Anomadarshi Barua is an Assistant Professor in the Department of Cyber Security Engineering at George Mason University, leading the System Design and Security research group. His work spans hardware-software co-design for securing cyber-physical systems (CPS), robotics, and sensors. Prior Affiliation: PhD from University of California, Irvine (2023) Industry Experience: Intel Corporation, Solidigm, Nordic Semiconductor, IDEAS Research Themes: Focuses on multimodal system security (audio, visual, electromagnetic data), analog-digital signal integrity, and quantum-inspired defenses in CPS. Key applications include healthcare systems, smart grids, and industrial control systems (ICS). Recent ACSAC 2024 paper acceptance Best Paper Award at ACSAC 2022 NSF panel reviewer (2024) Labs & Collaborations: Collaborates with University of Louisville on robotics and works on Commonwealth-funded UG research (2024). Publications in ACM CCS, USENIX, CHES, and IEEE Transactions (TDSC, TIFS).
Eunchun Park serves as an Assistant Professor in the Department of Agricultural Economics and Agribusiness at the University of Arkansas, concurrently holding the position of Director of the Experiment Station (DREX). A specialist in Bayesian spatial statistics and econometrics, his research focuses on agricultural risk analysis with particular emphasis on crop insurance mechanisms and financial commodity markets. His methodological expertise addresses critical data scarcity challenges in federal crop insurance premium calculations through advanced spatial modeling techniques. Dr. Park's academic foundation includes: Ph.D. in Agricultural Economics from Oklahoma State University (2017) M.S. in Food and Resource Economics from Korea University (2013) B.S. in Food and Resource Economics from Korea University (2010) His research program centers on extreme price and yield risk quantification in agricultural commodities, employing sophisticated Bayesian modeling frameworks to overcome data limitations in spatial risk assessment. Current work develops innovative approaches for measuring catastrophic risks in crop production systems and refining insurance rating structures through spatial smoothing of yield densities. This research bridges theoretical econometric advances with practical applications for risk management tools used by farmers and policymakers. Analysis of Dr. Park's recent publications reveals a consistent trajectory in spatial risk modeling for agricultural insurance systems, with increasing focus on prevented planting coverage factors, commodity market volatility around information releases, and climate-related production risks. His work demonstrates methodological progression from theoretical Bayesian frameworks toward actionable risk assessment tools, particularly through the application of kriging techniques to non-normal yield distributions and extreme event modeling. Dr. Park's scholarly contributions have been recognized through: Outstanding Contribution to Applied Risk Analysis Award (2020) from the Agricultural and Applied Economics Association Outstanding Graduate Student Paper Award (2018) from the Agricultural and Applied Economics Association Outstanding Doctoral Dissertation Award (2018) from the Southern Agricultural Economics Association While specific details of current advisees and grant funding are not provided in available materials, his active publication record in top agricultural economics journals suggests an ongoing mentorship role for graduate students and potential involvement in externally funded research initiatives related to agricultural risk management. His work on spatial smoothing techniques and extreme risk modeling likely informs collaborative projects with agricultural extension services and federal risk management agencies. No specific laboratory facilities or dedicated research teams are mentioned in the available documentation, though his methodological expertise suggests collaboration with spatial statistics and agricultural risk modeling groups within the university's research infrastructure.
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.
