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Megan OlsenView profile

Professor

• Complex Systems
• Self-organizing Systems
• Computational Biology
• +3 more

Megan Olsen is a Professor of Computer Science at Loyola University Maryland and serves as Director of the Hyman Science Scholars Program and Camp BaltiCode. She holds a Ph.D., M.S., and B.S. in Computer Science from the University of Massachusetts Amherst and Virginia Polytechnic Institute. Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2011 M.S., Computer Science, University of Massachusetts Amherst, 2009 B.S., Computer Science, Virginia Polytechnic Institute & State University, 2005 Research Interests: Her work focuses on complex systems, computational biology, and simulation validation. Recent efforts include improving agent-based model validation, parameter exploration, and tumor angiogenesis modeling. She develops methods to enhance simulation credibility through metamorphic testing and combinatorial approaches. Advising & Leadership: PhD Committee Member for Youssaf Menacer (University of Houston, 2024) Former Department Chair (2020–2023) Member of the Data Science Advisory Board (2020–present) Labs & Teams: Leads the Validation Lab, advancing simulation validation techniques. Collaborates on projects like the Hyman Scholars Program and Camp BaltiCode to promote STEM education.

Matthew HirnView profile

Associate Professor

• computational mathematics
• harmonic analysis
• data science
• +7 more

Matthew Hirn is a computational mathematician with expertise in harmonic analysis, data science, and machine learning. He was a tenured Associate Professor at Michigan State University (MSU), jointly appointed in the Department of Computational Mathematics, Science & Engineering and the Department of Mathematics. As the scientific leader of the ComplEx Data Analysis Research (CEDAR) Team at MSU, he developed innovative methods for analyzing complex data, including wavelet scattering transforms for atomistic systems and geometric deep learning techniques. He is currently a Senior Quantitative Researcher at Citadel LLC, contributing to algorithmic trading strategies. His research bridges theoretical mathematics and applied data science, with notable contributions to graph neural networks, scattering transforms, and privacy-preserving methods. His academic career included organizing conferences such as the SIAM Mathematics of Data Science mini-symposium on geometric learning and co-authoring influential software like the Kymatio library for wavelet scattering transforms. He advised PhD students Ryan LaRose, Xavier Brumwell, and Jieqian He, who have since pursued impactful roles in academia and industry. His work on 3D wavelet scattering networks for material science and geometric scattering on manifolds has advanced the intersection of mathematics and machine learning. Matthew’s research interests span signal processing, graph representation learning, and applications in bioinformatics and many-particle physics. He has published extensively in top journals and conferences, including the Journal of Chemical Physics and the Journal of Machine Learning Research.

• Image Processing
• Computer Science
• Mathematics
• +7 more

Carole Le Guyader is an Associate Professor at the Department of Mathematical and Software Engineering at the National Institute of Applied Sciences of Rouen (INSA Rouen). Her research focuses on hybrid variational/deep learning methods under topological and geometric constraints, with applications in biomedical imaging, geophysics, and high-performance computing (HPC). She supervises Ph.D. students and collaborates with institutions such as UCLA , Cambridge University , and University of Pau . Teaching: Mathematical Engineering (PDEs, CAGD in M1; Variational Methods in Image Processing in M2), L1/L2 STPI courses. Administrative Roles: LMI correspondent for GDR MIA (since 2021), Head of Mathematics Teaching at STPI INSA Rouen (since 2019), Deputy Director of GM Department (2018-2020). Her recent work emphasizes integrating deep learning with variational methods for tasks like semantic segmentation in medical imaging and nonlinear elasticity models for image registration. She has contributed to parallel computing implementations and industrial partnerships with Siemens-Gamesa and Dassault Systèmes .

• Numerical Probability
• Stochastic PDEs
• Multiscale Modeling
• +2 more

Charles-Edouard Bréhier is a Professor at the University of Pau and the Pays de l'Adour, specializing in numerical methods for stochastic systems. His research spans stochastic PDEs, multiscale modeling, metastable processes, and geometric numerical integration. He leads investigations into error analysis and computational efficiency for complex stochastic systems. Bréhier's work develops advanced numerical schemes for high-dimensional problems, including splitting integrators, Galerkin approximations, and structure-preserving algorithms. His recent publications focus on error bounds for SPDE discretizations, turbulence modeling, and stochastic geometric mechanics. Articles consistently demonstrate rigorous mathematical foundations for computational methods, with applications spanning fluid dynamics, plasma physics, and optimization. The research provides tools for simulating complex systems with inherent randomness across multiple temporal and spatial scales.

