Ian Bradley is an Assistant Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo, State University of New York. His research focuses on creating sustainable biological processes to address needs in engineered and natural systems for water and wastewater treatment and resource recovery. Education: PhD in Environmental Engineering, University of Illinois at Urbana-Champaign (2017) MS in Environmental Engineering, University of Illinois at Urbana-Champaign (2011) MS in Civil Engineering (Structures), University of Illinois at Urbana-Champaign (2010) Research Interests: Dr. Bradley specializes in microalgal-based nutrient recovery, wastewater surveillance for public health monitoring, PFAS degradation using nanomaterials, and sustainable resource recovery systems. His work integrates biological processes with environmental engineering to optimize wastewater treatment efficiency and develop predictive models for water quality and health outcomes. Publications: His recent research includes advancements in microalgal cultivation (EcoRecover process), wastewater-based epidemiology for SARS-CoV-2 tracking, and computational enzyme design for PFAS remediation. These studies demonstrate interdisciplinary expertise spanning environmental engineering, biotechnology, and public health analytics.
Steven A. Corcelli is a Professor and Interim Dean of the College of Science at the University of Notre Dame, with a research focus on Theoretical Chemistry and Molecular Dynamics Simulations . His work bridges Physical Chemistry and Biochemistry , targeting Energy Applications and Biomolecular Binding Mechanisms . He leads the Computational Molecular Science & Engineering Laboratory (CoMSEL). Ph.D., Chemistry, Yale University (2001) Sc.B., Chemistry, Brown University (1997) Research interests span ionic liquids for Carbon Capture , aqueous electrolytes in battery technologies , and molecular binding processes in immunology and DNA interactions . His group employs GPU-accelerated simulations and weighted ensemble methods to uncover structural and dynamic motifs. Recent publications highlight trends in vibrational spectroscopy , TCR-MHC binding , and CO2 solvation mechanisms . Awards include the Thomas P. Madden Award (2020) , ACS Fellowship (2016) , and NSF CAREER Award (2009) . Staff: Erin Brossard (Ph.D.), Nell Karpinski, Shuang Wu, Noah Vasconez, Kaitlyn Handy, Isabel Thompson
Professor Manolis Gavaises is a leading academic in the field of mechanical engineering and computational fluid dynamics at City St George's, University of London, where he holds the position of Professor in the School of Engineering and Mathematical Sciences. He earned his PhD from Imperial College London and has been a faculty member since 2001, progressing to full Professor in 2009. His research is centered on advanced modeling of multi-phase flows, cavitation, and fuel injection systems, with extensive collaborations across Europe and industry partners such as Delphi, Caterpillar, and BP. Education: DIC, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 PhD, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 Diploma (5 years), Mechanical Engineering, National Technical University of Athens, 1992 His research interests span computational fluid dynamics, cavitation, fuel injection, atomization, high-pressure and supercritical flows, and alternative fuels . He has developed advanced numerical models and experimental techniques, including X-ray phase contrast imaging and high-pressure test rigs. His work integrates fundamental DNS and LES simulations with industrial applications in automotive, marine, aerospace, and medical devices such as heart valves. The recent publications reflect a strong trend toward real-fluid thermodynamic modeling (e.g., PC-SAFT), multi-component fuel behavior, cavitation erosion, and advanced diagnostics . His research increasingly incorporates machine learning and high-fidelity imaging to understand complex flow phenomena across energy, transportation, and biomedical domains. Scientific Awards and Recognitions: Richard Way Prize (1998) Arch T. Collwell Merit Award (1998) Best Oral Paper, SAE World Congress (2006) PE Publication Award, IMechE (2007) Best Presentation Award, Engine Combustion Processes (2009) Fellow, IMechE (2013) Fellow, IMA (2015) As a dedicated mentor, Professor Gavaises has supervised 13 PhDs to completion and currently guides 23 doctoral students. He has secured over €16 million in EU and UK funding, including multiple Horizon 2020 Marie Skłodowska-Curie ITN projects (CAFÉ, HAOS, IPPAD), which support 46 early-career researchers globally. He has created academic opportunities for post-docs and junior faculty, significantly advancing the research profile of his institution. He leads the International Institute of Cavitation Research (IICR), co-founded in 2011 with partners from Loughborough University, TU Delft, and Imperial College, supported by The Lloyd’s Register Foundation. His lab maintains strong experimental capabilities, including a 2000bar pressure flow rig with micro-transparent nozzles and collaborations with Argonne National Laboratory for X-ray imaging.
