جست‌وجوی استاد و پژوهشگر

Xiaoning Qianمشاهده پروفایل

استاد

• machine learning
• Bayesian methods
• optimal experimental design
• +۵ مورد دیگر

Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.

• Applied Mathematics
• Mathematical Biology
• Multiscale Modelling
• +۵ مورد دیگر

Prof. Radek Erban is a Professor of Applied Mathematics at the Mathematical Institute , University of Oxford. He is affiliated with the Oxford Centre for Industrial and Applied Mathematics and works across interdisciplinary fields including Mathematical Biology, Stochastic Processes, and Reaction-Diffusion Systems. His research focuses on: Multiscale modeling of biological and chemical processes Stochastic simulation algorithms for reaction-diffusion systems Mathematical analysis of collective behavior in biological systems Computational methods for chemotaxis and gene regulatory networks Partial differential equation models for biological phenomena Recent work explores multi-resolution simulations of ions, morphogen gradient modeling, and hybrid numerical methods for stochastic processes. His publications span journals in applied mathematics, computational biology, and physical sciences. Current projects involve bridging atomistic and continuum models for chemical systems.

Rodrigo Freitasمشاهده پروفایل

استاد

• Materials Science
• Computational Materials Design
• Machine Learning in Materials
• +۵ مورد دیگر

Professor Rodrigo Freitas holds the TDK Professorship in Materials Science and Engineering at MIT. His research focuses on computational materials design, bridging atomistic simulations with mesoscale microstructural analysis. He leads the Freitas Research Group, specializing in machine learning-driven modeling of materials kinetics and solidification processes. Education: B.S. and M.S. in Physics, University of Campinas, Brazil M.S. and Ph.D. in Materials Science & Engineering, UC Berkeley Research Interests: Professor Freitas investigates microstructural evolution in metals and alloys using advanced computational methods. Key areas include solidification mechanisms, interstitial atom behavior in superalloys, and machine learning applications for materials discovery. His work emphasizes bridging atomistic and mesoscale phenomena to guide industrial applications like semiconductor manufacturing and battery design. Publications Trend: Recent work emphasizes machine learning potentials for alloy modeling, short-range order analysis in high-entropy alloys, and kinetic modeling of complex chemical systems. Themes include alloy phase stability, defect dynamics, and data-driven materials discovery. Labs/Teams: Leads the Freitas Research Group at MIT, which develops novel computational tools for materials engineering.

Rosana Collepardoمشاهده پروفایل

پژوهشگر ارشد

• Computational Biophysics
• Chromatin Structure
• Epigenetics
• +۴ مورد دیگر

Dr. Rosana Collepardo is a Winton Advanced Research Fellow at the Cavendish Laboratory, University of Cambridge, where she leads a research group within the Theory of Condensed Matter (TCM) Group and is also affiliated with the Biological and Soft Systems group. Her research focuses on developing multi-scale computational models to investigate chromatin nanostructure, epigenetic regulation, and biomolecular condensates, with applications in understanding genome organization and sustainable data storage. Her primary research interests include: Computational biophysics of chromatin and epigenetics Mechanisms of biomolecular condensates and phase separation Nanoscale structure of the genome and DNA accessibility Multi-scale modeling from atomistic to mesoscale Design principles for chromatin-inspired data storage Analysis of her recent publications (2023-2025) reveals a dominant focus on chromatin organization, epigenetic mechanisms, and biomolecular condensates. Key trends include the role of nucleosome spacing, linker histones, and epigenetic modifications in chromatin phase separation, alongside investigations into condensate aging, material properties, and the physical principles of phase transitions in RNA-protein systems. Her work consistently integrates computational modeling with experimental validation. Notable scientific awards include: Winton Advanced Research Fellowship ERC Starting Grant Dr. Collepardo actively mentors PhD and MPhil students, including Sivapalan Chelvaniththilan (MPhil in Physics, recipient of Gates and Winton Scholarships), Miguel Garcia Ortegon (MPhil in Scientific Computing), Stephen Farr (PhD in Computational Methods for Materials Science), Akshay Sridhar (MPhil in Scientific Computing), and Adiran Garaizar (PhD with EPSRC scholarship). Her group secures competitive funding through ERC grants and student scholarships. The Collepardo group, established in 2016 at the Maxwell Centre, Cavendish Laboratory, comprises postdoctoral researchers, PhD students, and MPhil candidates. They collaborate with experimental groups to study chromatin dynamics and biomolecular condensates using advanced computational techniques, contributing to fundamental biological understanding and potential biotechnological applications.

