. Recent applications include optimizing fire station locations, hospital placement during pandemics, and 5G mast deployment. His approach combines mathematical rigor with real-world impact, emphasizing solutions that improve efficiency and save lives. Teaching Initiatives: Co-created OR Base, a drop-in session for MSc students to discuss academic and personal challenges. This initiative fostered global community-building during lockdowns, supporting isolated students through online interactions. Labs/Teams: Active within the School of Mathematics' Operational Research group, collaborating on interdisciplinary projects at the intersection of mathematics, computer science, and public policy.

Robyn Goldsmithمشاهده پروفایل

مدرس

• Data Science
• Stochastic Modelling
• Inventory Optimisation
• +۴ مورد دیگر

Robyn Goldsmith is a Lecturer in Mathematics (Data Science) at the University of Greenwich, affiliated with the School of Computing and Mathematical Sciences within the Faculty of Engineering and Science. Her work bridges mathematical theory and practical applications in data science, with a focus on sustainable decision-making. Her primary research interests include: Stochastic Modelling Inventory Optimisation Forecasting for long-term planning Science communication and public engagement in mathematics Although no recent articles are listed in the provided text, her research trajectory emphasizes applied mathematics in real-world contexts, particularly sustainability and operational efficiency. Robyn has been recognized with prestigious early-career awards: Statistical Excellence Award for Early Career Writing by the Royal Statistical Society Graham Hoare Prize from the Institute of Mathematics and its Applications She previously served as chair of the Early Career Mathematicians Committee at the Institute of Mathematics and its Applications, demonstrating leadership in the mathematical community. No information is available regarding student supervision or external research grants. Robyn is actively involved in mathematical outreach and science communication, contributing to public understanding of data science and mathematical modelling.

Samuel C Zelibeمشاهده پروفایل

مدرس

• Mathematical Optimisation
• Operational Research
• Probabilistic Modelling
• +۷ مورد دیگر

Dr Samuel C Zelibe is a Lecturer in Mathematics (Data Science) at the School of Computing and Mathematical Sciences, University of Greenwich, Faculty of Engineering and Science. His work bridges theoretical mathematics with real-world applications in logistics, supply chains, and healthcare. His research interests include mathematical optimisation, operational research, probabilistic modelling, and lifetime processes. He focuses on integrated multi-echelon supply chain systems with response-time constraints, aiming to enhance efficiency, resilience, and decision-making in complex networks. He is also exploring the synergy between optimisation techniques and data science methodologies. Samuel is an experienced educator who designs and teaches courses in optimisation, operational research, and data-driven decision-making. He employs innovative teaching strategies to foster an inclusive and engaging learning environment, emphasizing the practical relevance of mathematical concepts across industries. He actively promotes interdisciplinary collaboration and supports students in achieving their academic and professional goals. His commitment to education and research ensures a strong impact on both pedagogy and applied problem-solving. Professional links: ORCID , LinkedIn .

Jean Lagaceمشاهده پروفایل

مدرس

• Spectral Geometry
• Geometric shape optimisation
• Eigenvalue asymptotics
• +۱ مورد دیگر

Jean Lagace is a Lecturer in Pure Mathematics at King's College London specializing in Mathematical Analysis. He joined King's as a lecturer in January 2022 after completing postdoctoral research at University College London and the University of Bristol. His academic journey began at Université de Montréal where he earned both his BSc and PhD under the mentorship of Prof. Iosif Polterovich. His educational background includes: Doctor of Philosophy in Pure Mathematics from University of Montreal (2014-2018), with dissertation titled "Asymptotiques spectrales et géométrie des nombres" BSc from Université de Montréal Jean Lagace's research focuses on several interconnected areas within spectral geometry and mathematical analysis. His work explores the relationship between geometric properties of domains and the spectrum of differential operators, particularly examining eigenvalue asymptotics and shape optimization problems. His research combines techniques from analysis, geometry, and number theory to address fundamental questions about how the shape of a domain influences its spectral properties. Through his investigations of Steklov problems, homogenization theory, and spectral invariants, Lagace contributes to our understanding of how geometric constraints affect physical phenomena modeled by partial differential equations. Jean Lagace has published multiple significant papers in top mathematical journals, with research spanning spectral decomposition, homogenization theory, and eigenvalue problems. His work demonstrates strong connections between spectral theory, differential geometry, and mathematical physics, with particular emphasis on Steklov problems, Dirac operators, and boundary homogenization techniques. His research output shows a consistent focus on understanding asymptotic behavior of eigenvalues and developing theoretical frameworks for spectral analysis on various geometric settings. The fingerprint analysis of his work reveals strong connections to eigenvalue mathematics (100%), Steklov problems (59%), and manifold theory (21%). Jean Lagace has established himself as a promising researcher in spectral geometry with multiple publications in high-impact journals including Journal of Spectral Theory, Annales Henri Lebesgue, and Archive for Rational Mechanics and Analysis. His work on Steklov problems, eigenvalue asymptotics, and homogenization theory has garnered citations in the mathematical community, with his research being recognized for addressing open questions in two-dimensional conformal mappings and Lipschitz boundaries.

