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Daniel CatchpooleView profile

Professor

• Childhood Cancer Research
• Genomics
• Biomedical Data Science
• +6 more

Professor Daniel Catchpoole serves as Deputy Head of School (Research) at the School of Computer Science, University of Technology Sydney (UTS), holding dual appointments at UTS and The Children's Hospital at Westmead. With over 20 years of research experience, he bridges computational sciences and pediatric cancer research through the Biomedical Data Science Lab in the Australian Artificial Intelligence Institute. His work integrates data analytics, artificial intelligence, and software development with molecular cancer biology to transform pediatric cancer treatment pathways. PhD in Cancer Cell Biology, University of New South Wales (1991-1995) Founding Fellow, Royal College of Pathologists Australasia (2010-present) Head, Children's Hospital at Westmead Tumour Bank (2001-present) Professor Catchpoole's research focuses on translational applications of genomics in childhood cancers, particularly acute lymphoblastic leukemia and neuroblastoma. His work combines high-throughput genomic technologies with advanced computational analysis to develop systems biology approaches for cancer patient assessment. Recent projects explore virtual reality applications for complex genomic data visualization and copper chelation therapies to enhance neuroblastoma immunotherapy. His research has received significant funding from Cancer Institute NSW, Sony Foundation, ARC, and NHMRC. His publication record spans biomedical data science, cancer genomics, and virtual reality applications in oncology. Recent work demonstrates leadership in 3D latent diffusion models for tumor segmentation, biobank economics, and innovative immunotherapies. His research consistently addresses the critical need for actionable knowledge from complex multidimensional biomedical data. Editorial Board Member, Cancers (2023) Associate Editor, Innovations in Digital Health, Diagnostics and Biomarkers (2019) Founding member and first President, Australasian Biospecimens Network Association Professor Catchpoole has supervised 17 Honours students (including 6 First Class Honours), 3 MSc students, and 12 PhD candidates across multiple institutions, with 6 current PhD students. His collaborative research bridges UTS's Faculty of Engineering and IT with The Children's Cancer Research Unit at The Children's Hospital at Westmead. Significant research funding includes Cancer Institute NSW grants, Sony Foundation VR projects, and ARC Discovery Projects focused on genomic data analysis and clinical decision support systems. His leadership extends to building frameworks for translational research, managing biobanks and clinical data linkages, and navigating governance requirements for cancer research. The Tumour Bank at Kids Research, CCRU, represents his long-standing commitment to pediatric cancer infrastructure development.

• Design and Analysis of Computer Experiments
• Adaptive Dynamic Programming
• State Transition Modeling
• +12 more

