جست‌وجوی استاد و پژوهشگر

• Robotics
• Computer Vision
• Simultaneous Localization and Mapping (SLAM)
• +۶ مورد دیگر

Niko Sünderhauf is a Professor in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT), specializing in robotics, computer vision, and autonomous systems. His research spans visual place recognition, simultaneous localization and mapping (SLAM), neural radiance fields, and out-of-distribution detection for robotics applications. His research interests focus on enabling robots to understand and navigate complex environments through advanced computer vision techniques. He has made significant contributions to visual place recognition, particularly for handling viewpoint and environmental changes. His work on switchable constraints has improved robustness in SLAM systems, while his more recent research explores neural radiance fields, Gaussian splatting, and integrating large language models with robotic perception. Prof. Sünderhauf's publication record demonstrates consistent research productivity with 115 publications spanning from 2005 to 2025. His recent work shows a clear progression from traditional SLAM techniques toward more advanced neural scene representations and integration with large language models. His research has been published in top venues including IEEE ICRA, CVPR, IROS, and the International Journal of Robotics Research. Among his scientific contributions are novel approaches to visual place recognition that work across seasons and viewpoints, robust SLAM techniques that handle outliers and non-linearities, and recent innovations in neural scene representations for robotics. His work on switchable constraints has been particularly influential in the robotics community. Prof. Sünderhauf actively supervises PhD students including Dimity Miller, Krishan Rana, and Jad Abou-Chakra, who frequently appear as first authors on publications. His collaborative network includes researchers from QUT and international institutions, demonstrating strong research leadership in the robotics community.

Ivan Laptevمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Human Action Recognition
• +۳ مورد دیگر

Ivan Laptev is a Professor at MBZUAI on leave from INRIA Paris, and serves as Head of Research at VisionLabs. He holds a PhD from the Royal Institute of Technology (2004) and a Habilitation from École Normale Supérieure (2013). His research focuses on computer vision, robotics, and action recognition, with over 150 publications in top-tier venues. He has served as Program Chair for ICCV'23 and CVPR'18, and organized events like the INRIA summer schools and Machines Can See summits. Key contributions include work on human action recognition, 3D reconstruction, and multimodal learning. His recent research emphasizes robotics manipulation, sim-to-real transfer, and large-scale video understanding. Awards include the ERC Starting Grant (2012) and the Helmholtz Prize (2017). Education: PhD (KTH, 2004), Habilitation (ENS, 2013) Grants: ERC Starting Grant (2012) Labs: Leads new lab at MBZUAI; Head of Research at VisionLabs

Camilla Graneمشاهده پروفایل

مدرس ارشد

• Human-Machine Interaction
• Engineering Psychology
• Usability
• +۷ مورد دیگر

Camilla Grane serves as a Senior Lecturer in Psychology at Luleå University of Technology, where she is affiliated with the Department of Health, Education and Technology and specifically works within the Division of Health, Medicine and Rehabilitation. In addition to her teaching responsibilities, she holds the position of deputy head of education for the Department of Health, Medicine, and Rehabilitation (HMR). Dr. Grane holds a Master of Science degree in Ergonomic Design and Production and earned her PhD in Engineering Psychology. Her educational background has prepared her for interdisciplinary work at the intersection of psychology, engineering, and human factors, focusing on the practical application of psychological principles to real-world technological challenges. Her research centers on human-machine interaction and psychosocial factors, with notable expertise in attention and distraction when using multimodal interfaces in vehicles. She has expanded her research scope to include workers' digital tools and wearable sensors in mining and process industries, examining critical aspects such as usability, efficiency, safety, acceptance, and privacy. Dr. Grane is particularly interested in how ongoing digital development creates new relationships between humans and technology, with emerging interests in generative AI and human-robot interaction as research areas. Her publication record demonstrates a strong focus on automotive human factors, with numerous studies on gear shifter usability, human-automation interaction, and safety in industrial contexts. Her work spans multiple disciplines including psychology, engineering, ergonomics, and industrial safety, reflecting her interdisciplinary approach to understanding human-technology interactions across different environments. Dr. Grane teaches across multiple academic programs including the Psychology Bachelor's program and the Master of Science program in Industrial Design Engineering. Her extensive teaching portfolio covers Introduction to Psychology, Engineering Psychology, Social Psychology: Psychological Perspectives, Work and Motivation Psychology, Human-Machine Interaction, Usability, and Ergonomics and Cognition, among other specialized courses. She has led and participated in several significant research projects including 'Working environment of control room operators' (Swedish Transport Administration), 'SIMS' (attitudes towards positioning technology in mining environments), 'Quicktag' (mobile applications for inspections at Boliden), 'The Future Operator' (digital tools for future operators at LKAB and Boliden), 'MODAS' (measuring trust in highly automated vehicles), and 'Life on Board' (usability of future gear selectors in vehicles).

