Kala Krishna is a Liberal Arts Research Professor in the Department of Economics at The Pennsylvania State University. His affiliations include being a Fellow of the Econometric Society, NBER Research Associate, CESifo Research Associate, and IGC Research Affiliate. He holds a Ph.D. in Economics from Princeton University (1984). His research focuses on International Economics, Development Economics, Industrial Organization, Game Theory, and Applied Microeconomics. Key themes include trade policy impacts, firm behavior under trade liberalization, educational testing dynamics, and policy design in high-stakes exams. Recent work explores topics like the U.S.-China trade war's reallocations (2023), Pareto improvements in college admissions contests (2025), and learning effects in trade agreements (2025). His articles often combine theoretical models with empirical data, such as Chinese firm-level analyses and experiments in Turkey's education system. Awards: Fellow of the Econometric Society (2025), NBER Research Associate (2025) Grants & Funding: Multiple NBER Working Papers, collaborations with international institutions like IGC His work bridges theoretical frameworks with real-world policy implications, emphasizing structural modeling and policy experimentation.
Michael Reimer is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC). His primary role involves advancing quantum photonic technologies through experimental and theoretical research. He is a full-time faculty member with expertise in semiconductor nanowire-based devices, quantum dot systems, and their integration with atomic ensembles for quantum communication applications. His research interests focus on developing high-efficiency single-photon sources and detectors, metamaterials for photonic applications, and leveraging cold atoms in hollow-core fibers to enhance quantum state manipulation. He also explores hybrid quantum-classical systems, particularly in optimizing signal-to-noise ratios (SNR) and mitigating nonlinear impairments in optical networks. His work bridges fundamental quantum optics with practical implementations in quantum computing and secure communication systems. Recent articles highlight trends toward achieving near-unity absorption in metamaterials, on-demand photon generation with quantum dots, and probabilistic constellation shaping for optical networks. While no scientific awards are explicitly mentioned, his contributions to quantum photonic devices are notable. No specific grants or advisees are detailed in the provided text, but his affiliation with IQC suggests involvement in collaborative research projects. He is affiliated with the Institute for Quantum Computing, a leading academic unit at the University of Waterloo.
Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.
Ivana Malenica is an Assistant Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. Previously, she was a HDSI Fellow at Harvard Data Science Initiative and Postdoctoral Fellow in Statistics at Harvard University. She holds a Ph.D. in Biostatistics from UC Berkeley and a B.S. in Mathematics from Arizona State University. Her research focuses on causal inference, machine learning, nonparametric statistics, efficiency theory, and precision health. She specializes in longitudinal and structured dependent settings including adaptive sequential experiments, online learning, and reinforcement learning applications in personalized health. Her recent publications demonstrate strong methodological contributions to causal inference and machine learning, with applications spanning clinical trials, public health, genomics, and reinforcement learning. Her work consistently develops novel statistical approaches for complex data structures. Awards include: Harvard Data Science Initiative Fellowship (2022) Berkeley Wellness Letter Fellowship (2020) Wellness Scholarship in Honor of Chin Long Chiang (2019) Berkeley Institute for Data Science Moore-Sloan Fellowship (2018) She teaches graduate courses including Advanced Probability and Statistical Inference I (BIOS 760). Her computational work includes contributions to the tlverse ecosystem for causal inference in R.
Andre R. Barbosa is the Cecil and Sally Drinkward Professor in Structural Engineering at Oregon State University's College of Engineering. He holds a Ph.D. from UC San Diego and degrees from Instituto Superior Técnico (IST), Portugal. His research focuses on earthquake engineering, structural reliability, and multi-hazard resilience, particularly in mass timber and advanced building systems. Notable awards include the 2023 J. James R. Croes Medal. Education : Ph.D., Structural Engineering, UC San Diego (2011) M.S., Structural Engineering, IST, Portugal (2002) Licenciatura in Civil Engineering, IST, Portugal (1998) Research Highlights : Barbosa leads the Barbosa Research Group, specializing in experimental testing and numerical tools for structural resilience. Recent work includes virtual damage assessment post-hurricanes, life-cycle assessment of mass timber buildings, and drone-based monitoring of seismic performance. Collaborations involve NSF, USDA, and industry partners like CoreBrace and Simpson Strong-Tie. Grants & Partnerships : Supported by NSF awards (e.g., 2120683) and USDA (Award No. 58-0204-9-165). Industry collaborations include KPFF, Holmes, and Freres Engineered Wood.
