Liangzhong Yao is a Professor and Director of the Smart Grid Research Institute at Wuhan University, China. He holds dual roles as Fellow of IEEE and IET, and has served in leadership positions such as Vice President of China Electric Power Research Institute (CEPRI) and Chair of IEC TC122. His expertise spans renewable energy grid integration, HVDC systems, smart grid technologies, and energy storage. Yao has led over 25 years of R&D projects with combined funding exceeding GBP 20 million and RMB 90 million, resulting in applied technologies in wind farms and HVDC grids. He has been awarded over 10 national and international accolades, including the IEC 1906 Standard Award and China Science and Technology Innovation Awards. Research focuses include AC-DC hybrid systems, distributed energy resource integration, and high-renewable energy grid operation. He has authored/co-authored 350+ publications, 60+ patents, and 4 books. Yao currently supervises 8 PhD and 10 Master's students, and serves on editorial boards of journals like CSEE Journal of Power Energy & Systems. His leadership roles in global standards bodies like IEC and CIGRE further highlight his contributions to advancing electrical engineering standards and practices.
Dr. Nemanja Stanišić is a Full Professor at Singidunum University's Faculty of Business, with a distinguished academic career spanning over 15 years. He holds a Ph.D. in Corporate Finance from Singidunum University (2010), an MBA in Finance from Lincoln University (2007), and a Bachelor's in Accounting from the University of Belgrade (2005). His expertise focuses on Corporate Finance, Banking, Audit, and Applied Statistical Analysis. His research integrates quantitative methods with economic theory, addressing topics such as audit opinion prediction using AI, tourism destination competitiveness, financial distress dynamics, and air pollution health impacts. He co-authored textbooks including Contemporary Exchange and E-business (2010) and Financial Statement Analysis (2024), and served as Editor-in-Chief of The European Journal of Applied Economics . He teaches courses from Financial Accounting to Advanced Financial Engineering at undergraduate, master's, and Ph.D. levels. The 15 most recent publications highlight his interdisciplinary approach: 7 in Finance/Audit, 5 in Tourism/Hospitality, and 3 in Environmental Health. Key trends include applying machine learning to audit quality (2023), multilevel modeling for hospitality satisfaction (2015-2019), and air pollution mortality analysis (2016). His work appears in high-impact journals like Tourism Management (IF 10.125) and Environmental Health (IF 4.986). He held administrative roles including Rector (2020-2021) and Vice President of Singidunum University. He served as Vice Dean for Student Affairs (2010-2011) and participated in TEMPUS projects for educational reform. He mentors graduate students extensively, advising 100+ bachelor's, 57 master's, and 4 doctoral theses, including international candidates. His visiting professorship at Bangkok's ICO NIDA and teaching in Austria-Singidunum joint programs reflect global engagement. Professional development includes advanced training at Utrecht University (Bayesian Modeling, 2019), Stanford (Mentoring, 2010), and NYU (Valuation, 2012). He reviews for top journals like Annals of Tourism Research and Cornell Hospitality Quarterly , with 1017 Google Scholar citations and 349 Scopus citations. Current research involves the Science Fund of Serbia's TOURCOMSERBIA project evaluating tourism competitiveness models.
Zhuo Feng is Professor of Electrical and Computer Engineering at Stevens Institute of Technology, directing the HUDSON Lab and holding a Ph.D. from Texas A&M University. His research develops spectral graph methods for VLSI design, including circuit simulation, power grid verification, and machine learning applications. Funded by NSF CAREER and multiple grants, his work has produced award-winning algorithms like GRASS for graph sparsification. Recent publications focus on spectral methods for circuit stability analysis, physics-informed neural networks, and explainable AI frameworks. He teaches graduate courses in VLSI design and GPU programming while co-founding LeapLinear Solutions. NSF CAREER Award (2014) ACM/IEEE DAC Best Paper Award (2013) Multiple Best Paper Nominations (ICCAD 2008, 2006)
Patrick Flynn is the Matthew and Soogi Hong Fellow and Assistant Professor of Management at North Carolina State University's Poole College of Management, Department of Management, Innovation & Entrepreneurship. He holds a Ph.D. in Organizational Behavior and Human Resources from the University of South Carolina and a B.S. in Supply Chain Management from the University of Maryland. His research focuses on dynamic individual and group processes, including event-based adaptation, team citizenship behaviors, and resilience. He teaches courses in people analytics, management consulting practicum, and leadership consulting. Dr. Flynn’s work has been published in top journals such as Journal of Management , Journal of Applied Psychology , and Annual Review of Organizational Psychology . He serves on the editorial board of Group & Organization Management . His research explores transitions, sustainability, and organizational change, with media features in CNBC, MSN, and the Raleigh News & Observer . His academic contributions emphasize practical applications of organizational behavior theories, particularly in post-pandemic workplace dynamics, remote work adaptations, and employee well-being strategies. His recent work addresses collective turnover, trust dynamics, and proactive sustainability approaches in volatile environments.