Kai Zengerمشاهده پروفایل

مدرس ارشد

• Control Systems Engineering
• Predictive Control
• Marine Engines
• +۹ مورد دیگر

Kai Zenger is a Senior University Lecturer and Docent at Aalto University, affiliated with the Department of Electrical Engineering and Automation and the Autonomous Systems research area within the School of Electrical Engineering. He holds a doctoral degree from Helsinki University of Technology, now part of Aalto University, and has maintained a long-standing academic and research career in control systems and engine technologies. Doctoral degree, Engineering and Technology, Helsinki University of Technology (2003) Licentiate degree, Engineering and Technology, Helsinki University of Technology (1992) Master's degree, Engineering and Technology, Helsinki University of Technology (1986) His research focuses on advanced control systems for combustion engines, particularly marine and diesel engines, with an emphasis on emission reduction and efficiency. Key areas include Reactivity Controlled Compression Ignition (RCCI), predictive control modeling, machine learning applications in engine systems, and neural network-based estimation of in-cylinder pressure. His work contributes to sustainable propulsion technologies and aligns with UN Sustainable Development Goals related to clean energy and industry innovation. The recent publications show a strong trend in integrating machine learning with traditional control theory for real-time monitoring and optimization of marine and diesel engines. Topics such as low-temperature combustion, emissions prediction, and phase estimation in RCCI engines dominate his recent scholarly output, reflecting a focus on environmentally sustainable propulsion systems. Kai Zenger has led several significant research projects, including the Clean Propulsion Technologies (CPT Zenger) initiative funded by Business Finland, demonstrating leadership in securing research grants and driving innovation. He has also been involved in organizing academic events and serving on scientific committees, indicating active engagement in the academic community. Principal Investigator, Clean Propulsion Technologies (CPT Zenger), 2021–2023 Principal Investigator, CleanShip: Sustainable Business through Clean Ship Technologies, 2019–2020 Organizer, EUROSIM Congress on Modelling and Simulation & SIMS Conference, 2016 He has supervised multiple theses and mentored early-career researchers such as Nguyen Khac H., Modabberian A., and Storm X. His lab and research team focus on simulation environments for controller design, virtual sensors, and intelligent control systems for next-generation combustion engines. Collaborations span across Finland and internationally, with strong ties to industry and government-funded innovation programs.

Daniel Göllerمشاهده پروفایل

دانشیار

• Law and Economics
• Contract Theory
• Bargaining Theory
• +۲ مورد دیگر

Daniel Göller is an Associate Professor at the Department of Economics and Finance within the School of Business and Law at the University of Agder, Norway. His academic journey includes a Ph.D. in Economics from the University of Bonn, Germany, where he also worked as a Postdoctoral researcher before joining the University of Agder. Education: Ph.D. in Economics, University of Bonn, Germany Göller's research primarily focuses on the intersection of law and economics, with particular emphasis on contract theory and bargaining theory. His work explores how legal frameworks influence economic behavior and decision-making, especially in contexts involving contractual relationships and negotiations. He has made significant contributions to understanding how parties structure contracts to mitigate holdup problems, how dual-SIM phone usage affects mobile telephony markets in emerging economies, and how mathematical methods can be integrated with microeconomic theory. His research spans theoretical contract design, empirical analysis of mobile markets, and methodological approaches to economics education. His publications reveal a consistent trend toward interdisciplinary research that bridges economics, law, and mathematics. Göller frequently publishes in top-tier journals such as the Journal of Economic Theory, American Law and Economics Review, and Journal of Institutional and Theoretical Economics. His recent work demonstrates increasing attention to practical applications of theoretical models in real-world economic contexts. Professional Service: Member of the study council at the School of Business and Law Former member of the faculty board Member of the BAKØKAD panel for bachelor program evaluation Organizer of the research seminar series at the School of Business and Law Referee for Review of Economics Studies, Journal of the European Economic Organization, and Journal of Law, Economics, & Organization Göller has applied his expertise through consultancy work for Kristiansand Municipality and as an expert advisor for the EEA mechanism project 'Regional convergence and social inclusion under technological change.' He also serves as founder and board member of Victoria Hotel Krageræ AS, demonstrating engagement beyond academia. Research Group: Member of CNI (Competition, Networks and Institutions) research group