Dr. Victoria C. P. Chen is a Professor in the Industrial, Manufacturing, and Systems Engineering (IMSE) department at The University of Texas at Arlington (UTA), where she has served since 2002. She previously held positions at the Georgia Institute of Technology from 1993-2001. Dr. Chen has held several leadership roles at UTA, including Interim Department Chair (2012-2014), Director of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) (2008-2012, and again from 2017-present), and Director of Doctoral Studies (2019-present). She was also the George & Elizabeth Pickett Professor from 2015-2017 and was inducted into the UT Arlington Academy of Distinguished Teachers in 2019. Dr. Chen is actively involved with INFORMS (Institute for Operations Research and the Management Science), where she currently serves as Secretary on the Executive Board. Dr. Chen earned her B.S. in Mathematical Sciences from The Johns Hopkins University, and her M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. Her academic journey includes visiting professorships at the University of Genoa, Italy, and Iowa State University. Dr. Chen's research utilizes statistical perspectives to create new methodologies for operations research problems appearing in engineering and science. Her expertise includes the design of experiments, statistical modeling, and data mining, particularly for computer experiments and stochastic optimization. Through her statistics-based approach, she has developed computationally-tractable decision-making methods for many high-dimensional complex systems. Her work spans multiple domains including sustainability, energy, water management, healthcare, and law enforcement. Specific application areas include inventory forecasting, airline optimization, water reservoir networks, wastewater treatment, air quality monitoring, green building design, nurse assignment systems, and pain management programs. Her recent publications demonstrate continued innovation in mixed integer programming for electric vehicle charging stations, vacuum ultraviolet spectroscopy prediction, and sustainable building education. Senior Member, Institute for Operations Research and the Management Sciences (INFORMS) (2024) Data Mining Prize (Lifetime Achievement Award), INFORMS Society on Data Mining (2023) College of Engineering Teaching Award, UT Arlington (2021) Third Place Award, C3.ai COVID-19 Grand Challenge (2020) Academy of Distinguished Teachers, University of Texas at Arlington (2019) George & Elizabeth Pickett Professorship (2015-2017) As an educator and mentor, Dr. Chen has advised over 25 doctoral students across diverse research topics in operations research and systems engineering. She has secured substantial research funding from multiple sources including the National Science Foundation (over $1.5 million in active projects), Environmental Protection Agency, National Institute of Justice, and industry partners like Luminant and Dallas-Fort Worth International Airport. Her current research projects focus on decision analytics for sustainable urban environments, optimization for Texas water management, and statistical methods for pain management programs. She has served as Principal Investigator or Co-PI on more than 20 externally funded research projects totaling over $3 million in funding. Dr. Chen co-founded the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at UTA with Dr. H. W. Corley. This research center brings together faculty and students from multiple disciplines to address complex problems through advanced statistical and optimization methods. She also leads interdisciplinary research teams working on projects related to sustainable infrastructure, energy systems, and healthcare optimization, frequently collaborating with researchers from civil engineering, environmental science, and medical fields.

Mesut BaranView profile

Professor

• Power Systems
• Smart Grid Technology
• Distributed Energy Resources
• +3 more

Mesut Baran is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He focuses on applying computer, control, and system analysis techniques to power system operation and planning, particularly in smart power distribution systems and distributed energy resource integration. His research emphasizes grid resilience, renewable energy integration, and advanced grid technologies. Education: Ph.D. in Electrical Engineering, University of California, Berkeley (1988) Master's in Electrical Engineering, Middle East Technical University, Turkey (1981) Bachelor's in Electrical Engineering, Middle East Technical University, Turkey (1979) Research interests include power electronics, smart grid technologies, distributed energy resource management, and grid resilience. His recent work addresses challenges in EV charging impacts, fault tolerance, and microgrid coordination. Baran has contributed to IEEE standards and received prestigious awards, including the IEEE Fellow designation (2010) and the William F. Lane Outstanding Teaching Award (2017). Publications highlight advancements in distribution system state estimation, fault mitigation, and microgrid management. Collaborations with industry (e.g., Strata Solar) and federal grants ($3.1M DOE award) underscore his applied research impact. He teaches foundational courses like ECE200 and advises on FREEDM Systems Center projects.

Aliasghar ArabmaldarView profile

Assistant Professor

• Operations Research
• Data Envelopment Analysis
• Robust Optimization
• +4 more

Aliasghar Arabmaldar is an Assistant Professor in Business Analytics and Systems (BAS) at the Hertfordshire Business School, University of Hertfordshire. He holds a PhD in Operations Research from the University of Sistan and Baluchestan (2017), an MSc in Applied Mathematics-Operations Research from the University of Guilan (2011), and a BSc in Applied Mathematics from the same institution (2009). His research focuses on robust optimization, data envelopment analysis (DEA), decision-making under uncertainty, and performance evaluation in complex systems. Key research interests include: Developing robust DEA models to handle uncertainty in data Resilience-efficiency trade-offs in retail logistics Network production processes and efficiency measurement Integration of undesirable outputs in performance evaluation Recent work emphasizes application in retail management, oil industries, and higher education sectors. His 2025 project, 'Enhancing Research Publication Skills Through Writing Retreats,' highlights his commitment to academic development. Peer-reviewed contributions appear in top journals like European Journal of Operational Research and Expert Systems with Applications. He serves as a reviewer for 12+ journals including OMEGA and Operations Research Spectrum, demonstrating his influence in operational research and analytics.

Lisa WillsView profile

Assistant Professor

• Computer Architecture
• Hardware Acceleration
• Hardware-Software Co-Design
• +5 more

Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.

