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Mingjian WenView profile

Adjunct Assistant Professor

• Data-driven methods
• Machine learning
• Materials design
• +3 more

Mingjian Wen is an Adjunct Assistant Professor in the William A. Brookshire Department of Chemical and Biomolecular Engineering at the University of Houston, affiliated with the Cullen College of Engineering. He holds the Presidential Frontier Faculty Fellow title. His research focuses on developing data-driven and machine learning methods, molecular simulations, and open cyberinfrastructure to design materials and chemical reactions for renewable energy. He completed a postdoc at Lawrence Berkeley National Lab, a Ph.D. in Aerospace Engineering & Mechanics from the University of Minnesota, and a B.S. in Chemical Engineering from Tianjin University. Education: Ph.D., Aerospace Engineering and Mechanics, University of Minnesota-Twin Cities, 2019 B.S., Chemical Engineering, Tianjin University, 2012 Postdoc, Energy Technologies Areas, Lawrence Berkeley National Laboratory, 2022 Research interests emphasize integrating machine learning with computational chemistry to address challenges in renewable energy, materials design, and sustainable processes. His work spans uncertainty quantification in atomistic models, graph neural networks for molecular properties, and open-source tools like Atomate2 and Jobflow for materials informatics. Scientific awards include the University of Minnesota Doctoral Dissertation Fellowship and the UH Alternative Textbook Incentive Award. His lab ( wengroup.github.io ) explores interdisciplinary approaches to accelerate materials discovery through AI-driven methods. Dr. Wen’s contributions span computational workflows, hydrolysis energy predictions (HEPOM framework), and equivariant neural networks for crystal elasticity. His research bridges theory and application, aiming to advance sustainable energy technologies through advanced modeling and automation.

• Chemical Education
• Physical Chemistry
• Laboratory Technology
• +3 more

Estel Sprague is a Professor of Chemistry at the University of Cincinnati, where he has served since 1974. He holds a BA in Chemistry from Asbury College (1966) and a PhD in Physical Chemistry from the University of Tennessee (1971). His postdoctoral research included stints at the Max Planck Institut in Germany and the University of Wisconsin. Dr. Sprague has been deeply involved in chemical education innovation, particularly in integrating technology into laboratory instruction. He led the development of MeasureNet® for networked data collection systems and pioneered the use of iPads in classroom settings. His service roles include committee chairs for General Chemistry Coordination and Undergraduate Curriculum Committees within the Chemistry Department, as well as membership in university-level committees like UC Dual Enrollment. His research focuses on enhancing STEM education through digital tools, including web-based data management and active learning strategies. Notable contributions include a National Science Foundation grant (#DUE-1022563) for a multi-disciplinary STEM initiative, awarded as Senior Personnel in 2010. He has presented over 30 workshops and conferences on topics like networked laboratory systems and tablet integration in education, spanning institutions from MIT to Spelman College. Grants: NSF Grant DUE-1022563 ($249,648, 2010-2013) Awards: 2006 University of Cincinnati Emerging Entrepreneur Award Teaching: Coordinates foundational chemistry courses (CHEM-101/102/103, Gen Chem I/II) at the undergraduate level His work emphasizes practical applications of technology to improve student learning outcomes, particularly in laboratory settings and first-year chemistry courses. Dr. Sprague continues to advocate for pedagogical innovation through committee leadership and cross-disciplinary collaborations.

Robert PastelView profile

Associate Professor

• Human-Computer Interaction
• Mobile App Design and Usability
• Software Development Processes
• +4 more

