Peter Ross is a Researcher at the Edinburgh Napier University , affiliated with the School of Computing Engineering and the Built Environment and the Centre for Algorithms, Visualisation and Evolving Systems . He collaborates with academics like Emma Hart, Alistair Lawson, and Andrew Webb on projects involving evolutionary swarm robotics , artificial immune systems , and optimisation algorithms . His research focuses on applying bio-inspired computing to robotics, including work on perceptual aliasing , adaptive scheduling , and swarm intelligence . He has supervised postgraduate research, notably serving as Director of Studies for Dr Neil Urquhart’s thesis on evolutionary machine learning in metamorphic malware analysis (1999-2003). His publications span 1998-2003 and explore intersections between immunology , sparse distributed memory , and robotic systems . Recent projects like VanFill Innovation Voucher (2025) indicate ongoing involvement in AI-driven solutions and human-robot interaction research.
Laura Dee is an Adjunct Assistant Professor jointly affiliated with the University of Minnesota and the University of Colorado Boulder . She is a conservation and global change ecologist whose work sits at the intersection of ecology, economics, and decision science. Education Ph.D. in Environmental Science & Management, University of California Research Focus Laura’s research aims to understand how ecosystems provide benefits to people under accelerating global change, and how management can be adapted to sustain both biodiversity and ecosystem services. She uses statistical and mathematical modelling to tackle questions such as: How biodiversity and species interactions drive ecosystem services. How climate variability and extremes alter service provision. How to design conservation strategies that remain robust under uncertainty. Her work spans forests, grasslands, and marine & coastal systems from local to global scales. Publication Trends Across 15 recent publications (2012–2018), Laura has advanced understanding of biodiversity–ecosystem-function relationships, climate impacts on fisheries, ecosystem-service valuation, and decision-analytic conservation planning. Studies appear in high-impact journals such as Ecology Letters , Journal of Applied Ecology , and Global Change Biology , reflecting a strong interdisciplinary footprint across ecology, conservation, and environmental economics. Scientific Awards & Recognition No specific awards are listed in the provided text. Students, Grants & Collaborations No individual students are named, but Laura collaborates extensively with international teams; co-authors include researchers from Australia, France, Sweden, and the USA. Grant details are not provided. Labs & Teams Laura leads an active research group that integrates global change ecology, community ecology, and conservation science, employing quantitative tools from multiple disciplines to support evidence-based environmental management.
Calvin Tsay is a Lecturer (Assistant Professor) in the Computational Optimisation Group at Imperial College London, holding the BASF/RAEng Senior Research Fellowship in Scale-Bridging Modelling. Education: PhD Chemical Engineering (UT Austin 2020), BS/BA (Rice University 2015). Research develops optimization methods bridging machine learning and process systems engineering. Specializes in mixed-integer programming for neural networks and Bayesian optimization for energy applications. Awards: President's Medal for Early Career Researcher RAEng Senior Research Fellowship CACE Best Paper (2023) COIN-OR Cup (2022) Leads research on AI-driven chemical process optimization. Supervises PhD students in ML optimization and process control. Collaborates with BASF on industrial applications.
