Bri-Mathias Hodge is an Associate Professor at the University of Colorado Boulder's Department of Electrical, Computer, and Energy Engineering, while also serving as Chief Scientist at NREL's Grid Planning and Analysis Center and Associate Director of the Renewable and Sustainable Energy Institute. His career spans academic and industry roles in power systems analysis and renewables integration. Bachelor's, Master's, and PhD in Chemical Engineering from Carnegie Mellon, Åbo Akademi, and Purdue respectively His research focuses on Renewables integration , Net-zero energy systems , and Wind and solar power forecasting . Recent publications explore energy storage dispatch modeling, electric vehicle wireless charging impacts, and AI-driven grid foundation models. His 299+ research outputs (2011-2025) emphasize Renewable energy grid integration Power system forecasting AI applications in energy systems EV infrastructure impacts Carbon capture technologies Scientific recognition includes Fulbright Fellowship (2016) Five IEEE Power and Energy Society Best Paper Awards (2015-2018) Active in professional networks as Member of IEEE Transactions on Sustainable Energy Member of multiple IEEE working groups Contributor to Journal of Renewable and Sustainable Energy
Professor Nagi Gebraeel serves as the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, where his research integrates predictive analytics, machine learning, and optimization for industrial IoT applications. His work focuses on real-time equipment diagnostics, prognostics, and operational decision-making in critical infrastructure systems. Education: Ph.D. in Industrial Engineering (2003), Purdue University M.S. in Industrial Engineering (1998), Purdue University Research Focus: Dr. Gebraeel develops statistical learning algorithms for IoT-enabled maintenance, repair, and operations (MRO), with emphasis on federated learning frameworks for distributed fault diagnosis and cybersecurity protection against Industrial Control System (ICS) attacks. His research spans manufacturing, power generation, and deep space habitats through NASA's HOME Space Technology Research Institute, where he pioneers self-aware habitat systems. Recent work addresses data heterogeneity in high-consequence industrial environments using causal-informed analytics. Publication Trends: His 2024-2025 publications demonstrate a strong trajectory toward distributionally robust optimization for maintenance logistics, federated learning architectures for distributed fault diagnosis, and prognostics for complex systems like offshore wind farms and industrial robots. Key themes include handling imbalanced data in fault diagnosis, state-space representations for interdependent systems, and cybersecurity integration in manufacturing networks. Awards and Recognition: NSF CAREER Award (2007) SAE Aircraft Electrical Power System Recognition Award (2008) SAE Materials Modeling and Testing Recognition Award (2006) IEEE-AUTOTESTCON Certificate (2006) Fellow of the Institute of Industrial and Systems Engineers Advising and Funding: Dr. Gebraeel mentors doctoral students including Michael Ibrahim (2025 IISE Best Student Paper winner), Heraldo Rozas (now Assistant Professor at University of Chile), Ayush Mohanty, and Nazal Mohamed. He secured a $500,000 NSF grant in August 2025 for AI-driven cybersecurity in distributed manufacturing networks and leads NASA-funded research on deep space habitat systems. His work bridges academic research with industry applications through Georgia Tech's Strategic Energy Institute collaborations. Research Infrastructure: He directs the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute and leads the Predictive Analytics and Intelligent Systems (PAIS) research group. Previously, he served as associate director of Georgia Tech's Strategic Energy Institute (2014-2019), fostering data science applications in energy systems.
Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.
Dr. Mark B. Smith is an Associate Professor in Modern European History at the University of Cambridge’s Department of History. His research focuses on Soviet and European history, particularly the Soviet Union’s social structures, welfare policies, urban development, and cultural dynamics from Stalinism through late socialism. He teaches courses on welfare states, Soviet life, and 20th-century European violence. Key research areas include Soviet urban housing programs, welfare state evolution, and Cold War-era cultural exchanges. His publications analyze topics like Khrushchev’s housing reforms, post-Stalin urban welfare systems, and Soviet pension policies. Smith has co-edited special issues on European urban societies and authored books such as *The Russia Anxiety* (2019) and the forthcoming *Exit Stalin* (2026). He advises doctoral students on Soviet history, welfare states, and Cold War topics. His work bridges legal history, social policy analysis, and cultural studies, emphasizing how everyday life reflected broader ideological and structural changes in Soviet society.
Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech and a field faculty member in Computer Science at Cornell University. Previously, she was a postdoctoral researcher at Microsoft Research New England and completed her PhD at Stanford University’s Institute for Computational & Mathematical Engineering. Her research focuses on algorithmic fairness, computational social science, and causal inference in public health, addressing disparities in automated systems like speech recognition and policy decision-making. Education : PhD, Stanford Institute for Computational & Mathematical Engineering MA/MS, Stanford University BA/BS, Massachusetts Institute of Technology Research Interests : Dr. Koenecke’s work bridges economics and computer science, emphasizing equity in AI systems. Key areas include: Algorithmic bias in speech recognition (e.g., racial disparities in voice assistants) Fairness in policy tools like environmental justice data systems Causal analysis in public health interventions Ethical implications of large language models in education Media & Impact : Her research has been featured in outlets like New York Times , Science , and Scientific American . Notable studies include exposing racial gaps in speech-to-text systems and advocating for inclusive dataset development. She also explores societal impacts of AI in education and governance. Awards : Sloan Research Fellow in Computer Science Forbes 30 Under 30 in Science NSF Awards Cornell CIS Teaching Excellence Award (2024) Teaching & Outreach : She teaches courses like Designing Fair Algorithms and Data Science for Global Development , emphasizing interdisciplinary collaboration. Her PhD Professionalization course addresses hidden curricula in academia. She advises on AI ethics for nonprofits, tech companies, and government agencies.
Christopher R. Knittel is the George P. Shultz Professor of Energy Economics and Professor of Applied Economics at the Massachusetts Institute of Technology (MIT) Sloan School of Management . He serves as Director of MIT’s Center for Energy and Environmental Policy Research (CEEPR), Director of the MIT Climate Policy Center, and Deputy Director for Policy at the MIT Energy Initiative (MITEI). PhD, University of California, Berkeley (1999) M.A., University of California, Davis (1996) B.A., California State University, Stanislaus (1994) His research focuses on energy economics, environmental policy, and machine learning applications in energy systems. He leads interdisciplinary initiatives like the Climate Action Through Education program for high schools and co-directs the E2e Project with UC Berkeley and University of Chicago. Recent publications analyze data center energy dynamics, hydrogen technologies, Russian oil sanctions, and transportation policy reforms. His work emphasizes policy cost-effectiveness, emissions equity, and energy market responses to regulation.
Dr. Can Bayram is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He serves as a resident faculty member at the Nick Holonyak, Jr. Micro and Nanotechnology Laboratory and leads the Innovative Compound Semiconductor Laboratory (ICORLAB). His research focuses on semiconductor technologies, particularly cubic GaN and diamond-based devices for next-generation electronics and photonics. Current research includes cubic GaN quantum wells for green LEDs Diamond semiconductor devices for high-power applications Photonic technologies and nanoscale device fabrication Investigation of efficiency cliff phenomena in micro-LEDs Recent Article Trends : His work spans photonic devices, power electronics, and nanoscale systems, with particular emphasis on diamond photoconductive switches and cubic GaN efficiency improvements. Notable contributions include record-breaking diamond diodes and efficiency droop mitigation in green LEDs. Scientific Awards : IEEE Electron Devices Society Early Career Award NSF CAREER Award AFOSR Young Investigator Award Dean's Award for Early Innovation (2024) SPIE Fellow (2025) Editor's Pick & Front Cover Article, Applied Physics Letters (2024) Dr. Bayram teaches courses in electronics, including ECE 110: Introduction to Electronics, ECE 443: LEDs and Solar Cells, and ECE 500: ECE Colloquium.
Anthony Rollett is a Professor in the Department of Materials Science and Engineering at Carnegie Mellon University , where he has been a faculty member since 1995. He serves as the Principal Investigator and Co-Director of the NASA-supported Institute for Model-Based Qualification & Certification of Additive Manufacturing (IMQCAM) and co-director of the Next Manufacturing Center . Prior to CMU, he held leadership roles at Los Alamos National Laboratory (1991-1995). Education: Ph.D., Materials Engineering, Drexel University (1987) MA, Metallurgy and Materials Science, Cambridge University (1977) Research Interests: Rollett’s work focuses on microstructural evolution and microstructure-property relationships in 3D using experiments and simulations. His expertise spans additive manufacturing , metal 3D printing , materials for energy systems , grain growth , recrystallization , and stereology , with techniques like high-energy diffraction microscopy (HEDM) and dynamic x-ray radiography (DXR) . Scientific Contributions: He has over 320 peer-reviewed publications and an h-index >80 . His recent articles highlight machine learning for laser processing , fatigue analysis of additively manufactured alloys, and design optimization for heat exchangers in supercritical CO2 and solar thermal applications . Scientific Awards: Fellow of ASM International (1996) Fellow of the Institute of Physics (UK) (2004) Fellow of The Minerals, Metals & Materials Society (TMS) (2011) Cyril Stanley Smith Award (TMS, 2014) Member of Honor, French Metallurgical Society (2015) US Steel Professor (2017) Francqui International Professor (2020-2021) International FAME Award (2023) Leadership & Impact: Rollett co-led the development of a NASA Space Technology Research Institute for additive manufacturing and established a new master’s program in additive manufacturing (2018). His research group is funded by industry , federal agencies , and Pennsylvania state grants . He also serves on the Basic Energy Science Advisory Committee and Defense Programs Advisory Committee for the Department of Energy.