Robert Pastel is an Associate Professor of Computer Science at Michigan Technological University (MTU), with an affiliation as an Associate Professor in Psychology and Human Factors. He has held academic roles since 2006, previously serving as a Lecturer (2001–2005) and holding visiting positions in Physics and adjunct roles at multiple institutions. His education includes a PhD in Physics (University of New Mexico, 1994) and MS/BS degrees in Computer Science and Mathematics. Education: PhD, Physics, University of New Mexico, 1994 MS, Computer Science, Michigan Technological University, 2001 MS, Engineering Science, University of Tennessee, 1980 BS, Mathematics, Virginia Polytechnic Institute, 1977 Research Interests: Focus on Human-Computer Interaction (HCI), mobile app usability, software development processes, and computer science education. His work bridges HCI with cognitive engineering, emphasizing user-centered design and novel interaction techniques. Recent projects include mobile citizen science apps and humane interface design for diverse industries. Articles Overview: Pastel’s research spans HCI, software engineering, and physics. Key areas include user interface design, laser trapping techniques, and collaborative project methodologies. His recent work highlights cross-disciplinary projects, such as mobile input methods and team dynamics in software development. Grants and Advising: Secured grants totaling over $5M for projects like human-robot interaction labs and environmental citizen science tools. Advised numerous students across PhD, MS, and undergraduate levels, guiding research in HCI, mobile systems, and usability testing. Labs/Teams: Faculty advisor for the Humane Interface Design Enterprise (HIDE), a multidisciplinary team developing mobile apps. Collaborates on projects like the Gravity Mouse and remote navigation systems. Active in industry-academia partnerships, such as with Boston Scientific and the Army Research Laboratory.

YingXing ChengView profile

Researcher

• Computational Material Science
• Frequency-Dependent Polarizable Force Fields
• Time-Dependent Density Functional Theory
• +3 more

YingXing Cheng is a Researcher and Postdoc at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation and the department of Numerical Mathematics for High Performance Computing. Their work focuses on advancing computational methods in material science and quantum chemistry, including frequency-dependent polarizable force fields, Time-Dependent Density Functional Theory (TDDFT), and machine learning applications. Research interests include molecular density decomposition, relativistic quantum effects, and high-throughput computational studies. They contribute to software engineering for scientific computing, particularly in developing frameworks like ACKS2ω and integrating machine learning for material property prediction. Publications highlight advancements in polarizable force fields, vacancy formation energy analysis, and algorithmic approaches for defect prediction. Their work bridges theoretical chemistry, numerical analysis, and high-performance computing to address challenges in material design and characterization. No scientific awards are listed, though their contributions to computational methodologies are notable. Cheng collaborates on projects involving molecular dynamics simulations and electronic structure analysis, contributing to both academic and applied research in computational materials science.

Michele NottoliView profile

Researcher

• Computational Chemistry
• Quantum Mechanics
• Molecular Dynamics
• +5 more

Michele Nottoli is a researcher at the University of Stuttgart's Institute of Applied Analysis and Numerical Simulation (Faculty 08), specializing in Numerical Mathematics for High-Performance Computing. He holds a Ph.D. and works as a Postdoc and Research Assistant. His research focuses on developing advanced computational methods for quantum mechanics and molecular dynamics, emphasizing polarizable embedding models, QM/MM coupling, and high-performance numerical algorithms. Key contributions include the development of the ddX library for polarizable continuum solvation and optimizing linear-scaling methods for large-scale molecular simulations. Nottoli's work addresses challenges in modeling light-driven processes in complex systems, such as photoreceptors and antenna complexes, with applications in biochemistry and materials science. His methodologies integrate domain-decomposition techniques, fast multipole methods, and symmetry-preserving approaches to enhance computational efficiency and accuracy. He actively contributes to open-source software development, including the OpenMMPol library, to facilitate multiscale simulations of embedded molecules in polarizable environments. His research also explores excited-state dynamics, solvation effects, and nonperiodic boundary conditions, with implications for understanding magnetic shifts and optical properties in chemical systems. Nottoli collaborates on projects involving light-harvesting mechanisms in purple bacteria and the molecular mechanisms of solvent shifts in nitroxide radicals. Despite his prolific publication record, no scientific awards are currently listed. Advising and grants: Nottoli is actively involved in postdoctoral research but no specific advisees or grant details are provided in the available information. His affiliation with the Numerical Mathematics group positions him at the intersection of algorithm design and applied computational science.

