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Sangtae HaView profile

Associate Professor

• Machine Learning
• Deep Learning Systems
• Networks
• +6 more

Sangtae Ha is an Associate Professor in the Computer Science Department at the University of Colorado Boulder. His research focuses on building practical computer systems spanning multiple disciplines, including machine learning/deep learning systems, networks and distributed systems, internet protocols, wireless networks, video streaming, storage systems, and security. He specializes in creating efficient and scalable solutions for real-world computing challenges. His work emphasizes interdisciplinary approaches, bridging theoretical computer science with practical system design. Key areas of exploration include optimizing neural network execution for edge computing, developing adaptive streaming protocols, and enhancing wireless network performance through novel signal processing and spectrum management techniques. Recent projects include frameworks for semantic offloading in neural networks and reinforcement learning-based cloud scheduling. No scientific awards or notable grants are explicitly mentioned in the provided materials. While no formal advisees are listed, his research team likely involves graduate students and collaborators. No dedicated labs or teams are named, though his work aligns with broader university initiatives in computer systems and networking.

• Quantum Information Theory
• Quantum Optics
• Quantum Computing
• +3 more

Dr. Daniel Malz is an Assistant Professor at the Department of Mathematical Sciences, University of Copenhagen. His research focuses on quantum many-body systems, quantum optics, and quantum computing, with affiliations to research groups QA, QMATH, and QfL. His work bridges theoretical physics and mathematical modeling, addressing topics like superradiance, entanglement dynamics, and quantum state preparation. Key research interests include quantum information theory, non-Markovian dynamics, and the development of efficient quantum simulation techniques. His recent publications explore advanced topics such as photonic cluster states, tensor network simulations, and cross-platform quantum network verification. Much of his work addresses foundational questions in quantum mechanics while maintaining practical relevance for quantum technologies. His contributions span both theoretical derivations and numerical methods, with a focus on bridging classical and quantum many-body dynamics.

• Scientific computing
• Computational mathematics
• Numerical methods
• +6 more

Christina C. Christara is a Professor in the Department of Computer Science at the University of Toronto, specializing in scientific computing and numerical methods. Her research focuses on numerical solutions of partial differential equations, high performance computing, parallel computation, and financial mathematics applications. She holds a Ph.D. in Computer Science from Purdue University (1988), an M.Sc. from Purdue (1986), and a B.Sc. in Mathematics from Aristotle University (1982). She has taught courses such as Numerical Methods for Optimization Problems (CSC466/2305), High-Performance Scientific Computing (CSC456-2306), and Numerical Algorithms (CSC436), emphasizing both theoretical and computational aspects. Her research interests span numerical weather prediction, computational finance, and advanced numerical techniques like spline collocation and penalty methods. She has supervised over 20 graduate students in topics ranging from GPU-accelerated PDE solvers to XVA pricing models. Her work often addresses challenges in computational efficiency and accuracy, with applications in engineering, finance, and environmental science. She is affiliated with the Numerical Analysis and Scientific Computing Group and actively contributes to high-performance computing methodologies.

• Applied Analytics
• Statistical Inference
• Time Series Analysis
• +7 more

Dr. Laleh Tafakori is a Senior Lecturer in the Department of Statistics and Analytics at RMIT University's School of Science. Her research focuses on the intersection of statistical modeling, machine learning, and their applications in healthcare, finance, and environmental science. She is affiliated with the university's City Campus and can be contacted at laleh.tafakori@rmit.edu.au . Her teaching interests include Applied Analytics , Statistical Inference , Time Series Analysis , Mathematical Statistics , Stochastic Processes , and Probability Theory . She actively supervises research projects in areas such as: Healthcare modeling (e.g., diabetes onset prediction, maternal risk assessment) Environmental data analysis (e.g., extreme precipitation estimation via satellite data) Financial risk analysis (e.g., Value-at-Risk forecasting, credit portfolio management) Machine learning applications in complex networks and smart grids Her recent research trends emphasize predictive modeling for public health challenges, leveraging advanced statistical techniques (copula models, functional volatility) and machine learning (CNNs, random forests). She collaborates with institutions worldwide, addressing issues like Saudi Arabia's healthcare indicators and European financial systemic risk. While no formal awards are listed, her work demonstrates impactful contributions to interdisciplinary data science. Dr. Tafakori has advised over 16 students on topics ranging from diabetes epidemiology to edge computing optimization. Her research outputs include 28+ peer-reviewed articles, with a focus on methodological innovation and real-world problem-solving. She maintains active engagement in collaborative projects without explicitly listed grants.

