Qifeng Li is an Associate Professor at the University of Central Florida specializing in Electrical Engineering with a focus on power and energy systems. His research integrates convex optimization and nonlinear dynamics to address challenges in renewable energy integration, microgrid stability, and energy-water-hydrogen nexus systems. Research Interests: Convex optimization, nonlinear systems, grid resilience, energy-water-food nexus Grants: NSF and DOE projects on microgrid stability and cross-system coordination Professional Roles: Editor for CSEE Journal, IEEE Battery Energy Storage Work Group member Honors: China National Scholarship 2012 His recent publications emphasize data-driven optimization methods, voltage stability analysis, and machine learning applications in power systems. Key trends include hybrid physics-data-driven models, stochastic disturbance analysis, and real-time grid control solutions. Scientific Awards: China National Scholarship (2012)
Associate Professor Wenhua Zhao is a globally recognized expert in offshore hydrodynamics and renewable energy technologies at The University of Queensland , School of Civil Engineering. With over 110 publications and 30 million AUD in secured research funding, his work bridges theoretical and practical advancements in marine engineering. Research focuses on Clean Energy , Artificial Intelligence , and Climate Change , specifically floating wind energy, floating solar, offshore aquaculture, and green hydrogen production. His 15 most recent articles (2024-2025) emphasize wave-structure interactions, gap resonance dynamics, and AI-driven wave prediction, published in top journals like Journal of Fluid Mechanics and Ocean Engineering . Scientific awards include the prestigious ARC Future Fellowship (2024-2028) and DECRA Fellowship (2019-2022) , recognizing his contributions to academia and industry. He teaches the 'Design of Offshore Energy Systems' course , training hundreds of students in coastal and ocean engineering, and serves as Deputy Editor for Ocean Engineering and Associate Editor for ASME's Journal of OMAE . Available for research supervision, Zhao actively collaborates with editorial boards of Applied Ocean Research and other Q1 journals.
Yang Weng is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University. He leads the U.S.-Israel International Consortium on Energy Cyber Initiative on Cybersecurity R&D and directs a research lab focused on smart grid resilience and machine learning applications. Previously, he was a TomKat Postdoctoral Scholar at Stanford University. Education: Ph.D. in Electrical and Computer Engineering, Carnegie Mellon University M.S. in Machine Learning, Carnegie Mellon University Research: His interdisciplinary work bridges power systems, machine learning, and cybersecurity, emphasizing renewable integration, grid optimization, and cyber-physical resilience. Key themes include physics-informed AI, adversarial robustness in energy infrastructure, and real-time control algorithms for dynamic grids. Publications: Recent articles (2024–2025) demonstrate strong trends in AI-driven grid security, adaptive control under uncertainty, and climate-impact modeling. Dominant domains include neural network applications for stability guarantees, cyber-attack mitigation, and data-efficient renewable integration. Awards: NSF CAREER Award (2021), Amazon Research Award (2023) Best Paper Awards at IEEE SmartGridComm (2012, 2013), PES GM (2014), PMAPS (2016) IEEE Senior Member, Sun Award (ASU), Centennial Award (ASU) Grants & Leadership: Secured DOE, NSF, and AFOSR funding for projects on AI-enhanced grid resilience. Advises PhD/postdoc candidates and chairs the U.S.-Israel Energy Center consortium. Organized international workshops (e.g., ICRDE 2023) and validated research via hardware-in-the-loop experiments. Lab & Team: Directs a research group developing deployable ML solutions for utilities (e.g., OPAL-RT collaborations). Focus areas: cybersecurity toolchains, reinforcement learning for grid control, and anomaly detection architectures.
Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He is a leading researcher in AI-driven computational design, additive manufacturing, and intelligent engineering systems, leading the Visual Design and Engineering Lab (VDEL) at CMU. Education: B.S., Mechanical Engineering, Middle East Technical University (1998) M.S., Mechanical Engineering, Carnegie Mellon University (2000) Ph.D., Mechanical Engineering, Carnegie Mellon University (2005) His research focuses on integrating machine learning, optimization, and geometric modeling to revolutionize engineering design and manufacturing. Key areas include topology optimization, CAD intelligence, digital twins, generative design, bioengineering, and electronic design automation. His work enables automation of traditionally labor-intensive design processes using deep learning and reinforcement learning. His recent publications reveal a strong trend toward physics-informed surrogate modeling, real-time simulation, manufacturability prediction, and AI-driven automation in mechanical, biomedical, and electronic systems. These works frequently appear in top journals such as Journal of Mechanical Design and Journal of Applied Mechanics , and at premier conferences like NeurIPS and DAC. Scientific Awards: National Science Foundation CAREER Award ASME Design Automation Society Young Investigator Award Google AI for Social Good Impact Scholar Kara advises several Ph.D. students and has secured significant funding from federal agencies such as the NSF and the U.S. Army Research Laboratory, as well as collaborations with industrial leaders including Cadence Design Systems and NVIDIA. His research is also supported by CMU’s NextManufacturing Center and the Critical Technology Initiative. He is actively involved in developing intelligent design systems that leverage AI to automate product design, optimize manufacturing processes, and improve medical diagnostics, particularly in oral cancer screening and organ preservation. His lab, VDEL, is a hub for innovation in AI-enabled engineering.
Dr. Alyas Widita is an Assistant Professor in the Urban Design program at Monash University, Indonesia. He is actively involved in research and teaching, focusing on urban planning, transportation, and smart city technologies. He serves as the Program Coordinator for Urban Design and teaches courses such as Urban Design Studio: Smart City and Smart City Technologies. Education: Ph.D. in City and Regional Planning, Georgia Institute of Technology, United States Dr. Widita's research centers on the built environment, transportation systems, and urban analytics, with a strong emphasis on developing Asian cities. His work explores congestion impacts of mass transit, ride-hailing effects on vehicle ownership, rural-urban migration, walking behavior, and spatial patterns of MSMEs. He employs advanced data analytics and causal evaluation methods to inform urban policy. His recent publications span high-impact journals such as Transport Reviews , Journal of Planning Education and Research , and Travel Behaviour and Society . The research trend shows a consistent focus on data-driven urban policy, sustainable mobility, and equity in urban development across Indonesia and Southeast Asia. Scientific Awards and Recognition: No specific awards listed, but research widely cited and featured in media outlets. Dr. Widita has secured and contributed to multiple research projects funded by international and national agencies, including the World Bank, Korea Transport Institute (KOTI), Central Bank of Indonesia, and Georgia Department of Transportation. He is currently leading or co-leading projects on Jakarta’s subsidence, flood risk management using remote sensing, and the Citarum River revitalization. He collaborates extensively with researchers across disciplines and institutions. His work contributes to UN Sustainable Development Goals, particularly those related to sustainable cities and communities. He is accepting PhD students interested in the built environment, transportation, and urban analytics in developing Asia. He is involved in key labs and research teams including the Citarum Action Research Program (CARP), Intelligent and Dynamic Remote Sensing for Flood Risk, and interdisciplinary urban analytics initiatives at Monash Indonesia.
Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.
Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University
Jennifer L. Clarke is a Professor in the Department of Statistics at the University of Nebraska–Lincoln and Director of the Quantitative Life Science Initiative. She holds leadership roles in enabling big data integration across the University of Nebraska system through collaborative research programs. Her affiliations include the Institute of Agriculture and Natural Resources (IANR) and the College of Agriculture and Natural Resources. Dr. Clarke's research focuses on statistical methodology for high-dimensional data, computational biology, bioinformatics, and bacterial genomics. Her work bridges statistical innovation with applications in oncology, microbiome analysis, and agricultural phenomics. Key areas include predictive modeling, machine learning, and genomic/metagenomic data integration. Her recent publications span cancer biomarker discovery, plant phenotyping methodologies, and microbial community analysis, reflecting her interdisciplinary approach. Articles emphasize translational applications like therapeutic target identification and precision agriculture. Dr. Clarke leads initiatives fostering collaboration between statisticians and domain scientists, including the Quantitative Life Science Initiative and contributions to the Agricultural Genome-to-Phenome Initiative (AG2PI). Her work advances data-driven solutions for healthcare and food security challenges. Notable projects include developing statistical tools for microbiome studies, analyzing root architecture via 3D imaging, and investigating cranberry-derived compounds' cancer-inhibitory mechanisms. Her methodological contributions include hybrid clustering techniques and predictive model validation frameworks.
