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Matthew John M. KraneView profile

Professor

• Materials Process Modeling
• Solidification of Metal Alloys
• Thermal Processing
• +3 more

Dr. Matthew John M. Krane is a Professor in the Department of Materials Engineering at Purdue University and a member of the Purdue Center for Metal Casting Research. His work focuses on the design, development, and modeling of materials processes , particularly solidification and thermal processing of metal alloys, with strong emphasis on defect prevention and uncertainty quantification in numerical models. Current projects include grain-refined particle transport in DC casting , exergy optimization in copper smelting , and boron segregation in continuous casting . Past research includes through-process modeling of Al alloys , microsegregation studies , and laser hardening techniques . Scientific awards include an invited keynote lecture at the 2015 International Symposium on Liquid Metal Processing and Casting and multiple invited papers in high-impact journals.

Angelos GeorghiouView profile

Associate Professor

• Stochastic Optimization
• Robust Optimization
• Operations Management
• +4 more

Angelos Georghiou is an Associate Professor of Operations Management at the Department of Business and Public Administration, University of Cyprus. He previously held academic positions as Assistant Professor at McGill University (2016–2019), Post-Doctoral Researcher at MIT (2012–2013), and Postdoctoral Researcher at ETH Zurich (2013–2016). His work bridges computational methods in stochastic and robust optimization with applications in healthcare, energy systems, and operations management. Ph.D. in Operations Research, Imperial College London (2012) M.Sci. in Mathematics, Imperial College London (2008) His research focuses on stochastic optimization , robust optimization , and decision rules , addressing challenges in healthcare analytics , energy-efficient control systems , and machine learning integration . Recent publications in Management Science and Operations Research highlight his contributions to decision-dependent information discovery and computational frameworks. Key trends in his 15 most recent articles include: Advancing robust optimization techniques for multistage problems Applications in healthcare (psychiatric risk prediction) and energy systems Integration of machine learning with stochastic programming Development of tractable algorithms for complex decision environments Scientific Awards : Esdras-Minville Best Student Paper Award (2023) He serves on editorial boards of Operations Research Letters (Area Editor) and Management Science , and collaborates with institutions like MIT, ETH Zurich, and McGill University. His work with undergraduate students includes a 2021 paper in SIAM Undergraduate Research Online .

David Hyten, Jr.View profile

Professor

• Plant Genetics
• Soybean Genomics
• Molecular Breeding
• +4 more

David Hyten, Jr. serves as the Haskins Professor of Plant Genetics within the Department of Agronomy & Horticulture at the University of Nebraska-Lincoln, where he leads research advancing soybean genomics and breeding methodologies for global food security. Education: Ph.D., University of Maryland, 2005 M.S., University of Tennessee, 2002 B.A., Southern Illinois University, 1999 Research Focus: Dr. Hyten's program integrates high-throughput genotyping with quantitative genetics to dissect complex traits including disease resistance (cyst nematode, stem borer), seed composition (protein/oil), and yield stability. His work pioneers genomic selection pipelines and recombination hotspot analysis to accelerate breeding cycles, with emphasis on translating genomic discoveries into practical cultivar development through marker-assisted selection and predictive modeling. Publication Trends: Recent outputs (2021-2025) reveal evolving emphasis from foundational genomics (recombination mapping, QTL identification) toward applied breeding solutions, including cost-effective DNA extraction methods, pedigree-based imputation techniques, and community strategic planning for soybean genomics. His work increasingly addresses genotype-environment interactions and stability of key traits across diverse production systems.

Roberto TronView profile

Assistant Professor

• Control Theory
• Robotics
• Computer Vision
• +4 more

Roberto Tron is an Assistant Professor in the Mechanical Engineering and Systems Engineering departments at the Boston University College of Engineering , with his office located at 110 Cummington Mall. His research integrates control theory, robotics, and computer vision to solve complex multi-agent coordination problems. His primary research interests focus on Riemannian geometry applications , distributed multi-agent systems , and safety-critical control . Key methodologies include Control Barrier Functions (CBFs), Riemannian optimization, and distributed consensus algorithms, with applications spanning autonomous aerial vehicles, robotic manipulation, and multi-robot security systems. Analysis of his recent publications reveals a strong emphasis on safety verification and real-time optimization for autonomous systems. His work consistently bridges theoretical foundations in nonlinear control with practical implementations in robotics, particularly addressing challenges in limited sensor fields of view, distributed task allocation, and noise-robust navigation. The research shows increasing integration of formal methods like Signal Temporal Logic with learning-based approaches. Tron received his Ph.D. from The John Hopkins University and previously conducted post-doctoral research at the GRASP Lab, University of Pennsylvania. His work demonstrates significant contributions to provably safe autonomous systems through frameworks like the Control Barrier Function Toolbox.

