جست‌وجوی استاد و پژوهشگر

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.

• Analytical Chemistry
• Biomedical Engineering
• Nanotechnology
• +۵ مورد دیگر

Professor Danny O'Hare is a Professor of Biosensor Technology at the Department of Bioengineering, Faculty of Engineering at Imperial College London. His affiliations include the Centre for Antimicrobial Optimisation, Institute of Chemical Biology, and the Leverhulme Centre for Cellular Bionics. He holds a PhD from Imperial College London (1991) in electrochemical sensors for intervertebral disc nutrition. Prior to his current role, he served as Senior Lecturer and Lecturer at the University of Brighton (1992–2001). Education: BSc Chemistry (Imperial College London, 1984); PhD in Physiological Flow Studies (Imperial College London, 1991) Professional Memberships: Royal Society of Chemistry, Electrochemical Society, British Society for Matrix Biology His research focuses on biosensor technology , integrating devices into organ-on-chip models, antimicrobial resistance monitoring, and real-time metabolic sensing. Key themes include electrochemical sensing of biomarkers, development of minimally invasive microneedle sensors, and personalized treatment strategies for infections. His work bridges analytical chemistry, biomedical engineering, and nanotechnology. Recent publications highlight advancements in biosensor applications for osteoarthritis, UTI management, and antibiotic efficacy. He has pioneered microneedle-based systems for continuous drug monitoring, emphasizing clinical translation. His contributions to sensor biofouling mitigation and electrochemical detection methods have advanced environmental and clinical diagnostics. Awards and recognitions include his Chartered Chemist status and advisory roles, such as the Advisory Board for Analytical Abstracts (Royal Society of Chemistry). His research programs often involve interdisciplinary collaborations, targeting translational healthcare solutions.

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.

• Failure probability
• Interval computation
• Statistics for imprecise data
• +۱۰ مورد دیگر

Marco De Angelis is a Lecturer at the Centre for Intelligent Infrastructure within the Department of Civil and Environmental Engineering at the University of Strathclyde's Faculty of Engineering. His work focuses on computational methods for handling uncertainty in engineering systems, with applications in structural reliability and health monitoring. Education: PhD in Risk and Uncertainty (2015) from University of Liverpool's Institute for Risk and Uncertainty Master of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Bachelor of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Dr. De Angelis specializes in computing with imprecision, developing methods to propagate uncertainty through models using interval analysis, probability bounds, and other mathematical frameworks. His research enables rigorous inference with scarce empirical data and builds trust in simulation for structural reliability assessment. His work intersects civil engineering, computer science, and statistics, with particular emphasis on practical applications in infrastructure monitoring and risk assessment. His recent publications demonstrate a strong focus on high-dimensional uncertainty analysis, optimization under uncertainty, and verified computational methods for reliability engineering. The research shows increasing sophistication in handling complex uncertainty representations while maintaining computational tractability for real-world engineering problems. Scientific Awards: Best student paper (June 18, 2025) The NASA and DNV Challenge on Optimization under Uncertainty (June 17, 2025) Bronze poster award (July 27, 2021) Teaching and Learning Award (May 17, 2017) ISIPTA-IJAR Young Researcher Award (August 2015) Dr. De Angelis teaches structural engineering theory, computer programming, interval computation, probability theory, and machine learning to undergraduate students. He has developed teaching materials from scratch for advanced dynamics courses. He serves as Co-investigator on the REUN project (Reduction of Uncertainties in risk assessment of structures and infrastructures against Natural hazards) funded by the Royal Society of Edinburgh, running from April 2025 to March 2027. His professional activities include conference participation, journal peer review, and invited talks in his specialty areas. He is actively involved with the Centre for Intelligent Infrastructure, where he contributes to research on digital twins and computational methods for infrastructure monitoring and assessment.

