Rebecca Dziedzic is an Assistant Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University. Her research focuses on asset management, water system sustainability, and infrastructure resilience. She holds a PhD in Civil and Environmental Engineering from the University of Toronto. Dr. Dziedzic's work integrates machine learning, data science, and policy analysis to address challenges in urban infrastructure systems. Her research explores topics such as water distribution network optimization, climate change adaptation in infrastructure, and circular economy strategies for construction. Recent projects include predicting water main breaks using multivariate models, developing frameworks for energy-efficient pump operation, and assessing carbon footprints in industrial facilities. Dr. Dziedzic supervises graduate students in Civil Engineering (MASc/PhD) and maintains an active research group through the UrbanLinks initiative. Her work has been published in over 30 peer-reviewed articles, with a strong focus on smart city technologies, disaster risk reduction, and sustainable infrastructure design.
Ramadan El Shatshat is an Associate Professor (Teaching Stream) and Director of the Electric Power Engineering Program at the University of Waterloo's Department of Electrical and Computer Engineering. He holds a PhD from the University of Waterloo (2001) and is a registered Professional Engineer in Ontario. His research focuses on distribution system engineering, smart grids, renewable energy integration, and electric vehicle impact analysis. Dr. El Shatshat has received multiple awards, including the James A. Field Teaching Excellence Award (2016) and the University of Waterloo Outstanding Performance Award (2010, 2017, 2021). He teaches courses like ECE 360 (Power Systems and Smart Grids) and ECE 668 (Distribution System Engineering). Education: PhD in Electrical Engineering, University of Waterloo (2001) MSc in Electrical Engineering, University of Garyounis, Libya (1992) BSc in Electrical Engineering, University of Garyounis, Libya (1984) Research Interests: Smart grids and microgrids Optimization for distribution systems Electric vehicles and renewable energy integration AI-based monitoring techniques Awards: 2021 University of Waterloo Outstanding Performance Award 2018 Marsland Faculty Fellowship 2016 James A. Field Teaching Excellence Award Advising & Grants: Supervised/co-supervised 26 students (undergraduate, master's, doctoral, and postdoctoral) His work spans over 50 peer-reviewed publications and patents in fault detection, voltage control, and EV integration.
Ayman El-Hag is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is affiliated with the Outdoor Insulation and Condition Monitoring Research Group, focusing on advancing technologies for high-voltage insulation systems and smart grid infrastructure. His work integrates machine learning, signal processing, and materials science to improve condition monitoring and diagnostic methods for power equipment. Research interests include partial discharge detection using UHF and acoustic sensors, machine learning applications for defect classification in outdoor insulators, and the development of non-invasive sensing techniques for real-time monitoring. He also explores energy management systems leveraging fuzzy logic and smart meter data analysis for residential and grid-level applications. Recent publications highlight advancements in capsule networks for insulator discharge prediction, deep learning-based hydrophobicity classification, and novel antenna designs for partial discharge localization. His work emphasizes practical solutions for power system reliability and environmental resilience of insulation materials under extreme conditions. El-Hag is a Full-time faculty member and holds Adjunct faculty status, contributing to interdisciplinary research projects. He actively engages in promoting condition monitoring methodologies through educational initiatives and industry partnerships.
Dr. Tao Hong is the Duke Energy Distinguished Professor and NCEMC Faculty Fellow at the Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte. He directs the Big Data Energy Analytics Laboratory (BigDEAL) and has been a Founding Chair of the IEEE Working Group on Energy Forecasting (2011-2019). Ph.D., Electrical Engineering & Operations Research (2010), NC State University M.S., Operations Research & Industrial Engineering (2008), NC State University B.Eng., Automation (2005), Tsinghua University His research focuses on Energy Forecasting with applications in power systems operations, renewable integration, risk management, and cross-sector forecasting for healthcare, transportation, and sports. He has led major Delivery point level load analysis (2017-present) Short-term probabilistic forecasting (2016) Demand response modeling using smart meter data (2014-2015) Dr. Hong's scientific contributions include 9+ journal articles on energy forecasting methodologies and 3 major forecasting competitions (GEFCom2012-2017, BigDEAL Challenge 2022). His work has been cited in leading journals like International Journal of Forecasting and IEEE Transactions on Smart Grid . Charlotte Business Journal Energy Education Leader of the Year (2017) IEEE PES PSPI Technical Committee Prize Paper Award (2016) As a dedicated educator , Dr. Hong has advised multiple PhD and Master's students including Shreyashi Shukla (2023), Yike Li (2022), and Jordan McCorey (2021). He teaches specialized courses in energy systems planning and computational intelligence.