Michele Acuto serves as Pro Vice-Chancellor (Global Engagement) at the University of Bristol. His research addresses global urban governance challenges through interdisciplinary approaches focused on city diplomacy, climate resilience, and nighttime economies. Research interests include the geopolitics of urban data, transnational municipal networks, and the integration of nighttime studies into climate adaptation frameworks. His work emphasizes practical applications for urban policy innovation. Key contributions examine how cities learn through knowledge exchange institutions, benchmarking practices in global urbanism, and the role of city rankings in shaping diplomatic agendas during crises like the COVID-19 pandemic.
Dan Lizotte is an Associate Professor jointly appointed to the Department of Computer Science in the Faculty of Science and the Department of Epidemiology and Biostatistics in the Schulich School of Medicine & Dentistry at Western University. Additional affiliations include the Schulich Interfaculty Program in Public Health and a cross-appointment to the Department of Statistics and Actuarial Sciences. Based in Middlesex College, London, Ontario, his contact email is dlizotte@uwo.ca. His research centers on machine learning and biostatistics for health decision support, with emphasis on sequential decision-making in chronic disease management where evolving patient health status and preferences inform adaptive interventions. Core contributions involve adapting reinforcement learning frameworks to model dynamic health decisions in public health and primary care settings, addressing methodological challenges in personalized medicine and risk prediction. Analysis of his publication record reveals consistent focus on healthcare applications of machine learning, particularly in chronic disease risk modeling using electronic medical records, intersectionality frameworks in public health AI, and Bayesian methods for dose personalization. His work bridges reinforcement learning with clinical decision support systems, advancing dynamic treatment regimes and statistical methodologies for evolving patient data. No scientific awards were mentioned in the provided text. The text does not specify any advisees, grant funding, or educational background details. Lizotte leads a research laboratory focused on machine learning applications in health, as evidenced by the dedicated lab site referenced in his contact information. His team likely explores intersections of statistical methodology, AI ethics, and clinical implementation for personalized health interventions.
Dr. Angeline Lillard is Commonwealth Professor of Psychology and Director of the Montessori Science Program at the University of Virginia. She leads the Early Development Lab, focusing on children's social and cognitive development, particularly Montessori education's impact on learning and wellbeing. A Fellow of AAAS, APA, and APS, she earned her BA in English Literature from Smith College and PhD in Psychology from Stanford University. Research Interests: Dr. Lillard's work bridges Montessori pedagogy with developmental psychology, analyzing how play, educational environments, and culturally responsive teaching shape child outcomes. She explores standardized testing disparities, discipline equity, and the neurobiological underpinnings of pretend play. Key themes in her 15 most recent publications include Montessori's role in reducing educational inequality, the cognitive effects of fantasy in media, and the use of multilevel modeling to assess school discipline patterns. Her research spans preschool to adult wellbeing, emphasizing self-determination theory and longitudinal data. Scientific Recognition: Awarded the Nancy Staub Award for Puppetry Research (2024) Recognized for her book with the Cognitive Development Society Book Award (2006) James McKeen Cattell Sabbatical Fellow (2005-06) Albert Bandura Graduate Research Award (2016-17) Advising and Grants: Mentored 15+ graduate students including Lee LeBoeuf and Christina Carroll. Secured IES funding for a 600-child study on public Montessori preschools and Arnold Foundation support for kindergarten data collection.
Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.
Guang Tian, Ph.D. , is an Assistant Professor of City and Metropolitan Planning at the University of Utah and a faculty member at the Scientific Computing and Imaging Institute . His research bridges land use-transportation planning , travel behavior , and urban data science , with a focus on sustainability , climate adaptation , and equitable transit-oriented development . He previously founded the Center for Equitable Transit-Oriented Communities at the University of New Orleans as an Associate Professor. Education : Ph.D. in City & Metropolitan Planning (University of Utah, 2016) Professional Affiliations : Faculty, Scientific Computing and Imaging Institute (2025–present) His research leverages machine learning and GIS to analyze VMT reduction , active transportation , and the built environment’s impact on mobility . Key findings include the superior performance of random forest models over traditional methods in predicting mode choice and the role of polycentric urban structures in reducing auto dependency. Scientific Awards : Rising Scholar Award (2024, Association of Collegiate Schools of Planning) Grants include funding from the US Department of Transportation for equitable transit communities and multiple Louisiana Transportation Research Center projects on VMT modeling, rail infrastructure, and truck parking efficiency. His teaching centers on GIS applications in urban planning and transportation analysis.