• Mechanical Engineering
• Structural Engineering
• Materials Science
• +۷ مورد دیگر

Prof Zhen (Jeff) Luo is a Professor at the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS). Since 2012, he has led the Advanced Metamaterials & Metastructures (AMM) and Engineering Computation and Optimisation (ECO) research groups, focusing on multi-disciplinary engineering innovations. Expertise : Advanced materials design, topology optimization algorithms, additive manufacturing, and computational mechanics. Education : PhD in Mechanical Engineering from Huazhong University of Science and Technology (2005). His research bridges Mechanical, Structural, Aerospace, and Biomechanical Engineering , developing cutting-edge metamaterials and computational methods. Recent work includes two-scale lattice optimization , stochastic bandgap analysis , and machine learning-aided virtual modeling for structural reliability. Key contributions span 3D-printed heat sinks , frequency-selective surfaces , and hydrogen storage systems . As a World’s Top 2% Scientist (Stanford, 2019–present), he has secured over AUD $7 million in grants, including from the Australian Research Council (ARC) and National Intelligence Discovery Research Grants (NI220100074). Awards : IAAM Scientist Award, IAAM Fellow. Leadership : Editorial roles in Structural and Multidisciplinary Optimization , Frontiers in Bioengineering and Biotechnology , and organizing roles in 21 international conferences.

• System Reliability Modelling
• Maintenance Optimisation
• Project Management
• +۲ مورد دیگر

Associate Professor Indra Gunawan is affiliated with the Adelaide Business School under the Faculty of Arts, Business, Law and Economics at the University of Adelaide. He received his PhD in Industrial Engineering and MSc in Construction Management from Northeastern University, USA, and has held academic roles at Monash University, Auckland University of Technology (New Zealand), and Malaysia University of Science and Technology (collaborating with MIT, USA). His research focuses on system reliability modelling, maintenance optimisation, project management, and applications of operations research in complex systems. Indra actively contributes to professional societies like the Asset Management Council (Engineers Australia) and serves on editorial boards of journals including the International Journal of Project Organisation and Management.

Carl Scarthمشاهده پروفایل

پژوهشگر

• uncertainty quantification
• data science
• machine learning
• +۴ مورد دیگر

Dr Carl Scarth is a Researcher in the Department of Mechanical Engineering at the University of Bath. His work focuses on data-driven methods for composite aircraft structures, addressing uncertainties, manufacturing defects, and process features. Key research areas include uncertainty quantification, machine learning, composite design, and aeroelasticity. Education: MEng in Engineering Design with study in industry from the University of Bristol (2010), specialising in structural mechanics and nonlinear dynamics; PhD in Advanced Composites from ACCIS CDT (2017), sponsored by Embraer. Carl's research applies Bayesian methods and finite element analysis to optimize composite aerospace components. Notable projects include the EPSRC ADAPT and CerTest programme grants, targeting high-rate manufacturing defect reduction and experimental-numerical data fusion. His collaborations span Embraer, Swansea University, and EPFL.

• Redistributed Manufacturing
• Healthcare Supply Chains
• Supply Chain Resilience
• +۱۲ مورد دیگر

Dr. Helen Sanderson is a Senior Research Fellow at the Faculty of Business and Law (University of the West of England). Her work bridges biochemical expertise with supply chain innovation through the Redistributed Manufacturing in Healthcare Network (RiHN) , focusing on transformative healthcare production models. BSc (Hons) Biochemistry, University of Sheffield PhD, University of Manchester Research themes include: Redistributed Manufacturing (RDM) for point-of-care medical products 3D printing applications in food and healthcare supply chains Supply chain resilience during geopolitical disruptions Regulatory frameworks for decentralised manufacturing Biochemical foundations for medical product development Publications reveal a dual focus on technical cell biology research (2006-2012) and applied supply chain studies (2022-present). Key trends demonstrate integration of biomanufacturing with business model innovation in healthcare. Professional expertise spans grant portfolio management, bid writing for multidisciplinary projects, and stakeholder coordination across industry-academic-clinical partnerships. Research outputs frequently examine temporary supply chains in crisis settings and commercialisation pathways for emerging healthcare technologies.

