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Line RoaldView profile

Professor

• Power Systems Optimization
• Renewable Energy Integration
• Grid Resilience
• +5 more

Professor Line Roald is a faculty member in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. Her research focuses on power system optimization, renewable energy integration, grid resilience, and wildfire risk mitigation using stochastic optimization and data-driven methods. Education : PhD (2016), MS (2012), BS (2009) from ETH Zurich Key Research Areas : Power Systems Optimization, Renewable Energy Integration, Wildfire Risk Mitigation, Stochastic Programming, Grid Decarbonization Her work addresses critical challenges in sustainable energy systems, including balancing grid efficiency and risk, optimizing electrolyzer scheduling for flexibility, and predicting cascading blackout severity using graph neural networks. She has developed frameworks for carbon intensity comparison and wildfire risk assessment in power systems. Scientific Awards : 2024 Inclusion, Equity and Diversity in Engineering Award 2024 Vilas Faculty Early Career Investigator Award 2023 IEEE Power Tech Best Student Paper Award 2021 NSF CAREER Award 2019 MTLE Fellow Professor Roald mentors graduate students and teaches courses including Introduction to Optimization and On-Line Control of Power Systems . Her publications highlight innovative approaches to grid security, carbon-efficient energy markets, and climate resilience in infrastructure systems.

Zhibao MianView profile

Lecturer

• Trustworthy AI
• Machine Learning
• Large Language Models
• +8 more

Dr. Zhibao Mian is a Lecturer in the School of Computer Science at the University of Hull, UK, and previously held an Associate Professor position at Northwest Normal University. He specializes in trustworthy AI, machine learning, and intelligent maintenance systems. His research integrates AI with IoT, blockchain, and digital twins in Industry 4.0/5.0 contexts. He leads projects on predictive maintenance for offshore wind turbines and AI-driven sustainable energy solutions. Dr. Mian holds a PhD from the University of Hull and an MSc from the University of Nottingham. Research interests include AI ethics, model-based safety analysis, and RCM. He has secured grants such as the CPHC-funded study on AI in software education and oversees multiple PhD scholarships. Notable roles include Editorial Board member of the American Journal of Artificial Intelligence and Reviewer for high-impact journals/conferences like JSS and IEEE. He is a Senior Fellow of the Higher Education Academy and received the Royal Academy of Engineering's 2024 Exceptional Talent designation. Recent publications (2023-2025) focus on ordinal networks, outlier detection, Belt and Road trade analysis, and carbon emissions modeling. He actively advises PhD students on topics like UAV-based anomaly detection and predictive maintenance frameworks.

Yizhou SunView profile

Professor

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +3 more

Professor Yizhou Sun is affiliated with the University of California Los Angeles (UCLA) and the Henry Samueli School of Engineering and Applied Science . Her academic work focuses on Machine Learning , Artificial Intelligence , and Graph Neural Networks within the Computer Science department. Her research spans High-Level Synthesis , Causal Inference , and Computational Biology , with recent publications addressing neural network compression, language model safety, and dynamical system modeling. The trends in her recent 2025 and 2024 publications emphasize Deep Learning , Graph Theory , and Language Model Optimization , reflecting interdisciplinary applications in Biomedical Data , Hardware Design , and Physical Simulation .

Eunjung LeeView profile

Assistant Professor

• Educational Technology
• Gifted Education
• Teacher Education
• +3 more

Eunjung Lee serves as Assistant Professor of Business Analytics in the College of Business at Lewis University, bringing extensive industry experience from Samsung and LG alongside prior academic roles at Indiana State University. Her expertise bridges business analytics and educational technology with a pronounced focus on equity-driven research. Her educational foundation includes: Ph.D. in Information Technology Management, University of Wisconsin-Milwaukee M.B.A., Korea University B.S., Kwangwoon University Dr. Lee's research trajectory evolved from business analytics toward educational equity, with recent work emphasizing computational thinking in mathematics education, teacher preparation models, and machine learning applications for gifted identification. She investigates how digital tools transform pedagogy while addressing systemic barriers for underrepresented students. Analysis of her 2022-2025 publications reveals dominant themes in equitable gifted identification through cross-cultural validation of the HOPE rating scale, computational thinking integration in teacher training, and longitudinal studies of enrichment program impacts. Her methodology consistently combines quantitative analysis with equity-centered frameworks. No scientific awards were documented in available sources. While specific advising details remain unreported, her courses in Business Intelligence and Forecasting demonstrate applied analytics instruction. Research grants weren't specified in source materials. Collaborative structures like the Lowell Stahl Center for Entrepreneurship provide institutional context, though dedicated labs or research teams weren't explicitly referenced.