Dr. Yang Xing is a Senior Lecturer in Applied Artificial Intelligence for Engineering at Cranfield University's Centre for Autonomous and Cyberphysical Systems, where he also directs the HUMAX Lab focused on human-centered autonomous vehicle validation. He holds a PhD from Cranfield University (2018) and an MSc with Distinction in Control Systems from the University of Sheffield (2014). Previously, he was a Research Associate at the University of Oxford (2020-2021) and Research Fellow at Nanyang Technological University (2019-2020). His research centers on human-autonomy collaboration frameworks with four key pillars: Cognitive autonomous systems using trustworthy AI Computer vision for human behavior/intention modeling Multimodal foundation models for autonomous driving Deep learning for sustainable transportation systems His recent publications (2022-2025) demonstrate strong trends in AI-driven transportation research : 40% focus on trajectory prediction and behavior modeling, 30% on computer vision applications, 20% on human-AI collaboration frameworks, and 10% on energy optimization. Key thematic evolutions include increased use of transformer architectures, graph neural networks for interaction modeling, and simulation-to-real transfer learning. Awards and Honors: IEEE Outstanding Associate Editor Award (TNNLS 2023-2024) Best Paper Award, China National Intelligence Technology Conference 2019 IEEE Outstanding Service Award, Smart World Congress 2023 Best Workshop Paper, IEEE IV 2018 He currently advises PhD student Isa Ismail and has secured funding from the Royal Society, EPSRC, DSTL, SAAB, QinetiQ, and Thales. As lab director of HUMAX, he leads projects on human-AI teaming for autonomous systems.
Susan L. Ustin is a Professor in the Department of Land, Air, and Water Resources at the University of California Davis, where she has been a faculty member since 1999. She is also the Associate Director of the John Muir Institute of the Environment and Head of the Center for Spatial Technologies and Remote Sensing (CSTARS). Her academic journey began with a Ph.D. in Botany from UC Davis in 1983, followed by a postdoctoral fellowship working with NASA's Jet Propulsion Laboratory on imaging spectroscopy. Ph.D. in Botany, University of California Davis, 1983 M.A. in Biology, California State University, Hayward, 1978 B.S. in Biology, California State University, Hayward, 1974 Dr. Ustin is a leading expert in remote sensing, with over 30 years of experience applying imaging spectroscopy, LiDAR, thermal, and multispectral data to ecological and environmental problems. Her research spans landscape and ecosystem ecology, focusing on vegetation mapping, invasive species detection, canopy water content estimation, wildfire risk modeling, and climate change impacts. She has developed novel methods for quantifying biophysical and biochemical properties of vegetation using remote sensing technologies. Her recent publications demonstrate a strong trend in integrating multiple remote sensing platforms (LiDAR, hyperspectral, thermal, satellite) to study complex ecological systems. Key research areas include fuel type and canopy structure mapping for wildfire risk, biochemical analysis of plant species, and monitoring environmental disturbances such as oil spills and hurricanes. She has been a key member of NASA's MODIS Science Team and the HyspIRI Preparatory Science Team, contributing to major Earth observation missions. Elected Fellow, American Geophysical Union (AGU), 2017 Honorary Doctorate, University of Zurich, Switzerland, 2010 Outstanding Service Award, American Society of Photogrammetry and Remote Sensing, 2004 Elected Senior Member, IEEE, 2004 SERDP Conservation Project of the Year Award, 2004 Elected Fellow, The Remote Sensing and Photogrammetry Society, 2002 Dr. Ustin has advised numerous graduate students and postdoctoral scholars and has led major research initiatives including the Center for Spatial Technologies and Remote Sensing. She has secured significant research funding from NASA, DOE, and other agencies to support her work on global environmental change and remote sensing applications. Her collaborations span across institutions and disciplines, including work with the National Research Council and Battelle on NEON. She leads the Center for Spatial Technologies and Remote Sensing (CSTARS), which focuses on developing and applying advanced remote sensing technologies for environmental monitoring. The center works on projects ranging from agricultural productivity to wildfire risk assessment and ecosystem health monitoring using airborne and satellite platforms.