Prof. Tahar Kechadi is a Principal Investigator at the Insight Centre for Data Analytics , specializing in Machine Learning & Statistics and Optimisation & Decision Analytics . His research spans interdisciplinary domains including agriculture, healthcare, and cybersecurity. University : Insight Centre for Data Analytics Role : Principal Investigator Ranks : Professor His current research focuses on applying machine learning to smart agriculture (e.g., crop yield prediction, data engineering), blockchain technologies (e.g., energy trading, e-voting), and privacy-preserving systems in healthcare and cybersecurity. Publications highlight advancements in multi-modal analysis , deep learning architectures , and game-theoretic clustering . Recent work explores data contamination in LLMs , privacy-aware blockchain systems , and distributed reputation management . His team develops tools for agro-climate modeling , medical diagnostics , and cloud forensic readiness . Contact: tahar.kechadi@insight-centre.org
Dr. Catherine Lou is an Associate Professor in supply chain and logistics at Victoria University Business School, part of the College of Arts, Business, Law, Education & IT. She serves as Discipline Leader for Transformative Research in Policy, Economy and Business at the Institute for Sustainable Industries & Liveable Cities, and is the Global Head of the WiLAT Capacity Building Centre. Her academic journey began at Victoria University where she completed her PhD in Supply Chain Optimisation in 2015, after which she joined as a lecturer. Dr. Lou's educational background includes: PhD in Supply Chain Optimisation, Victoria University (2015) Graduate Certificate in Tertiary Education, Victoria University (2018) MSc, BUAA, China (2010) Dr. Lou's research spans multiple interdisciplinary areas with a strong emphasis on quantitative approaches including optimisation modelling, statistics, and advanced data analysis. Her primary research areas include Supply Chain Management (focusing on sustainability, operations management, and risk management), Green Tourism and Visitor Economy, Leadership and Diversity initiatives, International Education, and Business Information Systems. Her work consistently integrates quantitative methods to address complex challenges in sustainable development and supply chain optimization, with particular attention to practical applications that benefit industry and society. Dr. Lou's publication portfolio demonstrates a clear trajectory toward increasingly interdisciplinary research that bridges supply chain management with sustainability, social justice, and wellbeing. Her recent work shows a growing emphasis on regional disparities in green manufacturing, social procurement systems, and the intersection of international student experiences with mental health. The thematic evolution of her research reflects a deepening commitment to addressing complex societal challenges through supply chain innovation, with publications appearing in D1 (top 5%) and Q1 journals across multiple disciplines. Dr. Lou has received numerous prestigious awards recognizing her contributions to academia and professional practice: International Student of the Year - Postgraduate by Victoria State (2013) VU Outstanding Student Alumni Award (2015) Young Professional of the Year award by the Chartered Institute of Logistics and Transport in Australia (2018) Excellence in Women's Leadership (Supply Chain Education) Award at the IEOM conference (2024) As an active researcher and mentor, Dr. Lou has secured over $$1.3$$ million in competitive research funding from diverse sources including international foundations, national and state government agencies, philanthropic organizations, and industry partners. She currently supervises multiple PhD students across various topics including AI adoption in SMEs, ESG activities, environmental sustainability in sports, and insurtech innovation. Her leadership extends to significant roles in professional organizations, particularly as Australia Chairperson and Global Vice Chairperson for Women in Logistics and Transport (WiLAT), where she champions initiatives that foster diversity and inclusion worldwide. Dr. Lou plays a pivotal role in the WiLAT Capacity Building Centre, leading global initiatives that develop leadership capabilities and promote gender diversity in the logistics and transport sector. Her work bridges academic research with practical industry applications, creating meaningful impact across multiple sectors including tourism, infrastructure, and international education, with alignment to UN Sustainable Development Goals including Partnerships for the Goals, Good Health and Well Being, Industry Innovation and Infrastructure, and Sustainable Cities and Communities.
