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François BouffardView profile

Associate Professor

• Power Engineering
• Smart Grids
• Systems and Control
• +3 more

François Bouffard is an Associate Professor at McGill University's Faculty of Engineering, specializing in Power Engineering and Systems Control. He holds the William Dawson Scholar title and serves as Associate Chair (Undergraduate Affairs). His research focuses on smart grids, renewable energy integration, and advanced control systems. Research interests include optimizing power systems through data-driven methods, demand response mechanisms, and energy storage solutions. He contributes to projects like the Group for Research in Decision Analysis (GERAD), emphasizing sustainable energy systems and grid resilience. Recent work explores flexibility in smart grid architectures, cold load management, and multi-agent reinforcement learning for energy systems. His publications highlight optimization techniques, stochastic modeling, and machine learning applications in energy control. Awarded the William Dawson Scholar distinction, his work bridges theoretical advancements with practical grid operations. He collaborates on interdisciplinary projects addressing energy transition challenges, such as hybrid storage systems and distributed energy resource coordination.

Yann QuilcailleView profile

Researcher

• Climate Extremes
• Climate Emulators
• Climate Modeling
• +3 more

Dr. Yann Quilcaille is a Researcher at ETH Zurich's Department of Environmental Systems Science, affiliated with the Professorship for Land Climate Dynamics. His work focuses on climate extremes, climate emulators, and socio-economic integration of climate models. He holds a PhD from Université Paris-Saclay and previously served as a research scholar at IIASA (Austria), advancing the OSCAR climate model. Key contributions include the MESMER-X emulator for spatially resolved climate extremes and research on fire weather, extreme event attribution, and climate overshoot scenarios. Dr. Quilcaille's research integrates physics-based and statistical approaches to develop low-cost climate emulators, aiming to bridge climate modeling with socio-economic frameworks. His expertise spans heatwaves, droughts, and fires, with a focus on global and regional climate impacts. Notable projects include analyzing fire weather overlaps between continents and quantifying carbon majors' contributions to historical heatwaves. His academic journey includes studies at Ecole Normale Supérieure de Paris-Saclay (Theoretical Physics), AgroParisTech (Economics of Sustainable Development), and Université Pierre & Marie Curie (Ocean, Atmosphere, Climate). He has authored over 50 publications on climate modeling, emulation techniques, and policy-relevant climate science.

Dennis B. McLaughlinView profile

Professor

• Hydrology
• Groundwater Hydrology
• Environmental Engineering
• +5 more

Dennis B. McLaughlin is a Professor in the Department of Civil and Environmental Engineering at MIT, affiliated with the Parsons Laboratory for Water Resources and Hydrodynamics. He holds a B.S.E.E. from Purdue University (1966), M.S.E. from Princeton University (1967), and Ph.D. from Princeton University (1985). Research focuses on hydrologic data assimilation, groundwater systems, contaminant transport, and sustainability. Active in projects involving remote sensing data integration, ensemble methods, and continental-scale hydrology. His work bridges hydrology with applied mathematics, economics, and ecology. Notable collaborations include NSF-funded initiatives on data assimilation in Earth sciences and NASA projects on soil moisture estimation. He has advised numerous graduate students and postdoctoral researchers, contributing to advancements in environmental modeling and resource management.

• Autonomous Vehicles
• Robotic Perception
• Sensor Fusion
• +2 more

Eduardo Nebot is Emeritus Professor and former Patrick Chair in Automation and Logistics at the University of Sydney, where he founded the Australian Centre for Robotics. His research develops perception and navigation systems for autonomous vehicles, focusing on robust operation in complex environments. Nebot's work enables autonomous systems for mining, transportation, and field robotics applications. Research interests include sensor fusion, cooperative perception, probabilistic tracking, and validation methods for autonomous systems. Current projects investigate V2X-enabled cooperative driving, pedestrian trajectory prediction, and robust localization under environmental changes. Publication trends highlight multi-sensor perception systems, with recent work emphasizing domain adaptation for 3D detection, safety validation frameworks, and human-robot interaction in autonomous driving. Articles consistently address real-world deployment challenges in industrial and urban settings. Fellow of the Australian Academy of Technology and Engineering (2016) Fellow of the IEEE (2016) The Australian Centre for Robotics collaborates with industry partners on autonomous haulage systems and intelligent transportation. Nebot has supervised numerous PhD candidates in robotics and maintains research partnerships with mining and automotive sectors.