Carlos A. Ocampo Martínez is an Associate Professor in the Department of Automatic Control (ESAII) at the Universitat Politècnica de Catalunya (UPC), BarcelonaTech, Spain. He is affiliated with the Institut de Robòtica i Informàtica Industrial, CSIC-UPC, a joint research center between UPC and the Spanish National Research Council. He has been with UPC since 2011 and served as Deputy Director of IRI from 2014 to 2018. Education: PhD in Control Engineering, Universitat Politècnica de Catalunya, 2007 MSc in Industrial Automation, National University of Colombia, 2003 BSc in Electronics Engineering, National University of Colombia, 2001 His research is centered on model predictive control (MPC) , particularly constrained and distributed MPC, with applications in energy, water, and smart manufacturing. He investigates large-scale systems management, partitioning strategies, and non-centralized control architectures. His work integrates IoT frameworks for smart industrial systems. Key domains include renewable hydrogen production, fuel cell vehicles, microalgae bioreactors, and solar thermal plants. The recent publications reflect a strong trend in applying control theory to sustainable energy systems and environmental management. There is a clear focus on integrating game theory, population dynamics, and optimization into MPC frameworks for distributed and coalitional control. Applications span hydrogen infrastructure, solar energy, water irrigation, and transportation electrification, demonstrating interdisciplinary impact. Scientific Awards: Juan de la Cierva Research Fellow Dr. Ocampo-Martínez actively supervises PhD students and leads research projects such as MASHED , which focuses on digitalized energy systems with hybrid storage. He has advised students on topics including alkaline electrolyzers, hydrogen production control, and alcohol steam reformers. His grant involvement emphasizes renewable integration, smart grids, and sustainable transport. He collaborates with researchers across Europe and contributes to high-impact journals and IFAC conferences. He is a key member of the Automatic Control research group at ESAII and contributes to the strategic direction of the Institut de Robòtica i Informàtica Industrial. His team integrates control theory, optimization, and real-world industrial applications, particularly in energy and water systems.
Stefan Leyk is a Professor of Geography at the University of Colorado Boulder within the Department of Geography in the College of Arts and Sciences. His research focuses on GIScience, spatial uncertainty modeling, and historical landscape analysis, with significant contributions to cartographic pattern recognition from historical maps and spatial dynamic modeling in public health. He holds a Ph.D. from the University of Zurich and the Federal Research Institute for Forest, Snow and Landscape (2005). His primary research interests include uncertainty in GIScience and spatial uncertainty modeling, land cover change modeling using historical spatial information, cartographic pattern recognition from historical maps, and spatial dynamic modeling approaches in public health. His work bridges historical geography with advanced computational methods, particularly in extracting settlement patterns from historical map archives and developing spatiotemporal datasets spanning centuries. Leyk's recent publications demonstrate strong trends in historical settlement analysis, with major projects like CHRONEX-US and HISDAC-US creating century-long datasets of urban infrastructure and settlement evolution. His work increasingly integrates machine learning with historical map processing, focusing on uncertainty quantification, built-up land validation, and environmental justice applications related to flood risk and coastal hazards. Key thematic areas include long-term urban growth patterns, rural poverty dynamics, and wildfire risk assessment at the wildland-urban interface. Leyk has received significant research funding through collaborative grants including 'HNDS-I: Building Long-term, National-scale Spatiotemporal Data Collections from Historical Map Archives' (2025) and 'HNDS-I: Data Infrastructure for Research on Historical Settlement and Population Growth in the United States' (2021). He actively mentors graduate students including Alek Berg, Caitlin McShane, and Yuying Ren, and teaches advanced GIS courses such as GEOG 4303/5303 GIS: Spatial Programming and GEOG 4103/5303 GIS: Spatial Analytics. His laboratory work centers on geospatial modeling of historical settlement and landscape analysis, with a focus on developing automated methods for processing historical map archives and creating linked spatiotemporal data. Current projects involve machine learning applications for feature extraction from historical maps, uncertainty prediction in built-up land layers, and the development of fine-grained datasets measuring 200 years of land development in the United States.
Professor M. Hashem Pesaran is a leading academic in Econometrics and Macroeconomics at the University of Cambridge's Faculty of Economics. His research focuses on dynamic panel data models, asset pricing, climate change impacts, and spatial econometrics. He has contributed extensively to methodologies addressing cross-sectional dependence, factor models, and policy analysis. Notable works include advancements in testing for alpha in asset pricing, analyzing pandemic transmission via stochastic networks, and assessing climate change's macroeconomic effects. His empirical studies utilize large datasets and advanced econometric techniques, often with real-world policy implications. Key research interests include: Econometric theory (panel data, factor models) Financial economics (asset pricing, risk premia) Macroeconomic policy (climate change, fiscal impacts) Spatial and network analysis (dominant units, SIR models) Recent articles highlight his work on heterogeneous dynamic panels, climate effects on US states, and pandemic modeling. No explicit awards are listed, but his publications indicate peer recognition. Advising and grants are not detailed here, but his collaborative projects suggest extensive academic leadership.