• Mathematics
• Applied Mathematics
• Pure Mathematics
• +۵ مورد دیگر

Amy Trang Nguyen is an Associate Lecturer of Mathematics at Flinders University specializing in calculus, differential equations, analysis, and applied mathematics. Her teaching philosophy emphasizes creating active learning environments to demonstrate mathematics' applications in engineering, health sciences, and finance through practical examples. Her academic qualifications include: Doctor of Philosophy (Mathematics), University of South Australia, 2020 Bachelor of Applied Science (Honours) (Industrial and Applied Mathematics), University of South Australia, 2015 Bachelor of Mathematical Sciences, University of South Australia, 2014 Nguyen's research spans pure and applied mathematics with focus areas in calculus, differential equations, complex analysis, optimisation, and linear algebra. Her work integrates curriculum design innovations to enhance mathematical thinking and problem-solving skills through real-world disciplinary applications. Her distinguished awards include: Lift-off Fellowship, Australian Mathematical Society (2020) Endeavour Postgraduate Scholarship, Australian Government (2016-2020) Hypatia Scholarship for Mathematically Talented Women (2012) B.H. Neumann Prize (2019) Three Minute Thesis Competition Runner-up (2019) B.H. Neumann Prize Runner-up (2016) University of South Australia Medal (2014) Nguyen actively mentors secondary students through AMSI's CHOOSEMATHS campaign and UniSA Connect program, with emphasis on inspiring female STEM participation. She has organized the Australian Mathematical Sciences Student Conference and serves as a media expert on mathematics topics, demonstrating commitment to public engagement beyond campus boundaries.

Sheng Chenمشاهده پروفایل

استاد

• Wireless Communication Systems
• Machine Learning for Signal Processing
• Underwater Robotics
• +۳ مورد دیگر

Professor Sheng Chen at the University of Southampton holds a prestigious academic rank in the Electronics and Computer Science department. His research interests span wireless communication systems, machine learning for signal processing, underwater robotics, and digital twin technology. IEEE Fellow Royal Academy of Engineering Fellow Chartered Engineer Research Focus: Professor Chen specializes in advanced wireless communication systems including MIMO technologies, channel estimation techniques, and next-generation 6G protocols. His work extends to industrial cybersecurity applications and underwater stereo matching algorithms. Recent Contributions: His latest publications demonstrate expertise in memristor-based signal processing circuits, label distribution learning using renormalization group theory, and innovative interference mitigation strategies in dynamic TDD systems. Current research projects: Optimising control system integrity (Royal Academy of Engineering), NEWCOM (European Union)

Yeaw Chu Leeمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Thermo-fluids
• Multi-phase Flow
• +۱۶ مورد دیگر

Dr. Yeaw Chu Lee is an Associate Professor in Mechanical Engineering at the School of Engineering, Computing and Mathematics, University of Plymouth. He is an active researcher and lecturer with over 15 years of experience in teaching and research at both undergraduate and postgraduate levels. His work spans computational fluid dynamics, multi-phase flows, and environmental fluid mechanics, with applications in energy systems, marine habitats, and transport technologies. Academic Qualifications: Postgraduate Certificate in Academic Practice, Heriot-Watt University (2011–2012) Ph.D. in Mechanical Engineering, University of Leeds (2000–2004) M.Eng. (Hons.) in Mechanical Engineering, University of Leeds (1996–1999) His research interests focus on thermo-fluids and computational engineering of multi-scale and multi-phase soft matter, especially interfacial flows. Key areas include computational fluid dynamics (CFD), smoothed particle hydrodynamics (SPH), thin films, droplets, rivulets, fluid-structure interaction, and surface tension phenomena. He also integrates machine learning and optimisation techniques into solver development for engineering problems. His recent publications highlight a strong trend in fluid dynamics applied to environmental and energy challenges, including sloshing in cryogenic fuels, thermal jet discharges in coastal waters, wave instabilities, and coral habitat modeling using SPH. These works reflect interdisciplinary engagement across mechanical engineering, oceanography, and environmental science. Scientific Awards: Fellow of the Higher Education Academy (FHEA) Dr. Lee has successfully secured 34 research grants, awards, and studentships. He has supervised over 59 B.Eng. and M.Eng. dissertations, 22 M.Sc. theses, and 16 Ph.D. theses (13 completed). He is currently accepting PhD students and leads several active research projects. He plays a principal or co-investigator role in grants funded by EPSRC, STFC, Interreg, and GW Shift, focusing on wave-structure interaction, sustainable energy storage, and hydrogen sloshing dynamics. He is actively involved in research teams and collaborative networks, particularly in projects related to marine renewable energy and environmental fluid mechanics. His lab work involves computational modeling and simulation of complex fluid systems, often in partnership with environmental scientists and engineers.

