جست‌وجوی استاد و پژوهشگر

• Online Algorithms
• Learning-Augmented Algorithms
• Theoretical Analysis of Algorithms
• +۷ مورد دیگر

Christian Coester is an Associate Professor of Computer Science at the University of Oxford and a Tutorial Fellow at St Anne's College. His research focuses on theoretical computer science, particularly in the design and analysis of algorithms for problems involving uncertainty and incomplete information. His primary research areas include: Online algorithms, with groundbreaking work on the k-server problem (including refuting the randomized k-server conjecture, which earned the STOC 2023 Best Paper Award) Learning-augmented algorithms (algorithms with predictions) that leverage machine learning predictions while maintaining robustness guarantees Fundamental problems such as the k-taxi problem, metrical task systems, and online shortest paths Coester's theoretical work aims to develop algorithms with provable performance guarantees, particularly focusing on competitive ratios that measure worst-case performance against optimal offline solutions. His research often addresses problems that are 'simple to state and hard to solve,' leading to techniques with broad applicability across theoretical computer science. His publications span top venues including STOC, FOCS, SODA, and ICML, showing consistent contributions to both classical online algorithms and the emerging field of learning-augmented algorithms. The publications reveal a strong focus on metric spaces, competitive analysis, and the integration of prediction models into traditional algorithmic frameworks. Coester has received significant recognition including the STOC 2023 Best Paper Award and a substantial ERC Starting Grant (EUR 1.5M) for 'Challenges in Competitive Online Optimisation' (2025-2029). He actively supervises PhD students and welcomes inquiries from mathematically skilled candidates interested in theoretical computer science.

Ehsan Pashajavidمشاهده پروفایل

مدرس ارشد

• Stochastic modeling/optimisation
• Microgrids/Smartgrid
• Renewable Energy Resources
• +۳ مورد دیگر

Dr. Ehsan Pashajavid is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, within the Faculty of Science and Engineering. His research focuses on stochastic optimization, renewable energy integration, microgrid control, and electric vehicle systems. Research interests include: Microgrid and smart grid control algorithms Renewable energy resource management Power system stability and operation Energy storage optimization Electric vehicle-grid integration His publications demonstrate significant contributions to power system resilience, with recent work emphasizing battery storage economics, fault-tolerant converters, and model predictive control for grid stability. Article trends show strong focus on renewable integration challenges and optimization techniques for modern energy systems. Awards include Senior Member status in IEEE and its Power & Energy, Industrial Applications, and Power Electronics societies. Teaching responsibilities encompass graduate courses in Renewable Power Generation Systems, Smart Grid Control, and Renewable Energy Principles.

• Climate and Energy Finance
• Corporate Risk Management
• International Economics/Finance
• +۳ مورد دیگر

Dr. Bernard Njindan Iyke is a Lecturer in Finance at La Trobe University, specializing in climate and energy finance, corporate risk management, and international economics/finance. He holds an ORCID identifier (0000-0002-9217-1856) and has extensive experience as a consultant for central banks and international organizations. His recent research includes leading the Australian Government-funded 'Quantum Enhanced Optimisation for Energy Efficient Data Centres' project. He actively supervises postgraduate research students focusing on Sustainable Development Goals 13 (Climate Action) and 7 (Affordable and Clean Energy). Dr. Iyke's research interests span climate finance, energy security, geopolitical risk analysis, and macroeconomic policy interactions. His work frequently explores emerging markets, particularly in Asia and Africa, with notable contributions on financial stability, currency dynamics, and pandemic economic impacts. He has presented at institutions like the University of Oxford and the Asian Development Bank Institute. His recent publications analyze topics such as financial cycle synchronization in emerging economies, energy investment under climate risks, and the interplay between monetary policy and asset prices in ASEAN countries. While no explicit awards are listed, his high citation counts (e.g., over 90 citations for his 2024 energy security paper) reflect significant academic impact. He teaches courses on commercial lending and bank strategy, integrating real-world financial technology and regulatory challenges. Dr. Iyke has secured government grants totaling millions AUD, notably the 2024 Quantum Optimisation project. He advises on policy matters related to energy transition and financial resilience, maintaining active collaboration with central banks and multilateral institutions. His research agenda emphasizes bridging theoretical economics with practical policy solutions for sustainable development.