Sijia DongView profile

Assistant Professor

• Theoretical Chemistry
• Biophysics
• Materials Chemistry
• +5 more

Sijia Dong is an Assistant Professor in Chemistry and Chemical Biology at Northeastern University, affiliated with Chemical Engineering, Physics, and multiple research institutes including the Institute for Chemical Imaging of Living Systems and the Quantum Materials and Sensing Institute. Her research focuses on developing computational methods to study chemical systems, with applications in renewable energy and biomedicine. She holds a PhD from Caltech and a B.Sc. from the University of Hong Kong. Education: Ph.D. in Chemistry, Caltech (2017) B.Sc., First Class Honours, The University of Hong Kong (2010) Research Interests: Her lab develops physics-based and data-driven computational methods to study multiscale chemical processes. Key areas include quantum phenomena in biological systems, light-driven chemical transformations, and computational design of materials for energy and medicine. Techniques include electronic structure theory, machine learning, and quantum computing. Awards: NIH MIRA Award (2024) Excellence in Mentorship Award (2024) Scialog Fellowship (2024) DOE and NSF Funding (2023-2025) Lab and Teams: Her lab, the Dong Theoretical and Computational Chemistry Lab, includes PhD students, postdocs, and undergraduates. Collaborations involve institutions like MITRE and Iowa State University. Current projects include quantum algorithm development for electronic structure simulations and photoenzyme design.

• Structural Mechanics
• Solid Body Mechanics
• Fracture Mechanics
• +3 more

Res.Asst.Dr. Bahar Ayhan is affiliated with the Department of Civil Engineering at Istanbul Technical University (ITU). Her research focuses on structural mechanics, solid body mechanics, and fracture mechanics. She holds a Ph.D. from École Normale Supérieure de Cachan (2009–2013) and completed a postdoctoral fellowship at Johns Hopkins University (2015–2016). She has been a Research Assistant at ITU since 2007 and has contributed to advanced computational models for material behavior, including damage-plasticity coupling and fracture analysis. Her work spans multiscale analysis, constitutive modeling, and structural failure mechanisms. Educational Background: Ph.D., École Normale Supérieure de Cachan (2013) Master's in Structural Engineering, Istanbul Technical University (2013) Bachelor's in Civil Engineering, Istanbul Technical University (2003) Research Highlights: Her publications address topics such as gradient-enhanced damage models, strain rate effects on concrete, and finite element analysis of composite structures. She collaborates internationally and has presented at conferences like the World Congress on Computational Mechanics (WCCM).

Murtagh FionnView profile

Professor

• Data Science
• Computer Science
• Pattern Recognition
• +5 more

Prof. Fionn Murtagh is a leading academic in data science and computer science, currently serving as Professor of Data Science at the University of Huddersfield (2017–present). He has held prominent roles including Head of the School of Computer Science and Informatics at De Montfort University (2013–2014), Head of the Information Communications and Emergent Technologies Directorate at Science Foundation Ireland (2007–2012), and Full Professor at Royal Holloway, University of London (2004–2013). He holds an Honorary Professorship in Computer Science at Royal Holloway (2013–present) and maintains part-time roles as Professor of Data Science at the University of Derby (2015–2017) and Visiting Professor at Goldsmiths University of London (2017–). His education includes a BA in Mathematics and BAI in Engineering Science from Trinity College Dublin (1976), an MSc in Computer Science from Trinity College (1979), a PhD in Mathematical Statistics from Université Paris 6 (1981), and an Habilitation à Diriger des Recherches in Astronomy from Université Louis Pasteur (1993). He is fluent in English, French, German, and Irish, with reading knowledge of Italian. Research interests focus on data analysis, pattern recognition, astronomical imaging, and multivariate statistical methods. His work emphasizes computational efficiency and applications in high-dimensional datasets. Notable contributions include pioneering work in clustering algorithms, image processing, and the application of mathematical-statistical methods in astronomy and computer science. He has received prestigious awards such as Fellowship from the International Association for Pattern Recognition (2008), Royal Irish Academy membership (2003), and Fellowships from the British Computer Society, Royal Statistical Society, and Royal Society of Arts. His leadership roles include Editor-in-Chief of The Computer Journal (2000–2007) and President of the Classification Society of North America (2008–2009).