Ruth Simsمشاهده پروفایل

مدرس ارشد

• Ergonomics
• Human Factors
• Behaviour Change
• +۳ مورد دیگر

Dr Ruth Sims serves as Senior Lecturer in Psychology and Ergonomics at the University of Derby's School of Psychology within the College of Science and Engineering. Her work integrates ergonomics, human factors, and inclusive design with particular focus on ageing populations, disability inclusion, and behaviour change interventions across domestic, transport, and workplace environments. Her academic credentials include: BSc (Hons) Psychology, University of Exeter (1998) MSc Ergonomics, Loughborough University (1999) PhD 'Design for All: methods and data to support designers', Loughborough University (2003) Certificates in Early Years Practice and Health & Social Care (Open University) Dr Sims' research centers on inclusive design solutions addressing real-world challenges for older and disabled populations. Her pioneering work on kitchen ergonomics examines historical usage patterns and contemporary adaptations, while her transport accessibility research spans public transit systems, road safety in emerging economies, and the development of the HADRIAN digital tool for virtual fitting trials. Recent projects focus on behaviour change interventions for musculoskeletal disorders and train driver safety, demonstrating practical applications of ergonomics in high-risk environments. Analysis of her publication history reveals consistent thematic evolution: from foundational anthropometric studies (2010-2012) toward integrated behaviour-change frameworks (2016-2023), with notable expansion into pandemic-responsive healthcare design (2023) and cross-species ergonomics applications (2021). Her work consistently bridges academic research and industry implementation through co-design methodologies. Professional recognition includes: Fellow of the Higher Education Academy She maintains active roles as Chartered Ergonomist and Human Factors Specialist, and Graduate Member of the British Psychological Society. As Pathway Lead for the MSc Behaviour Change (Ergonomics and Human Factors) programme, she supervises PhD candidates and delivers modules across undergraduate Psychology and postgraduate Ergonomics programmes. Her industry collaborations include the SaferBraIn project (India/Brazil road safety) and DEVICE initiative (child-focused design education), with grant funding supporting international data collection trials involving older/disabled participants. Dr Sims co-leads the Digital and Material Artistic Research Centre's inclusive design stream, advancing the HADRIAN toolkit for virtual accessibility assessment. Her global consortium work—particularly with Indian and Brazilian institutions—focuses on adapting European safety standards to emerging economies while developing culturally responsive design solutions for vulnerable road users.

Cyrus Nearyمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Reinforcement Learning
• +۵ مورد دیگر

Cyrus Neary is an incoming tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), where he will direct the Artificial Intelligence in Robotics & Engineering (AIRE) Lab. Previously, he was a postdoctoral researcher at Mila (Québec AI Institute) and l’Université de Montréal. His research bridges AI system engineering and engineering applications, focusing on compositional design, physics-informed learning, and reliable autonomous systems. He holds a Ph.D. and M.Sc. in Computational Science from The University of Texas at Austin’s Oden Institute, and a Bachelors of Applied Science in Engineering Physics from UBC. Education : Ph.D. in Computational Science (2024), University of Texas at Austin M.Sc. in Computational Science (2021), University of Texas at Austin B.A.Sc. in Engineering Physics (Honors Mathematics minor), University of British Columbia (2019) Research Focus : Developing AI systems that integrate engineering principles and physics-based knowledge for control, robotics, and autonomy. Key areas include multiagent reinforcement learning, physics-informed neural networks, and compositional system design. His work emphasizes reliability, certification, and efficient data utilization in high-stakes applications. Lab & Opportunities : The AIRE Lab at UBC seeks students (MASc/PhD) to explore AI-robotics intersections. Application deadlines are Jan 15, 2025, with priority given to early submissions (Dec 31, 2024).