• Stochastic Optimization
• Robust Optimization
• Data-Driven Decision-Making
• +3 more

Nam Ho-Nguyen is a Senior Lecturer in the Discipline of Business Analytics at the University of Sydney Business School. He holds a PhD from the Tepper School of Business at Carnegie Mellon University and an Honours degree in Mathematics from the Australian National University. His research focuses on stochastic, robust, and data-driven optimization techniques for decision-making under uncertainty, combining methods from statistics, machine learning, and traditional optimization. Education: PhD in Operations Research, Carnegie Mellon University (2019) Bachelor of Science (Honours) in Mathematics, Australian National University Research Interests: Nam’s work addresses decision-making in uncertain environments through optimization frameworks. He explores applications in finance, healthcare, transportation, and technology, emphasizing scalability and computational efficiency. Key areas include bilevel optimization, adversarial machine learning, and distributionally robust chance-constrained programming. Awards: INFORMS Optimization Society Young Researchers Prize (2022) Gerald L. Thompson Doctoral Dissertation Award (2019) Australian Research Council Discovery Early Career Researcher Award (DECRA) ($470K AUD) Teaching & Grants: Teaches QBUS1040, QBUS2310, BUSS4932, and QBUS6820 courses. Recipient of ARC grants for research on multi-stage optimization with contextual data and forecast reconciliation ($625K AUD). Supervises research students on scheduling problems and multi-agent systems. Lab/Team: His work involves collaborations on optimization tools for choice modeling, electronic trading, and solution design in IT services, supported by interdisciplinary teams at the University of Sydney and international institutions.

• Genomics
• Statistical Genetics
• Polygenic Risk Scores
• +7 more

Bogdan Pasaniuc is a Professor in the Departments of Pathology and Laboratory Medicine, Human Genetics, and Computational Medicine at the University of California, Los Angeles (UCLA). His academic career spans multiple disciplines within genomics and computational biology, with a focus on advancing precision medicine through genetic and genomic research. He holds a BSc in Computer Science from A.I. Cuza University of Iasi (2003) and a PhD in Computer Science and Bioinformatics from the University of Connecticut (2008). His postdoctoral training included positions at the Broad Institute of Harvard and MIT (2012), Harvard School of Public Health (2012), and ICSI at UC Berkeley (2010), where he specialized in statistical genetics and computational biology. His research interests encompass statistical genetics, polygenic risk scoring, and the application of computational methods to understand genetic architecture across diverse populations. He focuses on translational research in precision medicine, including the development of tools for polygenic risk assessment and their implications in healthcare disparities. Key areas include the genetic underpinnings of complex diseases such as cancer, neuropsychiatric disorders, and metabolic conditions, alongside the integration of genomic data with electronic health records (EHR) for clinical insights. Notable contributions include leadership in the UCLA ATLAS Community Health Initiative and the PRIMED Consortium, which aim to improve polygenic risk assessment in underrepresented groups. His work also extends to the All of Us Research Program, leveraging large-scale genomic and health data to enhance disease understanding and precision medicine applications. He collaborates across institutions and leads research teams focused on computational methods for admixture mapping, genetic epidemiology, and multi-omics integration. His projects often emphasize reducing disparities in genomic research and ensuring equitable access to precision health solutions.

Maggie MakarView profile

Assistant Professor

• Machine Learning
• Causal Inference
• Healthcare Applications
• +3 more

Maggie Makar is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of Michigan. Her research focuses on the intersection of machine learning and causal inference, particularly in developing robust and data-efficient methods for healthcare and resource-constrained settings. She received her PhD from MIT's CSAIL and a bachelor's degree in math and economics from UMass Amherst. Her work emphasizes causal reasoning to improve predictive models' reliability, with applications in healthcare decision-making and policy evaluation. Key areas include offline reinforcement learning for medical interventions, confounding variable handling, and mitigating biases in AI systems. Dr. Makar teaches courses such as CSE 598 (Causality and Machine Learning) and mentors PhD students in causal ML and healthcare AI. She actively seeks to expand her research group and collaborate on projects blending causal theory with real-world applications. Education: PhD in Computer Science, MIT (CSAIL), 2018 Bachelor's in Mathematics and Economics, University of Massachusetts Amherst, 2013 Awards: CAREER Award (NSF, 2024) CRII: SCH Grant (NSF, 2022) Research Themes: Causal inference in machine learning Data-efficient methods Healthcare applications Algorithmic fairness