Johanna ZiegelView profile

Professor

• Statistical Forecasting
• Probability Theory
• Financial Risk Management
• +3 more

Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.

Gabriel A. SilvaView profile

Professor

• Neuroscience
• Neural Computing
• Quantum Algorithms
• +7 more

Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.

Georg MartiusView profile

Professor

• Autonomous Learning
• Reinforcement Learning
• Deep Learning
• +7 more

Georg Martius is a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in Tübingen and a full Professor at the University of Tübingen since 2023. His work focuses on autonomous learning in embodied agents, drawing inspiration from child development to create self-motivated robotic systems. Research Areas: Model-based reinforcement learning, intrinsic motivation, deep learning, haptic sensor development, and integration of combinatorial algorithms into neural networks. Affiliations: Former postdoc at IST Austria (Christoph Lampert and Gašper Tkačik groups) and Max Planck Institute for Mathematics in the Sciences (Leipzig). Contact: georg.martius@tuebingen.mpg.de Education: PhD in 2009 (Göttingen), Computer Science studies in Leipzig and Edinburgh.

Upaka RathnayakeView profile

Professor

• Water Resources Management
• Hydrological Modeling
• Climate Analysis
• +6 more

Upaka Rathnayake is a Professor of Civil Engineering and Principal Investigator at the Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) research unit at Atlantic Technological University Sligo, Ireland. He has held academic and research roles globally, including in Sri Lanka, Japan, the UK, New Zealand, Australia, and Fiji. His research focuses on water resources management, hydrological modeling, climate analysis, and AI-driven solutions for environmental challenges. He holds a PhD from the University of Strathclyde and advanced certifications from Hokkaido University. Education: PhD in Optimal Management of Urban Sewer Systems (University of Strathclyde, 2013) Professional Memberships: Institution of Engineers Sri Lanka, Engineers New Zealand, International Association of Hydrological Sciences Research interests include: Hydrological modeling and climate change adaptation Multi-objective optimization and soft computing techniques Explainable AI applications in environmental systems Remote sensing and GIS for water resource management Recent articles highlight AI-driven solutions for air quality prediction, soil nutrient analysis, and flood risk assessment. Awards include the 2023 Presidential Award for Scientific Publications and multiple university excellence awards. He actively advises PhD students on projects like urban water systems optimization and climatic trends analysis. Rathnayake is an editorial board member of journals like Scientific Reports and PLoS ONE , contributing to peer review and policy-oriented research. His work bridges data-driven methods with traditional hydrological practices to address global environmental challenges.

Kurt MauteView profile

Professor

• Structural Topology Optimization
• Multidisciplinary Optimization
• Aeroelastic Systems Optimization
• +4 more

Kurt Maute is a Professor and Palmer Engineering Chair at the University of Colorado Boulder’s College of Engineering and Applied Science (CEAS). He currently serves as Associate Dean for Undergraduate Education. His academic journey includes a PhD in Civil Engineering (University of Stuttgart, 1998) and a Dipl.-Ing. in Aerospace Engineering (University of Stuttgart, 1992). He has held progressively senior roles at CU Boulder, including Associate Dean for Research (2012–2014), Associate Professor (2006–2012), and Assistant Professor (2000–2006). Maute’s research focuses on structural topology optimization, multi-disciplinary optimization, and aeroelastic systems. He has pioneered methods integrating XFEM, level-set techniques, and isogeometric analysis for complex engineering problems. His work spans fluid-structure interaction, hypersonic vehicle design, and additive manufacturing. His notable contributions include advancements in immersed boundary methods, multi-material optimization, and uncertainty quantification. Awards include the NSF Career Award (2004) and Palmer Endowed Chair (2016–present). Maute’s lab (Aerospace Mechanics Research Center, AMREC) addresses challenges in computational mechanics and multi-physics systems. He has advised numerous students and led grants in battery modeling, topology optimization, and aerospace systems. His research bridges theory and application, emphasizing industrial relevance and computational innovation.