Ashwani K. Gupta is a Distinguished University Professor at the University of Maryland, holding the Minta Martin Professorship in Engineering. He serves as Professor in the Department of Mechanical Engineering, Professor at the Institute of Physical Science and Technology, and Affiliate Professor in the Department of Aerospace Engineering. With over 45 years of experience in combustion engineering since graduating from Southampton University in 1970, Gupta has established himself as a leading authority in advanced combustion technologies. Dr. Gupta earned his Ph.D. from the University of Sheffield in 1973, followed by a D.Sc. from the same institution in 1986 and another D.Sc. from Southampton University in 2013. His academic journey includes six years at MIT as a research staff member and three years at Sheffield University as an independent research worker before joining the University of Maryland in 1983. Gupta's research focuses on revolutionizing combustion technology through innovations in swirl flows, high-temperature air combustion (HiTAC), and distributed combustion systems. His pioneering work on 'colorless distributed combustion' has enabled ultra-low emission combustion processes with significant applications in gas turbine engines and waste-to-energy conversion. His research spans biofuels, CO2 utilization, sulfur chemistry, waste conversion, and advanced laser diagnostics, addressing critical challenges in sustainable energy and environmental protection. Analyzing his recent publications reveals a strong emphasis on waste-to-energy conversion, biomass processing, and CO2-assisted technologies. Gupta's work demonstrates a clear trajectory toward sustainable energy solutions, with increasing integration of artificial intelligence for combustion optimization and emission control. His research bridges fundamental combustion science with practical engineering applications for cleaner energy systems. Among Gupta's numerous accolades are: Election to Fellowship of the Royal Academy of Engineering (2023) Honorary Fellowship of the Royal Aeronautical Society (2020) Recognition as one of the top 2% of scientists worldwide by Stanford University (2022-2024) Multiple prestigious medals from ASME and AIAA including the Soichiro Honda Medal (2018) and AIAA Air Breathing Propulsion Award (2014) Honorary doctorates from three international universities Gupta has secured substantial research funding throughout his career, resulting in over 850 technical papers, three books, 18 edited books, and 22 book chapters. He has delivered over 100 plenary/keynote/invited presentations at international conferences. His mentorship has shaped numerous graduate students who continue to contribute to the field of combustion engineering. Gupta directs the Combustion Laboratory at the University of Maryland, which serves as a hub for cutting-edge research in sustainable combustion technologies. The Combustion Laboratory, under Gupta's leadership, has become a center of excellence for advanced combustion research, particularly in distributed combustion systems, waste-to-energy conversion, and alternative fuels. The lab maintains strong collaborations with industry partners and international research institutions, facilitating technology transfer and practical implementation of research findings. Gupta's team employs state-of-the-art diagnostics and computational tools to advance fundamental understanding while developing practical engineering solutions for cleaner energy systems.