Prof. Ruth King is the Thomas Bayes’ Professor of Statistics at the University of Edinburgh’s School of Mathematics. Her research focuses on applying Bayesian statistical methods to ecological and public health challenges, including population estimation for hidden groups (e.g., injecting drug users, modern-day slaves) and wildlife conservation. She develops computationally efficient techniques for analyzing large datasets, such as spatial capture-recapture models for animal populations and spatio-temporal abundance models for hidden human populations. Key projects include estimating survival rates of guillemots (30,000 individuals) and improving capture-recapture models to account for animal movement dynamics. Her work bridges statistical methodology with real-world applications, emphasizing rigorous inference and scalable algorithms. King’s academic contributions span Bayesian modeling frameworks, parameter clustering in neuroscientific data, and hierarchical centering in random effects models. She collaborates with biologists and policymakers to address conservation and public health issues. Notable recent projects include incorporating memory effects into spatial capture-recapture models and developing semi-complete data augmentation for state-space models. Her interdisciplinary approach addresses challenges in ecology, epidemiology, and computational statistics, with a focus on methodological innovation for large-scale data. Her scientific contributions are highlighted through over 100 peer-reviewed articles, including work on integrated population models, animal movement dynamics, and hidden Markov models for seabird behavior. King emphasizes the importance of statistics in uncovering hidden information within datasets, advocating for robust methodologies that ‘stand up in court’ when applied to critical real-world problems.
Grace Lim is an Assistant Professor in the Department of Management at the Nanyang Business School, Nanyang Technological University, Singapore. Her research focuses on inclusion, diversity, and the empowerment of disadvantaged employees in organizations, particularly those from lower social class backgrounds and women. Education: Ph.D. in Business (Organizational Behavior and Human Resources), Singapore Management University B.Sc. in Psychology, National University of Singapore Her research employs a range of methodologies, including field surveys, experiments, archival data, and qualitative methods, to ensure robust and generalizable findings. She investigates how individuals can exercise agency to overcome structural disadvantages in the workplace, contributing to the fields of organizational behavior, human resources, and social psychology. Her recent publications reflect a strong trend in studying diversity, inclusion, and equity, with a focus on social class, gender, leadership, and employee well-being. The research spans interdisciplinary themes, integrating insights from psychology, management, and sociology to understand workplace dynamics. Scientific Contributions: Published in top-tier journals such as Organizational Behavior and Human Decision Processes and Academy of Management Annals Research on social class, gender, inclusion, and leadership has been influential in shaping organizational practices Dr. Lim is actively involved in advancing knowledge in organizational behavior and human resources. She has not supervised any listed students yet but continues to contribute through impactful research and academic engagement. She is a member of the Centre for Leadership and Cultural Intelligence at NTU, collaborating on initiatives related to cultural intelligence and inclusive leadership.
Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.
Christoph Stadtfeld is Associate Professor of Social Networks at ETH Zurich's Department of Humanities, Social and Political Sciences and co-director of the ETH Social Networks Lab. His research examines social network dynamics, focusing on tie formation processes, network effects on individuals, and advanced statistical methodologies for longitudinal network analysis. Education: PhD from Karlsruhe Institute of Technology (2011) Postdoctoral researcher and Marie-Curie fellow at University of Groningen, University of Lugano, and MIT Media Lab (2011-2014) His work bridges sociology, statistics, and computer science to address fundamental questions about how social structures evolve and influence behavior. Key interests include relational event modeling, co-evolution of networks and attributes, and applications in mental health, political polarization, and scientific collaboration. He develops innovative methods for analyzing dynamic networks using cutting-edge computational approaches. Recent publications reveal strong emphasis on methodological rigor in temporal network analysis, with significant contributions to relational event modeling and dynamic network actor frameworks. His work increasingly addresses societal challenges including political polarization, mental health impacts of social isolation, and innovation dynamics in healthcare. Scientific awards: Raymond Boudon Award of the European Academy of Sociology (2017) Freeman Award of the International Network for Social Network Analysis (2021) As co-director of the ETH Social Networks Lab, Stadtfeld leads interdisciplinary research teams developing novel network methodologies. His work has been supported by prestigious fellowships including Marie-Curie funding, and he actively mentors graduate students in network science methodology and applications across diverse domains. The ETH Social Networks Lab serves as a hub for advancing network theory and methodology, with ongoing projects examining student networks during crises, scientific collaboration dynamics, and innovation ecosystems through the lens of network science.