Andreas GrotheyView profile

Senior Lecturer

• Stochastic Programming
• Interior Point Methods
• Decomposition Methods
• +5 more

Andreas Grothey is a Senior Lecturer in the School of Mathematics at The University of Edinburgh, a position he has held since 2011. He completed his MSc in Numerical Algebra and Mathematical Computing at the University of Dundee (1995) and his PhD in Optimization at the University of Edinburgh (2001), supervised by Ken McKinnon. His research focuses on stochastic programming, interior point methods, decomposition approaches, high-performance computing, and energy systems optimization. He has contributed to energy planning, power grid reliability, and emergency response strategies for power networks. Grothey has advised seven PhD students, including work on unit commitment, top-percentile traffic routing, and power flow optimization. His projects include the OOPS solver, CESI energy integration center, and the Structured Modelling Language (SML). Recent work addresses pandemic policy optimization and exascale computational challenges. Education: MSc in Numerical Algebra and Mathematical Computing (University of Dundee, 1995) PhD in Optimization (University of Edinburgh, 2001) Research Interests: Stochastic Programming Interior Point Methods Decomposition Methods High-Performance Computing Energy Systems Optimization Advising & Projects: PhD Supervision (7 students, 2007–2022) OOPS Parallel Solver Development CESI Energy Systems Integration SML Structured Modelling Language Labs/Teams: Member of the Edinburgh Research Group on Optimization, leading projects in power grid stability and energy planning.

DİDEM SARI AYView profile

Lecturer

• Operations Research
• Stochastic Programming
• Wind Power Integration
• +3 more

Dr. Didem Sari Ay serves as a Lecturer in the Department of Industrial Engineering at Alanya Alaaddin Keykubat University's Rafet Kayış Faculty of Engineering. She earned her PhD in Industrial and Manufacturing Systems Engineering from Iowa State University (2017) following an MS in Operations Research from North Carolina State University (2013) and a BS in Industrial Engineering from Sakarya University (2008). Her educational background includes: PhD, Industrial and Manufacturing Systems Engineering, Iowa State University (2013-2017) MS, Operations Research, North Carolina State University (2011-2013) BS, Industrial Engineering, Sakarya University (2004-2008) Dr. Sari Ay specializes in Operations Research with emphases on stochastic programming and energy systems optimization. Her work develops statistical methodologies for scenario generation quality assessment in wind power integration and unit commitment problems, addressing uncertainty through advanced optimization frameworks. She bridges theoretical stochastic models with practical energy market applications. Her 2016-2019 publications demonstrate consistent innovation in stochastic unit commitment, introducing reliability metrics and validation techniques for wind power scenarios. These works establish statistical foundations for decision-making under uncertainty in power systems, with significant contributions to scenario quality assessment frameworks. She received the Teaching Excellence Award from Iowa State University in 2016 and maintains active INFORMS membership since 2014. Administrative service includes Department Head duties at Alanya Alaaddin Keykubat University (2018-2019). No information is available regarding graduate student supervision, research grants, or laboratory affiliations.

Bing YanView profile

Assistant Professor

• Power System Optimization
• Grid Integration of Renewables
• Microgrid Operation
• +3 more

Bing Yan is an Assistant Professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering. She holds a B.S. in Information Management from Renmin University of China (2010), and M.S. and Ph.D. degrees in Electrical Engineering and Statistics from the University of Connecticut (2012–2017). Prior to RIT, she was an Assistant Research Professor at the University of Connecticut. Dr. Yan’s research focuses on power system optimization , including grid integration of renewables (wind/solar), microgrid operations, distributed energy systems, and manufacturing scheduling. She has published over 30 peer-reviewed articles and secured grants from the National Science Foundation (including a CAREER Award), Department of Energy, and industry partners like Brookhaven National Laboratory and ABB. Her work emphasizes mixed-integer linear programming and machine learning applications in energy systems. Notable contributions include stochastic unit commitment models for wind farms, voltage control via deep reinforcement learning, and multi-layer weather models for PV prediction. She advises on projects involving grid resilience, smart manufacturing, and data-driven optimization. Awards: National Science Foundation Faculty Early Career Development (CAREER) Award Multiple NSF grants, DOE grants, and industry contracts Teaching: Courses include Circuits I , Electric Power Transmission & Distribution , and Advanced Power Systems . She also mentors students through co-op programs and independent studies. Labs/Teams: Leads the Intelligent Lab of Power and Manufacturing (ILPM), focusing on multidisciplinary solutions for energy and manufacturing systems. The lab emphasizes hands-on training and innovation in smart grid technologies and sustainable energy systems.