Mariko Oueمشاهده پروفایل

عضو هیئت علمی

• Radar Polarimetry
• Mixed-Phase Cloud Microphysics
• Radar Simulators
• +۳ مورد دیگر

Mariko Oue is a Research Assistant Professor at Stony Brook University's School of Marine and Atmospheric Sciences (SoMAS). She holds a Ph.D. in Science from Nagoya University (2010), followed by postdoctoral research at the Hydrospheric Atmospheric Research Center (2010–2012) and Pennsylvania State University (2012–2015). Her research focuses on radar polarimetry, mixed-phase cloud microphysics, and radar simulators. She uses forward radar simulators and field observations (e.g., millimeter-wavelength radars and lidars) to study cloud dynamics and microphysics, particularly in Arctic and midlatitude systems. Key contributions include the development of the Cloud Resolving Model Radar Simulator (CR-SIM), now in version 4.0, and leadership in radar science training programs. Her work integrates radar data with Doppler spectral analysis and lidar measurements to understand ice crystal habits, precipitation formation, and cloud properties. She has organized workshops like the 2019 CR-SIM Short Course and contributed to initiatives like the ARM Radar Network. Oue’s research emphasizes bridging observational and modeling approaches to advance understanding of cloud processes and their climatic impacts. She collaborates on projects such as the Tracking Aerosol Convection interactions ExpeRiment (TRACER) and the Investigation of Microphysics and Precipitation for Atlantic coast-threatening snowstorms (IMPACTS). Publications highlight her expertise in Arctic mixed-phase clouds, secondary ice production, and radar-based vertical velocity retrievals. She has led training programs for early-career scientists, including the 2023 Summer School on Cloud and Precipitation Observations at Stony Brook. Her lab, the Stony Brook Radar Observatory, operates cutting-edge radar facilities for atmospheric research.

Giulio Venturaمشاهده پروفایل

استاد

• Computational Mechanics
• Extended Finite Element Method
• Structural Monitoring
• +۱۱ مورد دیگر

Giulio Ventura is a Full Professor in the Department of Structural, Geotechnical and Building Engineering (DISEG) at Politecnico di Torino, where he conducts research and teaches in computational mechanics, structural monitoring, and artificial intelligence applications in civil engineering. He is a member of the Interdepartmental Center SISCON, focusing on the safety of infrastructures and constructions. Research Interests: His primary research areas include the Extended Finite Element Method (XFEM), where he developed the Equivalent Polynomials Quadrature for simplified integration of discontinuities; structural health monitoring through active monitoring systems that process sensor data in real time; and AI-driven analysis of structural data. He also leads research on railway sand mitigation and structural assessment of concrete and reinforced concrete structures. Publication Trends: His recent co-authored publications (2023–2024) are primarily in fusion energy engineering, particularly on the Divertor Tokamak Test (DTT) facility, covering thermal-hydraulic analysis, beam line components, vacuum vessel design, and neutral beam injector systems. These indicate a strong interdisciplinary collaboration in nuclear and mechanical engineering, though his core focus remains in civil and computational mechanics. Scientific Awards: No specific awards listed in the provided text. Advising and Grants: He supervises PhD students in Civil and Environmental Engineering, including Vida Bagherpour Aliabadi, Sasan Farhadi, and Sebastiano Fichera. He leads multiple funded research projects such as ERACLE (2024–2025), XFAST-SIMS (2019–2022), and several commercial and public-sector contracts on structural monitoring, bridge inspection, and sand mitigation. He is the Scientific Manager for ongoing projects related to the Mont Blanc Tunnel, railway viaducts, and seismic vulnerability assessments. Labs and Teams: He leads an active research team involving PhD students, colleagues, and private sector staff, collaborating on structural monitoring, computational modeling, and field testing for infrastructure safety. He chairs the X-DMS conference series and serves on the editorial board of Finite Elements in Analysis and Design .