• Nonlinear Partial Differential Equations
• Geometric Analysis
• Riemannian Manifold Learning
• +۷ مورد دیگر

Vahideh Vahidifar is a Research Fellow in the Department of Mathematics at the University of Sussex's School of Mathematical and Physical Sciences, holding a Global Talent Endorsement from EPSRC/UKRI. Her academic journey includes a PhD from Iran University of Science and Technology (2018-2022) and a Research Associate position at Iran's National Elites Foundation (2020-2022). Her research spans the intersection of pure mathematics and applied computational fields, with core expertise in Nonlinear PDEs , Geometric Analysis , and Geometric Deep Learning . Key interests include Riemannian Manifold Learning, Graph Neural Networks, Calculus of Variations, and applications in Material Sciences. Her work connects abstract mathematical frameworks with practical robotics and AI systems. Analysis of her 12 recent publications (2021-2025) reveals a strong focus on gradient estimates for nonlinear parabolic/elliptic equations on metric measure spaces, with significant contributions to Ricci flow theory and geometric analysis. Parallel work applies mathematical principles to surgical robotics, particularly tremor detection and Leap Motion-controlled systems. Global Talent Endorsement by EPSRC/UKRI (2022) Vahidifar maintains active international collaborations across the UK, Canada, and USA. Her robotics research demonstrates practical translation of mathematical frameworks into medical device control systems, particularly in surgical robotics where she has developed novel approaches using Leap Motion sensors and Kalman filtering. Current work bridges geometric analysis with emerging AI architectures like Graph Neural Networks. She leads research in geometric foundations of machine learning while maintaining strong ties to mechanical engineering applications, particularly in smart materials and continuum mechanics. Her laboratory work integrates theoretical mathematics with physical robotics systems for surgical applications.

• Computer Science
• Probabilistic Programming
• Semantics
• +۶ مورد دیگر

Fredrik Dahlqvist is a faculty member at the University College London , Department of Computer Science , focusing on theoretical and applied aspects of probabilistic programming , semantics , and formal verification . His work bridges computer science with mathematical logic , machine learning , and programming language theory . Education: PhD in Coalgebraic Logics from Imperial College London (2014) His recent publications (2016–2025) explore model pruning , reparameterisation invariance , probabilistic numerical analysis , and categorical approaches to machine learning . Key themes include optimisation , cosine similarity , and omega-complete cone duality in probabilistic systems. Contact: f.dahlqvist@ucl.ac.uk (institutional) or f.p.h.dahlqvist@gmail.com (private), located at Gower Street, London WC1E 6BT, United Kingdom .

• Operations Research
• Optimisation
• Game Theory
• +۲ مورد دیگر

Jerzy Filar is a Professor at Flinders University with research spanning Operations Research, Optimisation, Game Theory, Applied Probability, and Environmental Modelling. He co-authored the seminal textbook 'Competitive Markov Decision Processes' (Springer, 1996) and approximately 100 refereed research papers. His academic background includes: 1972: B.Sc.(Hons) Mathematical Statistics, University of Melbourne 1975: M.Sc. Statistics, Monash University 1977: M.A. Mathematics, University of Illinois, Chicago 1980: Ph.D. Mathematics, University of Illinois, Chicago Professor Filar's work bridges theoretical frameworks like Markov Decision Processes with practical environmental applications. As editor-in-chief of Environmental Modelling and Assessment and editorial board member for Operations Research and JMAA, he shapes discourse in mathematical optimization and sustainability science. His research integrates game-theoretic approaches with real-world environmental policy challenges. His distinguished honors include: OPSEARCH Best Paper Award (1981) AAAS Environmental Science Fellowship (1985) Australian Mathematical Society Fellowship (2003) Ren Potts Medal (2005) ARC Professorial Fellowship (2006-2010) South Australian Science Excellence Award (2007) Professor Filar has mentored 29 PhD students and secured major research funding from NSF, ARC, US EPA, World Resources Institute, DSTO, and the Sir Keith and Sir Ross Smith Foundation, demonstrating sustained impact across theoretical and applied domains.