Efthymios PavlidisView profile

Professor

• Macroeconomics
• International Finance
• Time Series Econometrics
• +4 more

Professor Efthymios Pavlidis is a faculty member in the Department of Economics at Lancaster University Management School (LUMS). He holds the rank of Professor and specializes in macroeconomics, international finance, and time series econometrics. His research focuses on housing market dynamics through collaborations like the International Housing Observatory (with the Federal Reserve Bank of Dallas) and the UK Housing Observatory. He is a Fellow of the Higher Education Academy, reflecting his commitment to academic excellence in teaching and research. His research interests include speculative bubble detection, real estate price forecasting, and testing parity conditions in financial markets. Pavlidis actively supervises PhD students in applied time series econometrics, emphasizing practical applications in financial markets and housing economics. He is involved in numerous academic activities, including organizing conferences and workshops such as the Dynare Conference and the Lancaster Economics Seminar. Key contributions include developing econometric methods for detecting market exuberance and analyzing real exchange rates. His work bridges theoretical econometrics with practical policy implications, particularly in housing and energy markets. Pavlidis collaborates internationally, evidenced by his participation in global academic networks and institutions like the European Economic Association and the Royal Economic Society. His teaching includes the course ECON222 Intermediate Macroeconomics I, and he maintains an office in the Management School (B015), with weekly office hours on Tuesdays. A comprehensive overview of his research and projects is available at his personal webpage: https://sites.google.com/view/etpavlidis/ .

Andrei KhodakovView profile

Professor

• Catalysis
• Heterogeneous Catalysis
• Fischer-Tropsch Synthesis
• +5 more

Andrei Y. Khodakov is a Research Director (Professor equivalent) at the Unité de Catalyse et de Chimie du Solide (UCCS), UMR CNRS 8181, affiliated with University of Lille. He serves as Coordinator of the CEMOP research team (Catalysis for Energy and Synthesis of Platform Molecules) within the Heterogeneous Catalysis Department. His academic journey began with a Master's in Chemistry from Lomonosov Moscow State University (1987), followed by a PhD from the Zelinsky Institute of Organic Chemistry (1991), and a Dr. Sci. (Habilitation) from University of Sciences and Technologies of Lille (2002). Khodakov's research focuses on heterogeneous catalysis, with particular expertise in Fischer-Tropsch synthesis, syngas conversion to fuels and platform molecules, photocatalysis, CO 2 utilization, and methane valorization. His work bridges fundamental catalyst design with practical applications for sustainable energy and chemical production. He has pioneered research on nanoconfined catalysts, mobile promoters, and single-atom catalytic systems, with significant contributions to understanding reaction mechanisms and kinetics. His publication record spans over 122 papers since 2008, with recent work emphasizing CO 2 hydrogenation, photocatalytic methane conversion, and advanced catalyst design using nanoreactors and single-atom techniques. The research shows a clear trajectory toward sustainable catalytic processes for renewable feedstocks and carbon-neutral chemical production. CNRS Prize of Excellence (2011) CNRS Ph.D. and Research Supervising Bonus (2016) Special Invited Scientist of the Brazilian Government (2013-2016) Khodakov has supervised 27 PhD students and 14 post-doctoral researchers, demonstrating strong commitment to academic training. He teaches at Centrale Lille and University of Lille's Biorefinery Master's program, and organizes international summer schools for Chinese and Brazilian students. His research is supported by 4 ANR projects, 3 European projects, and over 20 industrial contracts, reflecting both academic excellence and industrial relevance. His laboratory focuses on catalyst design for sustainable chemical production, with particular emphasis on reactor engineering, in-situ characterization techniques, and development of catalysts for renewable feedstocks conversion.