Yu He is an Assistant Professor of Applied Physics and Physics at Yale University, affiliated with the Department of Physics. His research focuses on condensed matter physics and experimental techniques such as angle-resolved photoemission spectroscopy (ARPES) and x-ray scattering to study correlated electronic systems and quantum materials. Prior to Yale, he completed a Miller Research Fellowship at UC Berkeley (2019) after earning his Ph.D. in Applied Physics from Stanford University. Key research areas include metal-to-insulator transitions, superconductivity, 2D magnetism, and solid-state quantum simulation. He has contributed to advancements in material characterization techniques, including high-resolution ARPES using tabletop lasers. His work integrates crystal synthesis, electric transport measurements, and surface decoration to explore material properties. Education: B.S. in Physics from University of Science and Technology of China (USTC); M.S. in Electrical Engineering and Ph.D. in Applied Physics from Stanford University. Research Interests: Experimental condensed matter physics, quantum materials, superconductivity, and light-matter interaction studies. His current projects aim to dissect microscopic degrees of freedom (electronic, lattice, spin) in novel materials using cutting-edge spectroscopic methods. The lab employs complementary techniques like electric transport measurements and crystal growth to characterize material properties comprehensively. Awards: Miller Research Fellow, UC Berkeley (2019) Advising & Grants: No student advisees listed. Research supported by Yale University and prior fellowships. Labs & Teams: Leads a research group at Yale focused on experimental condensed matter physics, collaborating on projects involving advanced material characterization and quantum material discovery.
Bryan K. Clark is an Associate Professor in the Department of Physics at the University of Illinois, with his office located in the Engineering Sciences Building. He leads the Clark Research Group, which works at the intersection of quantum information, condensed matter physics, machine learning, and computing. Clark's research spans four main areas: Quantum Computing , where his group develops quantum algorithms and collaborates with experimentalists on superconducting qubit systems; Quantum Many-Body Physics , where he applies computational methods to understand emergent behavior in strongly correlated systems; Algorithms for the Quantum Many-Body Problem , where his group has pioneered techniques like Neural Network Backflow (NNBF) that represent state-of-the-art accuracy for simulating fermions and frustrated magnetism; and Machine Learning for Experiment , where his group develops techniques to analyze experimental data like scanning transmission electron microscopy images. His publication record demonstrates consistent innovation in bridging theoretical quantum information science with practical applications. Recent work focuses on neural network approaches to quantum simulation, quantum error correction/mitigation, and novel qubit architectures like the Floquet Fluxonium Molecule. His research shows a clear trajectory from fundamental questions about the quantum-classical boundary to practical implementations in quantum hardware. Clark actively mentors graduate students, with recent thesis defenses by Faisal Alam, Matt Thibodeau, Chad Germany, James Allen, and Abid. His group has secured significant funding from the NSF and IBM's IIDAI institute to support research in quantum computing and machine learning applications for nano-photonics manufacturing and error mitigation. The Clark Research Group maintains strong connections with experimental teams, particularly in superconducting qubit development and materials characterization. They've developed computational tools like QOSY (Quantum Operators from SYmmetry) that are publicly available on GitHub and have gained recognition in the quantum information community.
Dr. Xiaofeng Qian is an Associate Professor in the Department of Materials Science & Engineering at Texas A&M University, with joint appointments in Physics and Astronomy, and Electrical & Computer Engineering. His research focuses on materials theory , quantum materials design , and high-throughput computational discovery , particularly for 2D materials and energy applications . Educational Background: Ph.D., Nuclear Science and Engineering, Massachusetts Institute of Technology (2008) B.S., Engineering Physics, Tsinghua University (2001) Research spans first-principles electronic structure methods , nonlinear optical responses , and multiscale modeling of electronic, thermal, and ionic transport. Key areas include quantum spin Hall effect , ferroelectric switching , and machine learning for materials prediction . Notable Awards: Dean of Engineering Excellence Award (2024) Engineering Genesis Multidisciplinary Award (2024) AZZ Faculty Fellow (2021) NSF CAREER Award (2018) Manson Benedict Fellowship (2006) Actively recruiting PhD, MS, and UG researchers with backgrounds in physics, materials science, or computational methods. Collaborates extensively on hybrid AI-materials projects and topological device concepts .
Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.