Thomas SiegertView profile

Researcher

• Gamma-ray Astronomy
• Nuclear Astrophysics
• Cosmic Rays
• +5 more

Dr. Thomas Siegert is a faculty researcher in the Department of Astronomy at the University of Würzburg's Faculty of Physics and Astronomy, leading the high-energy astrophysics research group focused on MeV gamma-ray observations. His work bridges thermal and non-thermal emission mechanisms through gamma-ray line studies to probe astrophysical processes in supernovae, novae, cosmic rays, and dark matter. His research centers on resolving the Positron Puzzle regarding the origin of Galactic 511 keV emission, investigating nucleosynthesis through radioactive isotopes (Al-26, Fe-60, Ti-44, Na-22), and developing advanced analysis tools for gamma-ray missions. He is deeply involved with the NASA COSI SMEX mission (launch 2027) as lead developer of the COSIpy data analysis software and collaborates with the INTEGRAL/SPI team at the Max Planck Institute for Extraterrestrial Physics. Recent publications reveal intense focus on: Time-variable diffuse gamma-ray foregrounds from solar system bodies Constraints on sub-GeV dark matter from INTEGRAL observations Gamma-ray line diagnostics for stellar nucleosynthesis Novae as potential lithium factories via Be-7 decay Siegert actively mentors PhD students including Eric Nanowsky (magnetic shielding for MeV telescopes) and Sean Penny. His group maintains critical infrastructure for the COSI mission and contributes to the INTEGRAL Science Data Center's background modeling. Notable projects include the COSIpy analysis framework, population synthesis models for Galactic nucleosynthesis, and pioneering studies of positron annihilation in stellar flares and cosmic-ray interactions. His work directly enables future MeV gamma-ray astronomy through instrument development and theoretical modeling of emission mechanisms.

Dmitry SavostyanovView profile

Lecturer

• Numerical Mathematics
• Tensor Decompositions
• High-Dimensional Problems
• +3 more

Dr. Dmitry Savostyanov is a Lecturer at the University of Essex, School of Mathematics, Statistics and Actuarial Science (SMSAS). His research focuses on developing efficient algorithms for high-dimensional problems using low-rank tensor product approximations, linear algebra, and numerical methods. He holds a PhD in Computational Mathematics from the Russian Academy of Sciences and a PGCert in Higher Education from the University of Brighton. Qualifications: PhD (2006), MSc (2003), BSc (2001), PGCert (2016). Appointments: Senior Lecturer at University of Brighton (2014–2020), Senior Research Fellow at University of Southampton (2012–2014), and Visiting Research Fellow at University of Chester (2011–2012). His research interests span numerical mathematics, tensor decompositions, and computational methods for high-dimensional systems. Recent work includes applications in epidemiological modeling, quantum control, and NMR simulation. He has collaborated on software tools like the TT-toolbox for tensor train formats. Publications highlight contributions to tensor interpolation, quantum algorithms, and efficient numerical solvers. No scientific awards are explicitly listed, but his work reflects sustained innovation in computational mathematics. No student advisees or lab affiliations are detailed in the provided materials. Grants and funding sections exist but lack specific details.

• Astrophysics
• High-Performance Computing
• Observational Astronomy
• +5 more

Dr. Oisin Creaner is an Assistant Professor in the School of Physical Sciences at Dublin City University. His research focuses on interdisciplinary applications of high-performance computing to astrophysical data challenges, including observational astronomy across radio, visible, and high-energy particle domains. He leads projects in exoplanet observation strategies, from citizen science initiatives like Exoplanet Watch to advanced ground-based and satellite observatories. His work also involves developing novel detector technologies like microwave kinetic inductance detectors (MKIDs) and optimizing algorithms for large-scale astronomical data analysis. Key technical contributions include the Locus Algorithm , a grid computing system for optimizing differential photometry fields, and advancements in MKID sensor design. He collaborates on major experiments such as the LUX-ZEPLIN dark matter detector and contributes to the automation of astronomical simulation frameworks like Opticks. Dr. Creaner's research integrates machine learning for data imbalance solutions, human-computer collaboration in citizen science, and precision instrumentation for astrophysical measurements. His professional affiliations include roles at DCU's Glasnevin Campus , where he is based in Room N106. He actively publishes on topics ranging from detector material science to dark matter detection methodologies, emphasizing both theoretical and applied aspects of modern astrophysics. No academic awards or grants are explicitly listed in available records.