Thomas Ebel is Professor and Head of the Centre for Industrial Electronics at the University of Southern Denmark (SDU) , Institute of Mechanical and Electrical Engineering. A leading expert in power electronics, high-voltage engineering and capacitor technology, he directs large, multi-partner research projects and teaches/supervises at both graduate and PhD levels. Education & Career Path Prof. Ebel holds the academic title Dr. rer. nat. and has been appointed full Professor at SDU. He concurrently serves as Head of Section at the Centre for Industrial Electronics, orchestrating cross-disciplinary research teams and infrastructure. Research Interests Power Electronics & Power Conversion: advanced converter topologies, WBG devices (GaN, SiC), high-frequency magnetics, grid-forming control. Dielectric Materials & Capacitors: polymer and hybrid nanocomposite dielectrics, self-healing metallized film capacitors, aluminium electrolytic capacitors, lifetime modelling and reliability. High-Voltage Engineering & Breakdown Physics: breakdown mechanisms in nanocomposites, corona and partial discharge, insulation coordination. IoT & Data-Driven Monitoring: real-time condition monitoring, digital twins, data-driven RUL estimation for power components. Publication Trends Across 133 research outputs (2018-2025) the dominant themes are (i) construction and reliability of 700 V-class aluminium polymer electrolytic capacitors, (ii) GaN-based power converter optimisation, (iii) hybrid AC/DC microgrid control and harmonic mitigation, and (iv) nanocomposite dielectrics for next-generation capacitors. The 15 most recent articles (2025) reinforce these directions while adding socio-technical energy analytics and green-vehicle powertrains. Scientific Awards Tek Innovation Prize 2023 – awarded for outstanding contributions to power electronics research and industrial innovation. Advising & Funding Prof. Ebel currently supervises ~10 PhD candidates and post-docs including L. Tavares, M. A. Khan, R. Maheshwari, S. Mateen, A. N. Pinky and others. He is Principal Investigator or Head Coordinator of six active projects (2024-2027) valued at >€8 M, spanning ultra-high-efficiency drives, hydrogen-PtX converters, self-healing capacitors and hybrid power-plant concepts. Laboratory & Teams He heads the High-Voltage Power Electronics Laboratory at SDU, equipped with 700 V/200 A capacitor test rigs, GaN/SiC converter prototyping benches, and environmental chambers for accelerated ageing studies. The centre collaborates with 20+ industrial partners and coordinates the international IEA Wind Task 50 on hybrid power plants.
Dr. Stuart Gibson is a Senior Lecturer in Physics and Astronomy at the School of Physics and Astronomy, University of Kent. He is the co-inventor of the EFIT-V facial composite system, widely adopted by UK police constabularies and international agencies. His academic contributions span interdisciplinary research bridging forensic science, computational methods, and machine learning. Research Interests: Forensic applications of digital image processing Machine learning in natural sciences Facial composites for criminal investigations Medical image analysis Computer vision with security applications Teaching: Stuart teaches numerical and computational methods, mathematical techniques for physical sciences, and digital forensics. His pedagogical focus integrates theoretical frameworks with practical forensic and computational tools. Publications & Collaborations: Over his career, Dr. Gibson has published extensively in journals such as Pattern Recognition Letters , ACS Nano , and Utilities Policy . His work includes innovations in evolutionary algorithms, facial composite systems, and applications of machine learning to muon spectroscopy and Raman spectroscopy.