Tony Janمشاهده پروفایل

استاد

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +۷ مورد دیگر

Professor Tony Jan leads the Centre for Artificial Intelligence Research and Optimisation (AIRO) at Torrens University Australia's Design and Creative Technology school. He holds a PhD in Computing Science from the University of Technology Sydney (2004) and a Bachelor of Engineering from the University of Western Australia (1999). His research focuses on federated machine learning for IoT security, ensembled machine learning for real-time applications, cognitive machines for human-centric computing, and smart sensor networks for healthcare and security. He has secured ARC grants and industry partnerships with NVIDIA, IBM, and Microsoft. Awards include the 2024 SEI Global Academic Excellence Award and the 2023 Torrens University Excellence Award. Research collaborations span global partners, with contributions to UN Sustainable Development Goals in education and industry. His work bridges academia and industry, expanding AI program enrollments by 2,000+ students and enhancing student satisfaction by 15%. He advises PhD students on topics like IIoT cybersecurity and smart cities, and has produced over 97 publications since 1999. Education: PhD (UTS, 2004), BEng (UWA, 1999) Research Themes: AI for Industry 5.0, Cybersecurity, Smart Cities, Healthcare Technology Key Partnerships: NVIDIA, CIMIC, Palo Alto Networks Recent Projects: Federated learning for health IoT, drone vision intelligence, ransomware detection His work emphasizes ethical AI adoption in design and healthcare, with publications exploring AI ethics, generative AI applications, and sustainable technology integration.

• Credit Scoring
• Consumer Credit Risk Modelling
• Predictive Analytics
• +۳ مورد دیگر

Christophe Mues is a Professor of Data Science and Information Systems at the University of Southampton's School of Management, within the Department of Decision Analytics and Risk. His research focuses on credit scoring, consumer credit risk modeling, and applications of predictive analytics, including machine learning techniques for credit risk assessment. He leads the Information Systems & Business Analytics section and supervises multiple PhD students in Business Studies and Management. His work spans advanced statistical methods for predicting Probability of Default (PD), Loss Given Default (LGD), and loan profitability. He is actively involved in the academic community, serving on the organizing committee for the Credit Scoring and Credit Control conference. His teaching includes topics in information systems and business analytics. Contact: C.Mues@soton.ac.uk. Research Interests: Credit Scoring and Consumer Credit Risk Modelling Predictive Analytics in Finance Machine Learning Applications (Deep Learning, Graph Neural Networks) Non-Traditional Data Integration Credit Model Transparency and Fairness Debt Collection Optimization PhD Supervision: Currently guiding four students in Business Studies & Mngt: Kameswara Rao Korangi, Sarthak Gurnani, Pablo Casas, and Nora Agyei-Ababio. Professional Activities: Leads research groups and contributes to international conferences. His work bridges academic research with practical financial risk solutions, emphasizing ethical AI and regulatory compliance in credit modeling. Biography: Holds a PhD in Applied Economics from KU Leuven (Belgium). Joined the University of Southampton in 2004, advancing from researcher to his current leadership role in Decision Analytics and Risk.

Naoko Ellisمشاهده پروفایل

استاد

• Multiphase reaction engineering
• Fluidized bed technology
• CO2 capture and utilization
• +۵ مورد دیگر