• Numerical linear algebra
• Matrix polynomials and spectral theory
• Stability and perturbation analysis
• +۳ مورد دیگر

Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.

• Mathematical Modelling
• Transportation Systems
• Optimisation
• +۲ مورد دیگر

Lucy Maybury serves as a Graduate Tutor in the School of Mathematics at Cardiff University while pursuing her PhD in the School of Computer Science and Informatics through an interdisciplinary project with the School of Engineering. Her role encompasses comprehensive mathematics support across computer science curricula. Her research specializes in mathematical modelling of transportation systems, with core expertise in optimisation and simulation techniques applied to the UK's transition from conventional to electric vehicles. This work bridges computational mathematics, environmental policy, and sustainable infrastructure development. Maybury's 2022 publication in Transportation Research Part D establishes her authority in systematic analysis of electric vehicle adoption models, contributing to interdisciplinary scholarship at the intersection of transportation engineering and mathematical sciences. Teaching responsibilities include: Maths Support Tutor for foundational courses Tutorials for CM2104: Computational Mathematics Support for CM3109: Combinatorial Optimisation Instruction for CM1208: Maths for Computer Science Group project supervision for CM2305 Her academic activities foster collaboration between the School of Mathematics and School of Engineering, advancing sustainable transportation research through computational methodologies.

Alessandro Checcoمشاهده پروفایل

استادیار

• Crowdsourcing
• Human Computation
• Distributed Systems
• +۷ مورد دیگر

Alessandro Checco is an Assistant Professor in the Computer Science Department at University of Rome La Sapienza. His research focuses on crowdsourcing, distributed systems, and privacy-preserving technologies, bridging theoretical computer science with practical applications that consider human factors in technological systems. He has established himself as a significant contributor to the field of human computation and privacy-aware systems. His educational background includes: 2020: Fellowship of Higher Education from The University of Sheffield, Higher Education Academy 2015: Ph.D. in Mathematics from Hamilton Institute (Design of decentralised algorithms applied to channel/code selection and convex optimisation for throughput fairness of 802.11 networks) 2010: M.Sc. in Mathematical Engineering from University of Roma "Tor Vergata" (110/110 with great distinction) 2009: Erasmus Scholarship at Universiteit Gent, Department of Telecommunications 2007: B.Sc. in Mathematical Engineering from University of Roma "Tor Vergata" (110/110 with great distinction) Checco's research spans multiple areas at the intersection of computer science and social implications of technology. He is particularly interested in Crowdsourcing for Human Computation, Distributed Private Recommender Systems, Information Retrieval, Data Privacy, Distributed Systems, User Data Obfuscation in Web Systems, Societal and Economic Analysis of Online Work, Crowd Workers Unionisation, and Algorithmic Bias. His work often examines how technological systems can be designed to respect user privacy while maintaining functionality, and how crowd work can be structured to be more equitable for workers. His recent publications demonstrate a clear evolution in research focus, beginning with foundational work in wireless networks and distributed algorithms, then shifting toward human computation and privacy-preserving systems. His most recent work increasingly addresses the societal implications of crowd work, including investigations into crowd worker unionization and cooperative models. Several publications examine gender bias in algorithmic systems, reflecting growing attention to fairness and ethical considerations in his field. Among his notable achievements: All That Glitters is Gold-An Attack Scheme on Gold Questions in Crowdsourcing (Best Paper Award) Checco has secured significant research funding and led important projects including the H2020-funded FashionBrain project as Research Director and the EPSRC-funded BetterCrowd project as Research Associate. His work on the FashionBrain project demonstrates his ability to lead large-scale, interdisciplinary research initiatives. He has also received the Technology Innovation Development Award (TIDA) from Science Foundation Ireland. His research has practical applications across multiple domains including recommendation systems (BLC: Private Matrix Factorization Recommenders), peer review assistance using AI, smart farming technologies, and cooperative models for crowd workers (CrowdCO-OP). He has developed frameworks for understanding worker behavior in crowdsourcing platforms and created methods for improving quality control in human computation systems.