Professor Zuduo Zheng is a faculty member at the University of Queensland, holding the position of Professor & Deputy TMR TAP Chair in the School of Civil Engineering. His research focuses on traffic flow theory, modeling, simulation, and optimization, particularly in the context of connected and automated vehicles (CAVs), traffic safety, and sustainable infrastructure systems for smart cities and major events like the 2032 Olympic and Paralympic Games. He earned his Doctor of Philosophy from Arizona State University and has served as a DECRA Research Fellow. Currently, he is a member of the Australian Research Council's College of Experts and ranks among the Top 2% of Scientists in Logistics and Transportation (Scopus & Stanford University). His work has led to prestigious awards and editorial roles in journals like Transportation Research Part B and IEEE Transactions on Intelligent Transportation Systems . Research interests include traffic flow dynamics, emerging mobility technologies, strategic transport planning, advanced data analysis techniques, and meta-research methodologies. His lab, the Connected and Automated Vehicle Driving Simulation Lab, utilizes cutting-edge tools like VR simulators and SUMO integration to explore mixed traffic scenarios and safety challenges. Key grants include projects on eco-driving strategies for CAVs, real-time traffic signal systems, and infrastructure design for future mobility. He has supervised numerous PhD and Master’s students, contributing to studies on CAV integration, traffic simulation, and policy analysis. His publications address topics such as road user charging theories, reinforcement learning for eco-driving, and paratransit system improvements. The lab’s facilities, including a mixed reality mobility testbed, support research into transitioning to CAVs and enhancing urban resilience.
Jean-Pierre Dubé is the James M. Kilts Distinguished Service Professor of Marketing at the University of Chicago Booth School of Business. He serves as director of the Kilts Center for Marketing at Booth, is a Research Associate at the National Bureau of Economic Research, an elected fellow for the Informs Society for Marketing Science, and a faculty fellow at the Marketing Science Institute. He has been working as a research consultant with Amazon since 2018 and previously consulted for Yahoo! from 2008-2010. B.Sc. in quantitative methods in economics from the University of Toronto (1995) M.A. in economics from Northwestern University (1996) Ph.D. in economics from Northwestern University (2000) Dubé's research focuses on the intersection of industrial organization and quantitative marketing. His empirical studies examine consumer preferences for branded goods, price discrimination, advertising, food deserts and nutrition policy, and the role of misinformation in consumer demand. His work frequently employs advanced econometric methods to analyze consumer behavior across various markets, including packaged goods, digital platforms, and media consumption. Many of his recent projects involve collaborations with companies in the US and China, reflecting the applied nature of his work. Dubé's publications span leading journals in economics and marketing, with recent work focusing on personalized pricing, media effects on consumer behavior during the pandemic, craft brand preferences among millennials, and nutritional inequality. His research demonstrates strong methodological rigor while addressing practical business and policy questions, often challenging conventional wisdom as seen in his work on food deserts which found neighborhood environments contribute minimally to nutritional inequality. 2023 Hillel J. Einhorn Excellence in Teaching Award Chicago Booth Class of 2016 Phoenix Award for service to extracurricular activities 2008 Paul E. Green Award for Best paper in the Journal of Marketing Research 2005 Faculty Teaching Excellence Award for Evening MBA and Weekend MBA Programs Elected fellow for the Informs Society for Marketing Science Multiple MSI Research Grants Kauffman grant Yahoo! Faculty Research Grant Professor Dubé serves as Department Editor at Management Science and has previously served as an area/associate editor for several leading journals including The Journal of Marketing Research, Management Science, Marketing Science, and Quantitative Marketing and Economics. His industry collaborations, particularly with Amazon and previously Yahoo!, have enabled him to conduct large-scale field experiments that inform both academic theory and business practice. Dubé joined the Chicago Booth faculty in 2000 and continues to be actively engaged in research, teaching, and professional service, influencing both academic scholarship and industry practice.
Yun-Hee Choi is a Professor at the Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University. Her research focuses on advanced statistical methodologies for complex biomedical data, particularly in genetic and cancer epidemiology contexts. BSc in Statistics MA in Statistics PhD in Biostatistics She specializes in joint modeling , dynamic prediction , correlated survival data analysis , and statistical genetics , with applications to Lynch syndrome and familial colorectal cancer . Her methodological work includes copula models , frailty models , and multistate models for analyzing competing risks and recurrent events. Recent publications demonstrate expertise in genetic risk estimation , cancer progression modeling , and family-based survival analysis . She develops computational tools like the FamEvent R package for time-to-event data in genetic studies. NSERC: Statistical methods for joint modeling (2019-2024) CIHR: Risk prediction models for HBOC (2019-2023, Co-PI) CANSSI: Genetic data analysis (2019-2022, Co-I) NSERC: Competing risks modeling (2014-2019) CIHR: Multistate models for Lynch syndrome (2013-2016) CBCF-Ontario: BRCA1/2 family screening (2014-2017) NSERC: Correlated survival data (2009-2014) Her work bridges biostatistical theory with cancer prevention applications , particularly in hereditary cancer syndromes.