Min Hu is an Assistant Professor in the Department of Economics, Philosophy, and Political Science at the University of British Columbia, Okanagan Campus. He holds an adjunct position at Dalhousie University's School of Health Administration. His primary research focuses on Health Economics, Applied Microeconomics, and Labour Economics, with an emphasis on vulnerable populations in Canada, Indigenous health, income inequalities, and cancer economics. He teaches courses in Microeconomics, Health Economics, and Statistics. Education: PhD in Economics (2019), Dalhousie University; Postdoctoral Fellowship in Health Economics (2019–2020), Dalhousie University. Previous degrees include an M.A. (2014–2016) and B.A. in Economics from Dalhousie University. Research interests include the socio-economic determinants of health disparities, particularly among Indigenous peoples and immigrants, and the economic impacts of cancer-related policies. He employs applied econometric methods to analyze labor market outcomes and social policy effectiveness. His recent articles explore sustainable biomass utilization, energy policies, and environmental engineering innovations. Key themes include biomass supply chain optimization, CO₂ reduction strategies, and waste-to-energy technologies. Scientific Awards: Recipient of SSHRC New Frontiers Grant (2023–2026), CIHR Team Grant (2024–2026), and multiple UBC startup grants. His work is supported by SSHRC, CRDCN, and Indigenous Services Canada. He advises students in Economics and Global Studies (IGS) programs. Grants and funding span health economics, Indigenous well-being, and sustainable energy systems. Collaborations include interdisciplinary teams in health administration and environmental policy. Labs/Teams: Engaged with UBC's Okanagan Sustainability Initiatives and Dalhousie's Health Economics Research Group. Affiliated with professional organizations like the American Economic Association and Canadian Health Economics Study Group.
Anwar Hithnawi is an Assistant Professor of Computer Science at the University of Toronto, where he leads the Privacy Preserving Systems Lab (PPS Lab). His research focuses on data privacy, applied cryptography, and secure systems, with emphasis on privacy-preserving machine learning, federated learning, and encrypted data processing. He holds a Ph.D. in Computer Science from ETH Zurich and was a postdoctoral researcher at UC Berkeley. Previously, he served as an Ambizione Fellow and research group leader at ETH Zurich. Research Interests: Data Privacy & Security Applied Cryptography (Homomorphic Encryption, Zero-Knowledge Proofs) Privacy-Preserving Systems (Federated Learning, Secure Analytics) IoT Security & Privacy Secure Collaborative Learning Awards: Google Research Award SNF Ambizione Grant ETH Medal for Outstanding Master Thesis (student Lukas Burkhalter) Microsoft Research Ph.D. Award (student Lukas Burkhalter) Lab Activities: The PPS Lab develops systems for privacy-preserving computation, secure collaborative learning, and encrypted data stream processing. Notable projects include Zeph, HECO, and Cohere. Recent achievements include acceptance of DPolicy at IEEE S&P 2025 and RoFL at Oakland 2023.
Dr. Xinan Zhang is an Associate Professor in the School of Engineering at The University of Western Australia (UWA), specializing in Electrical, Electronic, and Computer Engineering. He holds a BEng from Fudan University (2008) and a PhD from Nanyang Technological University (2014). Before joining UWA in 2019, he held roles as a Lecturer and Research Fellow in Singapore and Australia. His research focuses on power electronics, electrical machine drives, and renewable energy, with over 60 top-tier publications. He is the Portfolio Lead for Industry Engagement in UWA's School of Engineering and co-leads the Power and Clean Energy (PACE) research group. Education: BEng in Electrical Engineering, Fudan University (2004–2008) PhD in Electrical Engineering, Nanyang Technological University (2010–2014) Research Interests: Dr. Zhang’s work spans power electronics, renewable energy systems, energy storage, and smart grid technologies. He emphasizes practical applications, such as battery management systems for vanadium redox flow batteries and adaptive control strategies for microgrids. His contributions address challenges in energy efficiency, grid stability, and sustainable power solutions. Articles & Trends: Recent publications focus on advanced control algorithms for inverters, battery modeling, and renewable energy integration. His work combines data-driven methods with traditional control theory to enhance system efficiency and reliability. Notable areas include DC microgrid control, vanadium redox flow battery optimization, and model predictive control for power electronics. Awards: Listed in Stanford University’s Top 2% Scientists (2020–2022) Grants & Collaborations: He leads or co-leads projects funded by the Australian government and industry partners, including the GenX Betavoltaic Battery Pilot Manufacturing Process and Mine Electrification . These projects aim to advance clean energy technologies and industrial applications. Labs & Teams: As co-lead of the PACE group, he fosters interdisciplinary collaboration to tackle global energy challenges, aligning with UN Sustainable Development Goals for affordable and clean energy (SDG 7).