Karl Kunisch is University Professor at the Department of Mathematics and Scientific Computing, University of Graz , and simultaneously Scientific Director of the Radon Institute (RICAM) of the Austrian Academy of Sciences in Linz. A SIAM Fellow and recipient of the 2021 W.T. and Idalia Reid Prize, he leads the ERC Advanced Grant OCLOC and heads the research group “Optimization and Optimal Control”. Education: Dipl.-Ing. (1975), Dr. techn. (1978) and Habilitation (1980), Graz University of Technology Research Interests: His work centres on optimization and optimal control of partial differential equations , nonsmooth optimisation in function spaces , inverse problems and mathematical imaging , together with advanced numerical analysis . Current emphases are life-science applications , closed-loop control and machine-learning based feedback design. Publications Profile: With over 400 papers and two monographs, his recent output is dominated by high-impact studies on infinite-horizon optimal control , feedback stabilisation of semilinear parabolic and Navier–Stokes systems, risk-averse and data-driven control , and sparse control strategies . A clear trend is the fusion of rigorous PDE analysis with cutting-edge machine-learning techniques. Scientific Awards & Distinctions: W.T. and Idalia Reid Prize (2021) SIAM Fellow (2017) ERC Advanced Grant Horizon 2020 (2015) Alwin Walther Medaille (2008) ICM Invited Lecture, Hyderabad (2010) SIAM Outstanding Paper Prize (2006) Christian Doppler Laboratory Fellowship (1992) Fellowship of the Japanese Society for the Promotion of Science (1990) Max Kade Scholarship (1982/83) Fulbright Travel Grants (1979/80, 1985) Theodor-Körner-Fonds Research Award (1979) Pro Scienta Scholarship (1974–1977) Grants & Leadership: Principal Investigator, ERC Advanced Grant “ OCLOC – From Open to Closed Loop Control ” Scientific Director, Radon Institute (RICAM), Austrian Academy of Sciences Head of Research Group “Optimization and Optimal Control”, RICAM Co-Speaker, International Research Training Group IGDK Former member/consultant: MATHEON Scientific Advisory Board, Weierstrass Institute Scientific Advisory Board, Christian Doppler Forschungsgesellschaft Senate, DFG and INRIA evaluation boards Laboratory & Team: Prof. Kunisch currently leads the “Optimization and Optimal Control” group at RICAM, comprising post-docs, doctoral researchers and international visitors, focusing on interdisciplinary projects at the interface of PDE control, numerical optimisation and life sciences.
William Smith is a Professor in the Department of Computer Science at the University of York, where he leads the Vision, Graphics and Learning (VGL) research group. He holds a BSc and PhD in Computer Science from the University of York, completed in 2002 and 2007 respectively. He has served in key roles including Director of Admissions, PGT AI Programme Lead, and Research Group Lead. He is an Associate Editor for the journal Pattern Recognition and was a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow from 2019 to 2020. Education: BSc in Computer Science, University of York, 2002 PhD in Computer Vision, University of York, 2007 His research lies at the intersection of computer vision, computer graphics, and machine learning. He focuses on physics-based and 3D vision, shape and appearance modeling, and the application of statistical and machine learning methods. Key application areas include face and body analysis, surveying and mapping, object capture, and inverse rendering. His work leverages advanced mathematical tools such as convex and nonlinear optimization, manifold learning, computational geometry, and low-level vision techniques. His recent publications, spanning top venues like ECCV, NeurIPS, WACV, and IEEE T-PAMI, reflect a strong trend in neural scene representation, inverse rendering, and illumination modeling. Themes include relighting outdoor scenes, self-supervised pose estimation, spherical neural fields, and orientation-aware deep learning for omnidirectional inputs. These works demonstrate a consistent focus on integrating physical models with deep learning for robust and interpretable vision systems. Scientific Awards: Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow (2019–2020) William Smith supervises a team of nine PhD students and has advised several alumni who have gone on to research and industry roles. He has served in significant service roles including Area Chair for ICCV, CVPR, and 3DV, Programme Chair for BMVC 2020, General Chair for CVMP 2019, and reviewer for major conferences since 2008. He is actively involved in the academic community and continues to contribute to cutting-edge research through his leadership in the VGL group at York. The Vision, Graphics and Learning (VGL) group, which he leads, conducts research in computer vision, graphics, and machine learning, with projects such as Branching Out (historic tree mapping), Senior Research Fellowship (model-based vision meets deep learning), Google Daydream (VR/AR head modeling), and LEAPP (polar archival photography). The group fosters a collaborative environment with both current and former members contributing to impactful research.