Dr Nima Izadyar serves as a Senior Lecturer and Course Chair of the Bachelor of Building Surveying (NBBS) at Victoria University's School of Built Environment and Engineering within the College of Sport, Health and Engineering. He leads curriculum innovation, applied research, and student-industry engagement in building regulation and performance. His educational background includes a PhD from Queensland University of Technology (QUT), Australia (2020), with academic and research experience spanning Australia, Southeast Asia, and the Middle East. Dr Izadyar's research focuses on developing practical, data-driven solutions for sustainable built environments. His work connects engineering principles with building science to improve indoor environmental quality, enhance ventilation performance, and integrate renewable energy systems. He has secured competitive funding for projects targeting sustainable housing design, digital transformation in construction, and energy efficiency through nature-based and AI-enabled solutions. His research output appears in high-impact journals in building, energy, and sustainability fields. His scholarly contributions have been recognized with prestigious awards including the Signato Medal and the AIRAH Higher Education and Research Award. Signato Medal AIRAH Higher Education and Research Award As an academic supervisor, Dr Izadyar mentors HDR students on critical topics including greening private outdoor spaces for improved indoor environmental quality, regulatory frameworks in Class 2 buildings, ventilation system optimization, occupant-centric window design, and BIM integration in construction education. His supervision approach emphasizes applied systems thinking, interdisciplinary collaboration, and alignment with national sustainability goals. He has secured competitive funding for projects targeting sustainable housing design, digital transformation in construction, and energy efficiency solutions. Dr Izadyar actively contributes to industry engagement through international collaborations that generate tangible outcomes in the built environment, serving as a reviewer, editor, and guest editor for scholarly publications while maintaining strong connections with industry partners to bridge research and practice.
Juan Zhai is an Assistant Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. She co-directs the Laboratory for Advanced Software Engineering Research (LASER) and is a member of the UMass NLP group. Her research advances software engineering through automated techniques for building high-quality systems with emphasis on behavioral specifications, AI safety, and trustworthy AI. Her work addresses the fundamental challenge of aligning software behavior with intended specifications through two main directions: automated specification synthesis (translating natural language comments to formal specifications via tools like C2S and LLMCup) and defect detection/repair (developing frameworks for AI system testing, bias mitigation, and training diagnostics). Her vision integrates these into end-to-end assurance systems that continuously validate, repair, and audit evolving software in dynamic environments. Recent publications (2024-2025) reveal dominant trends at the software engineering/AI intersection: formal specification synthesis for IoT and code generation, comment maintenance using LLMs, deep learning framework testing (DevMuT, Citadel), bias detection in LLMs, and automated training repair (AutoTrainer, DREAM). These contributions appear in top venues including ICSE, FSE, ASE, ISSTA, and ACL. Professor Zhai currently advises PhD student Gehao Zhang (focusing on Software Engineering and AI Safety) and actively recruits new PhD/Master's students. Her LASER lab develops practical tools for specification inference, LLM-driven synthesis, and trustworthy AI, while collaborating with the UMass NLP group on language-centric software analysis. The LASER lab, co-directed by Zhai, pioneers techniques for behavioral specification enforcement across traditional and AI-powered systems. Key projects include CPC for bidirectional code-comment analysis, ModelMeta for deep learning framework testing, and frameworks for bias mitigation across the ML lifecycle. The lab emphasizes practical, scalable tools that enhance correctness, robustness, and fairness in critical AI applications.
Conor Ryan is a Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a Science Foundation Ireland-funded Investigator since 2002 and a member of multiple research centres including Lero – the Irish Software Research Centre and the Limerick Digital Cancer Research Centre. His research focuses on Genetic Programming, Grammatical Evolution, and their applications in domains like healthcare analytics, digital circuit design, and financial modeling. He has authored over 250 publications, with recent work emphasizing automated feature selection in medical diagnostics, neural architecture search, and blockchain ecosystems. Teaching includes courses on Foundations of Computer Science and Computer Games Programming. Research interests span evolutionary computation, machine learning, and interdisciplinary applications. Collaborations involve global institutions, reflecting his work's impact across computer science, engineering, and healthcare. His research has addressed challenges in breast cancer diagnosis via genetic algorithms, cryptocurrency volatility prediction using random forests, and automated generation of digital circuits. Ongoing projects explore interpretability in AI, energy-efficient computing, and sustainable transport systems through predictive analytics. Professional memberships include roles in the Centre for Research Training in Foundations of Data Science and the Data-Driven Computer Engineering Research Centre, underscoring his commitment to interdisciplinary innovation.