Marco A.R. FerreiraView profile

Associate Professor

• Bayesian Statistics
• Multi-Scale Modeling
• Spatial-Temporal Models
• +4 more

Marco A.R. Ferreira is an Associate Professor in the Department of Statistics at Virginia Polytechnic Institute and State University (Virginia Tech), affiliated with the College of Science. He holds a Ph.D. in Statistics from Duke University (2002), with a dissertation on Bayesian multi-scale modeling under M. West. He also earned an M.Sc. (1994) and B.Sc. (1993) in Statistics from the Federal University of Rio de Janeiro. Research Interests: Ferreira specializes in Bayesian statistics, multi-scale modeling, spatial-temporal models, computational methods (e.g., MCMC), and applications in environmental science, genomics, and epidemiology. His work emphasizes hierarchical models, inverse problems, and high-dimensional data analysis. Publications Trends: His research spans advanced statistical methodologies for environmental monitoring, civil unrest modeling, and genomic data analysis. Key themes include Bayesian hierarchical models, spatiotemporal fusion, and computational algorithms for optimal experimental design. Awards & Honors: OBAYES Poster Prize (2009) CNPq Fellowship (2003–2006) WNAR/COBAL 2 Award (2005) Springer Poster Prize (2003) Finalist, Savage Award (2003) Best Contributed Paper (JSM 2000) Professional Activities: He serves as an Associate Editor for Bayesian Analysis and is a member of the American Statistical Association and the International Society for Bayesian Analysis.

Nicholas OssiView profile

Visiting Assistant Professor

• Nonlinear Waves
• Integrable Systems
• Partial Differential Equations
• +2 more

Nicholas Ossi is a Visiting Assistant Professor in the Department of Mathematics at the University at Buffalo's College of Arts and Sciences. He holds a PhD in Applied Mathematics from Florida State University (2024) and a BS in Mathematics and Physics from the University of Central Florida (2018). His research focuses on integrable nonlinear partial differential and difference equations modeling wave propagation in dispersive media, particularly within the nonlinear Schrödinger (NLS) family. His work explores inverse scattering transforms for space-time-shifted integrable equations and investigates non-Hermitian systems with applications in wave dynamics and topological properties. Recent publications address discrete integrable systems, breather interactions, and effects of complex potentials. Office hours: Tuesday/Thursday 10:00-11:00am. Contact: 326 Mathematics Building, UB North Campus, Buffalo NY, 14260-2900; (904) 891-0164; nossi@buffalo.edu .

Alexandra Andreeva SoskovaView profile

Professor

• Computability Theory
• Computable Structure Theory
• Mathematical Logic
• +4 more

Alexandra Andreeva Soskova is a Full Professor in the Department of Mathematical Logic and its Applications at Sofia University's Faculty of Mathematics and Informatics. She holds a PhD in Mathematics (1990) and has held academic roles since 1979, including Head of Department (2008-2016) and Vice Dean (2015-2016). Her research focuses on computability theory, computable structure theory, and enumeration reducibility, with notable contributions to degree spectra and abstract structures. Education: BS (1978), MS (1979) in Mathematics, PhD (1990) in Mathematical Logic (Sofia University) Award: Honorary badge 'St. Kliment Ohridski' (2020) Her work explores effective properties of abstract structures, including coding in graphs, cohesive powers, and jump inversion theorems. She has supervised multiple PhD and MS students, including Stefan Vatev. Key grants include collaborations with institutions in Austria, Russia, and the US. She actively participates in academic service, including roles with the Association for Symbolic Logic and organizing international conferences.

Niels OtaniView profile

Associate Professor

• Cardiac Electrophysiology
• Computational Biology
• Biomechanical Imaging
• +9 more

Niels Otani is an Associate Professor in the School of Mathematics and Statistics at the College of Science, Rochester Institute of Technology (RIT). He holds a BA from the University of Chicago and a Ph.D. from the University of California, Berkeley. His research focuses on cardiac electrophysiology, computational biology, and biomechanical imaging, with a particular emphasis on understanding and controlling cardiac arrhythmias such as ventricular fibrillation. Dr. Otani has pioneered methods for visualizing action potential propagation using ultrasound and developed novel defibrillation strategies that reduce energy requirements. He also investigates the role of ephaptic coupling in cardiac dynamics and the mechanisms underlying spiral wave formation and termination. His work bridges mathematics, physics, and cardiology, with applications in clinical therapies and biomedical engineering. Dr. Otani's research has been supported by grants such as an NSF award for developing diagnostic tools for cardiac disease. He teaches advanced courses in multivariable calculus, linear algebra, and research thesis supervision. Collaborations span interdisciplinary teams, including veterinary cardiology and biomedical imaging groups. His contributions include over 50 publications on topics ranging from action potential dynamics to computational modeling of cardiac tissue, with a focus on translating theoretical findings into clinical solutions.