• Precision Antimicrobial Use
• Antimicrobial Resistance (AMR)
• Biosensor Technology
• +۶ مورد دیگر

Tim Rawson serves as Clinical Associate Professor in Infectious Diseases and Antimicrobial Resistance at Imperial College London's Department of Infectious Diseases, Faculty of Medicine. He concurrently holds an Honorary Consultant position in Infectious Diseases and Medical Microbiology at Imperial College Healthcare NHS Trust, and maintains affiliations with the Institute for Molecular Science and Engineering and NIHR HPRU in Healthcare Associated Infections and Antimicrobial Resistance. His academic credentials include: MBBS from Imperial College London BSc (hons.) in Infection & Immunity from Imperial College London PhD from Imperial College London (2018) Postgraduate Diploma in Medical Education from Cardiff University Diploma of Tropical Medicine & Hygiene from Royal College of Physicians Professionally certified as Member of the Royal College of Physicians (MRCP) and Fellow of the Royal College of Pathologists, his research pioneers precision antimicrobial use through biosensor technology development , pharmacokinetic dose optimization models , and machine learning applications targeting antimicrobial resistance. This work bridges Clinical Sciences, Medical Microbiology, and Pharmacology to transform infection treatment protocols. His distinguished recognition includes: 2017: BIA Barnet Christie Award for Excellence in Original Research 2022: ESCMID Young Investigator Award As core Fleming Initiative member and former Research Lead for the NIHR HPRU's Precision Prescribing Theme (2020-2025), he directs interdisciplinary teams advancing antimicrobial stewardship through the Centre for Antimicrobial Optimisation Network (CAMO-net).

• Optimisation Under Uncertainty
• Discrete Choice
• Social Choice
• +۴ مورد دیگر

Chung Piaw Teo is the Stephen Riady Professor and Executive Director of the Institute of Operations Research and Analytics (IORA) at the National University of Singapore (NUS) Business School. He has held significant academic roles including Head of Department, Acting Deputy Dean, Vice-Dean of Research and Ph.D. Programs, and Chair of the Ph.D. Committee at NUS. PhD in Operations Research from MIT (1996) Bachelor of Science (Honors) in Mathematics from NUS (1990) Research Interests: Optimisation Under Uncertainty Discrete Choice Modeling Social Choice Theory Inventory Theory Supply Chain Management Combinatorial Optimisation Operations Research Publications focus on stochastic optimization, supply chain resilience, and network design, with recent works spanning 2020–2015 in journals like Management Science , Operations Research , and Mathematical Programming . Key themes include urban logistics, risk mitigation, and decision analytics. Scientific Awards: Stephen Riady Professor (2024) Provost’s Chair (2014) Faculty Outstanding Researcher Awards (2014, 2006, 2003) Nominee for University Outstanding Researcher Award (2006) Grants & Leadership: Served on international committees (INFORMS, LANCHESTER Prize, Fudan Prize) and held visiting/fellow positions at MIT, Northwestern, and Sungkyunkwan University. Currently a department editor for Management Science and associate editor for multiple journals.

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.

• Software Engineering
• Artificial Intelligence
• Genetic Programming
• +۴ مورد دیگر

Dr. Anthony Ventresque is a Visiting Associate Professor at the University College Dublin, School of Computer Science , with a PhD in Computer Science from the University of Nantes & INRIA France (2008). He is also the Director of the TCD Complex Software Lab and a Senior Investigator with Lero, the SFI Irish Software Research Centre. MSc in Philosophy and Logic from the University of Nantes PhD in Computer Science from the University of Nantes & INRIA France His research focuses on Software Engineering , Genetic Programming , and Multi-objective Optimization , with applications to Autonomous Systems , Cloud Computing , and Sports Analytics . Recent publications highlight his work on grammar-obeying program synthesis using large language models, mutation testing for software quality, and domain adaptation for object detection in rugby data. His scientific awards include a Teaching Award from the UCD School of Computer Science in 2020. He has contributed to grants like Mutation Testing at Scale and Advancing Computational Archaeology , and his teaching activities encompass modules such as Big Data Programming and Operating Systems over multiple years. He leads the TCD Complex Software Lab , fostering interdisciplinary research and innovation.