David C. CollinsView profile

Assistant Professor

• Astrophysics
• Magnetohydrodynamics
• Star Formation
• +3 more

David C. Collins is an Assistant Professor in the Department of Physics at Florida State University (FSU), specializing in astrophysics and magnetohydrodynamic (MHD) simulations. His research focuses on simulating magnetized turbulence in molecular clouds, its role in star formation, and observational signatures of collapsing systems. PhD in Physics (2009), University of California, San Diego BS in Physics and BA in Mathematics (2001), University of Cincinnati Collins develops and utilizes the adaptive mesh refinement code Enzo , including contributions to its MHD package, and employs the analysis tool yt . His work spans topics such as: Galaxy cluster magnetic fields and turbulence Thermonuclear supernovae (Type Ia) and deflagration dynamics Collisions of giant molecular clouds as triggers for star formation Density and magnetic field distributions in self-gravitating turbulence Recent publications highlight his focus on supersonic turbulence, shock interactions, and statistical modeling of astrophysical fluids. He also contributes to software development for cosmological simulations and observational data analysis.

• Theoretical Condensed Matter Physics
• Quantum Mechanics
• Machine Learning in Materials Science
• +3 more

Alexandre Tkatchenko is a Full Professor of Theoretical Condensed Matter Physics at the University of Luxembourg, leading the Department of Physics and Materials Science and the Theoretical Chemical Physics group. His work integrates quantum mechanics, statistical mechanics, and machine learning to model complex materials, with over 150 peer-reviewed articles (h-index 58). He has held roles including Alexander von Humboldt Fellow at the Max Planck Institute (2008–2016) and has secured five ERC grants, including Advanced and Proof of Concept awards. Education: Bachelor in Computer Science and PhD in Physical Chemistry from Universidad Autónoma Metropolitana, Mexico City. Research focuses on quantum mechanical methods for molecular interactions, machine learning force fields, and computational tools like FHI-aims and libMBD. Key contributions include non-covalent interaction modeling, van der Waals forces, and AI-driven drug design. Awards include the 2020 Dirac Medal and APS Fellowship (2019). Grants include ERC Starting (2011), Consolidator (2017), and Advanced (2022) grants. His labs develop software for electronic structure calculations and explore materials for energy and drug discovery. Recent work addresses lithium diffusion in solid electrolytes and AI explainability in ADMET modeling.

• Materials Science
• Machine Learning
• Electronics Engineering
• +5 more

Associate Professor Bronson Philippa is an electronics and software engineer at James Cook University (JCU), serving as Deputy Head of Engineering and Chair of the Engineering Learning and Teaching Committee. He holds a PhD in Physics and dual degrees in Electrical and Electronic Engineering and Mathematics. His research focuses on computational modeling, materials science, and applied technologies in biosecurity, agriculture, and sensor systems. He has secured over $16.8M in research funding, including leadership roles in the $130M Fresh and Secure Trade Alliance (FASTA) and the ARC Training Centre for Plant Biosecurity. **Education**: BEng (Electrical/Electronic) & BSc (Mathematics), PhD in Physics (JCU). **Key Roles**: Deputy Head of Engineering, Component Lead for Smart Technologies (FASTA), Chief Investigator (ARC Plant Biosecurity Centre). **Research Interests**: Organic solar cells, photodetectors, OLEDs, biosecurity applications, and IoT-driven agricultural solutions. He develops technologies like closed-loop irrigation systems and near-infrared spectroscopy for pest detection. His work integrates machine learning and sensor networks to address environmental and industrial challenges. **Awards**: Multiple teaching citations (including national AAESE award), Early Career Advisor of the Year (2019), and Lindau Nobel Laureate Meeting attendance (2015). **Teaching**: Leader in project-based learning, video content creation, and cross-campus engineering education. Taught courses on sensors, embedded systems, and electronics. Serves as a Discipline Expert for university accreditation. **Labs/Teams**: Leads research projects in materials science, biosecurity, and IoT agriculture. Collaborates on initiatives like the Climate Smart Sugarcane Irrigation Partnerships and Black Soldier Fly larval nutrition optimization.