Oded RegevView profile

Professor

• Theoretical Computer Science
• Lattice-based Cryptography
• Quantum Computation
• +3 more

Oded Regev is a Silver Professor at the Courant Institute of Mathematical Sciences, New York University. He has previously held positions at Tel Aviv University and École Normale Supérieure, Paris (CNRS). His research spans theoretical computer science, cryptography, quantum computation, and machine learning applications in biological discovery. Education: PhD in Computer Science, Tel Aviv University (2001) Regev is renowned for his work in lattice-based cryptography, including the introduction of the Learning With Errors (LWE) problem and Gaussian measures. He also leads research using interpretable machine learning to decode RNA splicing logic and nuclear speckle dynamics, with implications in disease and therapeutics. His recent articles focus on quantum factoring, RNA localization, and geometric lattice bounds. Scientific Awards: European Research Council (ERC) Starting Grant (2008) 2018 Gödel Prize 2019 Simons Investigator Award Best Paper Awards: STOC 2003, Eurocrypt 2006 Regev mentors students and postdocs in both theoretical computer science and computational biology. His lab has secured funding from NSF, NIH, and Additional Ventures. He co-founded the TCS+ online seminar series and serves as Associate Editor-in-Chief for Theory of Computing .

Arthur BousquetView profile

Associate Professor

• Applied Mathematics
• Numerical Analysis
• Computational Fluid Dynamics
• +3 more

Arthur Bousquet is an Associate Professor of Mathematics at Lake Forest College, affiliated with the Math and Computer Science department. He holds a PhD in Applied Mathematics from Indiana University (Bloomington, IN) and a MS in Engineering in applied mathematics and scientific computing from SuP Galilee Engineering School (Paris, France). His research focuses on numerical methods for partial differential equations, including finite volume and finite element techniques, with applications to geophysical fluid dynamics, climate modeling, and biomedical problems like viral shell mechanics. Notable areas include shallow water equations, phase field modeling, and computational methods for atmospheric dynamics. Bousquet has published extensively on topics such as numerical weather prediction, electrokinetic equations, and virus nanoindentation modeling. His work often combines theoretical analysis with computational simulations to address complex systems in fluid dynamics and materials science. He has received the Rothrock Award for teaching excellence (2014) and held research fellowships including an NSF Graduate Fellowship (2009-2013). His teaching includes courses like Computational Mathematics, Multivariable Calculus, and Real Analysis.

Susanne JaeggiView profile

Professor

• Cognitive Training
• Executive Functions
• Individual Differences in Cognition
• +4 more

Susanne M. Jaeggi is a Professor of Psychology at Northeastern University, with additional affiliations in the Bouve College of Health Sciences and the College of Arts, Media, and Design. Her research focuses on cognitive training, executive functions, and individual differences in cognition across the lifespan. She holds PhDs in Cognitive Psychology and Neuroscience from the University of Bern (Switzerland), and completed postdoctoral work in Cognitive Neuroscience at the University of Michigan. Her work has been funded by NIH, NSF, IES, ONR, and the Advanced Education Research and Development Fund (AERDF). She leads the Working Memory & Plasticity Lab , which develops interventions to improve working memory and executive functions, and co-leads the Brain Game Center for Mental Fitness and Well-Being , creating evidence-based brain fitness tools. Jaeggi’s research emphasizes understanding mechanisms of cognitive improvement through training, including neuroplasticity and individual variability. Key areas include cognitive aging interventions, gamification, sensory-cognitive interactions, and the impact of socioeconomic factors on academic achievement. Her work integrates behavioral experiments, neuroimaging, and digital health technologies to address real-world challenges in education and healthcare. Recent studies explore music/art-based interventions, brain stimulation (e.g., tDCS), and scalable cognitive assessments. Collaborations span disciplines, with publications addressing topics like neural correlates of training, motivational features in interventions, and cross-modal perception. Her labs emphasize translating findings into public-facing tools, such as freely accessible brain fitness apps. Current projects include optimizing interventions for ADHD populations and leveraging digital platforms for global mental fitness.