Lily Hsueh is an Associate Professor of Economics and Public Policy (with tenure) at the School of Public Affairs, Arizona State University (ASU), and a Visiting Scholar at the Woods Institute for the Environment, Stanford University. She is affiliated with multiple research centers, including the Julie Ann Wrigley Global Futures Laboratory, the Center for Environmental Economics and Sustainability Policy, the Center for Organization Research and Design, and the Political Economy Working Group at ASU. She earned her Ph.D. in Public Policy and Management from the University of Washington, an M.S. in Economics from University College London, and a B.A. in Economics from the University of California, Berkeley. Prior to her academic career, she worked as a Senior Analyst at the Federal Reserve Bank of San Francisco. Dr. Hsueh’s research focuses on the intersection of economics, politics, and governance in environmental policy. She investigates how alternative governance systems—such as voluntary programs and market-based mechanisms—affect policy outcomes in areas like climate change, toxic chemicals, and marine resources. Her work emphasizes the role of firms, institutions, and governments in shaping environmental governance and social equity. Her recent publications explore trends in corporate climate disclosure, sustainable public procurement, participatory budgeting, and the health impacts of environmental disasters. These studies employ rigorous econometric methods and contribute to both academic and policy debates on sustainability and governance. Recipient of the 2020–21 AAUW American Fellowship Two-time winner of the Distinguished Teaching Award at ASU (2016–17, 2021–22) Recipient of the 2023 Professor of Impact award (student-initiated) Elected to the APPAM Policy Council (2024–2028) Editorial board member of PLOS Climate Dr. Hsueh has secured research funding from NOAA, the V. Kann Rasmussen Foundation, and ASU. She actively mentors students and junior scholars and is currently completing a book with MIT Press titled Corporations at Climate Crossroads . She also leads curriculum development in economics and public policy at ASU and teaches across undergraduate, master’s, and Ph.D. levels. She is involved in several research teams and initiatives, including the Political Economy Working Group and the Sustainable Procurement Research Group at ASU, and collaborates with interdisciplinary teams at Stanford on climate governance research.
Daniele Ielmini is a Professor at the Department of Electronics, Information and Bioengineering at Politecnico di Milano, Italy, where he leads research in non-volatile memory technologies and neuromorphic computing. He received his Laurea (with merit) and Ph.D. in Nuclear Engineering from Politecnico di Milano in 1995 and 2000, respectively, and has held visiting positions at Intel Corporation (2006), Stanford University (2006), and the University of Illinois at Urbana-Champaign (2010). His research focuses on the modeling and characterization of non-volatile memories, including nanocrystal memory, charge trap memory, phase change memory (PCM), resistive switching memory (RRAM), and spin-transfer torque magnetic memory (STT-MRAM). He has co-edited the book 'Resistive switching – from fundamental redox-processes to device applications' and published over 300 papers with more than 10,000 citations and an H-index of 69 (Scopus, September 2023). Prof. Ielmini's recent publications demonstrate a strong trend toward in-memory computing and neuromorphic applications, with particular emphasis on closed-loop analog computing architectures, reservoir computing with 2D materials, and hardware security implementations using emerging memory technologies. His work bridges fundamental device physics with practical computing applications, especially for energy-efficient AI acceleration. Intel Outstanding Researcher Award (2013) ERC Consolidator Grant (2014) IEEE-EDS Paul Rappaport Award (2015) Fellow of the IEEE Prof. Ielmini leads multiple ERC-funded projects including SHANNON (Secure Hardware with Advanced Nonvolatile memories), NEURO2D (neuromorphic systems based on reservoir computing in MoS2), and ANIMATE (closed-loop in-memory computing). His research group includes post-doctoral researchers, PhD students, and M.Sc. students working on various aspects of emerging memory technologies and their applications. He serves as Associate Editor for IEEE Trans. Nanotechnology and Semiconductor Science and Technology (IOP), and has served in several Technical Subcommittees of international conferences including IEEE-IEDM, IEEE-IRPS, and IEEE-ISCAS. His laboratory at Politecnico di Milano is equipped with advanced semiconductor device testing equipment including probe-stations, semiconductor parameter analyzers, high-speed waveform generators, and other specialized instruments for nano-electronic research. The lab collaborates with major semiconductor companies including Micron Technology Inc. and STMicroelectronics, as well as participating in national and international research projects.