Naoko Ellis is a Professor in the Department of Chemical and Biological Engineering at the University of British Columbia, Faculty of Applied Science. Her work bridges advanced multiphase reaction engineering with pressing sustainability challenges, focusing on fluidized-bed technologies for CO₂ capture, biomass valorisation, and clean energy production. Research Interests Multiphase reaction engineering & fluidized beds CO₂ capture via calcium looping and chemical looping combustion Biomass gasification, pyrolysis, and tar mitigation Bio-oil upgrading and biochar engineering for environmental applications Engineering education innovation for sustainability literacy Recent publications (2020-2024) reveal an integrated approach combining rigorous reactor modeling, novel catalyst development, and educational scholarship. Energy and environmental engineering dominate the portfolio, with emerging use of machine-learning tools for process optimisation and extensive exploration of biochar and bauxite-residue valorisation. Scientific Recognition Guest editor for memorial special issues honouring Prof. John R. Grace (Canadian Journal of Chemical Engineering, Powder Technology, 2023-2024) Invited prefatory contributions on chemical engineering education (Canadian Journal of Chemical Engineering, 2024) Advising & Grant Landscape No specific student names or funding details were provided in the supplied text; however, the breadth of collaborative publications and education-focused papers indicates active supervision of graduate researchers and leadership in curriculum-development grants. Labs & Teams Operates within the multiphase reaction engineering laboratory environment at UBC Chemical and Biological Engineering, leveraging pilot-scale fluidized-bed facilities and advanced analytical instrumentation for thermochemical conversion studies.

Andrea Meilán-Vilaمشاهده پروفایل

استادیار

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Sustainable Transport
• Electric Vehicles
• Reverse Logistics
• +۵ مورد دیگر

Dr. Erica Ballantyne is a Senior Lecturer in Operations and Supply Chain Management at the University of Sheffield Management School, where she leads the Logistics & Supply Chain Management Research Theme. With a PhD from the University of Leeds and industry experience at major logistics firms, her work focuses on sustainable transport solutions. She holds a PG Certificate in Learning & Teaching and is a Fellow of the Higher Education Academy. Education: BSc Hons Business Administration & Transport Management (Cardiff University) MSc Logistics and Supply Chain Management (University of Hull) PhD in Transport Studies (University of Leeds) Research Interests: Dr. Ballantyne specializes in electric vehicle applications in logistics, urban freight planning, and sustainable transport policy. Her interdisciplinary approach integrates energy storage solutions with transport systems, reverse logistics optimization, and behavioral aspects of supply chain management. Publication Trends: Recent works demonstrate a strong focus on data-driven modeling of EV energy consumption, infrastructure integration for urban fleets, and policy frameworks for sustainable freight. Methodologies emphasize empirical analysis and cross-sector applications. Awards: Fellow of the Higher Education Academy (FHEA) Research Leadership: Principal Investigator for multiple grants including EPSRC-funded 'Fleet Energy Optimisation' and EU H2020 project 'ProSFeT'. Actively supervises PhD candidates investigating EV charging infrastructure and last-mile delivery solutions. Professional Engagement: Committee member of Chartered Institute of Logistics & Transport (CILT) South Yorkshire group, with research cited in international policy frameworks.

• Smart Systems
• Optimum Resource Allocation
• Massive MIMO Systems
• +۱۳ مورد دیگر

Dhammika Jayalath is an Associate Professor at Queensland University of Technology (QUT) in the School of Electrical Engineering & Robotics within the Faculty of Engineering. He has been with QUT since 2007, initially as a Senior Lecturer and later promoted to Associate Professor. Prior to joining QUT, he worked as a Senior Researcher at National ICT Australia Ltd and held a Fellowship at the Australian National University. His educational background includes a PhD in Wireless Communications from Monash University and a Graduate Certificate in Higher Education from QUT. He is a Senior Member of IEEE and active in multiple IEEE societies including Communications, Signal Processing, and Vehicular Technology. Research Interests: Dhammika's research focuses on Smart Systems with particular expertise in wireless communications and networking. His work spans Physical Layer Security, Massive MIMO Systems, Internet of Things, Optimum Resource Allocation, Cooperative communications, Cognitive radio, and Vehicular communications. He has made significant contributions to 5G New Radio, Chaotic Communications, Orthogonal Frequency Division Multiplexing (OFDM), and Space-Time Signal Processing. Publication Trends: His recent publications demonstrate a strong focus on 5G/6G networking technologies, physical layer security for IoT devices, and optimization of wireless communication systems. His work bridges theoretical communications theory with practical implementation challenges, particularly in vehicular networks, secure communications, and resource allocation for heterogeneous networks. The articles show increasing interdisciplinary work, combining machine learning techniques with traditional communications engineering approaches. Scientific Awards: 2007: Early Career Academic Recruitment and Development (ECARD) award from QUT 2009: Elevated to Senior Member Grade of IEEE 2000: IEEE travel grant Multiple scholarships during graduate studies at Monash University Supervision and Grants: Professor Jayalath has supervised numerous PhD students to completion with research topics including chaotic communication systems, resource allocation in heterogeneous networks, and vehicular communication systems. He has secured multiple research grants totaling over AU $300,000, including projects from ARC, QUT internal grants, and industry partnerships with Queensland Fire and Emergency Services. His current research includes physical layer security frameworks for IoT devices and optimization of massive MIMO systems for dense mobile networks. Laboratory and Team Work: He has been instrumental in establishing wireless communications research capabilities at QUT, including securing equipment grants for Software Defined Radio platforms. His work often involves interdisciplinary collaboration with researchers in signal processing, cybersecurity, and transportation systems.