Massimo Bongiorno is an Assistant Professor in Electrical Engineering at Chalmers University of Technology. He holds a Master’s degree in Electronics Engineering from the University of Palermo (2002) and earned his Licentiate and PhD from Chalmers University. His research focuses on power electronics applications in power systems, particularly grid-forming converter systems, power quality, and renewable energy integration. MSc in Electronics Engineering (University of Palermo, 2002) Licentiate and PhD (Chalmers University of Technology) Research interests include: Power electronics in power systems Grid-forming converter stability Renewable energy integration Modular multilevel converter design Small-signal and large-signal stability analysis Energy storage system applications Recent publications highlight trends in: Converter control strategies for grid stability Dynamic modeling of power electronics systems Applications in offshore wind and hydro microgrids Impedance analysis and resonance mitigation Advanced fault ride-through techniques Multi-terminal HVDC grid control
Dr. Ahmed Badawy is a Lecturer in Electrical Engineering at Lancaster University's School of Engineering, where he has been serving since 2017. His academic journey includes advanced degrees in electrical engineering and significant research experience in power electronics and renewable energy systems. Dr. Badawy's educational background includes: B.Sc. in Electrical Engineering from the Faculty of Engineering, Alexandria University, Egypt (2008) M.Sc. in Electrical Engineering from the Faculty of Engineering, Alexandria University, Egypt (2012) Ph.D. in Electrical Engineering from the Electric and Electronic Engineering Department at the University of Strathclyde, Glasgow, U.K. (2016) Dr. Badawy's research focuses on power electronics and energy conversion systems, with particular expertise in DC-DC converters, multi-level converters, and electric machines. His work emphasizes digital control of power electronic systems for applications in renewable energy integration, electric vehicles, and power quality improvement. His research has significant implications for sustainable energy systems and transportation electrification. His recent publication record demonstrates a strong focus on electric vehicle power systems, particularly modular converter topologies for on-board charging applications. There's a clear trend toward integrated solutions for EV charging that combine renewable energy sources with grid connectivity. His work also shows significant contributions to control methodologies, including model predictive control and hierarchical control systems for power electronic converters. Dr. Badawy mentors several PhD students working on cutting-edge research in power electronics and renewable energy systems. His research group is actively involved in projects related to sustainable energy conversion and electric transportation. Dr. Badawy is affiliated with Energy Lancaster and the TALOS research group at Lancaster University, where he contributes to interdisciplinary research on sustainable energy systems and power electronics applications.
Yuanyuan Shi is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, San Diego (UCSD), with affiliations at the Center for Energy Research and the MICS. Her research integrates machine learning with control theory, focusing on energy systems, cyber-physical systems, and PDE-governed systems, aiming to provide reliable and efficient decision-making in complex environments like power grids and buildings. Assistant Professor, UCSD (2021–present) Postdoctoral Fellow, Caltech (2020–2021) Ph.D., Electrical and Computer Engineering, University of Washington (2020) M.Sc., Electrical Engineering and Statistics, University of Washington B.Eng., Nanjing University, China Her work spans machine learning, optimization, and control theory, with applications in power systems, PDEs, and intelligent systems. She develops algorithms that combine learning with control guarantees, enabling robust solutions for energy management and grid stability. Recent publications highlight her focus on neural operators for PDE and delay systems, stability-constrained reinforcement learning, and multi-agent control in sustainability contexts. These works advance physics-informed models, grid frequency regulation, and commercialized energy storage integration. She has received prestigious awards, including: NSF CAREER Award (2025) Schmidt Sciences AI2050 Early Career Fellowship (2025) Hellman Fellowship (2023) Jacobs School Early-Career Faculty Acceleration Award (2024) MIT Rising Star in EECS (2018) Clean Energy Institute Scientific Achievement Award (2020) At UCSD, her lab collaborates on projects like FedNeMO (federated neural operators) and BEAR-Data (multi-zone building dataset). She co-organized Control Meets Learning seminars and serves as guest co-editor for the Applied Energy special issue on Trustworthy Machine Learning.