Dr. Aris Dimeas is a Researcher at the National Technical University of Athens in the Department of Electric Power and Industrial Applications . He holds a diploma and PhD in Electrical and Computer Engineering from NTUA and has extensive experience in power systems operations, renewable energy integration, and smart grid technologies. Specialized in AI applications for power systems Developed control software for demand side management Consultant for PPC (2007-2012) Research Focus : Smart grids and digital twin implementations Renewable energy market dynamics Microgrid optimization and control algorithms Collaborations : Active participant in EU research projects, collaborating with HEDNO and other energy grid operators on electronic meters and intelligent network deployments. Teaching : Instructs courses on electric energy systems, power system analysis, and energy management.
David Macii is Associate Professor at the Department of Industrial Engineering, University of Trento, Italy, where he teaches "Digital Signal Processing for Mechatronics" and co-leads the "Laboratory of Internet of Things." His core expertise lies in digital signal processing, measurement science, smart-grid instrumentation, indoor positioning and industrial IoT applications. Research interests revolve around four pillars: (i) advanced estimation algorithms for frequency, ROCOF and synchrophasors to enhance power-quality monitoring in future smart-grids with high PV and EV penetration; (ii) design and metrological characterisation of low-cost PMU and smart-meter solutions; (iii) radar- and RFID-based indoor localisation and tracking for robotics and assisted-living scenarios; and (iv) embedded, IoT-enabled measurement systems bridging DSP, mechatronics and industrial electronics. Recent publications (2023-2025) reveal a clear methodological trend: development of fast, uncertainty-aware DSP algorithms (interpolated DFT, Kalman filtering, harmonic whitening) validated against real-world noise, interference and contingency conditions, followed by their embedding into resource-constrained hardware platforms for EV charging coordination, grid-support converters and robotic navigation. Although the supplied text does not list specific grants or doctoral students, the steady stream of joint publications with European colleagues and his leading teaching role in two inter-departmental master courses indicate an active, well-integrated research and educational profile within the University of Trento.
Diego F. Aranha is an Associate Professor in the Department of Computer Science at Aarhus University . His research focuses on cryptographic systems, cybersecurity, and privacy-preserving technologies with applications in voting systems, post-quantum cryptography, and secure computation. He has contributed extensively to homomorphic encryption, secure multiparty computation (MPC), and cryptanalysis of cryptographic implementations. Key projects include: MPCC (2025-2028) : Multi-Party Computation in the Confidential Cloud SCI (2024-2027) : Secure Computation Infrastructures for the Retail Industry RENAIS (2021-2026) : Residue Number Systems for Cryptography His work emphasizes practical efficiency and formal verification of cryptographic protocols. Recent publications highlight advancements in lattice-based cryptography, secure voting schemes, and mitigating side-channel vulnerabilities in post-quantum algorithms. He actively collaborates on open-source cryptographic libraries and standards, with a focus on bridging theoretical security and real-world implementation challenges.
Evan Franklin is an Associate Professor in Energy and Power Systems within the School of Engineering at the University of Tasmania. He also serves as Associate Head of Research, reflecting his leadership in advancing engineering research at the institution. His academic work is centered on modern power systems with a strong emphasis on renewable integration, grid stability, and sustainable energy technologies. His primary research interests include energy and power systems, renewable energy integration, grid frequency control, harmonic analysis, distributed energy resources (DER), battery and compressed air energy storage, agrivoltaics, and hydrogen integration. His work bridges engineering fundamentals with real-world applications in sustainable energy systems, contributing to Australia's transition toward clean energy. The recent publications of Dr. Franklin span high-impact journals such as Energies , IEEE Transactions on Industry Applications , Renewable and Sustainable Energy Reviews , and Journal of Energy Storage . The research trends reflect a strong focus on power system stability, microgrid control, harmonic mitigation, and innovative energy storage solutions. His work increasingly integrates AI and machine learning techniques for power quality and system monitoring, while also exploring interdisciplinary applications like agrivoltaics and offshore energy systems. Dr. Franklin has successfully supervised both PhD and Master’s students, including Ahmadreza Eslami and Md Ruhul Amin, with research topics ranging from harmonic analysis to frequency control using battery storage. He has secured substantial research funding from major national and international bodies, including the Australian Research Council (ARC), Australian Renewable Energy Agency (ARENA), CSIRO, and the Blue Economy CRC. Notable projects include the ARC Training Centre in Energy Technologies for Future Grids, MoorPower wave energy projects, and studies on hydrogen integration and black-start capabilities. He leads and participates in research teams focused on renewable energy systems, including the Centre for Renewable Energy and Power Systems at UTAS. His collaborative network includes key researchers such as Professor Michael Negnevitsky, industry partners like Carnegie Clean Energy and TasNetworks, and government agencies including Hydro Tasmania and Aurora Energy. His work is instrumental in shaping resilient, sustainable, and intelligent power systems for the future.