Yinglong Heمشاهده پروفایل

مدرس

• Intelligent Transportation Systems
• Energy Management & Decarbonisation
• Vehicle & Powertrain Dynamics
• +۷ مورد دیگر

Dr. Yinglong He is a Lecturer in Automated Electrified Transport (AcT) Systems at the School of Mechanical Engineering Sciences, University of Surrey, UK, and an Honorary Assistant Professor at the School of Engineering, University of Birmingham. His research focuses on intelligent transportation systems, energy management, vehicle dynamics, and AI-driven optimisation. He has held positions including Postdoctoral Research Associate at the University of Cambridge and Technology Expert at the European Commission's Joint Research Centre (JRC). Research interests include autonomous vehicle control, traffic simulation, hybrid/electric vehicle dynamics, and sustainable transport solutions. Notable contributions include advancing microscopic traffic models for automated vehicles and optimising energy systems for hybrid powertrains. His work integrates machine learning, multi-agent systems, and data-driven approaches to address challenges in transport decarbonisation and safety. Recent publications highlight advancements in hybrid vehicle dynamics simulation, lithium-air battery modelling, and energy mapping of urban buildings. He has received the Chinese Government Award for Outstanding Self-Financed Students (2022) and is a Fellow of the Institute for Sustainability (IfS). His expertise spans interdisciplinary collaborations in automotive engineering, energy systems, and smart infrastructure.

Agni Orfanoudakiمشاهده پروفایل

دانشیار

• Operations Research
• Machine Learning
• Healthcare Operations
• +۶ مورد دیگر

Agni Orfanoudaki serves as an Associate Professor of Operations Management at the University of Oxford's Saïd Business School and holds a Fellowship in Management Studies at Exeter College. She concurrently maintains a visiting scholar position at Harvard Kennedy School as a Harvard Data Science Initiative fellow. Her academic journey includes a Ph.D. in Operations Research from MIT and undergraduate studies at Athens University of Economics and Business. Her research focuses on developing machine learning algorithms to address real-world data imperfections—including missing values, censored observations, and unobserved counterfactuals—with applications in healthcare and insurance. Key contributions include predictive models for cardiovascular/cerebrovascular diseases and COVID-19 patient care, alongside pioneering the field of algorithmic insurance through quantitative frameworks for litigation risk assessment of ML models. She emphasizes interpretability and system design for seamless healthcare integration. Orfanoudaki leads the Data Driven Decisions Lab (3DL), collaborating with eight+ US/European hospitals, major medical societies, and international reinsurers. Her teaching spans MBA/EMBA Technology and Operations Management courses and the Oxford AI Ethics, Regulation and Compliance Programme, supplemented by machine learning instruction for Exeter College undergraduates.

Dan Steinbergمشاهده پروفایل

عضو هیئت علمی

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +۷ مورد دیگر

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

Laura Deeمشاهده پروفایل

استادیار مدعو

• Conservation ecology
• Ecosystem services
• Global change ecology
• +۳ مورد دیگر

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.