Shujuan MaoView profile

Assistant Professor

• Environmental Seismology
• Hydrogeophysics
• Geothermal Energy
• +5 more

Shujuan Mao is an Assistant Professor in the Department of Earth and Planetary Sciences at the University of Texas at Austin, part of the Jackson School of Geosciences. She holds a B.S. from Peking University, a Ph.D. from MIT, and conducted postdoctoral research at Stanford University and the Institut des Sciences de la Terre in France. Her research focuses on environmental seismology, hydrogeophysics, and geothermal energy, with an emphasis on understanding subsurface fluid dynamics using seismic interferometry. Key areas include groundwater monitoring, carbon sequestration, and volcanic unrest. She leads the 'Seismo4D' research group, which develops cutting-edge seismic techniques for 4D subsurface imaging. Mao's work spans academic contributions (over 15 peer-reviewed articles), student supervision (two current advisees), and international collaborations. Her group actively recruits PhD students and postdocs interested in environmental seismology and energy transition challenges. Her mailing address is Jackson School of Geosciences, Austin, TX 78712-1692, with contact via smao@jsg.utexas.edu or shujuan.c.mao@gmail.com.

Peter H.M. BovendeerdView profile

Assistant Professor

• Cardiovascular Biomechanics
• Mathematical Modeling in Clinical Data
• Cardiac Electromechanics
• +5 more

Peter H.M. Bovendeerd is an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), specializing in the Cardiovascular Biomechanics research group. He holds affiliations with the Eindhoven MedTech Innovation Center and EAISI Health. His work focuses on developing mathematical models to interpret clinical patient data, particularly in cardiac electromechanics, perioperative monitoring, and perinatal monitoring, collaborating with Catharina Hospital and Máxima Medical Center. Academically, Bovendeerd earned his MSc in Applied Physics (cum laude, 1985) and PhD (1990) from TU/e and Maastricht University, respectively. His research integrates fundamental physical/physiological principles to estimate pathologies from patient data through inverse modeling, aiding diagnosis and treatment planning. He teaches courses including Biomechanics, Cardiac Function, and Continuum Mechanics. Recent research emphasizes fetal blood circulation modeling, cardiac mechanics frameworks, and post-infarct ventricular function. His work contributes to UN Sustainable Development Goals through advancements in healthcare technology and innovation.

Suchuan DongView profile

Professor

• Computational and Applied Mathematics
• High-Order Numerical Methods
• Multiphase Flows
• +7 more

Suchuan Dong is a Professor in the Department of Mathematics at Purdue University , affiliated with the Center for Computational and Applied Mathematics . His work bridges Computational Mathematics and Machine Learning , focusing on High-Order Numerical Methods and Multiphase Flows . Academic Background Post-Doc in Applied Mathematics, Brown University (2004) Ph.D. in Mechanical Engineering, SUNY Buffalo (2001) M.S. in Physics, Zhejiang University (1995) B.S. in Aerospace Engineering, National University of Defense Technology (1992) His research centers on Neural Network-Based Numerical Methods and Data-Driven Scientific Computing , with applications to Computational Fluid Dynamics , Contact Line Dynamics , and High-Performance Computing . Publications highlight Physics-Informed Neural Networks , Energy-Stable Schemes , and Extreme Learning Machines for PDEs. The articles reflect trends in Neural Network Applications to Dynamic PDEs , Phase Field Modeling , and High-Dimensional Computing , often combining High-Order Numerical Methods with Interfacial Phenomena . His recent work focuses on Exact Time Integration Algorithms and Hidden-Layer Concatenation for stability and efficiency. He leads research in the Center for Computational and Applied Mathematics , emphasizing Thermodynamically Consistent Modeling and Flow-Structure Interactions . His teaching includes MA-36600: Ordinary Differential Equations (Spring 2025).

Ian HowatView profile

Professor

• Glaciology
• Remote Sensing
• Climate Change
• +2 more

Ian Howat is ENGIE-Axium Professor and Distinguished University Scholar at Ohio State University, where he directs the Byrd Polar & Climate Research Center. His research focuses on ice sheet dynamics using remote sensing and field observations to predict sea-level rise. Research Leadership: Develops advanced satellite observation techniques to monitor glacier retreat, ice shelf stability, and ice-ocean interactions. Leads international collaborations on cryospheric change including NASA-funded projects. Field Expertise: Conducted extensive fieldwork in Greenland, Antarctica, and Iceland. Current research examines Greenland Ice Sheet response to climate forcing using high-resolution elevation models and velocity mapping. Recent Advances: Pioneered methods for generating continent-scale elevation models and detecting glacial lake outburst risks. Authored foundational studies linking ice sheet mass loss to sustained glacier retreat.