Walter StruhalView profile

Associate Professor

• Neurology
• Autonomic Nervous System
• Alzheimer's Dementia
• +4 more

Prim. Assoc. Prof. PD Dr. Walter Struhal MSc FEAN serves as Head of the Clinical Department of Neurology at University Hospital Tulln since 2017 and holds a shared leadership role at the University Clinic for Neurology (Karl Landsteiner Private University of Health Sciences) since 2019. His career bridges clinical neurology and translational research in autonomic nervous system disorders. Research Focus : Struhal specializes in autonomic dysfunction, particularly its role in Alzheimer's dementia, post-COVID-19 neurological sequelae, and neurodegenerative diseases like multiple system atrophy. His work emphasizes modern diagnostic tools such as wearable ECGs and power modulation spectrum algorithms for signal quality control. Recent collaborations include developing the NEUROGED Guidelines for neurogenic urinary/sexual symptoms and mapping autonomic education gaps in European neurology training. Scientific Contributions : As a Fellow of the European Academy of Neurology (FEAN) , he co-led international studies on ECG quality indices (PDQI/MSQI) and participated in the AFFRICATE Project analyzing autonomic impacts of cerebral vascular interventions. His 15 most recent publications (2020-2025) span stroke care economics, dysphagia management, and autonomic testing innovations, reflecting his commitment to clinical-neurological research infrastructure development. Scientific Awards : Metric-based scholarship for Austro-Mir space research Fellow of the European Academy of Neurology (FEAN)

• Ancillary Services
• Operation of Electric Power Systems
• Planning of Electric Power Systems
• +5 more

David Soler Soneira is a Professor in the Department of Electrical Engineering and Systems at the School of Engineering (ICAI) of Comillas Pontifical University, where he has been teaching since 1994. He is also a Visiting Researcher at the Institute for Research in Technology (IIT), focusing on electric power systems. His academic and professional work is deeply rooted in the technical and economic aspects of modern power systems. Research Interests: His primary research areas include ancillary services, operation and planning of electric power systems, and electricity market economics. He specializes in voltage control, reactive power management, and regulatory models for grid services. His work bridges engineering and market design, contributing to the efficient and stable operation of power networks. Publications and Research Trends: His most recent publications, primarily in IEEE journals, focus on voltage regulation, secondary reserve markets, and reactive power markets. These works emphasize market mechanisms, economic valuation, and operational strategies for ancillary services in competitive electricity environments. His research consistently addresses real-world challenges faced by transmission and distribution operators. Scientific Awards: Director of prizewinning work in Honorary Distinction to the best Doctoral Thesis on Engineering «Modelo regulatorio del control de tensiones en los sistemas eléctricos de potencia» by Pablo Frías Marín, Universidad Pontificia Comillas, Jan 2009 Advising and Grants: He has supervised at least one doctoral thesis and has led or participated in numerous research and consulting projects funded by major energy companies such as Iberdrola, Unión Fenosa, Duro Felguera Energía, and REE (Red Eléctrica de España). These projects have focused on voltage control strategies, automation of power plants, and regulatory modeling. Labs and Teams: He is affiliated with the Institute for Research in Technology (IIT), a leading research center in energy systems at Comillas Pontifical University, where he contributes to advanced research in power systems and energy policy.

Iliev AtanasView profile

Associate Professor

• Electric Power Systems
• Optimization Algorithms
• Renewable Energy Systems
• +3 more