Lyon Brad KingView profile

Professor

• Space Propulsion
• Plasma Physics
• Optical Diagnostics
• +3 more

Lyon Brad King serves as the Richard and Elizabeth Henes Endowed Professor in the Department of Mechanical and Aerospace Engineering within Michigan Technological University's College of Engineering. He directs the Space Systems Research Group and acts as Faculty Advisor for the Aerospace Enterprise student team. Dr. King earned his PhD from the University of Michigan. His research focuses on experimental space propulsion systems that utilize electromagnetic forces to accelerate ionized plasma propellant, achieving significant fuel savings over chemical rockets through on-orbit solar power generation. His expertise spans space propulsion , plasma physics , and optical diagnostics , with specific interests in Hall-effect thrusters, ion engines, arcjets, electrostatic probe design, ion-energy analysis, time-of-flight mass spectrometry, Doppler laser cooling of trapped ions, and antimatter confinement. His work bridges fundamental plasma phenomena with practical spacecraft propulsion applications. Analysis of his publication record reveals sustained innovation in electric propulsion systems over two decades, with recent emphasis on thermal management for Hall thrusters, alternative propellants like bismuth and magnesium, advanced plasma diagnostics techniques, and novel concepts such as magnetoelectrostatic jets from ionic liquids. Dr. King actively mentors students through the Aerospace Enterprise program and leads experimental research at the Ion Space Propulsion Laboratory, where his team develops next-generation propulsion technologies for spacecraft.

Venkatesh SaligramaView profile

Professor

• Machine Learning
• Video Analysis
• Statistical Signal Processing
• +3 more

Venkatesh Saligrama is a Professor in the Department of Electrical and Computer Engineering at Boston University, with affiliations in Systems Engineering, the Center for Information and Systems Engineering, and the College of Arts & Sciences’ Computer Science division. He holds a PhD from MIT (1997) and leads the Data Science & Machine Learning Lab. His research focuses on Machine Learning, Video Analysis, Statistical Signal Processing, and Network Science, with notable contributions to vision-language models, bandit algorithms, and synthetic data strategies. He has received prestigious awards like the NSF CAREER Award (2005) and Presidential Early Career Award (2003). His teaching includes courses on signals, stochastic processes, and digital communication systems. Saligrama’s work bridges theoretical foundations and applied systems, emphasizing efficient learning under constraints. His recent publications explore zero-shot detection, data-efficient pretraining, and safe optimization techniques. Beyond academia, his research impacts domains like autonomous systems, healthcare prediction, and cybersecurity. He actively contributes to editorial roles, conferences, and interdisciplinary initiatives, fostering collaborations across engineering and computer science.

• Robotics
• Swarm Robotics
• Collective Decision Making
• +3 more

Eliseo Ferrante is an Assistant Professor at the Faculty of Science, Department of Artificial Intelligence, Vrije Universiteit Amsterdam. He is also affiliated with the Network Institute. His research focuses on swarm robotics, collective decision-making, and autonomous systems, contributing to UN Sustainable Development Goals. He teaches courses such as 'Learning Machines' and 'Collective Intelligence.' His research explores topics including robot swarms in challenging environments, evolutionary algorithms for adaptive robotics, and morphological evolution in robots. Key contributions include methods for robust swarming in GNSS-denied areas and optimized control strategies for evolvable morphologies. Dr. Ferrante has supervised 2 PhD theses and contributed to datasets on collective motion models and modular robotics. His work emphasizes practical applications of AI in robotics, with a focus on resilience and adaptability in complex systems.