Alex TownsendView profile

Associate Professor

• Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +6 more

Alex Townsend is an Associate Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds the Stephen H. Weiss Junior Fellowship and has been recognized for both research and teaching excellence. His research focuses on numerical analysis, scientific computing, and theoretical aspects of deep learning, with contributions to spectral methods, low-rank techniques, and computational algebraic geometry. Education: Townsend earned a DPhil (PhD) in Mathematics from the University of Oxford in 2014. Research Interests: Townsend's work spans several areas: novel spectral methods for differential equations, low-rank matrix and tensor techniques, theoretical foundations of deep learning, and computational algebraic geometry. His research emphasizes developing fast, accurate, and robust numerical algorithms with applications in science and engineering. Teaching & Mentoring: Townsend is a dedicated educator, having taught courses at MIT and Cornell on topics ranging from linear algebra and numerical analysis to advanced graduate-level subjects like kernel-based learning and top-ten algorithms of the 20th century. He has mentored numerous PhD students and postdocs, many of whom now hold academic and industry positions. Awards & Honors: 2022 Stephen H. Weiss Teaching Award 2022 Simons Fellowship in Mathematics 2018 SIAG/LA Early Career Prize 2015 Leslie Fox Prize in Numerical Analysis Grants & Funding: Townsend has secured significant funding, including an NSF CAREER grant (2021), to support his work on operator learning and spectral methods. Labs & Collaborations: While not tied to a specific lab, his research frequently intersects with computational mathematics and machine learning communities. He collaborates widely, contributing to open-source tools like Chebfun and Diskfun.

Katie BoumanView profile

Associate Professor

• Computational Imaging
• Astrophysics
• Machine Learning
• +3 more

Katie Bouman is an Associate Professor of Computing and Mathematical Sciences (CMS), with courtesy appointments in Electrical Engineering and Astronomy at the California Institute of Technology (Caltech). She is also a Rosenberg Scholar and an Investigator at the Heritage Medical Research Institute. Her research focuses on computational imaging, integrating algorithm and sensor design to observe phenomena traditionally inaccessible through conventional methods. She leads the Computational Cameras Group, which develops systems for scientific discovery and technological innovation in fields such as astrophysics, medical imaging, and computer vision. Bouman holds a B.S.E. in Electrical Engineering from the University of Michigan (2011), an S.M. and Ph.D. in Electrical Engineering and Computer Science from MIT (2013 and 2017). Before joining Caltech, she was a postdoctoral fellow with the Event Horizon Telescope (EHT), contributing to the groundbreaking first image of a black hole in 2019. Her research interests span signal processing, machine learning, and physics-driven imaging solutions. Notable projects include 3D black hole gas reconstruction, MRI acceleration, and neural field-based tomography. She emphasizes interdisciplinary collaboration, merging computational methods with domain-specific challenges in astronomy and biomedical engineering. Awards: PECASE Award, Sloan Fellowship, NSF CAREER Award, Royal Photographic Society Progress Medal, Breakthrough Prize (co-recipient). Grants: Okawa Research Grant, HMRI Investigator funding. Bouman advises students in CMS and collaborates with industry through the Caltech Center for Sensing to Intelligence (S2I). Her lab fosters diversity and inclusion, prioritizing equitable participation in STEM fields.

Yashar HezavehView profile

Associate Professor

• Dark Matter
• Machine Learning and Deep Learning
• Galaxy Clusters
• +3 more

Yashar Hezaveh is an Associate Professor at the University of Montreal's Faculty of Arts and Sciences, Department of Physics. He holds the Canada Research Chair in Astrophysical Data Analysis and Machine Learning. His work focuses on using gravitational lensing and machine learning to map dark matter distributions in galaxy halos, advancing our understanding of dark matter's nature. He completed his PhD at McGill University in 2013, earning recognition for groundbreaking research on high-redshift dusty star-forming galaxies. Education: PhD in Physics (McGill University, 2013) Affiliations: Kavli Institute for Theoretical Physics, Flatiron Institute's Center for Computational Astrophysics Research interests include applying deep learning to analyze gravitational lensing data, Bayesian neural networks for dark matter mapping, and cosmological simulations. Notable projects include the CASTOR mission and advances in radio interferometry image reconstruction. His work bridges astrophysics and machine learning, addressing challenges in cosmic structure analysis. Awards: Hubble Fellowship (2015), Top 10 Quebec Science Discoveries (2013). Grants: Leads multiple projects on dark matter, AI-driven stellar mass measurement, and astrophysical data analysis funded by NSERC, FQRNT, and the Simons Foundation. Students: Supervised four Master's theses on topics like Bayesian lensing inversion and machine learning for galactic archaeology. He contributes to collaborative initiatives like the Centre de recherche en astrophysique du Québec (CRAQ), fostering interdisciplinary astrophysics research.