Rafael Brüschweiler is a Professor and Ohio Research Scholar at The Ohio State University, holding joint appointments in the Department of Chemistry and Biochemistry and the Department of Biological Chemistry and Pharmacology. He serves as the NMR Executive Director for the Ohio State Campus Chemical Instrument Center and the NSF-funded National Gateway Ultrahigh Field NMR Center. His research focuses on biophysical chemistry, analytical chemistry, and computational modeling, emphasizing protein dynamics, metabolomics, and NMR method development. He received his Ph.D. from ETH Zurich and completed a postdoc at the Scripps Research Institute. His research integrates experimental NMR, molecular dynamics simulations, and machine learning to study protein structure-function relationships, metabolic pathways, and biomolecular interactions. Key areas include the dynamics of oncogenic K-Ras, glucokinase glucose sensing, and nanoparticle-assisted NMR techniques. His work is funded by the NIH and NSF, with applications in biomedical diagnostics and drug discovery. Dr. Brüschweiler leads a multidisciplinary lab training students and postdocs in NMR spectroscopy, computational methods, and metabolomics. His lab developed tools like DEEP picker and COLMAR for automated NMR data analysis, contributing to the SECIM metabolomics center. He actively recruits students interested in protein dynamics, computational modeling, or metabolomics.
Asst. Prof. OU Pengfei is an Assistant Professor and NUS Presidential Young Professor in the Department of Chemistry at the National University of Singapore, Faculty of Science. He leads the AI for Chemistry (AI4Chem) research group, focusing on computational catalysis, machine learning, and materials science. Previously, he was a Research Associate at Northwestern University and a Postdoctoral Fellow at the University of Toronto under Prof. Edward H. Sargent, and earned his Ph.D. from McGill University. Education: Ph.D., McGill University, 2020 M.Eng., Central South University, 2015 B.Eng., Central South University, 2012 Research interests include catalyst design for electrochemical reactions using ab initio DFT, molecular dynamics simulations, and AI-driven methods. He develops dynamic simulations of chemical processes under reaction conditions and machine learning tools for accelerated catalyst discovery. His work addresses challenges in energy and environmental applications such as CO2 reduction and hydrogen evolution. Notable awards include the NUS Presidential Young Professorship (2024), Climate Positive Energy Postdoctoral Fellowship (2021), and Chinese Government Award for Outstanding Self-Financed Students Abroad (2020). Labs/Teams: The AI4Chem group integrates theory-guided and data-driven approaches to advance computational catalysis, with three core research directions: (1) reaction mechanism exploration and catalyst optimization, (2) dynamic structure-performance relationships under reaction conditions, and (3) machine learning algorithms for high-throughput screening.
Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science at the University of Birmingham's School of Mathematics. His research focuses on numerical methods for stochastic differential equations, with applications in computational mathematics, statistics, physics, and data science. He is affiliated with the Optimisation and Numerical Analysis Group, Statistics and Data Science Group, and the Institute for Data and AI. Shang holds a PhD in Applied and Computational Mathematics from the University of Edinburgh (2016) and completed postdocs at the University of Edinburgh, Brown University, and ETH Zurich before joining Birmingham in 2019. His academic achievements include fellowships from The Alan Turing Institute, the LMS Emmy Noether Fellowship, and the EUniWell Leadership Fellowship. He has secured funding from EPSRC, the Royal Society, and the Isaac Newton Institute. Shang is actively involved in supervising PhD students and co-organizing research initiatives such as the Data Science and Computational Statistics Seminar. Research interests include structure-preserving integrators, Bayesian sampling techniques, and machine learning applications in dynamical systems. His work bridges numerical analysis, probability theory, and multiscale modeling in materials science. Recent projects involve neural networks for complex dynamical systems and numerical algorithms for deterministic/stochastic systems.
Alfredo Pasquarello is a Full Professor at the Chair of Atomic Scale Simulation within the Condensed Matter Theory Laboratory (CSEA) at the Ecole Polytechnique Fédérale de Lausanne (EPFL) . He teaches courses such as Computer Simulation of Physical Systems I and General Physics: Quanta . Education: Physics at Scuola Normale Superiore of Pisa (1986), University of Pisa (1986), PhD at EPFL (1991). Research: Focuses on atomic-scale simulations using density functional theory (DFT) and many-body perturbation to study defects in oxides , oxide-semiconductor interfaces , and energy materials like perovskites and photocatalysts. Recent Publications: 15 most recent articles (2022–2024) address band gaps, polarons, water splitting, and defect engineering in materials for photovoltaics and electrochemistry. Awards: Recipient of the EPFL Latsis Prize (1998) . Students: Supervised PhD/Master's students including Stefano Falletta, Thomas Bischoff, Patrick Gono, and Zhendong Guo. Labs: Leads the Chair of Atomic Scale Simulation at EPFL SB IPHYS CSEA.