Wei Zhang is a tenured Professor at the Southern University of Science and Technology (SUSTech) , Shenzhen, China, and a Senior Member of IEEE. He serves as Associate Editor for IEEE Transactions on Control System Technology and leads the CLEAR Lab (Control & Learning for Robotics and Autonomy) within the School of Automation and Intelligent Manufacturing (AiM). His career spans institutions including the University of California, Berkeley (postdoc), and The Ohio State University (Assistant/Associate Professor). Education: PhD in Electrical Engineering from Purdue University (2009), MS in Electrical and Computer Engineering from University of Kentucky (2005), BS in Automation from University of Science and Technology of China (2003) Research Interests focus on integrating control theory, optimization, and machine learning to develop intelligent systems. Key areas include: Legged Robots: Dynamic locomotion control, bio-inspired gait design, and push recovery mechanisms Autonomous Systems: Real-time motion planning, obstacle avoidance, and safe navigation in adversarial environments Smart Grids: Distributed control for energy systems and transactive energy optimization Machine Learning: Reinforcement learning for robotics, Q-learning convergence analysis, and hybrid control-learning frameworks Publication Trends highlight interdisciplinary work at the intersection of robotics and control systems. His recent 2024 papers address: Whole-body control for wheeled-quadrupedal robots Geometric object pose refinement in computer vision Task-space Riccati feedback for underactuated systems Teacher-student reinforcement learning architectures Scientific Awards include: 2016 : NSF CAREER Award 2015 : Lumley Research Award (Ohio State University) 2013 : AFOSR Summer Faculty Fellowship 2018 : National Distinguished Expert (Young, China) 2019 : International Underwater Robot Competition 2nd Prize (team advisor) Academic Leadership involves editorial roles at IEEE Transactions on Control System Technology and IEEE Transactions on Power Systems. His lab provides state-of-the-art robotics platforms including quadruped robots, Kuka manipulators, and UAVs for algorithm validation. Research collaborators span The Ohio State University , UC Berkeley , CMU , and The University of Hong Kong .
Joseph Bentsman is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He also holds affiliate appointments in the Department of Aerospace Engineering (since 2015) and the Department of Electrical and Computer Engineering (since 2018). His academic journey began with an M.S. from Byelorussian Polytechnic Institute in Minsk, USSR (1979), followed by a Ph.D. in Electrical Engineering from Illinois Institute of Technology (1984). Professor Bentsman's research focuses on control of nonlinear and distributed parameter systems, nonlinear oscillations, network control, stability theory, and stochastic multiscale methods. He pioneered a new class of dynamical systems with active singularities that admit control actions during singular phases of motion, which represent a novel category of hybrid systems characterized by impulsively controlled discrete transitions. His recent work has expanded into biomedical applications, particularly thermophysical modeling of tissue during electrosurgery and control of phase change processes. His recent publications (2021-2024) reveal a strong trend toward biomedical applications of control theory, particularly in modeling heat conduction in biological tissues, electrosurgical processes, and phase change phenomena. Approximately 60% of his recent work focuses on biomedical applications, while the remainder continues his foundational work on nonlinear control systems, distributed parameter systems, and systems with active singularities. Key subfields include Stefan problems, enthalpy-based control, telegraph equation modeling, and PDE-based control of complex physical processes. NSF Presidential Young Investigator Award (1989) Life Fellow of American Society of Mechanical Engineers Life Senior Member of IEEE IEEE Control Systems Society Technical Committee Chair on Power Generation (2015-2019) International Society of Automation POWID Achievement Award (2014) 2018 AIST Computer Applications Best Paper Award Featured in 'People in Control', IEEE Control Systems Magazine (2018) Professor Bentsman has been instrumental in developing educational approaches that integrate signal processing, instrumentation, control, and machine learning, as evidenced by his two textbooks. His work on the steel continuous casting process, particularly the mold oscillation system, has led to practical industrial applications. He has also made significant contributions to power plant control systems and boiler/turbine control. His research group appears to focus on both theoretical control systems development and practical implementation in industrial and biomedical settings, with strong connections to steel manufacturing, power generation, and medical device industries.