• Robotics
• Mechatronics
• Control Systems
• +5 more

Pieter van Goor is a Researcher in Robotics and Mechatronics at the Digital Society Institute. He holds a PhD from the Australian National University (2018–2022), awarded in July 2023, and served as a Postdoctoral Research Fellow there from May 2022 to June 2024. His research focuses on geometric methodologies in control systems, state estimation, and navigation, with applications to Lie groups, inertial systems, and multi-agent data fusion. Recent work emphasizes fusion of Gaussian distributions on Lie groups, observer design for navigation systems with delayed measurements, and equivariance in tracking controllers. Key research areas include robotics, mechatronics, and mathematical control theory, with publications addressing topics such as parallel transport in Kalman filtering, collaborative attitude estimation, and geometric approaches to data fusion. His work integrates theoretical mathematics (e.g., Lie group theory) with practical engineering challenges in navigation and robotics. Collaborations span institutions globally, with notable contributions to inertial navigation systems and multi-agent systems. Despite no explicitly listed awards, his h-index of 62 and 4 citations (as of 2023) reflect significant scholarly impact. Advising and grant details are not provided in the text.

David BergerView profile

Professor

• Macroeconomics
• Monetary Policy
• Labor Economics
• +3 more

David Berger is a Professor of Economics at Duke University's Trinity College of Arts & Sciences. His research focuses on macroeconomics, monetary policy, labor markets, housing economics, and public policy. He holds a Ph.D. from Yale University (2012). Key research areas include the effects of minimum wages on labor market efficiency, the macroeconomic implications of imperfect risk sharing, and the dynamics of housing markets. He has explored topics such as monopsony power in labor markets, the role of testing in pandemic reopening strategies, and the impact of mortgage policies on monetary policy transmission. Recent grants include a National Science Foundation award (2022–2025) for studying macroeconomic implications of economic policy in imperfectly competitive labor markets. He teaches courses on housing economics, macroeconomic policy design, and applied microeconomics. External affiliations include the Federal Reserve Bank of Dallas and the National Bureau of Economic Research (NBER). His work spans both theoretical and empirical contributions, with a focus on policy-relevant analysis in labor markets, fiscal stimulus, and financial systems.

Michael TempletonView profile

Professor

• Environmental Engineering
• Infectious Diseases
• Public Health Engineering
• +6 more

Professor Michael Templeton is a leading academic in Public Health Engineering at Imperial College London , affiliated with the Faculty of Engineering and the Department of Civil and Environmental Engineering . He also holds the Royal Academy of Engineering Research Chair in Global Sanitation Technology , working closely with organizations like Oxfam , Water For People , and the International Worm-Based Sanitation Association . His work addresses critical issues in water supply , sanitation , and public health , particularly in regions affected by schistosomiasis and water scarcity . Professor Templeton earned a Bachelor's degree with honours in Engineering Science and a PhD in Civil-Environmental Engineering , both from the University of Toronto . He is a chartered civil engineer , Fellow of the Institution of Civil Engineers , and elected Fellow of the African Academy of Sciences . His professional experience includes advisory roles for the World Health Organization , Ethiopian Federal Ministry of Health , and various research councils. Environmental Engineering : Water treatment Infectious Diseases : Schistosomiasis control Global Health : Sanitation in low-income settings Climate Resilience : Cold region sanitation Waste Valorization : Biochar from faecal sludge Diagnostic Innovation : Biosensors for pathogens His most recent research explores stormwater odorants , UV disinfection , and smartphone-based water quality monitoring . He has also advanced tiger worm toilets and citizen science approaches in data-scarce regions. His work spans 15 countries including Ghana , Sierra Leone , India , and Ethiopia . Scientific awards and honors include: Fellow of the Institution of Civil Engineers Elected Fellow of the African Academy of Sciences Royal Academy of Engineering Research Chair Professor Templeton has no listed students in the provided data but has contributed extensively to sanitation innovation , waterborne disease prevention , and capacity-building in developing nations. He has led field projects and published on vermifiltration systems , biochar production , and disinfection byproducts .

