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. 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 .
Lina Bertling Tjernberg is a Professor at the Department of Electrical Engineering, KTH Royal Institute of Technology, and Deputy Head of the School of Electrical Engineering and Computer Science (EECS) with responsibility for research conditions and impact. She served as Director of KTH's Energy Platform during 2018-2024 and holds memberships in IVA (Swedish Royal Academy of Engineering Sciences) and the IEEE Power & Energy Society. Research Focus: Applying mathematics (statistics, optimization, life cycle assessment) to enhance reliability and predictive maintenance in electric power systems, with emphasis on future electricity grids integrating microgrids, battery storage, HVDC, nuclear/pumped/hydro/wind/solar power, hydrogen, and electrified transport. Collaborations: Engaged with Comillas Pontifical University (Madrid), Addis Ababa University, Norwegian University of Science and Technology (NTNU), and IEA Wind. Key Research Trends: Recent articles highlight advancements in microgrid control (2025), SMR nuclear energy integration (2025), AI-driven asset management (2024), hydrogen sector coupling (2024), and renewable forecasting techniques (2024). Awards: 2021 Power Woman of the Year 2022 Energy Power List (Sweden’s top 20 energy influencers) Leadership Roles: Swedish Electromobility Center (SEC) board Chair of Swedish Electrical Standards (SEK Svensk Elstandard) Member, IEEE PES ISGT Europe steering committee
Rebecca Schulman is an Associate Professor in the Department of Chemical and Biomolecular Engineering at the Whiting School of Engineering, Johns Hopkins University. She holds secondary appointments in Chemistry and Computer Science and is affiliated with multiple interdisciplinary institutes, including the Institute for NanoBioTechnology, the Hopkins Extreme Materials Institute, the Chemistry-Biology Interface Program, the Center for Cell Dynamics, and the Laboratory for Computational Sensing and Robotics. She currently co-directs the Passport to Future Technology Leadership program for PhD students. Research Interests: Schulman's research lies at the intersection of DNA nanotechnology, synthetic biology, and smart materials. Her group develops intelligent, adaptive biomolecular materials and nanostructures by integrating concepts from materials science, biochemistry, circuit design, and soft matter physics. The team focuses on engineering dynamic self-assembly processes using DNA to create reconfigurable materials, molecular circuits, and autonomous soft micro-robots. Key themes include self-healing nanostructures, feedback-regulated crystallization, programmable hydrogels, and synthetic genetic networks for materials control. Publication Trends: Her recent publications demonstrate a consistent focus on using DNA-based chemical reaction networks to program spatial and temporal behavior in materials. The work spans from fundamental mechanisms like catalytic polymerization and crystal growth regulation to applications in soft robotics, self-wiring circuits, and synthetic pattern formation. The research is highly interdisciplinary, combining synthetic biology with materials engineering to achieve life-like functionalities in non-living systems. Scientific Awards: AIMBE Fellowship Award Vannevar Bush Faculty Fellowship Award Hartwell Individual Biomolecular Research Award President’s Early Career Award in Science and Engineering (PECASE) DARPA Young Faculty Award DARPA Directors Fellowship NSF CAREER Award Turing Scholar Award DOE Early Career Award Advising and Grants: Schulman mentors graduate students and leads a vibrant research group focused on next-generation biomolecular engineering. Her work is supported by major federal grants, including the NSF CAREER, DOE Early Career, DARPA, and the Vannevar Bush Fellowship—a prestigious Department of Defense award for basic research. She is actively involved in training future leaders through programs like the Passport to Future Technology Leadership. Labs and Teams: The Schulman Lab at Johns Hopkins is a multidisciplinary team working on DNA-powered materials and molecular programming. The lab is embedded within several collaborative centers, enabling strong cross-departmental and cross-institutional research. Their work combines experimental biochemistry with theoretical modeling to design and implement complex molecular systems.
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.