Rosemarie Parz-Gollner is a distinguished Professor at the Institute of Wildlife Biology and Game Management, Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). With a career spanning over four decades since earning her PhD in 1984, she has established herself as a leading expert in wildlife management and conservation biology in Austria. Her work focuses on the ecology and management of key species including beavers, cormorants, and otters, with particular emphasis on human-wildlife conflict resolution and population monitoring. Her research interests encompass wildlife management, beaver ecology and conservation, cormorant population dynamics, otter research, bird population monitoring (particularly grey herons), conservation genetics, and ecosystem management. Dr. Parz-Gollner has led numerous research projects funded by Austrian federal states, the European Commission, and other institutions, addressing critical wildlife management challenges across Austria. Analysis of her recent publications reveals a strong focus on genetic studies of beaver populations, continued monitoring of waterbird populations (particularly cormorants and grey herons), and innovative approaches to studying otter ecology through non-invasive genetic sampling and stable isotope analysis. Her work demonstrates a consistent commitment to applied conservation science with direct relevance to wildlife management policy. Among her notable scientific achievements is the Österreichischer Staatspreis für Angewandte Ökosystemforschung MAB-Projekt Altenwörth, BMWF, awarded in 1990, recognizing her early contributions to applied ecosystem research. Dr. Parz-Gollner has actively supervised numerous graduate students, guiding doctoral and master's theses on diverse wildlife topics. Her research has been supported through various grants focused on wildlife management and conservation. She maintains strong connections with conservation organizations including BirdLife Austria and the Cormorant Specialist Group, and has served on scientific committees such as the advisory board of the Neusiedler See-Seewinkel National Park. Her laboratory and field work primarily focuses on non-invasive monitoring techniques for wildlife populations, particularly genetic methods for population assessment and conflict resolution strategies for species like beavers that interact with human infrastructure and activities.
Dr. Hendra Nurdin is a Senior Lecturer in the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW), where he has been employed since 2012. His academic journey began with a Sarjana Teknik (equivalent to a Bachelor of Engineering) in Electrical Engineering from Institut Teknologi Bandung, Indonesia, followed by an MSc in Engineering Mathematics from the University of Twente in the Netherlands, and culminated with a PhD in Engineering and Information Science from the Australian National University in 2007. His educational background includes: PhD in Engineering and Information Science, Australian National University, 2007 MSc in Engineering Mathematics, University of Twente, The Netherlands Sarjana Teknik (ST, equivalent to Bachelor of Engineering) in Electrical Engineering, Institut Teknologi Bandung, Indonesia Dr. Nurdin's research lies at the intersection of control engineering and systems theory with quantum physics and energy systems. He has made significant contributions to quantum control systems, quantum information processing, and microgrid control. His work combines theoretical advances in quantum stochastic processes with practical applications in quantum computing and renewable energy systems. He has developed novel approaches to quantum reservoir computing, quantum parameter estimation, and control of distributed energy resources. His recent publications reveal a strong focus on quantum reservoir computing, non-Markovian quantum systems, and the intersection of quantum information with machine learning. There's a clear trend toward practical implementations of quantum information processing systems, particularly exploring how quantum systems can enhance computational capabilities. His work bridges fundamental quantum theory with engineering applications, demonstrating how quantum phenomena can be harnessed for practical computing and sensing tasks. Dr. Nurdin has received recognition including an ARC APD Fellowship (2009-2011). His research has resulted in numerous publications in top-tier journals including Nature Communications, Physical Review series, and IEEE Transactions. He has successfully supervised multiple PhD students to completion, including Dr. Jiayin Chen (2022), Dr. Jiacheng Li (2021), Dr. Muhammad Ali (2021), and Dr. Zhan Shi (2016). Currently, he is supervising Mr. Wen Liu as a PhD candidate. His research is supported by various funding mechanisms including Sydney Quantum Academy scholarships and UNSW research grants, enabling him to pursue cutting-edge research in quantum systems and control. Dr. Nurdin is actively involved with the Sydney Quantum Academy, supervising research in quantum systems and control. His work contributes to Australia's growing quantum technology ecosystem, collaborating with researchers across multiple institutions to advance quantum information processing and quantum engineering applications.