Professor Winston Hsu is a distinguished faculty member in the Department of Computer Science and Information Engineering at National Taiwan University, where he has served as a full professor since 2015. He is the co-director of the Communications and Multimedia Laboratory (CMLab) and founder of the MiRA (Multimedia indexing, Retrieval, and Analysis) research group. Additionally, he serves as the Founding Director for NVIDIA AI Lab at NTU, the first such lab in Asia. Professor Hsu received his Ph.D. in Electrical Engineering from Columbia University in 2007 under the supervision of Professor Shih-Fu Chang. Prior to his academic career, he was a founding engineer and research manager at CyberLink Corp., now a public image/video software company. National Taiwan University (2007-Present): Professor (2015-Present), Assistant/Associate Professor (2007-2015) MobileDrive (2021-2024): CTO and Vice President (Joint Venture between Foxconn and Stellantis) IBM TJ Watson Research Center (2016-2017): Visiting Scientist Microsoft Research Redmond (2014): Visiting Researcher Columbia University (2007): Ph.D. in Electrical Engineering Professor Hsu's research focuses on machine learning, computer vision, large-scale image and video search and recognition, and embedded AI. His work spans from fundamental research in visual recognition to practical applications in automotive systems, medical imaging, and e-commerce. He has pioneered work in disguised face recognition, low-resolution face hallucination, 3D model search, and virtual try-on systems. His current research emphasizes Embodied AI, integrating perception, action, and learning technologies for applications in automotive and robotics domains. His research group has produced numerous influential publications, particularly in top computer vision and multimedia conferences like CVPR, where they won first place in the Disguised Face Recognition competition in 2018. Their work spans diverse application areas including security, medical diagnostics, automotive systems, and e-commerce solutions, demonstrating strong translation from academic research to real-world impact. IBM Research Pat Goldberg Memorial Best Paper Award (2018) First Place, IARPA Disguised Faces in the Wild Competition (CVPR 2018) Best Brave New Idea Paper Award, ACM Multimedia 2017 NVIDIA AI LAB Award (First in Asia, 2016) First Place, MSR-Bing Image Retrieval Challenge (2013) World's Top 2% Scientists (2023) Professor Hsu actively mentors students and researchers, with his group consistently recruiting PhD students, postdocs, and research assistants. He has successfully bridged academia and industry through multiple collaborations, including his role as CTO at MobileDrive (a joint venture between Foxconn and Stellantis) from 2021-2024. His research has been supported by significant industry partnerships with Microsoft, IBM, and NVIDIA, as well as government grants from Taiwan's Ministry of Science and Technology. His laboratory, the Communications and Multimedia Laboratory (CMLab), maintains strong industry connections and focuses on cutting-edge research in visual AI. The lab has produced numerous award-winning projects and maintains active collaborations with global technology companies, particularly in the automotive and consumer electronics sectors.
James C. Hoe is Professor of Electrical and Computer Engineering at Carnegie Mellon University (College of Engineering). He is on sabbatical at MangoBoost and directs research in computer architecture, reconfigurable computing, and high-level hardware design. Education Ph.D., Electrical Engineering and Computer Science, MIT (2000) M.S., Electrical Engineering and Computer Science, MIT (1994) B.S., Electrical Engineering and Computer Science, UC Berkeley (1992) Research Interests Professor Hoe’s work spans computer architecture , reconfigurable computing , FPGA architectures , and high-level hardware synthesis . His group created the CoRAM abstraction for virtualized FPGA computing and leads efforts in power-efficient accelerators, in-network computing, and security-oriented FPGA systems. Scientific Awards IEEE Fellow (2013) Intel Outstanding Researcher Award (2021) Research Funding & Projects Intel / VMware Crossroads 3D-FPGA Academic Research Center – co-leading exploration of FPGA roles in future datacenters. DARPA BRASS program ($2.7 M, 4 years) – ensuring long-lived software systems remain robust to resource changes. Pigasus open-source IDS – world’s fastest FPGA-accelerated intrusion-detection system (100 Gb/s on one server). Labs & Teams He heads activities within the Computer Architecture Lab at Carnegie Mellon (CALCM) , supervising graduate researchers on CoRAM++, SPIRAL autotuning, and FPGA overlays for stream processing.