Amin RezaeiView profile

Assistant Professor

• Computer Security
• Machine Learning
• Computer Architecture
• +3 more

Amin Rezaei is an Assistant Professor in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He directs the Computer Architecture, Reliability, and Security Laboratory (CARS-Lab) and serves as the undergraduate advisor for Computer Engineering. His expertise spans computer security, machine learning, and computer architecture, with a focus on hardware security and protecting hardware intellectual property. Rezaei holds a Ph.D. in Computer Engineering from Northwestern University. He is a senior member of IEEE and a lifetime member of ACM and AAAI. His research has been funded by NSF grants totaling $500,000, and he has published over 50 peer-reviewed articles in top venues like DAC, ICCAD, and DATE. He has also served on technical program committees for major conferences such as IPDPS and GLSVLSI. His research interests include hardware security, machine learning applications in secure systems, and novel logic locking techniques. Key projects include developing CycPUF (a cyclic physical unclonable function) and frameworks for detecting hardware Trojans using multimodal deep learning. His lab emphasizes democratizing research by publicly sharing source code. Education: Ph.D. in Computer Engineering, Northwestern University Awarded Academic Excellence Award, University of Louisiana at Lafayette Awards: Walter P. Murphy Fellowship, Northwestern University Royal E. Cabell Fellowship, Northwestern University Grants: NSF CISE-MSI and CRII programs. Rezaei’s advising focuses on undergraduate Computer Engineering students, with Spring 2025 hours on Zoom. His lab’s current research includes projects on hardware Trojan prediction, ML-based security evaluations, and dynamic logic locking.

Shinichi NakajimaView profile

Researcher

• Bayesian Inference
• Generative Modeling
• Explainable Artificial Intelligence
• +4 more

Dr. Shinichi Nakajima is a Senior Research Lead at the Technical University of Berlin, affiliated with the BIFOLD (Berlin Institute for the Foundations of Learning and Data) and the AIP – RIKEN Center of Advanced Intelligence Project . He leads the research group “Probabilistic Modeling and Inference” at BIFOLD. His academic journey includes a Master’s in Physics from Kobe University (1995) and a PhD in Computer Science from Tokyo Institute of Technology (2006). Prior to academia, he worked at Nikon Corporation (1995–2014) on statistical analysis, image processing, and machine learning. His research focuses on Bayesian inference , generative modeling , explainable AI , and quantum computing , with applications in computer vision, natural language processing, and scientific computing. Notable projects include developing NeuLat (a neural sampling toolbox for lattice field theories) and advancing techniques for symbolic XAI to enhance AI transparency. Dr. Nakajima has published extensively on topics such as diffusion models, federated learning, and physics-informed neural networks. His work bridges theoretical foundations (e.g., Bayesian learning) with practical applications in quantum computing and biomedical imaging. He actively contributes to open-source tools and collaborates with industry and academic institutions globally. Key technical achievements include improving sampling efficiency in quantum eigensolvers, enhancing brain source reconstruction via 3D neural networks, and developing anomaly detection systems using self-supervised autoencoders. His research emphasizes computational efficiency and robustness against adversarial attacks, leveraging Langevin dynamics and gradient-based optimization methods.

Dan M. FrangopolView profile

Professor

• Life-cycle civil engineering
• Structural reliability
• Infrastructure sustainability
• +5 more

Prof. Dan M. Frangopol is the Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture at Lehigh University's P.C. Rossin College of Engineering and Applied Science. He is a global leader in life-cycle civil engineering, focusing on probabilistic methods, infrastructure resilience, and sustainability. His research spans structural reliability, risk-based decision-making, and multi-hazard mitigation under climate change. Affiliations: Lehigh University (current), University of Colorado Boulder (23 years), and institutions in Romania and Belgium. Education: Dipl.-Ing. from Bucharest (1969), Doctor of Applied Sciences (summa cum laude) from University of Liège (1976), and multiple honorary doctorates. His research interests include life-cycle cost optimization, probabilistic mechanics, and infrastructure systems management. He pioneered the International Association for Bridge Maintenance and Safety (IABMAS) and the International Association for Life Cycle Civil Engineering (IALCCE). Key contributions include frameworks for resilient infrastructure under climate change and extreme events. Frangopol has authored/co-authored over 500 journal articles, 5 books, and 70 book chapters. He has supervised 50 PhD and 56 M.Sc. students, many of whom are now leading academics and practitioners. His awards include the inaugural Dan M. Frangopol Medal (2023), ASCE Noble Prize (2015, 2024), and multiple honorary memberships in national and international academies. He has advised numerous high-profile projects funded by NSF, FHWA, NASA, and others. His labs and initiatives include the Fazlur R. Khan Distinguished Lecture Series and the journal Structure and Infrastructure Engineering .