Rohan P. Singhمشاهده پروفایل

پژوهشگر

• Robotics
• Reinforcement Learning
• Humanoid Locomotion
• +۴ مورد دیگر

Rohan P. Singh is a Postdoctoral Researcher at the CNRS-AIST Joint Robotics Lab (JRL) in Tsukuba, Japan, since April 2024. Previously, he worked as a full-time Robotics Engineer (2017-2019) and Research Assistant (2019-2024) at the same lab, mentored by Fumio Kanehiro . His research bridges robotics, machine learning, and computer vision , with a focus on practical applications for humanoid robots. Education: Ph.D. in Reinforcement Learning for Humanoid Locomotion (2024), University of Tsukuba MS in 6-DoF Object Pose Estimation (2021), University of Tsukuba Research Interests include: Deep Reinforcement Learning for humanoid movement Sim-to-real transfer methods 6-DoF object pose estimation with minimal human effort Multi-contact locomotion planning Open-source robotics tools development Notable Scientific Awards : JST SPRING Fellowship (2021-2024) Excellent Master's Thesis Award (2021) Projects include: LearningHumanoidWalking: Training humanoid robots with PPO reinforcement learning RapidPoseLabels: Tool for automated 6-DoF object labeling ObjectKeypointTrainer: CNN training framework for pose estimation ANA Avatar XPRIZE team member

• Artificial Intelligence
• Robotics
• Sensitivity Informatics
• +۷ مورد دیگر

Professor Shohei Kato holds a primary appointment at Nagoya Institute of Technology's Graduate School of Engineering (Omohi College) and serves as a Visiting Researcher at the National Center for Geriatrics and Gerontology. His career includes prior roles as Associate Professor at Nagoya Institute of Technology (2002–2015), Lecturer at Toyota National College of Technology (1999–2002), and Research Assistant (1998–1999). He earned his Doctor of Engineering from Nagoya Institute of Technology, where he also completed undergraduate studies in Electrical and Computer Engineering. Kato's research integrates artificial intelligence, robotics, and affective computing, with applications spanning healthcare, optimization, and human-machine interaction. Key themes include kansei (sensitivity) robotics, computational intelligence, dementia screening via speech/fNIRS analysis, and multi-agent systems. His work emphasizes adaptive algorithms, emotion modeling, and real-world AI deployment. Recent publications focus on sim-to-real transfer learning, reinforcement learning for multi-agent cooperation, and non-invasive dementia diagnosis. A consistent trend involves applying particle swarm optimization and deep learning to industrial and biomedical challenges, such as shield tunneling automation and EEG-based emotion recognition. Awards & Honors: Minister of Education Commendation for Science and Technology (2015) IEEE Outstanding Paper Awards (GCCE 2017, 2018) 日本感性工学会論文賞 (2012) Multiple Workshop on Informatics (WiNF) prizes for student collaboration (2011–2020) Kato leads externally funded projects on dementia screening, affective robotics, and optimization algorithms. He directs the Kato Lab (katolab.nitech.ac.jp), collaborating with entities like Shimizu Corporation on AI-driven construction planning. His team explores human-robot symbiosis, BCI interfaces for Duchenne muscular dystrophy patients, and therapy robot design.

• Machine Learning
• Biologically Inspired Computing
• Hyper-heuristics
• +۴ مورد دیگر

Dr. Kevin Sim is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University, with research interests spanning machine learning, biologically inspired computing, hyper-heuristics, and combinatorial optimization. His work focuses on developing advanced algorithm selection techniques and optimization methodologies applicable to real-world domains such as logistics, forestry, and infrastructure engineering. PhD in Hyper-heuristics for Optimization (Edinburgh Napier University, 2014) MSc in Advanced Software Engineering (Edinburgh Napier University, 2010) BSc in Software Technology (Edinburgh Napier University, 2009) His research explores: Feature-free algorithm selection models using recurrent neural networks Evolutionary approaches to instance-space layout optimization Hybrid feature construction methods for environmental risk prediction Ensemble hyper-heuristics for scheduling and packing problems Integration of large language models with evolutionary algorithms Scientific awards include: Bronze Award, International Humies Competition (2018) for wind damage prediction research Recent publications demonstrate expertise in: Neural algorithm selection LLM-evolved heuristics Wind damage modeling Instance-space visualization Feature engineering Real-time optimization Kevin supervises research students in algorithm design and optimization, and has secured funding from the Data Lab (£19,599) and EPSRC (£238,068) for projects related to infrastructure optimization and lifelong learning systems.

