Lars Ulander is a Professor at Chalmers University of Technology specializing in radar remote sensing. His research focuses on synthetic aperture radar (SAR) signal processing, particularly for applications in forest biomass mapping and ground imaging using VHF/UHF-band systems. He is a key proposer for ESA's BIOMASS satellite mission (launching 2025) and leads the BorealScat project, utilizing a 50-meter tower-based tomographic radar to study boreal forest dynamics. His work spans radar system development, SAR tomography techniques, and environmental monitoring of forests and sea surface currents. Current research areas include vegetation water content estimation, bistatic radar configurations, and optimization of SAR data processing algorithms for multi-temporal analysis. Recent publications demonstrate expertise in P-band/L-band SAR for biomass retrieval, passive radar systems, and interferometric techniques. His articles investigate radar backscatter sensitivity to forest structure, moisture parameters, and seasonal changes, while contributing to mission design frameworks like SLAINTE and SESAME.
Peter Nilsson is a Professor of Economics at the Institute for International Economic Studies (IIES) , Stockholm University. He also holds a guest professorship at Linnaeus University and serves as a research fellow at IFAU , Uppsala Center for Labor Studies , CESifo , and CEPR . Previously, he was a postdoctoral fellow at Stanford University and obtained his PhD from Uppsala University (2010). Research Focus: His work bridges Labor Economics , Health Economics , and Environmental Economics , analyzing how early-life exposures (alcohol, lead), workplace dynamics, and policy interventions (unemployment insurance, congestion pricing) shape long-term socioeconomic outcomes. Key themes include Environmental Health Impacts on Cognition and Crime Labor Market Responses to Insurance Policies Peer Effects in Workplace Behavior Policy Design for Social Equity Scientific Awards: Nilsson has been recognized through research fellowships at leading institutions and editorial roles, including Associate Editor at The Economic Journal since 2021. His work has been featured in American Economic Review , Journal of Political Economy , and NBER platforms. Key Contributions: He provided testimony for Connecticut’s HB-5045 to reduce childhood lead exposure and contributed to Sweden’s Corona Commission report on pandemic responses. His empirical methods combine natural experiments with administrative datasets to identify causal relationships in health, labor, and environmental economics.
Anastasia Semykina is a Professor of Economics and Deputy Dean (Research and Innovation) at RMIT University's School of Economics, Finance & Marketing. She holds a PhD from Michigan State University (2006) and previously served as Charles and Joan Haworth Professor of Economics at Florida State University. Her expertise spans theoretical and applied econometrics, with a focus on panel data models, missing data estimation, and their application in labor economics, education economics, transition economies, and economic psychology. She teaches advanced econometrics and microeconomics courses at both undergraduate and graduate levels. Research Interests: Theoretical and Applied Econometrics Labor Economics Economics of Education Transition Economies Economics and Psychology Health Economics Her recent publications address topics such as panel data methodologies, healthcare cost-effectiveness analysis, and educational policy evaluation. She is actively involved in supervising PhD and Master's research students in econometrics and applied economics.
Peter Werner is a Full Professor of Behavioral Economics and Policy Design at Maastricht University's School of Business and Economics, where he leads research in the Department of Economics (Microeconomics & Public Economics section). He progressed from Assistant Professor (2016-2017) to Associate Professor (2018-2023) before attaining his current full professorship, demonstrating sustained academic contribution within the institution. His educational foundation includes: Ph.D. in Economics, University of Cologne (2005-2009), thesis: 'Behavioral Economics and Agency Problems: Empirical Studies' Diplom-Volkswirt in Economics, University of Cologne and Stockholm School of Economics (1999-2005) Werner's research centers on applying behavioral insights to real-world systems , with dual focus on fundamental social preference mechanisms and practical policy design. His work bridges laboratory experiments with field applications, particularly in wage transparency , pension decision architecture , and organizational incentive structures . Recent methodological innovations integrate population-level surveys with experimental economics to measure preference stability during crises like the COVID-19 pandemic. Analysis of his 2022-2025 publications reveals expanding scope from traditional labor economics into climate behavior and intergenerational solidarity, while maintaining core expertise in experimental methodology. Key trends include increased focus on Dutch pension systems, risk communication frameworks, and cross-generational discrimination patterns using nationally representative samples. His primary recognition includes: Marie Skłodowska-Curie Individual Fellowship (2017-2019) for 'Wage transparency in companies' (Horizon 2020 Grant 745894) Werner actively secures competitive funding through major initiatives: Current: Netspar Theme Project on risk preference measurement (Instituut Gak) Recent: Netspar pension savings project (2019-2023) Past: Marie Curie Fellowship (2017-2019) He coordinates the UM Behavioral Insights Center and co-organizes the M-BEES/M-BEPS symposia series, fostering research-practice integration. As co-director of the UM Behavioral Insights Center—one of the university's research spearheads—Werner leads interdisciplinary teams applying experimental economics to public policy challenges, with particular emphasis on pension communication and labor market transparency through partnerships with Dutch governmental and financial institutions.
