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Anirban MondalView profile

Associate Professor

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Spatial Statistics
• +5 more

Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu

Wanja HoferView profile

Researcher

• Embedded Operating Systems
• Real-Time Systems
• Aspect-Oriented Programming
• +3 more

Dr. Wanja Hofer is a former research staff member at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). She specialized in embedded systems, real-time operating systems, and aspect-oriented programming. Her research focused on optimizing hardware-centric systems like Sloth and CiAO, addressing challenges in interrupt handling, scheduling, and software product line variability. Her academic journey includes a PhD in 2014 titled Sloth: The Virtue and Vice of Latency Hiding in Hardware-Centric Operating Systems . She contributed to key projects such as Sloth (time-triggered RTOS) and CiAO (aspect-oriented OS family), emphasizing scalability and configurability for automotive and embedded domains. Hofer also held roles like Web chair for EuroSys 2009 and co-maintained the EuroSys Research Directory. Her teaching involved Basics of Systems Programming in C and OS-related seminars. She advised over 15 graduate students on topics ranging from MPU-based task isolation to filesystem-level variability management. Notable contributions include hardware-accelerated interrupt handling, aspect-oriented OS design, and embedded system energy optimization. Hofer currently works at Brose Fahrzeugteile, applying her expertise in embedded systems and real-time computing to automotive technologies. Her work bridges academic innovation with industrial applications, particularly in safety-critical and resource-constrained environments.

Brandon KarchewskiView profile

Teaching Professor

• Computational Geoscience
• Engineering Geology
• Natural Hazards
• +2 more

Brandon Karchewski is an Associate Head (Undergraduate) and Teaching Professor in the Department of Earth, Energy and Environment at the University of Calgary. He earned his PhD in Civil Engineering from McMaster University and teaches courses including Engineering Geology, Computational Methods, and Natural Disasters. His research program focuses on: Computational methods in geophysics and geomechanics Geoscience education innovation Climate change impacts on frozen soils Inverse modeling applications Karchewski has pioneered virtual field experiences and developed open-source tools for modeling climate impacts on permafrost. His educational research examines metaphor use in geoscience communication and field pedagogy. Recent computational work includes Python-based permafrost modeling and stochastic inversion methods for biogeochemical transport. He leads the geophysics field school program emphasizing team-based learning. Award recognition includes: Geoscience Teaching Award (2019) Best Poster Award for teaching innovation research (2018) Team Teaching Excellence award (2016) Multiple teaching assistant awards

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +3 more

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.

• Control Theory
• Applied Mathematics
• Computational Mechanics
• +7 more

Goong Chen is a Professor at Texas A&M University (TAMU) within the College of Arts & Sciences. His research focuses on Control Theory, Applied Mathematics, and interdisciplinary applications in computational mechanics, fluid dynamics, and quantum systems. He holds a Ph.D. from the University of Wisconsin (1977) and a B.S. from National Tsing-Hua University (1972). His research interests span computational biomechanics, forensic modeling, nanofluidics, and mathematical physics. Recent work includes modal analysis of animal motion, crash mechanics of aircraft, and forensic reconstruction of disasters. He has contributed to theoretical advancements in PDEs, nonlinear dynamics, and quantum computing. Chen’s articles address cutting-edge topics like thermoelastic plates, Volterra equations, and DFIM systems. He has pioneered numerical methods for complex systems using OpenFOAM and finite element techniques. His computational models have been applied to real-world scenarios such as submarine implosions and chemical warfare forensics. No academic awards are explicitly listed, but his extensive publication record reflects significant contributions to applied mathematics and engineering. He leads research teams focused on interdisciplinary challenges in computational science and engineering.

• Statistical Signal Processing
• Pattern Recognition
• Machine Learning
• +5 more

Ulisses Braga-Neto is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from The Johns Hopkins University (2002), with earlier degrees including an M.S. from the Federal University of Pernambuco (1992). His research focuses on statistical signal processing, pattern recognition, and machine learning, with applications in bioinformatics, materials informatics, and environmental modeling. He leads the TAMIDS Scientific Machine Learning Lab and has authored over 100 publications. Key research areas include physics-informed neural networks for environmental and engineering problems, error estimation in classification systems, and gene regulatory network inference. His recent work emphasizes machine learning applications in agriculture (e.g., cotton detection in corn fields using UAS), CO2 sequestration modeling, and wildfire prediction. He has contributed to foundational texts like Error Estimation for Pattern Recognition (Wiley-IEEE, 2015). Prof. Braga-Neto's interdisciplinary approach bridges computer science, engineering, and biology. His lab develops algorithms for data-poor environments, with applications in genomics, proteomics, and metagenomics. He advises graduate students through Texas A&M's ECE program, emphasizing rigorous statistical methods alongside machine learning innovation.