• Multiscale Modeling
• Fluid Dynamics
• Numerical Analysis
• +3 more

Stefano Piccardo is a Research Fellow at RMIT Europe in Barcelona, Spain, as part of the ALCOAT Project. His affiliation with RMIT Europe highlights his role in advancing computational and mathematical research within the institution. While biographical details are limited, his work centers on multiscale modeling, fluid dynamics, and numerical methods such as hybridizable discontinuous Galerkin (HDG) approaches and NURBS-based geometries. Research Interests: Multiscale Modeling Fluid Dynamics Numerical Analysis Scientific Computing Computational Mathematics Advection-Diffusion-Reaction Systems His recent publications (2023–2025) focus on developing high-order numerical methods to address complex fluid flow problems, including two-fluid Stokes dynamics and advection-diffusion-reaction phenomena. These studies emphasize computational efficiency and accuracy in handling intricate geometries using NURBS and unfitted mesh techniques. No scientific awards or formal grants are explicitly mentioned in the provided texts. Stefano collaborates closely with researchers like Conni G, Perotto S, Giacomini M, and Huerta A, contributing to advancements in hybrid high-order methods and level-set schemes for multiphase flow simulations. He is affiliated with RMIT Europe’s research group, working on projects like ALCOAT that bridge theoretical and applied computational science.

• Computational and Mathematical Biology
• Data Science
• Bioinformatics
• +3 more

Leili Shahriyari is an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst . Her research focuses on developing computational frameworks that integrate machine learning, mathematical modeling, and statistical methods to advance personalized cancer therapies. Key initiatives include: Digital Twin Platforms: Creating patient-specific models like My Virtual Cancer to predict treatment responses for breast cancer and rare cancers like uveal melanoma. QSP Modeling: Enhancing quantitative systems pharmacology (QSP) approaches to personalize treatments using patient-specific data rather than generic datasets. TumorDecon Software: A Python package for deconvolving tumor cell composition, supported by NIH-NCI funding. Education: Ph.D. in Mathematics, Johns Hopkins University (2013) M.S.E. in Computer Science, Johns Hopkins University (2012) Research Themes: Computational and Mathematical Biology Data-Driven Oncology Cancer Microenvironment Modeling Collaborations: Her work intersects interdisciplinary teams in computational biology, bioinformatics, and clinical oncology to advance translational cancer research.

Yanzhi ZhangView profile

Professor

• Fractional Partial Differential Equations (PDEs)
• Scientific Computing
• Numerical Analysis
• +6 more

Dr. Yanzhi Zhang is a Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (Missouri S&T), where she has held academic positions since 2010. She earned a Ph.D. in Mathematics from the National University of Singapore and a B.Sc. in Applied Mathematics (with a Computer Science minor) from Chongqing University. Her research spans computational and applied mathematics, focusing on fractional PDEs, nonlocal models, machine learning applications in materials science and business, Bose-Einstein superfluids, and seismic wave inversion. Dr. Zhang has been awarded the Faculty Excellence Award from Missouri S&T and other accolades. Her work is supported by grants from the National Science Foundation, including projects on fractional viscoacoustic wave equations, Bose-Einstein superfluids, and dispersive wave equations. She advises Ph.D. students in interdisciplinary research, such as Yumeng Wang (machine learning for eye-tracking data) and Yixuan Wu (seismic inversion). Her research integrates mathematical rigor with computational innovation, addressing real-world challenges in geophysics, materials science, and business analytics. Collaborations with industry and academia enhance her contributions to data-driven modeling and open-source strategies for digital transformation.