Scott D. Landes is a Professor in the Sociology Department at Syracuse University's Maxwell School of Citizenship and Public Affairs. He holds affiliations with the Aging Studies Institute, Lerner Center for Public Health Promotion, and Center for Aging and Policy Studies. Landes earned his Ph.D. in Sociology from the University of Florida in 2014. His research focuses on disability mortality disparities, veteran health outcomes, and the intersection of disability with medical and social theory. Education: Ph.D. in Sociology, University of Florida, 2014 Research Interests: Landes investigates mortality trends for disabled adults and veterans, disability theory integration with life course frameworks, and data equity in disability measurement. His work addresses systemic undercounting of disability in federal surveys and explores racial/ethnic disparities in lifespan outcomes for individuals with intellectual/developmental disabilities (IDD). Recent Research Trends: His publications analyze pandemic impacts on IDD populations, combat veteran longevity, and diagnostic overshadowing in mortality reporting. Recent work critiques Washington Group disability measures' limitations and advocates for improved data collection methods. Awards: 2025 Excellence in Graduate Education Faculty Recognition Award O’Hanley Faculty Scholar (Syracuse University) NIH-funded research projects on aging policy and mortality disparities Advising & Grants: Recipient of multiple National Institutes of Health grants. Advises on disability data equity initiatives and participates in interdisciplinary teams addressing veteran health disparities. Supervises graduate research on IDD lifespan trajectories and pandemic vulnerability. Labs/Teams: Core member of Syracuse’s Aging Studies Institute and Lerner Center for Public Health Promotion. Collaborates with the Center for Aging and Policy Studies on federal health data projects. Engages with national disability advocacy groups through research briefs and policy consultations.
Catherine H. Hausman is an Associate Professor at the Gerald R. Ford School of Public Policy, University of Michigan, and a Research Associate at the National Bureau of Economic Research (NBER). Her work bridges environmental economics, energy policy, and climate change mitigation. BA in Economics from University of Minnesota PhD in Agricultural and Resource Economics from University of California, Berkeley (2013) Her research explores: Electricity transmission barriers and corporate profit alignment Methane emissions and carbon intensity in fossil fuel production Climate change impacts on grid reliability and pricing Pollution inequality and nuclear power economics Recent publications examine decarbonization strategies, ancillary service market spillovers, and methane leak quantification. She has received a Fulbright grant for pre-graduate work in Peru and contributes policy insights through the Hamilton Project and NBER working papers.
Prof Christina Lim is a Professor at the Department of Electrical and Electronic Engineering, University of Melbourne, Australia. She serves as the Associate Dean of Research for the Faculty of Engineering and Information Technology (FEIT) and manages the Tucker Lab. Previously, she held roles as Research Group Leader of the Electronics and Photonics System group and Deputy Head of Department (Teaching and Operations) Education: PhD and Bachelors from University of Melbourne Research Interests: Radio-over-Fibre, Optical Wireless Communications, Microwave Photonics, Augmented Reality Displays, Reservoir Computing, Optical Crosshaul Networks Recent publications demonstrate expertise in optical waveguide design for AR, underwater optical wireless communications, photonic switching, and network optimization. Her projects focus on next-generation wireless infrastructure, including Photonics Computing Enabled Ultra-Broadband Wireless Communications (2024-2027, $598k ARC grant) and Additive Manufacturing of Optical Elements (2025). She has secured significant funding, including ARC Discovery Projects and Future Fellowships. Scientific Honors IEEE Fellow (2022) Optica Fellow (2018) ARC Future Fellow (2009-2013) ARC Australian Research Fellow (2004-2008) Professional Service Vice-President of Conferences, IEEE Photonics Society Deputy Editor, IEEE/Optica Journal of Lightwave Technology ARC College of Experts (2014-2016)
Naresh R. Shanbhag is the Jack Kilby Professor in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He serves as Director of the Systems on Nanoscale Information fabriCs (SONIC) Center and held the D.J. Gandhi Distinguished Visiting Professorship at IIT Mumbai from 2015-2020. Previously, he was a visiting faculty member at National Taiwan University (2007) and Stanford University (2014). Dr. Shanbhag received his doctorate from the University of Minnesota (1993) in Electrical Engineering. From 1993 to 1995, he worked at AT&T Bell Laboratories as the lead chip architect for AT&T's 51.84 Mb/s transceiver chips over twisted-pair wiring for Asynchronous Transfer Mode (ATM)-LAN and very high-speed digital subscriber line (VDSL) chip-sets. His research focuses on the design of energy-efficient machine learning, communications, and signal processing systems on resource-constrained embedded platforms. He explores fundamental trade-offs between energy efficiency, latency and accuracy of decision-making systems implemented in nanoscale technologies, with applications to computer vision, biomedicine, automatic target recognition, and imaging. His work spans four primary focus areas: Resource-efficient Machine Learning for the Edge, In-memory Computing (IMC), Energy-efficient High Data Rate Communications, and Shannon-inspired Statistical Error Compensation (SEC). Analysis of his recent publications reveals a strong emphasis on in-memory computing architectures (SRAM, MRAM, RRAM) for machine learning acceleration. His work consistently addresses energy-accuracy trade-offs, with increasing attention to security aspects of hardware implementations and applications to MIMO signal processing and edge AI systems. His research demonstrates a progression from theoretical foundations to practical silicon implementations. 