Laurent LessardView profile

Associate Professor

• Control Theory
• Optimization Algorithms
• Machine Learning
• +4 more

Laurent Lessard is an Associate Professor of Mechanical and Industrial Engineering at Northeastern University, with courtesy appointments in Electrical and Computer Engineering and the Khoury College of Computer Sciences. He is a core faculty member of the Institute for Experiential AI. Previously, he was at the University of Wisconsin-Madison. His research focuses on control theory, optimization algorithms, machine learning, and decentralized systems. He holds a PhD from Stanford University (2011) and completed postdocs at Lund University and Berkeley. Education: PhD in Aeronautics and Astronautics, Stanford University, 2011 MS in Aeronautics and Astronautics, Stanford University, 2005 BASc in Engineering Science (Aerospace), University of Toronto, 2003 Research Interests: Control theory, optimization algorithms, machine learning, distributed systems, and robust control. His work bridges control theory and optimization, emphasizing algorithm design and analysis with applications to AI and autonomous systems. Key Projects: Includes NSF-funded research on distributed optimization, Army-funded work on cognitive distributed sensing, and studies on algorithm equivalence and robustness. Awards: NSF CAREER Award (2018), Hugo Schuck Best Paper Award (2013), Gerald Holdridge Teaching Excellence Award (2019). Grants & Labs: Principal investigator on multiple NSF grants, including work on decentralized control and optimization. Active in the Kostas Research Institute for Homeland Security. Leads a research group with a focus on theoretical and applied control systems. Publications: Over 50 peer-reviewed articles, including foundational work on optimization algorithms and control theory. Recent work emphasizes robustness, distributed systems, and machine learning integration.

Guido MontúfarView profile

Professor

• Deep Learning Theory
• Mathematical Machine Learning
• Graphical Models
• +4 more

Guido Montúfar is a Professor in the Departments of Mathematics and Statistics & Data Science at the University of California, Los Angeles (UCLA), effective since 2024. He also leads the Mathematical Machine Learning Group at the Max Planck Institute for Mathematics in the Sciences (MPI MIS) in Leipzig, Germany since 2018. His academic journey includes a PhD in Mathematics from Leipzig University (2012), and Diplom degrees in Physics and Mathematics from TU Berlin (2009 and 2007). Montúfar's research focuses on the theoretical foundations of deep learning, mathematical machine learning, and the interplay between geometry and learning. Key areas include neural network architecture theory, optimization landscapes, and information geometry. His work bridges algebraic statistics, graphical models, and topological data analysis. His grants and awards include an ERC Starting Grant (2018-2023), a Sloan Research Fellowship (2022), and an NSF CAREER Award. He has advised numerous PhD students and postdocs, contributing to significant advancements in machine learning theory and applications. Montúfar teaches courses on applied mathematics, optimization, and machine learning at UCLA. His research also explores topics like oversquashing in graph neural networks and the geometry of policy gradients in reinforcement learning.

Robert PicheView profile

Professor

• Kalman Filters
• Global Positioning Systems (GPS)
• Signal Processing
• +5 more

Robert Piche is a Professor at the Computing Sciences Mathematics Research Centre, specializing in advanced signal processing, positioning systems, and sensor fusion. He holds a Doctor of Science (Technology) and Master of Science from the University of Waterloo, Canada (1986 and 1982, respectively). His research focuses on Kalman filters, Global Positioning Systems (GPS), particle filters, and indoor positioning technologies. He has contributed extensively to fields like satellite orbit prediction, non-line-of-sight (NLoS) positioning, and machine learning applications in biomechanics and robotics. Dr. Piche has authored over 230 publications and received recognition through an invitation/ranking in a 2014 competition. He actively participates in academic activities, including conference presentations and peer-review roles. His work bridges theoretical advancements and practical applications, with contributions to autonomous systems, sensor data analysis, and wearable technology. Collaborations span international institutions, reflecting his global impact in engineering and computer science disciplines.