Haye HinrichsenView profile

Professor

• Quantum Information Theory
• Exactly Solvable Systems
• Tensor Networks
• +4 more

Prof. Haye Hinrichsen holds a C3 professorship at the University of Würzburg within the Chair of Theoretical Physics III . His academic journey includes roles as Acting Professor at the University of Wuppertal (2001), Senior Assistant at Duisburg-Essen (2000), and postdoctoral research at institutions like the Weizmann Institute of Science (1995–1997). He earned his PhD in 1993 from the University of Bonn under Prof. V. Rittenberg, focusing on quantum groups. His research interests span quantum information theory , exactly solvable systems , statistical physics far from equilibrium , reaction-diffusion processes , and non-equilibrium phase transitions . Notable achievements include the 2007 Award for Good Teaching and contributions to understanding entropy-based tuning systems in music. His recent work explores topics like causal set propagators in anti-de Sitter spacetime, eigenmodes on hyperbolic lattices, and renormalization techniques in quantum field theories. He has also applied physics principles to music acoustics, developing adaptive tuning schemes using entropy maximization. Key Research Themes: Quantum gravity, non-equilibrium dynamics, topological complexity, and interdisciplinary applications in music. Teaching: Courses include Theoretical Physics I, Computational Physics, and General Relativity. Lab/Affiliation: Chair of Theoretical Physics III at Hubland South, Building M1.

Michelle Starz-GaianoView profile

Professor

• Cell Biology
• Developmental Biology
• Molecular Biology
• +6 more

Dr. Michelle Starz-Gaiano is a Professor and Chair of the Department of Biological Sciences at the University of Maryland, Baltimore County (UMBC). She joined UMBC as an Assistant Professor in 2008 following a postdoctoral fellowship at Johns Hopkins University. Her research focuses on collective cell migration mechanisms in Drosophila, JAK/STAT signaling pathways, and quantitative biology education. She has mentored over 50 undergraduates and six graduate students, earning the 2014 Donald Creighton Outstanding Faculty Member Award for mentorship excellence. Dr. Starz-Gaiano's leadership roles include serving as Graduate Program Director (2015–present) and her current role as department chair since August 2023. Her work integrates interdisciplinary approaches to address challenges in cell migration dynamics, tissue architecture impacts, and aging-related immune decline. She actively promotes innovative teaching methods through initiatives like multi-institutional quantitative biology modules and faculty development programs. Key research themes include: collective cell migration regulation in developing tissues, steroid hormone signaling's role in migration timing, and modeling physical/chemical cues' synergistic effects. Her lab uses Drosophila models to study border cell migration and phagocytic ability changes with age. Recent projects explore lisinopril's lifespan effects and enzyme kinetics modeling. Award-winning mentorship practices emphasize individualized student support, with continuous engagement even post-graduation. Her leadership vision emphasizes strengthening departmental collaboration, leveraging UMBC's R1 research status while maintaining commitment to undergraduate success. She advocates for pedagogical innovation through programs like CNMS Entrepreneurial Skills Training and the Teacher Scholar Program. Key collaborations include multi-institutional quantitative biology curriculum development and interdisciplinary modeling efforts. Current priorities as chair include post-pandemic recovery, flood-damaged lab restoration, and fostering cross-departmental research partnerships.

• Solidification Modeling
• Transport Phenomena
• Computational Mechanics
• +5 more