2024 Semiconductor Research Corporation Innovation Award 2018 Semiconductor Industry Association/Semiconductor Research Corporation University Researcher Award 2018 IEEE International Symposium on Circuits and Systems Best Paper Award 2006 IEEE Fellow 1996 National Science Foundation CAREER Award Professor Shanbhag has mentored over 50 graduate students who now work at leading technology companies including Qualcomm, Amazon, Nvidia, Intel, and Apple. His research has been generously supported by the National Science Foundation, DARPA, AFRL, Semiconductor Research Corporation, Texas Instruments, Sandia National Laboratories, and industry partners including IBM, GlobalFoundries, and Intel Corporation. He led the Alternative Computational Models research theme (2006-2012) and was the founding Director of the SONIC Center (2013-2017), a 5-year multi-university center funded by DARPA and SRC. Currently, he leads research themes in the SRC and DARPA funded JUMP 2.0 Program's Center for Co-Design of Cognitive Systems and the Center for Ubiquitous Connectivity, and in the NSF IUCRC Center for Advanced Semiconductor Chips with Accelerated Performance (ASAP). As Director of the Systems on Nanoscale Information fabriCs (SONIC) Center, Professor Shanbhag leads a multidisciplinary team exploring novel computing paradigms for the nanoscale era. His group has benchmarked an extensive collection of in-memory computing and digital accelerator IC designs, maintaining a publicly available IMC benchmarking repository of metrics extracted from published IC prototypes. His research philosophy integrates concepts from information theory, statistical signal processing, detection and estimation, VLSI architectures, and digital and analog integrated circuits to develop energy-efficient systems from algorithms to silicon implementations.
Baris Fidan is a Professor in Mechanical & Mechatronics Engineering at the University of Waterloo, with cross appointments in System Design Engineering and Electrical & Computer Engineering. He is a senior member of IEEE and AIAA. His research focuses on cooperative/adaptive control, autonomous systems, multi-agent networks, and vehicular control applications. He leads the Cooperative & Adaptive Mechatronic Systems (CAMS) Lab, which develops control strategies for autonomous vehicles, robotic systems, and intelligent transportation. Education: PhD in Electrical Engineering, University of Southern California (2003) Masters in Electrical & Electronic Engineering, Bilkent University (1998) Bachelor's in Electrical & Electronic Engineering & Mathematics, Middle East Technical University (1996) Research Interests: His work spans adaptive control theory, sensor networks, multi-agent coordination, autonomous vehicle networks, and biomedical systems control. He emphasizes practical applications in intelligent transportation, robotic navigation, and distributed system optimization. Grants & Projects: He has led major grants including NSERC Discovery Programs on cooperative mechatronic systems and 3D autonomous vehicle coordination. Industrial projects include autonomous driving strategies, vehicle control optimization, and high-precision gear manufacturing technologies. Labs/Teams: Directs the CAMS Lab, which collaborates on projects involving distributed motion planning, sensor localization, and autonomous vehicle networks. Current projects address challenges in urban autonomous driving, cooperative robotic systems, and resilient sensor networks.
Prasanna (Sonny) Tambe is a Professor at the Wharton School of the University of Pennsylvania, specializing in the economics of technology and labor markets. His research explores AI’s impact on workforce dynamics, HR algorithms, and the gender wage gap in tech industries. Education: Ph.D. in Managerial Science and Applied Economics (Wharton, UPenn); S.B. and M.Eng. in Electrical Engineering and Computer Science (MIT). His work leverages internet-scale data from job platforms and patent databases to analyze trends in skill acquisition, remote work diversity, and algorithmic bias in hiring. Recent studies examine AI’s role in HR decision-making, the economics of emerging technologies, and labor market responses to IT innovation. Scientific Awards: Best Undergraduate Professors (Poets & Quants, 2020) Best Paper Awards (Management Science, Information Systems Research) ISS Sandra A. Slaughter Early Career Award (2016) Tambe co-directs Wharton Human-AI Research, focusing on ethical AI integration in organizations. His teaching includes award-winning courses on AI’s societal implications and data-driven business strategies.
Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.
Roberto Manduchi is a Professor of Computer Science and Engineering at the University of California, Santa Cruz, within the Baskin School of Engineering. His primary affiliation is with the Computer Science and Engineering department where he leads research in assistive technology for visual impairments. He holds a Dottorato di ricerca in Electrical Engineering from the University of Padova, Italy, and previously worked at Apple and NASA JPL before joining UCSC in 2001. His research focuses on mobile computer vision, inertial sensors, and location-aware systems to enhance spatial awareness and information access for blind and low-vision individuals. Key research areas include indoor navigation systems, screen magnification for low-vision readers, obstacle detection using augmented reality, and text accessibility assessment through specialized OCR pipelines. His work bridges computer vision, human-computer interaction, and accessibility design. Analysis of his recent publications (2022-2025) reveals strong emphasis on inertial-based indoor navigation (e.g., PALMS localization system, backtracking algorithms), screen magnification usability studies, and novel approaches to scene text access for blind users. His research consistently targets practical applications for visual impairment, with significant contributions to pedestrian dead reckoning, magnetic signature localization, and gaze-contingent interfaces. Manduchi serves on the scientific advisory board of Aira and is a board member of the Vista Center for the Blind and Visually Impaired. He leads the UCSC Computer Vision Lab where his team develops accessible computing solutions. His work includes both theoretical contributions to computer vision and tangible assistive applications, with recent projects focusing on smartphone-based inertial odometry, multi-scale tactile maps, and real-time obstacle cueing systems.
Luca Peretti is an Associate Professor in Electric Machines and Drives at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Department of Electrical Engineering, Division of Electric Power and Energy Systems. He works as a researcher in the EMD (Electric Machines and Drives) group and serves as Partner Director for KTH's strategic partnership with ABB. Education: M.Sc. in Electronic Engineering (2005) from University of Udine, Ph.D. from University of Padova (2008) Professional Experience: Postdoc at University of Padova (2009-2010), Principal Scientist at ABB Corporate Research (2010-2018), Associate Professor at KTH (2018-present) His research focuses on: Automatic parameter estimation in electric machines Multiphase drive systems Sensorless control algorithms Loss segregation in drive systems Condition monitoring of industrial and transportation applications Recent publications demonstrate expertise in variable phase-pole machines, harmonic plane decomposition, predictive control algorithms, and advanced modeling of permanent magnet motors. Key application areas include transportation electrification, wind energy systems, and industrial drive technologies. Scientific roles include: Associate Editor, IET Electric Power Applications Journal (2019-present) Theme Co-Leader, Swedish Electromobility Center (2020-present) Member, IEEE (2021-present) and IET (2006-present) He leads the strategic partnership with ABB and contributes to doctoral program committees at University of Padova.
Andrea Bonfiglio is an Associate Professor at the Department of Naval, Electrical, Electronic and Telecommunication Engineering (DITEN) within the School of Engineering at the University of Genoa. His work focuses on power systems, smart grids, and renewable energy integration with a particular emphasis on innovative control strategies and energy storage solutions. Teaching areas: Electrical Systems, Industrial Measurements, Energy Security Key research themes: Smart distribution networks, synthetic inertia, microgrid control, vehicle-to-grid technologies, battery energy storage Recent work explores virtual energy partitioning, load flow optimization, and inertia allocation in transmission networks Contact: a.bonfiglio@unige.it His publications highlight advanced control methodologies using machine learning and sliding mode control for both transmission and distribution networks, with applications to renewable integration and grid stability challenges.
Lam James is a Chair Professor of Control Engineering at the University of Hong Kong (HKU), affiliated with the Faculty of Engineering. He holds a BSc (1st Hons.) in Mechanical Engineering from the University of Manchester, and MPhil/PhD degrees from the University of Cambridge. His academic journey includes roles as a Croucher Fellow, Lecturer at the University of Melbourne, and faculty member at City University of Hong Kong before joining HKU in 1993. Professor Lam serves as Editor-in-Chief for four international journals including IET Control Theory and Applications and Journal of The Franklin Institute . His research focuses on networked control systems, vibration control, control theory, and multi-agent systems. With 600+ peer-reviewed publications, he maintains an H-index of 102 (Web of Science) and 108 (Scopus), recognized as a Highly Cited Researcher in multiple fields. Education: BSc Manchester (Mechanical Engineering), MPhil/PhD Cambridge Editorial Roles: 20+ journals, including leadership roles since 2012 His awards include Foreign Membership of Academia Europaea (2024), National Academy of Artificial Intelligence membership (2025), and two State Natural Science Awards (2015, 2019). He is a Fellow of multiple institutions including IEEE, IMechE, and HKIE.