Dr. Jessica A Eisma is an Assistant Professor of Water Resources in the Department of Civil Engineering at the University of Texas at Arlington. Her research focuses on urban and dryland hydrology, remote sensing, machine learning, citizen science, and climate change adaptation. She holds a PhD from Purdue University (2020) and prior degrees from Purdue and Michigan State University. Her work emphasizes community-centered solutions for flood resilience and green infrastructure planning in vulnerable areas. Education: PhD, Civil Engineering, Purdue University, 2020 MS, Civil Engineering, Purdue University, 2015 BS, Civil Engineering, Michigan State University, 2012 Research Interests: Urban hydrology and climate impacts on rainfall patterns Remote sensing applications for water resource management Machine learning in hydrological modeling Citizen science for environmental data collection Green infrastructure design for flood mitigation Recent Article Trends: Dr. Eisma’s recent work addresses urbanization effects on extreme rainfall, UAV-based thermal mapping of micro-urban heat islands, and equity-focused green infrastructure planning. Her publications often bridge technical innovation and community-driven solutions for climate resilience. Awards: 2024: Faculty/Staff Graduating Student Impact Reception (UTA) 2023: Emerging Leaders Award (Purdue CEG SAC) 2020: Magoon Award for Excellence in Teaching (Purdue College of Engineering) 2015: NSF Graduate Research Fellowship Advising & Grants: Advises 3 PhD students and multiple undergrad researchers Principal Investigator on grants totaling over $1M from NOAA, NSF, and others Focus areas: Houston flood resilience, Texas urban stormwater systems Lab & Teams: Leads the SEUSI Lab, dedicated to socio-environmental solutions in urban sustainability and infrastructure. Collaborates with national and international partners on water security and climate adaptation projects.
Eugenia Rho is an Assistant Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), part of the College of Engineering. Her research focuses on data analytics, machine learning, natural language processing, and human-computer interaction, with particular emphasis on social media discourse, online identity dynamics, and ethical AI applications. Education includes a Ph.D. in Information and Computer Sciences from the University of California, Irvine (2020), and a B.A. in Political Science from Columbia University (2011). Her interdisciplinary background bridges computer science and social sciences. Research interests span AI-assisted communication tools, counterspeech strategies for online hate mitigation, and neurodivergent perspectives in technology design. Her work often integrates computational methods with social science theories to address real-world challenges such as bias detection, mental health support, and ethical AI deployment. Recent publications highlight themes like AI collaboration in writing, identity-driven online interactions, and the efficacy of counterspeech. Her projects frequently involve designing human-centered technologies that prioritize accessibility and ethical considerations. No scientific awards are explicitly mentioned in the provided text. She maintains an active Google Scholar profile and a personal homepage (URLs not provided in the text).
Stelios Timotheou is an Associate Professor at the Department of Electrical and Computer Engineering and faculty member at the KIOS Research and Innovation Center of Excellence, University of Cyprus. He holds a Dipl-Ing from National Technical University of Athens, and MSc/PhD from Imperial College London. His research focuses on developing real-time distributed methodologies using mathematical optimization, machine learning, and computational intelligence to enhance efficiency in critical infrastructure systems. Research interests center on data-driven decision making for urban mobility management, traffic control systems, and energy optimization. Key areas include: Intelligent UAV-based sensing for traffic monitoring Cooperative control algorithms for multi-agent systems Optimization of renewable energy integration Robust infrastructure protection strategies Recent publications demonstrate strong focus on traffic state estimation (76%), energy system optimization (16%), and UAV applications (8%). Awarded the Cyprus Research Award (2017) and ERC Consolidator Grant for URANUS project. Secured multiple research grants focusing on real-time control systems.