Bharath SriperumbudurView profile

Professor

• Non-parametric statistics
• Machine learning
• Optimal transport
• +5 more

Bharath Sriperumbudur is a Professor in the Department of Statistics (with a courtesy appointment in the Department of Mathematics) at the Pennsylvania State University. His research focuses on non-parametric statistics, machine learning, optimal transport, statistical learning theory, regularization and inverse problems, functional and topological data analysis, and reproducing kernel Hilbert spaces. His work is supported by grants such as NSF-DMS-CAREER-1945396 and NSF-DMS-2413425. PhD in Statistics, University of California, San Diego (2010) His research interests span foundational aspects of statistical learning, kernel methods, optimal transport theory, and their applications. Recent work includes advancements in Gromov-Wasserstein geometry, Stein variational methods, and robust topological data analysis. Key contributions include publications on kernel-based quadrature rules, regularization theory, and functional data analysis. His research bridges theoretical statistics, machine learning, and applied mathematics.

Wenyuan FanView profile

Associate Professor

• Array seismology
• Marine geophysics
• Environmental seismology
• +4 more

Dr. Wenyuan Fan is an Associate Professor at the Institute of Geophysics and Planetary Physics (IGPP) within the Scripps Institution of Oceanography at UC San Diego. His research focuses on array seismology, marine geophysics, environmental seismology, and earthquake dynamics. He holds a PhD from UC San Diego and degrees from Peking University. Research Interests: Array seismology and waveform modeling Marine geophysics and ocean-solid earth interactions Earthquake source kinematics and dynamic triggering mechanisms Microseismicity imaging and fault zone processes Recent Work Trends: His recent publications emphasize understanding earthquake rupture mechanics through dense array data, exploring submarine landslide dynamics via hydroacoustic signals, and applying machine learning to seismic catalog development. Studies often bridge tectonic processes with environmental factors, such as storm-induced seismicity and human-induced seismic events. Advising & Grants: No students listed; active in NSF-funded projects on fault dynamics and marine geohazards. Labs/Teams: Affiliated with IGPP's Seismology and Geodynamics Group, collaborating with international networks like IRIS and CTBTO.

Victor TsaiView profile

Professor

• Seismology
• Geomechanics
• Fluid-Solid Interactions
• +5 more

Victor Tsai is a Professor of Earth, Environmental, and Planetary Sciences at Brown University. He specializes in seismology, geomechanics, and theoretical glaciology, with a focus on earthquake mechanics, glacial dynamics, and wave propagation. His research bridges geophysical theory and observation, addressing topics like fault network complexity, subglacial hydrology, and seismic tomography. Tsai holds a PhD from Harvard University (2009) and has been recognized with awards including the NSF CAREER Award (2015) and the Charles F. Richter Award (2014). Education: PhD (Harvard, 2009), AM (Harvard, 2006), BS (Caltech, 2004) Affiliations: Brown University (since 2019), previously at Caltech (2011–2019) Research highlights include modeling earthquake source complexity, understanding high-frequency ground motion, and developing new seismic imaging techniques. His work on glacial earthquakes and meltwater pulses has advanced climate-ice interaction studies. Collaborations span seismology, glaciology, and planetary science.

Stephen LaneView profile

Teaching Professor

• Computer Graphics
• Computer Animation
• Virtual Reality
• +5 more

Stephen H. Lane is a Teaching Professor at the University of Pennsylvania's School of Engineering and Applied Science, Department of Computer and Information Science. He serves as Director of the Computer Graphics and Game Technology (CGGT) Master's Program and teaches courses such as Computer Animation (CIS462/562), Advanced Topics in Computer Graphics and Animation (CIS660), and Game Design and Development (CIS564). He also supervises the Game Design Practicum (CIS568) capstone course. Education: B.S. in Mechanical and Aerospace Engineering from Cornell University (1980) M.S. in Systems Engineering from UCLA (1982) Ph.D. in Mechanical and Aerospace Engineering from Princeton University (1988) Dr. Lane's research focuses on the intersection of robotics, physically-based character animation, embodied intelligent agents, and virtual reality user interfaces. His work integrates control theory, artificial intelligence, and computer animation techniques to develop advanced simulation and training systems. His publications since 1987 cover topics such as inverse kinematics, neural networks for motion control, B-spline receptive fields, robotic skill acquisition, and gesture recognition systems. His recent work (2010-2011) emphasizes sensor fusion for gesture recognition and immersive training interfaces. Scientific Awards: Co-inventor of four US patents related to robotic animation and motion control systems Contributions to hybrid controller hierarchies and neural network training methods As founder of soVoz, Inc., Dr. Lane commercializes behavioral animation technology for virtual environments. His academic-industry collaboration includes contracts with Microsoft, Disney, and the US Army. He has developed tools like ProScena™ to integrate interactive 3D simulation capabilities into gaming and training applications.