Dr. Joshua Brinkerhoff is an Associate Professor in Mechanical Engineering at the University of British Columbia Okanagan Campus. He serves as the Associate Director for Research & Industrial Partnerships in the School of Engineering and leads the UBC-Okanagan Computational Fluid Dynamics Laboratory. His research spans computational fluid dynamics, turbomachinery, multiphase flows, hydrogen safety, wind energy, and biofluid mechanics. He teaches courses in mechanics of materials, alternative energy systems, turbulence, computational fluid dynamics, and aircraft design. PhD, Aerospace Engineering (Carleton University, Ottawa, ON) BEng, Aerospace Engineering (Carleton University) Dr. Brinkerhoff’s research interests include: Computational Fluid Dynamics (CFD) for laminar-to-turbulent transition and instability analysis Wind energy systems and turbine aerodynamics Hydrogen storage and safety protocols for transportation Biofluid mechanics for respiratory diseases and aneurysm modeling Multiphase flows in industrial and environmental contexts His publications focus on CFD simulations for: Aerosol dispersion and mitigation in indoor environments Wind farm interactions and atmospheric gravity waves Cavitation and phase transitions in cryogenic and LNG systems Heat transfer optimization in industrial and thermal systems Instability dynamics in buoyancy-driven and swept flows Turbulent structures in fluidized beds and reactors Dr. Brinkerhoff has no listed scientific awards in the provided data but has extensive contributions to renewable energy, hydrogen safety, and medical fluid dynamics. His laboratory develops open-source tools like TOSCA for large-eddy simulations and investigates practical applications in urban air quality, dental aerosol control, and turbine wake modeling.
Chun Liu is Chair and Professor of Applied Mathematics at the Department of Applied Mathematics, Illinois Institute of Technology (IIT), within the College of Computing. His research focuses on Nonlinear Partial Differential Equations , Complex Fluids , and Multiscale Modeling , with applications in electrophysiology and materials science. He earned a Ph.D. from New York University’s Courant Institute, an M.S. from Duke University, and a B.S. from Fudan University. Prof. Liu leads projects on General Diffusion Systems , Ion Channel Dynamics , and Viscoelastic Fluids . He has secured grants from NSF, BSF, and DAAD for research in energetic variational approaches, multiscale materials modeling, and biomolecular systems. Key contributions include the development of Poisson-Boltzmann models , coarse-grained dynamics , and energetically stable numerical methods . He serves on editorial boards for Communications in Mathematical Sciences , SIAM Journal on Mathematical Analysis , and others. His work bridges applied mathematics with engineering and biophysics, addressing challenges in fluid mechanics, ion transport, and nonlinear systems.
Lande Liu is a Senior Lecturer in Chemical Engineering at the University of Huddersfield's School of Applied Sciences. Previously, he held a Lectureship at the University of Manchester (2010-2014), and earlier worked as an industrial consultant and research fellow at Leeds and Sheffield Universities. His academic journey began with a MEng in Chemical Engineering and a PhD in kinetic theory of aggregation from Sheffield (2004), preceded by a visiting PhD at Twente University (2002). Education: PhD in Chemical Engineering (University of Sheffield, 2004) Visiting PhD (Twente University, 2002) MEng in Chemical Engineering (Tsinghua University, 1999) BSc in Applied Mathematics (Tsinghua University, 1996) Liu's research focuses on multi-scale particle interactions (molecular to granular) using kinetic theory of aggregation, with applications spanning nanotechnology, pharmaceutical engineering, and sustainable chemical processes. His work aligns with UN Sustainable Development Goals for environmental protection and industrial innovation. Recent publications examine particle deposition in turbulent flows, enhanced heat exchanger designs, and nanofluid stabilization techniques. He teaches core chemical engineering topics including transport phenomena, unit operations, and process design. Active in collaborative research, Liu has partnered with institutions across Europe on projects involving spectroscopy, ultrasonics, and dynamic modeling. His technical expertise includes particle size analysis, tomography, and computational simulation of complex systems.
Gustav Amberg is a Professor at KTH Royal Institute of Technology, affiliated with the Flow Mechanics research group within the School of Engineering Sciences. His primary appointment is in the Department of Mechanics, where he focuses on fluid dynamics, multiphase flow, and interfacial phenomena. He holds a permanent full professorship with no indication of兼职 roles. Research interests center on dynamic wetting mechanisms, phase-field modeling, and computational fluid dynamics applied to complex fluid systems. His work explores contact line behavior, microstructured surface interactions, and material phase transformations. Notable areas include rapid droplet spreading, viscoelastic fluid dynamics, and boiling heat transfer on engineered surfaces. Recent studies investigate the interplay between surface topography and wetting dynamics, oscillatory contact line phenomena, and numerical benchmarking across molecular and continuum models. He has pioneered methods for simulating surfactant effects in multiphase flows and developed novel approaches for analyzing weld pool behavior during sintering processes. No academic awards or grants are explicitly listed in the provided materials. His advising record remains undisclosed, though his prolific publication history suggests active research supervision. Laboratory affiliations are not detailed, but his work aligns with KTH's broader initiatives in computational mechanics and materials science.