Scott FieldView profile

Associate Professor

• Gravitational Wave Data Science
• Discontinuous Galerkin Methods
• Computational Relativity
• +2 more

Scott Field is an Associate Professor in the Department of Mathematics at the University of Massachusetts Dartmouth, where he co-directs the Engineering & Applied Science PhD program. His research integrates gravitational wave data science, computational relativity, and high-performance computing to model astrophysical phenomena like black hole mergers. His primary research areas include developing discontinuous Galerkin methods for efficient waveform computation, creating surrogate models to accelerate gravitational wave predictions, and applying machine learning to astrophysical inverse problems. His work supports gravitational wave observatories like LIGO and LISA. He has secured substantial research funding, including NSF and ONR grants exceeding $1.6 million. Notable projects focus on neural ODE dynamics for orbital modeling and high-order methods for long-duration waveform simulations. As co-developer of the GWSurrogate Python package, he enables efficient gravitational wave data analysis. He collaborates with the Simulating eXtreme Spacetimes (SXS) consortium and contributes to the LISA mission through waveform modeling.

Vijay VarmaView profile

Assistant Professor

• Gravitational Waves
• Numerical Relativity
• Computational Astrophysics

Vijay Varma is an Assistant Professor in the Department of Mathematics at the University of Massachusetts Dartmouth. His research focuses on gravitational waves, numerical relativity, and computational astrophysics. He completed his PhD at Caltech in 2019 and previously held positions as a Marie Curie Fellow at the Albert Einstein Institute and a Klarman Fellow at Cornell. Education: PhD, Caltech (2019) His primary research explores the dynamics of binary black hole systems, gravitational wave signal analysis, and surrogate modeling techniques. He develops computational tools for gravitational wave astronomy and collaborates on projects like the SXS Collaboration and Einstein Telescope initiative. Dr. Varma's publications predominantly focus on gravitational wave physics, including black hole spectroscopy, numerical relativity simulations, and waveform modeling. His most cited work involves surrogate models for gravitational wave analysis and studies of spin precession in binary systems. Scientific Awards: None reported No information is currently available regarding student advising, funded grants, or laboratory affiliations.

Jeb KegerreisView profile

Assistant Professor

• Theoretical Chemistry
• Computational Chemistry
• Quantum Dynamics
• +3 more

Jeb Kegerreis is an Assistant Professor of Physical Chemistry at Shippensburg University within the College of Arts & Sciences. His research integrates theoretical and computational chemistry with chemical education, emphasizing hands-on learning through programming and mathematical software. Collaboration with Dr. John Richardson on analytical/physical chemistry intersections Collaboration with Dr. Dan Predecki on imine kinetics Research focuses on quantum dynamics and Monte Carlo integration for thermal energy calculations, alongside educational projects translating complex mathematics into physical chemistry curricula using Mathcad. Former students have pursued graduate work at institutions like the University of Illinois, Johns Hopkins, and Notre Dame. His computational work includes Fourier transforms , quantum correlation functions , and gold/silica nanoparticle modeling . Collaborative kinetic studies of imines involve UV-Vis spectroscopy and data modeling. Current group member: Nick Bigham (solvation energies of heavy metal ions) Former students: Connor Daly (Chemical Physics, U. Illinois), Mike Denchy (Physical Chemistry, Johns Hopkins), Kelsey Mengle (Engineering, U. Michigan), and others Dr. Kegerreis’ group combines theoretical chemistry , computational laboratory development , and interdisciplinary research between physical and analytical chemistry.

Charles A. TrappView profile

Professor

• Physical Chemistry
• Magnetic Properties
• Materials Science
• +4 more

Charles A. Trapp is a Professor of Physical Chemistry at the University of Louisville. His research focuses on magnetic properties of matter, particularly using electron spin resonance (ESR) spectroscopy to study free radicals in aromatic organic compounds, transition metal ions in catalysts, high-temperature superconductors, and photoacoustic crystals. He holds a B.S. from Loyola College (1958), M.S. (1960), and Ph.D. (1963) from the University of Chicago. His theoretical work involves molecular orbital calculations to complement experimental studies. Recent publications include co-authored textbooks on physical chemistry and computational tools for the field, alongside a 1993 microbiology study on manganous ion uptake in bacteria. No scientific awards are explicitly listed, and no advisees are mentioned in the provided data. His research integrates spectroscopic techniques with computational modeling to explore materials' electronic structures and chemical behavior.