Dr Damian Flynn is an Associate Professor at the UCD School of Electrical and Electronic Engineering , University College Dublin. His research focuses on the challenges of integrating renewable energy sources like wind and solar into power systems while maintaining stability and reliability. Research Themes: High renewable penetration, grid-forming converters, energy storage, and smart grid technologies. Collaborations: EirGrid, Glen Dimplex, Electricite de France, and General Electric. Dr Flynn’s work addresses the technical and economic feasibility of transitioning to 100% renewable energy systems, particularly for islanded grids like Ireland’s. His models explore scenarios for 2030–2050, emphasizing the need for adaptive infrastructure and policy frameworks. Key challenges include balancing unpredictable renewable supply with demand, managing grid congestion, and leveraging technologies such as electric vehicles and blockchain for system stability. Recent Publications highlight trends in grid-forming converter design, renewable curtailment reduction, and multi-carrier energy systems. He investigates solutions like transportable storage and dynamic line rating to enhance grid flexibility. Scientific Awards: Smurfit Kappa Newman Fellowship Award.
Mar Reguant is an Associate Professor (with tenure) at Northwestern University's Department of Economics (Weinberg College of Arts & Sciences) and an ICREA Researcher at CSIC-IAE. Her research focuses on energy economics, climate policy, and environmental markets, with emphasis on electricity markets, carbon pricing, and renewable energy integration. She holds a PhD in Economics from MIT (2011) and a Llicenciatura from Universitat Autònoma de Barcelona (2006). Her work examines electricity market design, climate policies, and the economic impacts of the energy transition. Key areas include border carbon adjustments, emissions leakage, and the efficiency of renewable energy policies. She has advised numerous PhD students and leads the EconLab at Northwestern for undergraduate research. Dr. Reguant is a Research Associate at NBER (IO and EEE programs) and a Research Affiliate at CEPR (IO program). She serves as Co-Editor of Journal of the Association of Environmental and Resource Economists , and holds editorial roles at Review of Economic Studies and RAND Journal of Economics . Her honors include the Presidential Early Career Award for Science and Engineering (2019), the EAERE Award (2019), and the Sloan Research Fellowship (2016). She leads the ERC-funded ENECML project on energy transitions and has contributed to policy reports for entities like the California Air Resources Board and the International Growth Center. Her teaching includes Energy Economics and PhD-level Industrial Organization courses. Professional service includes organizing conferences like the Midwest Energy Fest and serving on the Sloan Foundation's Energy & Environment Program advisory committee.