Alessandro Rigolon is an Associate Professor and MCMP Program Coordinator in the Department of City and Metropolitan Planning at the University of Utah, where he has been on the faculty since 2019. A dual-PhD scholar (Design & Planning, University of Colorado Denver; Architecture, University of Bologna), he is internationally recognized for research on environmental justice, green-space equity, and the public-health consequences of urban greening. Education: Ph.D. in Design and Planning, University of Colorado Denver (2015) Ph.D. in Architecture, University of Bologna, Italy (2012) B.Arch. & M.Arch. in Architecture and Urban Design, University of Bologna, Italy (2007) Research Interests: Rigolon’s work sits at the intersection of environmental justice, urban planning, and public health. He investigates four interconnected themes: (1) policy drivers of inequity in green-space provision; (2) the mechanisms and resistance to green gentrification; (3) green infrastructure’s role in equitable climate adaptation; and (4) health impacts of urban nature on marginalized communities. His studies span multiple scales—from census microdata in Miami-Dade County to machine-learning analyses across 263 Chinese cities—deploying mixed-methods, spatial analytics, and community-engaged research. Publications & Impact: Across 89 peer-reviewed outputs, recent work (2024-2025) reveals complex pathways by which gentrification both precedes and follows greening, quantifies disparities in park access among racial/ethnic groups, and evaluates policies aimed at achieving green-space equity. Collectively, these studies highlight the need for fine-scale spatial data, intersectional analyses, and robust procedural justice when designing equitable greening interventions. Scientific Awards & Recognition: Stanford/Elsevier Top 2 % Scientist (2024) Clarivate Highly Cited Researcher (2024) APA-Utah High Achievement Award (2022) Urban Studies Editor’s Featured Articles (2021) University of Utah Celebrate U Researcher Honoree (2020) Arnold O. Beckman Award (2019) Grants & Advising: Rigolon currently leads or co-leads six funded projects totaling over one million dollars from the Center for Equitable Transit-Oriented Communities, Center for Climate Smart Transportation, Prevention Institute, and University of Illinois. These grants support interdisciplinary teams examining transit-oriented green gentrification, climate adaptation for active transportation, and equitable park policy implementation. Graduate students and post-docs are active collaborators on all projects. Teaching & Community Engagement: He teaches graduate courses including Design Ecologies , Plan Making , Professional Project Studio , and Research Design . Through studio courses, students partner with local governments (South Salt Lake City, Liberty Wells Community Council) to produce actionable plans advancing environmental justice.
Professor Masashi Okubo at Waseda University's School of Advanced Science and Engineering specializes in electrochemistry and energy materials development. With cross-appointments at Kyoto University and the Advanced Collaborative Research Organization for SmartSociety, his work focuses on sustainable battery systems including aqueous proton batteries, MXene-based electrodes, and oxygen-redox chemistry. His research bridges fundamental materials science with practical energy storage applications through combined experimental-theoretical approaches. Education : Ph.D. in Basic Science (2005) and M.Sc./B.Sc. in Basic Science from The University of Tokyo Research Strengths : Solid-state ionics and intercalation chemistry MXene electrode engineering Oxygen-redox reaction mechanisms High-rate energy storage systems Hydrate-melt electrolyte optimization Scientific Contributions include: Discovering near-zero-volume-phase battery materials Developing distortion-relieving voids in host structures Elucidating multiorbital bond formation in oxygen-redox reactions Advancing aqueous redox-flow battery catholyte design Prominent Awards : Waseda Research Award (2021) ACS Reviewer Excellence Award (2018) Ministry of Education Young Scientist Award (2017) Multiple Young Investigator Awards (2016)
Olivier Tougait is a Professor at the Chemistry, materials and processes for sustainable nuclear power (CIMEND) department within the Unité de Catalyse et Chimie du Solide (UCCS) at Université Lille . He specializes in solid-state chemistry, nuclear materials, and actinide-based compounds, with a focus on understanding fuel cycle processes for nuclear energy. Academic Background: PhD in Chemistry (1998, Université de Rennes1), Postdoctoral Fellow at Northwestern University (1998-2000). Career: Lecturer at Rennes1 (2000-2014), now Professor at UCCS since 2014. Collaborations include the French Alternative Energies and Atomic Energy Commission (CEA) , Orano , and Framatome . Research Interests: Actinide-based intermetallic compounds Phase diagrams of nuclear materials Magnetocaloric properties Fuel cycle process optimization Synthesis and thermodynamic behavior of uranium alloys Collaborative industrial nuclear R&D Publications since 2012 focus on: Uranium-molybdenum fuel characterization Germanium/Aluminum substitution in actinide systems Thermal stability of uranyl peroxide nanoclusters Crystallographic analysis of heavy-fermion materials Labs: Directs the joint research laboratories LR4CU and LRC PUMA, which collaborate with Orano and Framatome on nuclear fuel cycle innovations.