Dr. Sebastiaan Breedveld is an Associate Professor in the Department of Radiation Oncology at Erasmus University Medical Center in Rotterdam, The Netherlands. His research focuses on improving radiation therapy through applied mathematics, multi-criteria optimization, and automated treatment planning. He developed the clinically implemented Erasmus-iCycle algorithm and leads projects like TROTS (Radiotherapy Optimisation Test Set). Education includes a PhD in Medical Physics (cum laude) from Erasmus University Rotterdam and an MSc in Applied Mathematics from Delft University of Technology. Research interests center on developing computational methods for real-time, personalized radiotherapy. Key areas include: Large-scale optimization for treatment planning Deep learning for instantaneous dose prediction Multi-criteria decision analysis for balancing treatment trade-offs Proton therapy and VMAT techniques High-performance computing applications Recent publications demonstrate strong focus on AI-driven automation in brachytherapy, IMPT robustness, and FLASH proton therapy. Work consistently addresses clinical translation of mathematical optimization. Significant scientific awards include: NWO Vidi Grant (2021) for INSTORAD project NWO Veni Grant (2016) MCDM Doctoral Dissertation Award (2015) Erasmus MC Efficiency Grant (2015) PhD with honors (2013) Leads research group supervising 2 post-docs, 11 PhD students, and numerous master/bachelor projects. Major grants support work in proton therapy optimization and automated planning systems. Developed TROTS dataset for benchmarking radiotherapy optimization algorithms and collaborates internationally through projects like BiCycle for automated brachytherapy planning.
Dr Euan W McGookin is a Senior Lecturer in Autonomous Systems & Connectivity at the University of Glasgow, based in the Aerospace Sciences division of the James Watt Building South. He coordinates Glasgow-delivered aerospace degree programmes in Singapore and serves on the IFAC Technical Committee on Marine Systems, underlining his sustained engagement with both local and international academic activities. Education: 1st Class Honours Master of Engineering in Avionics, University of Glasgow PhD in Optimisation of Sliding Mode Controllers for Marine Applications, University of Glasgow (1997) Research Interests Dr McGookin’s core expertise lies in the design, simulation, control and physical realisation of autonomous robotic systems. His work spans Autonomous Underwater Vehicles (AUVs) , Unmanned Aerial Vehicles (UAVs) , Planetary & Terrestrial Rovers , and Biomimetic Robotics . He is particularly recognised for applying biologically inspired principles to robotic locomotion, navigation and control. Complementary themes include advanced control methodologies—Sliding Mode Control, H-infinity, Inverse Model Control—optimisation heuristics, guidance & navigation, fault detection & isolation (FDI), and system health monitoring for both terrestrial and space applications. Publication Trends Across 80 publications from 1995 to 2025, his work has evolved from early genetic-algorithm-based controller optimisation for marine vessels to cutting-edge multi-rover mission planning and health monitoring for planetary exploration. Recent outputs (2022–2025) concentrate on micro-rover coordination, friction modelling for planetary soils, reinforcement-learning-driven sensor fusion, and robust health-monitoring architectures, reflecting a strategic pivot toward space robotics while retaining strong roots in control theory and autonomous systems. Scientific Awards & Fellowships Member, IFAC Technical Committee on Marine Systems Grant & Advising Narrative While specific grant values are not disclosed, his continuous funding stream is evidenced by sustained publication output, international conference leadership, and ongoing supervision of postgraduate projects. Dr McGookin advises a steady cohort of PhD and MSc students whose theses align with his research themes—ranging from rover fault diagnosis to biomimetic AUV coordination—thereby fostering the next generation of control and robotics engineers. Laboratory & Team Dr McGookin heads research activities within the James Watt Building South, leveraging interdisciplinary laboratories that integrate simulation suites, rapid-prototyping facilities for AUV and UAV subsystems, and dedicated test rigs for biomimetic propulsion and rover mobility studies. Collaborative networks extend across the University of Glasgow’s Aerospace Engineering group, Singapore Institute of Technology partners, and international consortia such as ESA and IFAC.