Hao Wang is a Senior Lecturer in the Department of Data Science and Artificial Intelligence at Monash University, affiliated with the Monash Energy Institute and Monash Data Futures Institute. He holds an ARC DECRA Fellowship and leads interdisciplinary research on applied machine learning and optimization in smart grids, energy systems, and smart cities. His work focuses on leveraging AI for energy data analytics, EV integration, and distributed energy resource management. Education: PhD from The Chinese University of Hong Kong, supervised by Prof. Jianwei Huang. Postdoctoral research at Stanford University (Prof. Ram Rajagopal) and University of Washington (Prof. Baosen Zhang). Research interests include reinforcement learning for energy systems, decentralized control, and incentive mechanisms for prosumer participation. He serves on editorial boards of journals like Energy Conversion and Economics and IET Renewable Power Generation , and organizes conferences like ACM e-Energy and IEEE SmartGridComm. Awards: Best Paper Awards at IEEE PECON 2016, IEEE ICC 2017, and IEEE SmartGridComm 2020. Current projects include the Human-in-the-loop Microgrid Project and AI for Clean Energy and Sustainability initiative.
Jiazhen Zhou is an Associate Professor and Chair of the Department of Computer Science at the University of Wisconsin-Whitewater. His research focuses on Internet of Things security and privacy, cryptography, machine learning-based attack and defense mechanisms, and emergency communications. He teaches courses in cybersecurity and computer networking including CYBER 101, COMPSCI 354, and COMPSCI 455. His educational background includes: Ph.D. in Computer Science from the University of Missouri–Kansas City, USA M.S. in Intelligent Control Engineering from Chinese Academy of Sciences–Shenyang Institute of Automation, P. R. China B.S. in Mathematics from Shandong University, P. R. China Dr. Zhou's research centers on IoT security vulnerabilities in medical devices, cryptographic solutions for privacy protection, and machine learning applications for attack detection. His emergency communications work develops caching strategies for stressed networks during disasters, with recent emphasis on protecting older adults using smart medical devices. The Wireless Systems Lab he founded in 2012 serves as the primary research hub for these investigations. His publication record spans 14 years with 11 significant works, showing an evolution from fundamental networking research (2009-2013) toward applied IoT security (2016-2023). Recent publications demonstrate increasing focus on healthcare applications and privacy concerns for vulnerable populations, particularly in his 2023 HIMSS conference paper on medical devices for older adults. Scientific recognition includes: Grant writing fellowship ($5,000) in 2013 Summer research fellowship advisor awards ($2,500 each) in 2016, 2018, and 2019 Dr. Zhou has supervised over 30 undergraduate researchers and three graduate students through the Wireless Systems Lab, producing journal publications, conference posters, and educational demonstrations. His secured funding includes a $105,757 Tommy Thompson Center grant for IoT privacy in older adults (2021-2022), a $50,000 UW System Regent Scholar grant for wireless vehicle communications (2021-2022), and a $2 million U.S. Department of Labor Cybersecurity Apprenticeship grant (2020-2024). The Wireless Systems Lab, established in 2012, conducts hands-on research in medical IoT security, emergency communication protocols, and real-time monitoring systems. Current projects include security attacks on medical IoT devices with Jesse Ostrander and Jackson Richman, while past work has resulted in classroom demonstrations for K-12 students and novel cache deployment solutions for disaster scenarios.
Peiyuan Chen is an Associate Professor at the Department of Electric Power Engineering, Chalmers University of Technology. He holds a B.Eng. from Zhejiang University (2004), an M.Sc. from Chalmers (2006), and a Ph.D. from Aalborg University (2010). His research focuses on power system operation and planning with wind power integration, emphasizing time series modeling, statistical analysis, and optimization. He contributes to projects on grid-forming converters, inertia estimation, frequency control, and renewable energy system stability. Research Interests: • Power Systems and Renewable Integration • Grid-Forming Converters and Stability Analysis • Time Series Modeling and Statistical Methods • Machine Learning for Energy Applications • Frequency Control and Synthetic Inertia Recent Publication Trends include studies on deep learning for heating load classification, wind turbine type optimization, fault ride-through capabilities, and inertia estimation in converter-dominated grids. His work bridges theoretical power system analysis with practical implementations in Nordic and European energy networks. Projects (2017-2024) include grants from the Swedish Energy Agency, Swedish Research Council (VR), and collaborations with institutions in Sweden, China, and Italy. Key areas: grid strength metrics, multiport converter applications, and citizen energy communities.