Dr. Nanpeng Yu is a Full Professor at the University of California, Riverside , serving as Vice Chair and Graduate Advisor in the Department of Electrical and Computer Engineering . He maintains cooperative faculty affiliations with the Department of Computer Science and Department of Statistics , and directs the Energy, Economics, and Environment Research Center at UCR. Education : B.S. in Electrical Engineering from Tsinghua University (2006), M.S. in Electrical Engineering and Economics, and Ph.D. from Iowa State University (2010) Prior Industry Experience : Senior Power System Planner and Project Manager at Southern California Edison (2011-2014) Dr. Yu’s research bridges smart grid technologies , data-driven optimization , and machine learning for energy systems. Key areas include voltage control , renewable energy integration , transportation electrification , and grid resilience . His recent work focuses on physics-informed graph learning for unit commitment problems and adversarial purification in power system classifiers. The AI-Energy Nexus Laboratory under Dr. Yu has produced impactful publications in Applied Energy , IEEE Transactions on Power Systems , and Transportation Research series, covering topics like heavy-duty EV charging infrastructure and dynamic distribution network reconfiguration . Lab members have won the American-Made Digitizing Utilities Grand Prize ($300,000) from the U.S. DOE. Leadership Roles : Chair, IEEE Power and Energy Society Distribution System Operation and Planning Subcommittee Chair, IEEE Power and Energy Society Working Group on Data-Driven Modeling for Power Distribution Networks Associate Editor, IEEE Transactions on Smart Grid and IEEE Power Engineering Letters Scientific Awards : Regents Faculty Fellowship Regents Faculty Development Award Multiple IEEE Best Paper Awards
Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.
Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.
Lyubov Zhyznomirska is an Associate Professor of Political Science at Saint Mary's University, specializing in European studies, migration governance, and democratization processes. She holds a PhD in Political Science from the University of Alberta (2012), with a focus on EU migration cooperation with Eastern neighbors. Her current research examines the reception of war-displaced Ukrainians in Canada, the UK, and Germany. She has held a Jean Monnet Post-Doctoral Fellowship and is active in research networks such as the European Community Studies Association-Canada and the Migration Research Network. Teaching focuses on comparative politics, European Union politics, and migration studies. Recent SSHRC-funded projects include studies on Ukrainian refugee integration in Nova Scotia and the impact of temporary status on newcomer youth. Awards include the Jean Monnet Post-Doctoral Fellowship (2011-2012). Research interests include forced displacement, post-war societies, and EU-Ukraine-Russia dynamics. She co-edited The Routledge Handbook of the Politics of Migration in Europe (2019) and publishes in journals such as Comparative European Politics . Professional roles include Treasurer of ECSA-Canada (2021-2024) and President of the Atlantic Provinces Political Science Association (2022-2023).
Ferdinando Fioretto is an Assistant Professor of Computer Science at the University of Virginia, leading the Responsible AI for Science and Engineering (RAISE) group. His research focuses on foundational challenges in AI, privacy, fairness, and the intersection of machine learning and optimization. He holds a dual PhD in Computer Science from the University of Udine and New Mexico State University. Affiliations: University of Virginia (current), Syracuse University (former), Georgia Institute of Technology (postdoc), University of Michigan (research fellow) Education: PhD (Udine & NMSU), B.S. (University of Parma) Research Interests: Machine Learning, Responsible AI, Optimization, Differential Privacy, Algorithmic Fairness. His work emphasizes practical applications in energy systems, court scheduling, and privacy-preserving machine learning. Recent projects include neuro-symbolic diffusion models, fairness-aware optimization, and privacy guarantees in LLMs. Grants & Funding: NSF CAREER Award, Google Faculty Research Award, Amazon Research Award, NVIDIA Academic Grant, and grants from the LaCross Institute and 4-VA. His group collaborates with institutions like George Mason University and Virginia Tech. Key Awards: NSF CAREER (2022), IJCAI Early Career Spotlight (2022), Caspar Bowden PET Award (2022), ACP Early Career Researcher Award (2021) Labs/Teams: RAISE group at UVA, focused on trustworthy AI, fair optimization, and privacy-preserving systems. Active in organizing workshops like NeurIPS Algorithmic Fairness and AAAI Privacy-Preserving AI.