Oliver Kosut is an Associate Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has worked since August 2012. He was promoted to Associate Professor in 2018 and received the NSF CAREER award in 2015. His research spans information theory, machine learning, cybersecurity, and power systems, with a focus on theoretical foundations and applications to privacy, security, and smart grid resilience. Education: B.S. in Electrical Engineering and Mathematics from MIT (2004), Ph.D. in Electrical and Computer Engineering from Cornell University (2010) His recent work explores differential privacy, adversarial robustness in decentralized networks, and information-theoretic approaches to cybersecurity. He advises graduate students with strong mathematical backgrounds, particularly those interested in fundamental theory for applied problems. Scientific accolades include the IEEE Information Theory Society Distinguished Lecturer (2023–2024) and NSF CAREER award. Key research areas: Information Theory, Privacy, Machine Learning, Power System Security Students: Obai Bahwal, Atefeh Gilani, Naima Tasnim (current); Nima Bazargani, Andrea Pinceti, Jingwen Liang, Zhigang Chu, Fatemeh Hosseinigoki, Nematollah Iri, Kousha Kalantari, Roozbeh Khodadadeh (former)
Alan Briones Delgado is a researcher at the La Salle School of Engineering , Universitat Ramon Llull , with a focus on Internet of Things , Cybersecurity , and Transport Protocols . His work spans projects funded by the European Commission and national grants, including EXCEL4HOUSING4.0 , WeB-Nimbus , and NG-SOC , addressing challenges in cloud computing education, ecological monitoring, and security operations. His research integrates Artificial Intelligence and Wireless Sensor Networks for sustainable solutions. Key research areas include Quality of Service in heterogeneous networks, Environmental Conservation via IoT, and Teaching and Learning strategies for Big Data. Projects like EcoSentinel and BTL-COP highlight his commitment to Environmental Monitoring and Community Policing applications. His collaborations extend to institutions in the UK , Albania , and Western Balkans . Contact: alan.briones@salle.url.edu
Luciano Lavagno is a Full Professor at the Department of Electronics and Telecommunications, Polytechnic University of Turin, with over two decades of academic and research contributions. His work bridges hardware acceleration, low-power electronics, and intelligent system design. Research Focus: Hardware-accelerated machine learning, high-level synthesis (HLS) for FPGA/ASIC, heterogeneous CPU/GPU/FPGA platforms Key Projects: SPACE (predictable acceleration), REBECCA (secure AI acceleration), HPC-National Center (quantum computing), and oral history preservation via "Ti racconto una storia" initiative His recent publications analyze CNN inference optimization, subgraph isomorphism, and superword-level parallelism exploitation. Lavagno supervises multiple PhD students working on FPGA acceleration, neural network hardware, and embedded systems. As Principal Investigator for national and EU-funded projects (PRIN, JTI-ECSEL, PNRR), he drives advancements in sustainable computing infrastructure. His patented technologies include MIx&Latch timing methodology, capacitive sensing innovations, and 5G acceleration frameworks.