Derk H. Bos is a postdoctoral researcher in the Department of the Built Environment at Eindhoven University of Technology (TU/e), specialising in 3D concrete printing and quality control engineering. He completed his MSc cum laude in 2019 and defended his PhD dissertation in October 2024, both at TU/e. Education MSc in Structural Engineering and Design, Eindhoven University of Technology (cum laude, 2019) PhD in Concrete Structures, Eindhoven University of Technology (2024) Research Interests Derk’s research integrates additive manufacturing, materials science, and process engineering to advance 3D concrete printing. He develops parametric mortar design methodologies, rheological models for mixing and pumping, and novel in-line quality-control tests. His work addresses the challenges of ensuring structural integrity and process reliability in large-scale digital construction. Key focus areas include: – Rheology and yield stress optimisation of printable cementitious composites – Real-time monitoring techniques such as dye-tracer and gravity-induced compression tests – Multi-scale quality assessment from material to structural level Scientific Awards Digital Concrete Conference – Best Paper Award (2022) Service & Collaboration Since 2019, Bos chairs the PhD Network Civil Engineering, fostering early-career researcher engagement. He co-organised the Second RILEM International Conference on Concrete and Digital Fabrication in 2020, strengthening international collaboration in digital construction. Labs & Teams He is embedded within the Concrete Structures chair of the Unit Structural Engineering and Design, working closely with the TU/e 3D Concrete Printing research group led by Prof. Theo Salet and Dr. Rob Wolfs.
Professor Andrea Da Ronch is a faculty member at the University of Southampton's School of Engineering, holding the academic rank of Professor and serving as Director of the Boeing Flight Simulators Laboratory. She maintains active research leadership and teaching responsibilities within the Aeronautics and Astronautics program, with a demonstrated commitment to high-quality education evidenced by two nominations for "Outstanding Lecturer" and Fellowship of the Higher Education Academy. Her research spans Air Vehicle Design (Derivative and Unconventional), Computational Aerodynamics, Aeroelasticity, Weather Modeling, and Multidisciplinary Optimisation. She develops scalable computational methods at the intersection of aerospace engineering, mathematics, and computer science to support next-generation sustainable air transportation, targeting aviation decarbonization, Advanced Air Mobility (AAM) connectivity, and accelerated industry product development through physics-informed machine learning approaches. Recent publications (2024-2025) reveal a pronounced trend toward geometric deep learning and graph neural networks for transonic flow prediction, morphing wing analysis, and propeller-wing interaction. These works emphasize computational efficiency in unsteady aerodynamics while addressing critical challenges in aircraft design validation and dynamic maneuver simulation. Scientific awards include: Rotary International Award (2012) Aerospace Speakers Travel Grant (2010) T.I.M.E. Double Degree Award (2008) AIAA Atmospheric Flight Mechanics Best Paper Award (2019) Fellow of the Higher Education Academy (2014) Senior Member of AIAA (2017) She currently supervises three PhD students (Declan Salazar Clifford, Gabriele Immordino, Giuseppe Morichetti) with funding secured from EPSRC, European Union H2020, US Air Force Office of Scientific Research, and Royal Academy of Engineering projects including IMPACT anti-ice coatings optimization, Sparsified Reduced Modeling, and transonic buffet analysis. As Director of the Boeing Flight Simulators Laboratory, she provides hands-on curriculum enhancement for engineering students. Her active membership in the Aerodynamics and Flight Mechanics Research Group facilitates collaboration on sustainable aviation initiatives while supporting her leadership in major projects like HOMER- with Professor Bharath Ganapathisubramani.