Assoc. Prof. Iliev Atanas is affiliated with the Faculty of Electrical Engineering and Information Technologies (FEIT) at the Ss. Cyril and Methodius University of Skopje. He holds the position of Associate Professor at the Institute for Power Plants and Switchgear. His academic journey includes a PhD (2003), MSc (1993), and BSc (1987) in Electrical Engineering from FEIT. His work experience spans over three decades, starting as a Junior Assistant (1987–1994), progressing to Assistant Professor (1994–2008), and attaining his current rank in 2008. His research focuses on optimizing power systems through advanced algorithms, particularly genetic algorithms applied to unit commitment, hydrothermal scheduling, and microgrid management. Key areas include renewable energy integration, grid reliability, and security-constrained optimization. He has contributed to over 50 publications addressing topics like dynamic programming for hybrid power systems, fuzzy logic for hydroelectric project evaluation, and reliability modeling of substations under distributed generation uncertainty. Notable contributions include developing novel self-adaptive genetic algorithms for short-term scheduling and energy management in hybrid systems. His work bridges theoretical optimization with practical grid challenges, emphasizing sustainability and resilience in power infrastructure.

• Renewable Energy
• Statistical Data Analysis
• Energy Management Systems
• +3 more

Yu Fujimoto is a Professor at Waseda University's Advanced Collaborative Research Organization for SmartSociety. Previously, he served as an Associate Professor at Waseda University's Advanced Collaborative Research Organization for Smart Society (2015-2023) and Institute for Nanoscience & Nanotechnology (2012-2015), and as an Assistant Professor at Aoyama Gakuin University's Department of Integrated Information Technology (2009-2012). He holds a Doctorate from Waseda University's Graduate School, Division of Science and Engineering and is a member of IEEE and the Information Processing Society of Japan. His research focuses on statistical science applied to energy systems, with specific interests in renewable energy forecasting, statistical data analysis for energy management systems, and statistical machine learning theory. Fujimoto has published 109 papers with 1,191 citations and maintains an h-index of 16, demonstrating significant impact in his field. His recent publications show a strong trend toward integrating machine learning techniques with power system applications, particularly focusing on renewable energy integration, electric vehicle charging optimization, smart grid technologies, and carbon reduction strategies. His work frequently addresses the challenges of grid stability with high renewable penetration and develops innovative solutions using advanced data analytics. Best Paper Award for Big Earth Data (2025) for wind generation dataset research Best Paper Award in International Conference on Power, Energy and Electrical Engineering (2025) Second Best Paper Award in International Conference on Renewable Energy Research (2024) Top Downloaded Article in IET Smart Cities (2023) Top Downloaded Article in IEEJ Transactions (2022) Fujimoto's research bridges theoretical statistical methods with practical energy system applications, contributing significantly to Japan's efforts in smart grid development and renewable energy integration. His work often involves collaboration with industry partners and government research organizations to address real-world power system challenges.

Tan Wen ShanView profile

Lecturer

• Stochastic Generation Scheduling
• Power System Flexibility
• Electricity Market Operation
• +8 more

Dr. Tan Wen Shan is a Lecturer in Mechatronics Engineering at Monash University Malaysia, specializing in power systems and renewable energy integration. He holds a PhD in Electrical Engineering from Universiti Teknologi Malaysia (2017), an MEng in Electrical Engineering (2013), and a BEng in Electrical and Electronics Engineering (2011). His research focuses on stochastic generation scheduling, energy storage systems, smart grids, and blockchain applications in energy markets. Education: BEng (Electrical and Electronics Engineering), Universiti Malaysia Sabah, 2011 MEng (Electrical Engineering), Universiti Teknologi Malaysia, 2013 PhD (Electrical Engineering), Universiti Teknologi Malaysia, 2017 Research Interests: Stochastic generation scheduling with renewable integration Power system flexibility and resilience Electricity market operations and blockchain Artificial intelligence in energy systems Publications and Projects: Recent work includes P2P energy trading, transportable energy storage, and resilience-based scheduling under natural disasters. Active projects include sustainable energy bike lanes in Kuala Lumpur and AI-assisted energy storage planning. His articles emphasize renewable energy forecasting, smart grid optimization, and decentralized energy systems. Awards: ITEX 2023 Prize (2023) IBM Call for Code 2022 Green Practice Challenge Accelerator Prize (2022) Teaching and Supervision: Unit Coordinator for TRC3500 (Sensors and Artificial Perceptions) and TRC3600 (Modelling and Control) Supervised multiple final-year projects on energy systems and smart grids (2023–2024).