• Robotics
• Autonomous Systems
• Data Fusion
• +7 more

Hugh Durrant-Whyte is a Professor at the University of Sydney and Director of the Centre for Translational Data Science. He holds a BSc (Eng) from the University of London, an MSE, and a PhD from the University of Pennsylvania. Previously, he served as CEO of National ICT Australia (NICTA) and Director of the Australian Centre for Field Robotics (ACFR). His research focuses on robotics, autonomous systems, and data fusion, with over 350 publications and four successful startups. Education: BSc (Eng) in Engineering, University of London MSE in Robotics, University of Pennsylvania PhD in Robotics, University of Pennsylvania Affiliations: Director, Centre for Translational Data Science Former CEO, NICTA (2010-2014) Former Director, ACFR (1995-2010) His research interests span robotics, autonomous systems, and sensor networks. Notable contributions include foundational work on SLAM (Simultaneous Localization and Mapping) and decentralized data fusion. He has pioneered applications in mining automation, autonomous vehicles, and environmental modeling. His work emphasizes practical real-world systems, with projects like autonomous straddle carriers for container terminals and terrain mapping for mining operations. Awards & Honors: NSW Scientist of the Year (2010) Fellow of the Royal Society (FRS), Australian Academy of Science (FAA), and IEEE (FIEEE) Recipient of multiple IEEE Best Paper awards Grants & Labs: Leadership in securing multi-million-dollar grants for robotics and data science initiatives Centre for Translational Data Science: Focuses on translating data science into real-world impact He advises on numerous government and industry projects, bridging academic research with industrial applications. His research teams have developed influential algorithms for autonomous navigation and multi-agent systems.

Christine DeMarsView profile

Professor

• Educational Measurement
• Psychometrics
• Item Response Theory (IRT)
• +4 more

Christine DeMars is a Professor of Graduate Psychology and Senior Assessment Specialist at James Madison University (JMU), affiliated with the Center for Assessment & Research Services (CARS). She holds a PhD in Measurement and Quantitative Methods from Michigan State University (1998), an M.Ed. in Elementary Education from Arizona State University (1993), and a B.A. in Psychology from Wichita State University (1990). Her research focuses on psychometric methods, including item response theory (IRT), differential item functioning (DIF), equating, and large-scale testing challenges. She teaches courses in IRT and classical test theory and supervises PhD students. As CARS liaison to Cluster 4 (Stewardship and Sustainability) and Communications Studies (SCOM), she advises on assessment design and implementation. Recent work addresses low-stakes testing validity, remote assessment integrity, and pandemic-related testing adaptations. Her publications emphasize methodological rigor and practical applications in educational measurement. Her scholarly contributions include over 50 peer-reviewed articles on topics like response validity, IRT parameter recovery, and DIF detection. Her work bridges theoretical advancements and real-world testing challenges, particularly in addressing examinee motivation, external event impacts, and model misfit implications. She collaborates with institutions to enhance assessment practices through evidence-based strategies. Ms. DeMars is actively involved in professional development, offering workshops on assessment design and psychometric analysis. Her efforts in CARS support institutional learning outcomes and program evaluation initiatives, reflecting her commitment to advancing educational measurement practices.

Nuria Pelechano GomezView profile

Professor

• Character Animation
• Virtual Humans
• Immersive Environments
• +3 more

Nuria Pelechano Gomez is a Professor at the Department of Computer Science, Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Informatics (FIB). She leads research in the ViRVIG group (Visualització, Realitat Virtual i Interacció Gràfica), focusing on character animation, virtual humans, and crowd simulation. Her work emphasizes immersive environments and real-time rendering applications. Education: PhD in Computer Science from UPC (thesis: 'Real-Time Avatar Animation Synthesis in Virtual Reality' by Yun, H. 2023). Research interests include motion capture, crowd behavior modeling, and VR/AR applications. Recent work explores sparse data reconstruction, avatar customization, and disease spread simulations. She has contributed to over 140 publications, including high-impact journals like ACM Transactions on Graphics and Computer Graphics Forum. Awards include 'Best Teaching Assistant' in 2005 and 2006. Active in academic governance, she serves on program committees for top conferences (e.g., Eurographics, SIGGRAPH Motion in Games). Her projects (e.g., MPACT, XR4ED) are funded by national and EU grants. Labs/Teams: ViRVIG Group at UPC, collaborating with international institutions on projects like CAVAST (Crowd Animation, Visualization, and Simulation Testbed).