Carlos Errando Herranz serves as an Assistant Professor in the Quantum and Computer Engineering Division at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) and as a Principal Investigator at QuTech. His research focuses on developing scalable quantum photonic integrated circuits using semiconductor fabrication processes compatible with existing infrastructure for quantum internet applications. He received Bachelor's and Master's degrees from Universitat Politècnica de València (2013) and a PhD in Micro and Nanosystems from KTH Royal Institute of Technology (2018), followed by postdoctoral positions at KTH and MIT as a Marie Curie fellow. His lab investigates quantum photonics, integrated photonics, and color centers with emphasis on diamond tin-vacancy systems and silicon-based quantum emitters. Recent publications demonstrate strong expertise in tuning quantum emitters via strain engineering, heterogeneous integration of spin-photon interfaces, and MEMS-enabled reconfigurable photonics. Key advancements include cavity-enhanced quantum memories, superconducting detector integration, and spectral control of solid-state emitters for quantum networks. Dr. Herranz advises seven graduate students including PhD candidates Vicky Dominguez Tubio, Arjan Mejas, Matteo Pirro, Christian Primavera, Jan Riegelmeyer, and Elena Volkova, along with Master student Bram Zijlstra. His team comprises postdocs Lin Jin and Pat Laferriere, and interns Elsa Herranz Valiente and Ernest Staffetti Cruañas. The Errando Herranz Lab operates within QuTech's Quantum Internet Division at Delft University, maintaining specialized facilities for nanofabrication and optical characterization of quantum photonic devices. Current research directions include developing CMOS-compatible quantum memories operating at telecom wavelengths and scalable architectures for quantum repeaters.
Prof. Dr. Taner Akbay is a faculty member at Yeditepe University, Faculty of Engineering , Department of Materials Science and Nanotechnology Engineering. He has held academic positions at institutions including Kyushu University, Oita University, and Imperial College London. Education: PhD in Materials Engineering (1993, Imperial College London); Master’s (1989) and Bachelor’s (1986) degrees from Middle East Technical University. His research spans Materials Engineering , Metallurgy , and Solid Oxide Fuel Cells (SOFCs) , with a focus on oxide ion conductivity, laser surface treatment, and phase transformations. Recent work explores photocatalysis , anion intercalation , and CO2 reduction using computational and experimental approaches. Key article trends include SOFC optimization (2004–2009), strain effects on catalysts (2015–2020), and dual-carbon battery technology (2016–2020). His work bridges fundamental metallurgy and advanced energy materials . Scientific Awards: Postdoctoral Research Sponsorship Award (EPSRC, UK) JSPS Fellowship (Japan) Daiwa Adrian Prize (2016, UK) PhD Studentship at Imperial College (European Commission) He has supervised multiple PhD and Master’s theses, including projects on dual-carbon batteries , microwave absorption nanocomposites , and rare earth recovery . Administrative roles include Head of Department (2020–2021). Non-University Experience: Worked with Mitsubishi Materials Corporation (2001), Çolakoğlu Metalurji (2010), and National Research Council Canada (2009).
Nathan Schine is an Assistant Professor at the University of Maryland, specializing in quantum physics and quantum information science. He leads the Schine lab, which explores controlled coherent dynamics and engineered dissipation in quantum systems, particularly using optical cavities coupled to tweezer-trapped cold atoms. His research bridges atomic physics, quantum optics, and condensed matter physics. Education: B.A. in Physics, Williams College (2013) Ph.D. in Physics, University of Chicago (2019) Research interests focus on quantum many-body systems, optical cavities, and applications such as quantum information processing and ultra-coherent atomic clocks. The lab’s work includes developing state-of-the-art strontium tweezer array apparatuses for precision metrology and quantum simulation. Recent publications highlight advancements in Dicke state preparation, optical pumping of quantum Hall states, and cavity-enhanced measurements. Advising and grants involve mentoring graduate students and postbaccalaureate researchers, including Shardul Rao and Siddharth Taneja. The lab collaborates with groups like AMPED, QuICS, and RQS at UMD. Members include postdoctoral researchers and graduate students working on theoretical quantum optics and experimental setups. Labs/Teams: The Schine lab integrates atomic, optical, and condensed matter physics approaches to address fundamental and applied questions in quantum science.
Gary Rochelle is the Carol and Henry Groppe Professor in Chemical Engineering and a faculty member at the University of Texas at Austin . His research focuses on developing fundamental insights into kinetic and mass transfer phenomena in aqueous technologies for air pollution control and acid gas treating, particularly for carbon dioxide and mercury removal. Education: Ph.D. in Chemical Engineering from UC Berkeley (1977), M.S./B.S. from MIT (1971) His work addresses critical challenges in CO2 capture using amine scrubbing, including process design optimization, solvent degradation mitigation, and pilot plant validation. Recent studies emphasize energy efficiency, oxidation inhibition, and environmental impacts such as amine aerosol emissions. The Texas Carbon Management Program , which he contributes to, aims to improve amine scrubbing technologies for retrofitting power plants and enabling geological sequestration or enhanced oil recovery. His group has validated concentrated aqueous piperazine (PZ) with an advanced flash stripper as the most efficient open-literature system.