Dr. Sergio Felicelli is a Professor and Chair in the Department of Mechanical Engineering at the University of Akron's College of Engineering and Polymer Science. He holds a Ph.D. in Mechanical Engineering from the University of Arizona (1991) and B.S./M.S. in Nuclear Engineering from Instituto Balseiro (1985). His expertise spans Solidification Modeling, Transport Phenomena, and Computational Mechanics. Dr. Felicelli has authored pioneering works on computer modeling of freckle segregation during solidification and has directed projects on casting, additive manufacturing, and parallel simulations of microstructures. His research focuses on advancing computational methods for material processing, including dendritic solidification under microgravity conditions, multiphase flow dynamics, and large-scale parallel simulations. He has secured $15M in grants through 23 funded projects and is an ASME Fellow. His work integrates experimental validation with numerical modeling, particularly using lattice Boltzmann and phase field methods. Dr. Felicelli has led international collaborations on casting defects and serves in academic leadership roles. Education: Ph.D., Mechanical Engineering, University of Arizona, 1991 B.S./M.S., Nuclear Engineering, Instituto Balseiro, 1985 Dr. Felicelli's research trends emphasize computational modeling of materials under extreme conditions, with recent articles addressing microgravity solidification effects, multiphase interactions, and high-performance computing for microstructure prediction. His work bridges fundamental science and industrial applications in additive manufacturing and energy storage systems. Awards: ASME Fellow (prestigious honor in mechanical engineering) Grants & Funding: Over $15M secured through 23 grants, focusing on solidification science and advanced manufacturing technologies. He advises on courses such as Heat Transfer and Numerical Methods, and his lab explores cutting-edge techniques in computational materials science. Collaborations include NASA and international institutions, with a focus on solving complex industrial and aerospace material challenges.

Adrianus van DuinView profile

Professor

• Reactive Force Fields (ReaxFF)
• Catalysis
• Combustion Chemistry
• +5 more

Adrianus van Duin is a Distinguished University Professor at Pennsylvania State University, holding joint appointments in Mechanical Engineering, Chemical Engineering, Engineering Science and Mechanics, Chemistry, and Materials Science and Engineering. He leads the Materials Computation Center and specializes in developing and applying the ReaxFF reactive force field for simulating chemical and physical interactions in materials and molecules. His work spans catalysis, combustion, nanomaterials, and environmental chemistry. Van Duin has held prestigious fellowships, including the Marie Curie and Royal Society awards, and has contributed over 70 publications. His research integrates computational methods with experimental validation, focusing on applications like fuel cells, corrosion, and energy storage. Education: Ph.D. in Chemistry, Delft University of Technology, Netherlands (1996) M.Sc./B.Sc. in Chemistry, University of Amsterdam, Netherlands (1986-1991) Research Interests: ReaxFF development and integration into computational tools like ADF/BAND Water/metal oxide interactions and surface catalysis Combustion chemistry and reaction mechanism derivation Material degradation and corrosion mechanisms Environmental applications, including carbon mineralization Recent Trends in Articles: Recent work emphasizes ReaxFF applications in 2D materials (e.g., graphene, MoS₂), energy systems (hydrogen storage, batteries), and environmental remediation (PFAS removal). Simulations frequently address interfacial chemistry and phase transitions under extreme conditions. Awards: Marie Curie Research Fellowship (1997-1999) Royal Society Fellowship (1999-2002) Named Distinguished University Professor at Penn State Grants & Labs: Leads the van Duin Group, collaborating with over 80 academic institutions globally. Active in NSF-funded initiatives and industry partnerships (e.g., SCM, NASA/AMES). Current projects include ReaxFF workshops, code distribution, and experimental-computational synergy in material design. Labs/Teams: Directs the Materials Computation Center and oversees interdisciplinary collaborations across departments at Penn State.

Ludovic AutinView profile

Researcher

• Molecular Modeling
• Scientific Visualization
• Structural Biology
• +3 more

Ludovic Autin, PhD is an Institute Investigator in the Department of Integrative Structural and Computational Biology at The Scripps Research Institute. His research bridges scientific visualization with artistic illustration to enhance understanding of complex biological systems. He leads development of software frameworks including cellPACK, mesoscope, ePMV, and cellPAINT/VR for multiscale molecular modeling. Education: PhD in Structural Biology from Paris 5 University (2002-2005), focusing on blood coagulation protein complexes. Professional experience spans roles from Assistant Lecturer (2005-2007) to Senior Staff Scientist (2015-2024), with key contributions to molecular docking methods and cellular environment modeling. Research emphasizes induced-fit molecular recognition, viral assembly mechanisms, DNA structure dynamics, and cellular crowding phenomena. Software innovations like cellPACK enable real-time modeling of crowded cellular environments, while mesoscope provides web-based integration of molecular data. Notable achievements include the first whole Mycoplasma cell structural model (2022) and collaborative work on HIV virion modeling. Active in computational tools development for both scientific research and educational outreach.