Erick DelageView profile

Professor

• Optimization under Uncertainty
• Operations Research
• Robust Optimization
• +7 more

Erick Delage is a Professor in the Department of Decision Sciences at HEC Montréal, holding the Canada Research Chair in Decision Making Under Uncertainty. He is a member of the Group for Research in Decision Analysis (GERAD) and an associate academic member of MILA. His research focuses on optimization under uncertainty, robust and stochastic optimization, machine learning, and risk management, with applications in finance, energy systems, and transportation. Delage holds a Ph.D. in Electrical Engineering from Stanford University, where he worked with renowned scholars like Andrew Y. Ng and Yinyu Ye. His teaching includes courses on Quantitative Risk Management, Decision Analysis, and Robust Optimization at institutions like HEC Montréal, Politecnico di Milano, and EPFL. He has supervised numerous PhD and master's students, leading to impactful contributions in areas like distributionally robust optimization and deep reinforcement learning for financial engineering. Delage's work emphasizes bridging theory and practice, with notable contributions to contextual optimization methods, energy transition pathways, and risk-averse decision-making. His research has been recognized with awards such as the Nicholson Award (2008) and membership in the Royal Society of Canada's College of New Scholars (2020). His laboratories and collaborations include the Supply Chains and Mobility research cluster funded by IVADO, focusing on data-driven decision-making for resilient systems. Key grants include leadership in energy transition optimization and robust supply chain frameworks.

Hwan ChoiView profile

Assistant Professor

• Prosthetic Devices
• Exoskeletal Systems
• Musculoskeletal Analysis
• +3 more

Dr. Hwan Choi is an Assistant Professor at the University of Central Florida (UCF), affiliated with the College of Engineering. He leads the Rehabilitation Engineering and Assistive Device Lab, focusing on advancing prosthetic and exoskeletal technologies to enhance mobility for individuals with disabilities. His work integrates biomechanical analysis, neural control strategies, and assistive device design to address challenges in rehabilitation engineering. Education: Dr. Choi earned his Ph.D. in Engineering from the University of Washington (2016), a Master of Engineering (2006), and a Bachelor of Engineering (2004) from Korea University. He completed a Postdoctoral Research Fellowship at the University of Michigan in 2018. Research Interests: His studies prioritize prosthetic device optimization, exoskeleton system development, musculoskeletal modeling, and neural control mechanisms. He investigates how biomechanical insights can improve device performance and user adaptation, with applications in transtibial amputee mobility, cerebral palsy gait correction, and aging-related mobility issues. Article Trends: Recent work emphasizes multimodal data integration (e.g., IMU sensors, sEMG), adaptive prosthetic systems, and machine learning for gait analysis. Key themes include improving clinical outcomes through smart technologies and addressing unmet needs in orthotic/prosthetic design. Awards: Gatzert Child Welfare Fellowship (2016), Travel Grant from Gait and Clinical Movement Analysis Society (2016), University of Washington Research Assistant Scholarship (2011–2016), Louis and Katherine Marsh Memorial Fellowship (2012). Advising & Grants: While specific advisees are not listed, Dr. Choi’s lab actively engages in collaborative projects, with funding support from scholarships and institutional grants. His research bridges academic and clinical settings, emphasizing translational outcomes. Labs/Teams: The Rehabilitation Engineering and Assistive Device Lab focuses on developing innovative solutions for mobility impairments, including smart adaptive prosthetic sockets and variable-stiffness orthoses. Current projects explore machine learning-driven gait analysis and self-sanitizing medical devices.