Anna Stuhlmacher is an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University. Her research focuses on the optimization of uncertain distributed energy resources (DERs) and the coordination of the power grid with other critical infrastructure systems including water and agricultural networks. Dr. Stuhlmacher received her academic credentials from prestigious institutions: PhD in Electrical Engineering, University of Michigan MS in Electrical Engineering, University of Michigan BS in Electrical Engineering, Boston University Her research program addresses a critical challenge in modern energy infrastructure: how distributed generation, storage, and flexible loads can be optimized to provide grid flexibility while coordinating with other essential infrastructure systems. Dr. Stuhlmacher specializes in modeling and optimizing the inherent flexibility and uncertainty propagation between power systems and other infrastructure systems such as drinking water, wastewater treatment, and agricultural systems. This interdisciplinary approach is vital for improving grid reliability, particularly during periods of network stress, by increasing demand flexibility through coordinated management of multiple infrastructure systems. Dr. Stuhlmacher's publication record reveals a consistent progression from fundamental optimization techniques for water distribution networks to more complex systems involving wastewater treatment biogas and agrivoltaics. Her research demonstrates sophisticated application of advanced optimization methods including chance-constrained programming, robust optimization, and machine learning techniques like input convex neural networks to address uncertainty in coupled infrastructure systems. The majority of her work focuses on the water-power nexus, with recent expansion into agrivoltaics as renewable energy and food production compete for land resources. Her notable achievements include: Best paper award for the Electric Energy Systems Track, HICSS 2025 Dr. Stuhlmacher actively secures research funding as Principal Investigator on multiple significant grants including an NSF award focused on biogas from wastewater treatment, a PSERC award on flexible load dispatch (as Co-PI with Georgia Tech researchers), and a Michigan Tech Research Excellence Fund grant on agrivoltaics. While she indicates she is not actively seeking graduate students for the 2025-26 academic year, she remains open to working with exceptional students with strong foundations in power systems and mathematics. Her undergraduate teaching includes courses on Distributed Energy Resources, Electrical Energy Systems, and Power System Optimization, building on her previous teaching experience at the University of Michigan. Her research leverages Michigan Tech's DOE-designated Regional Test Center for Emerging Solar Technologies, particularly for her agrivoltaics research. She has established connections with national laboratories including NREL, where she interned during her PhD studies, and maintains active collaborations with researchers at institutions like Georgia Tech. Her work bridges theoretical optimization techniques with practical applications that have immediate relevance to utility companies and infrastructure operators.
Erin Bell is a Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire . She holds a Ph.D. in Structural Engineering from Tufts University and has extensive experience in structural health monitoring, finite element modeling, and infrastructure sustainability. B.C.E., Georgia Institute of Technology M.S., Civil Engineering, Tufts University Ph.D., Structural Engineering, Tufts University Her research focuses on structural health monitoring, bridge condition assessment, and integrating AI techniques like artificial neural networks and deep reinforcement learning for infrastructure asset management. Recent work includes equitable maintenance strategies for aging bridges in flood-prone zones and tidal energy conversion for sustainable bridge monitoring systems. Key trends in her publications include the application of machine learning to structural analysis, finite element model calibration, and climate change adaptation in transportation infrastructure. She has led projects on deep reinforcement learning for bridge scour maintenance, modal-based uncertainty quantification, and multi-scale modeling of steel bridges. Grants and Collaborations : Erin Bell has secured funding from the National Science Foundation (NSF) , US Department of Energy (DOE) , and New Hampshire Department of Transportation . Notable projects include the Living Bridge initiative for tidal energy-powered smart infrastructure and statewide data exchange systems for bridge condition assessment.
Kamal Al Haddad is a Lecturer in the Department of Electrical Engineering at École de technologie supérieure (ÉTS). He holds a Doctorate from INTP, Toulouse, and advanced degrees from UQTR. His research focuses on power electronics, renewable energy integration, and smart grid technologies. He leads the GREPCI research group, specializing in Power Electronics and Industrial Control. Education: B.Eng., M.Sc.A. (UQTR), Doctorate (INTP, Toulouse). Research interests span energy conversion, industrial electronics, power quality, and electromagnetic interference. He emphasizes sustainable energy solutions, electric traction systems, and high-efficiency power sources. His work includes developing advanced power electronic converters and grid stability solutions. Recent articles highlight advancements in modular converters for STATCOM, AI-driven fault detection in hydrogenerators, and renewable energy policy frameworks. He has received notable awards, including the 2014 IEEE Eugene Mittelmann Prize and Fellowships from IEEE and other institutions. Supervised over 60 students, including doctoral theses on topics like hydrogenerator diagnostics, EV charging systems, and renewable energy integration. His research also involves real-time simulation of power systems and FPGA-based implementations. Labs/Teams: GREPCI – Power Electronics and Industrial Control Research Group, leading projects on smart grids and energy efficiency.