Yang ZhangView profile

Academic

• Aerospace Engineering
• Fluid Dynamics
• Noise Reduction
• +3 more

Yang Zhang is a Research Assistant Professor at the Illinois Institute of Technology (Illinois Tech), affiliated with the Department of Mechanical, Materials, and Aerospace Engineering within the Armour College of Engineering. His research focuses on experimental and computational aerodynamics, particularly in noise reduction mechanisms, flow control, and advanced measurement techniques such as particle image velocimetry (PIV). Key research interests include slat noise mitigation in aircraft systems, turbulent flow dynamics, and the application of smart materials and spectral analysis methods. Zhang’s work addresses challenges in high-lift systems, supersonic cavity flows, and aerodynamic efficiency optimization. His publications highlight contributions to noise prediction, flow visualization techniques, and passive/active control strategies for aerodynamic systems. Recent studies emphasize the integration of porous materials and dielectric elastomers for acoustic liner design, alongside advancements in data assimilation frameworks for turbulent boundary layer analysis.

• Machine Learning
• Cybersecurity
• Biometric Authentication
• +3 more

Dr Wencheng Yang is a Senior Lecturer in Computing at the School of Mathematics, Physics and Computing , University of Southern Queensland. He holds a PhD in Computing from UNSW, an MSc from Korea, and a BMgmt from Wuhan University of Technology. His research spans multiple domains including machine learning security , biometric authentication , privacy-preserving systems , and IoT applications . Education : BMgmt (Wuhan University of Technology), MSc (Korea), PhD (UNSW) Yang’s work emphasizes privacy and security in AI systems, particularly in biometric authentication (e.g., fingerprint, face, ECG) and IoT security. His recent publications focus on model inversion attacks , homomorphic encryption , and federated learning for healthcare applications. Key trends include secure face-swapping , Alzheimer’s disease prediction , and anti-forensic detection . His research has been cited 3378 times, with 1458 total downloads and 31 monthly views. While no explicit scientific awards are listed, his interdisciplinary work bridges computer science , healthcare , and cryptography . Current projects include privacy-preserving frameworks for implantable medical devices and military health systems .

• Probabilistic Forecasting
• Statistical Evaluation
• Climate Science
• +3 more

Prof. Tilmann Gneiting is a Professor of Computational Statistics at Karlsruhe Institute of Technology (KIT) and Head of the Computational Statistics Working Group at the Heidelberg Institute for Theoretical Studies (HITS). His research focuses on probabilistic forecasting, statistical evaluation of predictive models, and applications in environmental science, epidemiology, and renewable energy. He has held academic roles in mathematics and statistics departments across multiple institutions. His work emphasizes rigorous statistical methodology for forecasting systems, including ensemble models, uncertainty quantification, and validation frameworks. Notable contributions include developing scoring rules for probabilistic predictions, Bayesian model averaging techniques, and geostatistical methods for weather and climate modeling. Courses taught include Forecasting: Theory and Practice and Time Series Analysis . Prof. Gneiting’s research spans diverse areas such as earthquake forecasting, solar energy prediction, and pandemic modeling. He has contributed to R packages like ensembleMOS and ProbForecastGOP , advancing computational tools for statistical analysis. His work bridges theoretical statistics with practical applications in environmental and public health domains. Professional awards and recognitions are not explicitly listed in the provided texts. His advising and grants involve collaborations in interdisciplinary projects, though specific student names or grant details are not mentioned. He leads computational statistics teams at HITS and actively publishes in top-tier statistical and applied journals.

• Soil Organic Carbon
• Digital Soil Mapping
• Soil Data Standardization
• +7 more

Niels Batjes is a leading researcher at ISRIC - World Soil Information , specializing in soil data standardization, digital soil mapping, and carbon sequestration modeling. His work focuses on creating harmonized global soil databases like WoSIS and SoilGrids to support climate mitigation and environmental conservation. Key Research Areas : Soil Organic Carbon, Digital Soil Mapping, Pedotransfer Functions, Measurement Error Analysis Projects : Development of soil databases with quantified uncertainties, HoliSoils initiative for European forest soils Recent publications highlight his contributions to global soil water retention modeling, interoperable soil data exchange frameworks, and machine learning applications for carbon stock assessment. He plays a central role in international soil data infrastructure development.

• Empirical Asset Pricing
• Derivative Markets
• Real Options
• +3 more

Anastasios Kagkadis is a Senior Lecturer (Associate Professor) in Finance at the University of Liverpool Management School and an Honorary Researcher at Lancaster University Management School. His research focuses on empirical asset pricing, derivative markets, and real options. He teaches modules such as Empirical Asset Pricing (ULMR804) and Theory of Finance II (ACFI234), serving as Module Coordinator for both in the 2024-25 academic year. Research interests include analyzing hedge fund performance through bear factor dynamics, evaluating political risk pricing in equity options, and exploring stock-level investment anomalies related to real uncertainty. Recent work examines factor timing using portfolio characteristics and the predictive power of dispersion in investor versus analyst expectations. No scientific awards or grants are listed in available records. He maintains affiliations with both Liverpool and Lancaster universities without documented part-time status. No advisee or lab-team details are provided in the source materials.