Jack Collinsمشاهده پروفایل

پژوهشگر

• Robot Simulation
• Sim-to-Real Methods
• Robotics Foundation Models
• +۷ مورد دیگر

Jack Collins is a Researcher and AI Research Scientist at Collaborative Robotics, focusing on robotics foundation models for collaborative robots. Previously, he served as a Postdoctoral Researcher at the University of Oxford's Applied Artificial Intelligence Lab and completed his PhD at Queensland University of Technology (QUT) in collaboration with CSIRO. His research emphasizes sim-to-real methods, robot simulation, and bridging the 'Reality Gap.' He holds a Bachelor of Engineering (Mechatronics) with First Class Honours from QUT. Education: PhD in Robotics (2018-2022), QUT/CSIRO BEng (Mechatronics) (Honours) (2014-2017), QUT Research Interests: Robot simulation and sim-to-real transfer Bimanual/mobile robotic manipulation Generative models and world modeling Task planning for long-horizon tasks Evolutionary robotics and component design Publications: Over 15 peer-reviewed articles focusing on simulation realism, robotic manipulation benchmarks, and generative modeling techniques. Key work includes the COMBO-Grasp framework and TWIST distillation method for sim-to-real transfer. Lab Affiliations: QUT Centre for Robotics (QCR), Oxford Robotics Institute's Applied AI Lab.

Anders Andersenمشاهده پروفایل

استاد

• Adaptive System Architectures
• Multimedia Applications
• Mobility and Context-Sensitive Systems
• +۲ مورد دیگر

Anders Andersen is a Professor and Head of the Department of Computer Science at UiT The Arctic University of Norway. His research focuses on adaptive systems, multimedia applications, context-aware systems, security, and AI/ML. He leads projects like NUDGE (smart nudging for sustainable behavior) and NFC City (NFC-based services). He has extensive teaching experience, supervising numerous master's and PhD students. Affiliations: UiT Department of Computer Science, Open Distributed Systems (ODS) research group Education: Extensive academic background in computer science and related fields (details not explicitly listed). Research Interests: His work spans adaptive system architectures, multimedia systems, mobility/context-aware computing, privacy-preserving techniques, and AI integration. Key projects include secure distributed health data analysis (SNOOP), IoT applications for green transportation, and NFC infrastructure development. Articles Overview: Recent work emphasizes smart nudging methodologies, distributed deep learning optimization, and privacy-preserving frameworks. His publications address cross-platform multimedia systems, context-aware recommendation systems, and security in mobile and cloud environments. Grants/Projects: NUDGE, NFC City, SNOOP, PIA, and others focusing on IoT, health informatics, and adaptive middleware. Labs/Teams: Member of the ODS group, contributing to middleware for distributed applications, adaptive systems, and secure computation.

Baibing Liمشاهده پروفایل

استاد

• Bayesian Statistical Modelling
• Data Mining
• Financial Markets Analysis
• +۳ مورد دیگر

Baibing Li is a Professor of Business Statistics and Management Science at Loughborough Business School, Loughborough University. He holds a BSc from Yunnan University, an MSc from Vrije Universiteit Brussel, and a PhD from Shanghai Jiao Tong University. His research focuses on statistical machine learning, data mining, and their applications to financial markets, transportation systems, and management science. Prior to his current role, he served as a Lecturer at Newcastle University’s School of Mathematics and Statistics before joining Loughborough in 2004, where he was promoted to Reader in 2007 and Professor in 2011. His expertise spans Bayesian statistical modelling, predictive analytics, and traffic flow analysis. Notable research areas include stochastic modeling of vehicle dynamics, hedge fund strategies, and liquidity analysis in financial markets. Baibing’s work bridges theoretical statistical methods with practical business and transport challenges. His publications reflect interdisciplinary contributions, with recent trends emphasizing stochastic processes, financial market dynamics, and intelligent transportation systems. Research highlights include adaptive forecasting for urban mobility and econometric analysis of hedge fund behaviors. He has contributed to editorial roles, such as the 2020 guest editorial on sustainably intelligent mobility (SIM). Baibing’s academic journey includes affiliations with institutions like the Management Science and Operations research group at Loughborough. His work integrates advanced statistical techniques with real-world applications, addressing complex problems in both business and transportation sectors.