• Statistical Mechanics
• Probability Theory
• Discrete Geometry
• +3 more

Richard Kenyon is the Erastus L. DeForest Professor of Mathematics at Yale University, serving as Director of Undergraduate Studies. His research focuses on statistical mechanics, probability, and discrete geometry, with notable contributions to dimer models, random tilings, and integrable systems. He has explored topics such as limit shapes, phase transitions, and combinatorial configurations. His work intersects algebraic geometry, combinatorics, and mathematical physics, often involving the analysis of lattice models and their applications. His research includes open problems such as tiling optimization, geometric spanning surfaces, number theory questions, and rigidity of tilings. Kenyon's gallery showcases visualizations of mathematical concepts, including random triangulations, Vinnikov curves, and conformal mappings. His academic contributions span over three decades, with recent articles addressing dimers, webs, and eigenvalue properties. He actively collaborates on projects like the six-vertex model, multiwebs, and renormalizable dynamical systems. Kenyon’s academic service includes directing undergraduate studies at Yale and contributing to initiatives in discrete differential geometry. His work highlights the interplay between pure mathematics and applied probability, with applications in physics and combinatorial optimization.

• Health Care Management
• Operations Research
• Patient Scheduling
• +5 more

Professor Patrick Jonathan is a Full Professor at the Telfer School of Management, University of Ottawa. His research focuses on applying operations research methods to healthcare management challenges, particularly in patient scheduling, capacity planning, and disaster response logistics. He holds a B.A.Sc. from McMaster University, M.Sc. and Ph.D. from the University of British Columbia (UBC). Key research areas include healthcare system efficiency, stochastic optimization, approximate dynamic programming, and resource allocation in emergency and long-term care settings. He has contributed to projects like optimizing inpatient unit staffing during pandemics and developing robust logistics models for disaster relief operations. Education: B.A.Sc. (McMaster), M.Sc. (UBC), Ph.D. (UBC) Affiliations: INFORMS, CORS, Centre for Health Care Management (UBC) Recent work emphasizes pandemic-related healthcare operations, including staff scheduling under crisis conditions and elective patient admission strategies during the COVID-19 pandemic. His research also addresses dynamic home care routing and multi-priority patient scheduling policies. He has secured over $2.3 million in research funding through grants from NSERC, MITACS, and the National Research Council. Notable projects include the 'Resilience and Adaptation to Climatic Extreme (RACE) Wildfires' initiative and 'Stochasticity in Approximate Dynamic Programming.' Labs/Teams: Collaborates with interdisciplinary teams on healthcare logistics and emergency department optimization projects.

Scot M. MillerView profile

Associate Professor

• Atmospheric Science
• Greenhouse Gases
• Air Pollution
• +11 more

Scot M. Miller is an Associate Professor in the Department of Environmental Health and Engineering at the Whiting School of Engineering, Johns Hopkins University. He leads the Greenhouse Gas Research Group, focusing on quantifying emissions of greenhouse gases and air pollutants using satellite, aircraft, and tower observations. His research spans global scales, from Arctic ecosystems to urban and industrial sources in the U.S. and China. His research interests include atmospheric science, greenhouse gas emissions, inverse modeling, big data analytics, and climate policy. He integrates tools from statistics, high-performance computing, and satellite remote sensing to improve emission estimates and inform environmental regulations. Recent publications highlight trends in methane, ethane, and sulfuryl fluoride emissions, carbon cycle dynamics, and innovative methods for analyzing massive satellite datasets. His work increasingly leverages OCO-2 and OCO-3 satellite data to study carbon dioxide and methane fluxes across diverse ecosystems. Scientific awards include the NSF CAREER Award, JHU Catalyst Award, and the Carnegie Distinguished Postdoctoral Fellowship. He was also recognized with Harvard’s Certificate of Excellence in Teaching. Miller advises multiple PhD students, including Mingyang Zhang, Dylan Gaeta, and Leyang Feng, and has secured grants from NASA, NSF, NOAA, and JHU. He is a member of the NASA OCO Science Team and collaborates with institutions such as Northern Arizona University, Carnegie Institution, and NOAA. His lab is involved in urban environmental monitoring in Baltimore and interdisciplinary climate policy research.