Dr Dobrila Petrovic is an Associate Professor of Operational Research and Business Analytics in the Department of Strategy, Analytics and Operations at Nottingham Business School, Nottingham Trent University. With over 20 years of academic experience in the UK, she serves as Research Coordinator for the department and teaches Management in Organisations, Research Methods, and Data Driven Decision Making on postgraduate programs. Operational Research Business Analytics Fuzzy Logic Applications Supply Chain Optimization Multi-Objective Optimisation Her recent research focuses on fuzzy scenario-based optimization for supply networks, type-2 fuzzy cognitive maps, and multi-attribute decision-making for autonomous vehicle safety. She has supervised 17 PhD students and 12 postdoctoral researchers throughout her career. EPSRC Reviewer ESRC Reviewer Leverhulme Trust Reviewer European Science Foundation Reviewer International Journal of Systems Science Associate Editor
Belen Martin-Barragan is a Reader in Management Science at the University of Edinburgh Business School , with a focus on the Department of Management Science and Business Economics . Her research bridges Machine Learning and Mathematical Programming , emphasizing Explainable Artificial Intelligence (XAI) and applications in Operational Research , including classification , clustering , inventory management , and routing optimization . Her work includes developing interpretable machine learning models for credit scoring, healthcare data, and sustainable logistics. She has led EPSRC-funded projects on Optimisation Models for Interpretable Analytics and contributed to journals like European Journal of Operational Research , Risk Analysis , and Computers and Operations Research . Key methodologies involve Mixed-Integer Linear Programming , Support Vector Machines , and stochastic dynamic programming . Her research fingerprint spans Machine Learning (97%), Mathematical Programming (75%), and Optimization Algorithms (20%). She has been a Research Champion and Deputy Director of Research (Ethics and Integrity) at the Business School, with affiliations to the Credit Research Centre and Edinburgh Strategic Resilience Initiative .
Dr. Ian Morris is a Reader in Mathematics at Queen Mary University of London and Deputy Director of Postgraduate Research Studies. Previously, he held positions at the University of Surrey (2012–2020) and postdoctoral roles at institutions including the University of Rome Tor Vergata and the University of Warwick. His research focuses on ergodic theory, with applications to fractal geometry, matrix analysis, and dynamical systems. Education: PhD in Mathematics, University of Manchester (2006), supervised by Dr. Charles Walkden. Bachelor’s degree in Mathematics, University of Warwick. Research Interests: Morris specializes in ergodic theory and its applications to fractal geometry, joint spectral characteristics of matrices, and dynamical systems. His work includes studies on self-affine fractals, Lyapunov exponents, and thermodynamic formalism for linear cocycles. He has explored topics such as marginal instability in switched systems and the interplay between self-affine measures and fractal dimensions. Grants: Leverhulme Trust Research Project Grant (2024–2028): "An ergodic optimisation approach to stability of linear switched systems" (£189,097). Leverhulme Trust Grant (2017–2022): "Lower bounds for Lyapunov exponents" (£267,776). Advising and Collaborations: Morris supervised PhD student Jonah Varney (2018–2022) and postdoctoral researchers Natalia Jurga and Argyrios Christodoulou. His collaborations include work with Balázs Bárány, Antti Käenmäki, and Çağrı Sert on topics like self-affine measures and matrix equilibrium states. Labs/Teams: Morris is affiliated with the Centre for Complex Systems at Queen Mary University of London, focusing on interdisciplinary research in dynamical systems and fractal geometry.
Chuan He is an Assistant Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research bridges continuous optimization and machine learning, focusing on algorithmic efficiency and theoretical foundations. Education: Ph.D. in Industrial and Systems Engineering, University of Minnesota, USA (2019–2023) B.S. in School of Mathematical Sciences, Xiamen University, China (2015–2019) His research interests include deep learning, decentralized and large-scale optimization, high-order methods, and applications in healthcare, scientific computing, and engineering. He develops algorithms with strong theoretical guarantees, particularly in nonconvex optimization settings. His work emphasizes improving the speed, reliability, and scalability of machine learning training processes. His recent publications focus on Newton-CG based methods, augmented Lagrangian techniques, and federated learning under constraints. These contributions span top journals in operations research, optimization, and machine learning, reflecting a strong trend toward integrating second-order optimization with practical machine learning challenges. Scientific Awards: No awards explicitly mentioned in the text. Chuan He advises and collaborates within the WASP Mathematics research environment, particularly in the 'Optimisation for machine learning' group. He previously held a postdoctoral position at the University of Minnesota under Professor Ju Sun. His research is supported through institutional affiliations with WASP and Linköping University. He actively contributes to the academic community through conference presentations at INFORMS, SIAM, and NeurIPS workshops. He is involved in the 'Optimisation for machine learning' research group at MAI, which aims to develop more efficient and theoretically sound algorithms for machine learning. The group focuses on replacing heuristic methods with principled approaches, reducing computational costs in training models.