• Algorithm development
• Data analytics
• Time-series data analysis
• +۶ مورد دیگر

Emran Ali is a Graduate Researcher (Ph.D. candidate) and Part-Time Lecturer at Deakin University's School of Information Technology within the Faculty of Science, Engineering and Built Environment. He holds concurrent faculty appointments at Hajee Mohammad Danesh Science & Technology University (HSTU) in Bangladesh where he teaches computer science courses while on study leave. His academic journey includes a Master of Science (Research) in Information Technology from Deakin University (2022) and a Bachelor of Science in Computer Science and Engineering from HSTU. Doctor of Philosophy (Ph.D.) in Information Technology, Deakin University (2023–present) Doctor of Philosophy (Ph.D.) in Machine Learning, Coventry University (Cotutelle program, 2023–present) Master of Science (Research) in Information Technology, Deakin University (2020–2022) Bachelor of Science in Computer Science and Engineering, HSTU Bangladesh (2007–2012) Ali's research focuses on algorithm development and applied machine learning in health informatics, specializing in biosignal processing for neurological and sleep disorder detection. His work integrates time-series data analysis with explainable AI techniques to develop clinical decision support systems. Current projects include ML/DL modeling of sleep-stage transitions in aging populations and causal relationship analysis in sleep disorders using EEG data. Analysis of his 10 recent publications reveals strong concentration in biomedical ML applications (60%), particularly EEG-based neurological disorder detection and mental health diagnostics. Secondary focus areas include environmental monitoring systems (20%) and foundational computer science (20%). His work consistently employs ensemble methods and feature optimization techniques across diverse datasets, with increasing emphasis on real-world clinical applicability in recent publications. Deakin University Post-graduate Research Scholarship (DUPRS) through Cotutelle program with Coventry University National Fellowship from Bangladesh Ministry of Science and Technology (2020) Best Presentation Award at Deakin School of IT Conference (2021) AWS AI/ML Scholarships (2023, 2024) Next Generation Tech Booster Scholarship (2024) Ali provides research supervision at HSTU while serving as a Graduate Research Teaching Fellow at Deakin University for Machine Learning and Data Analytics units. His industry collaborations include projects with Monash University, Alfred Health, and AETMOS Australia focused on health informatics applications. Current funding includes AWS-sponsored nanodegrees and Deakin University research scholarships supporting his sleep disorder research. His technical work integrates cloud-based AI/ML platforms (AWS, Azure) with biosignal processing pipelines, utilizing collaborations across Australian healthcare institutions to validate clinical applications. Recent projects emphasize explainability in deep learning models for medical diagnostics, particularly in resource-constrained environments relevant to Bangladesh healthcare contexts.

David Howeyمشاهده پروفایل

استاد

• Energy Storage Systems
• Battery Modeling and Control
• Renewable Energy Integration
• +۳ مورد دیگر

Professor David Howey is the Professor of Engineering Science at the University of Oxford and Tutorial Fellow at St Hilda's College. He holds an MEng from the University of Cambridge and a PhD from Imperial College London (2010), specializing in thermal management of electrical machines. His research focuses on modeling and managing energy storage systems, particularly batteries for electric vehicles and grid applications. He leads the Battery Intelligence Lab, emphasizing interdisciplinary approaches combining machine learning, electrochemistry, and engineering to improve battery performance and longevity. Notable collaborations include work with BBOXX Ltd on off-grid battery diagnostics and the Faraday Institution. He has over 125 peer-reviewed publications, 7 patents, and co-founded Brill Power Ltd. His current projects include the Oxford Martin School Programme on Circular Battery Economies, aiming to optimize second-life EV battery use in India and Africa. Education: MEng in Electrical and Information Sciences, University of Cambridge (2002) PhD in Engineering, Imperial College London (2010) Research Interests: Electrochemical energy storage, battery modeling and control, renewable energy integration, machine learning applications in energy systems, grid and off-grid power systems, thermal management of batteries. Key Contributions: Developed impedance-based battery condition monitoring techniques. Pioneered advanced modeling for grid-scale battery systems, improving revenue by 20% and reducing degradation by 30%. Co-developed the Brill Power intelligent battery management system. Grants & Collaborations: EPSRC, European Union, Korean Government, InnovateUK, Faraday Institution, industry partnerships (e.g., BBOXX, Brill Power). Labs/Teams: Battery Intelligence Lab at Oxford, collaborating with interdisciplinary teams including social scientists for circular economy initiatives.