JULIEN BONNELView profile

Professor

• Ocean Acoustics
• Signal Processing
• Machine Learning
• +12 more

Julien Bonnel is a Professor at the Scripps Institution of Oceanography, University of California, San Diego, where he is affiliated with the Marine Physical Laboratory. His research focuses on the application of signal processing and machine learning techniques to ocean acoustics, with significant contributions to underwater sound propagation and monitoring. His educational background includes an HDR (Habilitation to Direct Research) from Université de Bretagne Occidentale (2017), a Ph.D. from Université de Grenoble (2010), and an M.Sc. from Grenoble INP (2007). 2017: HDR, Université de Bretagne Occidentale, France 2010: Ph.D., GIPSA-Lab, Université de Grenoble, France 2007: M.Sc., Université de Grenoble (Grenoble INP), France Dr. Bonnel's research interests center on ocean acoustics, particularly signal processing and machine learning applied to passive acoustic monitoring , bioacoustics , and ambient noise analysis . He investigates geoacoustic inversion , acoustical tomography , and source localization using normal mode propagation models in underwater environments. His work integrates numerical modeling and ocean instrumentation , contributing to remote sensing and satellite oceanography . These efforts are critical for understanding marine ecosystems and underwater communication. His scientific contributions span applied ocean sciences, with a strong emphasis on developing advanced methodologies for interpreting underwater acoustic signals. While specific publications are not listed here, his research trajectory indicates a sustained focus on inference systems and ocean acoustic modeling. Dr. Bonnel leads the Signals, Inference and Ocean Acoustics Lab , where he mentors students and researchers in cutting-edge acoustics projects. He is actively involved in research grants and collaborative oceanographic studies, though specific funding sources are not detailed in the provided text. He maintains a presence on academic platforms including Google Scholar and ResearchGate, supporting broad dissemination of his work.

Nicholas E. BarrandView profile

Associate Professor

• Glaciology
• Glacier Mass Balance
• Sea-Level Rise
• +7 more

Dr. Nicholas E. Barrand is Associate Professor of Glaciology and Senior Admissions Tutor at the School of Geography, Earth and Environmental Sciences, University of Birmingham, UK. His research focuses on glacier mass balance, sea-level rise, and glaciers as critical water resources in a changing climate. Education: P.G. Cert. Academic Practice, University of Birmingham (Distinction) Ph.D. Glaciological Remote Sensing, University of Leeds and Swansea University M.Sc. Geographical Information Science, University of Leeds (Distinction) B.Sc. Physical Geography, University of Newcastle-upon-Tyne (1st Class Honours) Dr. Barrand’s research interests lie at the intersection of glaciology, climate change, and hydrology. He investigates glacier and ice sheet dynamics using field observations, numerical modeling, and optical/microwave remote sensing. His group explores the role of glaciers in sea-level rise and water security, with a focus on polar and high-latitude regions. Key methodological interests include glacio-hydrological modeling, ice flow and melt modeling, and interdisciplinary approaches linking earth system science with socio-environmental studies. His recent publications demonstrate a strong trend in Antarctic and Arctic glaciology, with a focus on ice shelf stability, glacier retreat under climate change, Greenland albedo dynamics, and mountain water security. His work increasingly integrates hydrological modeling with glaciological data to assess future water availability in deglaciating basins, particularly in the Andes and Himalayas. Teaching Responsibilities: Research Methods and GIS (co-module lead) Environmental Research in High Latitudes (module lead) Swiss Alps Fieldclass Contributions to Fieldwork & Tutorials, Global Environmental Issues, Research Projects Dr. Barrand supervises research projects on glacier mass balance, remote sensing of ice dynamics, ice flow modeling, and environmental applications of remote sensing in polar regions. He has led research funded by NERC and other agencies, contributing to international assessments of glacier change and sea-level rise. He is actively involved in academic leadership as Senior Admissions Tutor and promotes interdisciplinary collaboration across earth system sciences. Research Group Focus: Mass balance and dynamics of glaciers and ice sheets Sea-level rise contributions Glaciers as water resources Glacio-hydrological modeling Remote sensing of snow and ice melt Climate-glacier-hydrology modeling chain