Jeffrey C. Suhlingمشاهده پروفایل

استاد

• Materials Science
• Mechanical Engineering
• Electronics Packaging
• +۳ مورد دیگر

Jeffrey C. Suhling is the Quina Distinguished Professor and Department Chair of Mechanical Engineering at Auburn University . His research focuses on the mechanical and thermal behavior of lead-free solder alloys , particularly in automotive electronics and high strain rate applications . He has extensively studied the reliability of hybrid SAC-LTS solder joints under thermal cycling, vibration, and shock. Scientific awards : Quina Distinguished Professor His work integrates finite element modeling , microstructural analysis , and machine learning to predict solder joint failure and optimize material performance. Key areas include creep behavior , damage accumulation , and interfacial reliability in extreme environments.

• Materials Science
• Condensed Matter Physics
• Computational Modeling
• +۷ مورد دیگر

Alejandro Strachan is an Assistant Professor of Materials Engineering at Purdue University's College of Engineering. His research focuses on molecular modeling of advanced materials, with specific emphasis on atomistic and mesoscale simulations of condensed-phase chemistry, active materials, nanotechnology, and mechanical properties of structural materials. Ph.D. in Physics, University of Buenos Aires (1998) Postdoctoral Research, Caltech's Materials Process Simulation Center (1999-2002) Strachan's work integrates computational methods with machine learning to study material behavior under extreme conditions, including shock waves and high-pressure environments. His research spans energetic materials, phase transitions, and multiscale modeling frameworks. Recent publications highlight trends in combining quantum-accurate simulations with deep learning for non-equilibrium systems, FAIR data infrastructure for materials discovery, and multiscale reactive models for energetic composites. He also explores mechanochemistry, defect dynamics, and microstructure-property relationships. His computational simulations often address practical challenges in material stabilization, polymer interactions, and hotspot formation mechanisms. Strachan actively contributes to open science initiatives through platforms like nanoHUB and HUBzero.

Tom Wooمشاهده پروفایل

استاد

• Theoretical Chemistry
• Physical Chemistry
• Catalysis
• +۶ مورد دیگر

Tom Woo is a Professor in the Department of Chemistry at the University of Ottawa's Faculty of Science. His research focuses on computational quantum chemistry, catalysis, and energy-related systems. Using advanced molecular simulations, his group explores microscopic chemical processes and develops novel methods for energy storage, pharmaceutical catalysis, and material design. Key projects include studying catalytic systems for energy conversion and pharmaceutical synthesis, leveraging computational tools to uncover reaction mechanisms inaccessible via experiments. Research interests span computational chemistry, quantum chemistry, molecular dynamics, and nanotechnology. The Woo Group applies these methods to design metal-organic frameworks (MOFs) for CO2 capture, hydrogen storage, and other energy applications. Their work bridges theoretical models with experimental validation, emphasizing high-throughput screening and machine learning. Publications highlight breakthroughs in MOF design, computational validation of material databases, and carbon capture technologies. Collaborations focus on interdisciplinary challenges in energy sustainability and pharmaceutical catalysis.

Chandrika Sadanandمشاهده پروفایل

استادیار

• Low Dimensional Topology
• Geometry
• Hyperbolic Geometry
• +۳ مورد دیگر

Chandrika Sadanand is an Assistant Professor in the Department of Mathematics at Bowdoin College. She holds a PhD from Stony Brook University and a BS from the University of Toronto. Her research focuses on low-dimensional topology and geometry, specifically exploring curves on surfaces, hyperbolic geometry, billiards, translation surfaces, and 3-manifolds. She has held postdoctoral positions at the University of Illinois Urbana Champaign, Technion, and Hebrew University of Jerusalem. Dr. Sadanand teaches courses in mathematical reasoning, geometry, and topology. Her recent courses include MATH 2020 (Introduction to Mathematical Reasoning), MATH 2404 (Geometry), and MATH 3402 (Topology). She has also mentored undergraduate research projects on flat surfaces and participated in outreach activities through programs like the Stony Brook Math Summer Camp and WISE. Her research contributions include studies on translation surfaces of infinite type, billiards dynamics, and geometric structures. She has contributed to innovative conferences like the Nearly Carbon Neutral Geometry and Topology Conferences through video presentations. Her work bridges pure mathematics with computational methods, addressing questions in geometric topology and dynamical systems.

Chong Liuمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Optimization
• Bandit Algorithms
• +۳ مورد دیگر

Dr. Chong Liu is an Assistant Professor of Computer Science at the State University of New York at Albany (SUNY Albany) in the College of Nanotechnology, Science, and Engineering. He received his PhD in Computer Science from UC Santa Barbara in 2023 and completed a postdoctoral fellowship at the University of Chicago's Data Science Institute (2023-2024). His research focuses on Machine Learning and AI for Science, particularly Bayesian optimization, bandit algorithms, generative models, and AI applications in drug discovery. He has received the SUNY IITG/OER Impact Grant and serves as Associate Editor for IEEE-TNNLS, Area Chair for ICML/AISTATS, and editorial board reviewer for JMLR. PhD: UC Santa Barbara (2023), advised by Yu-Xiang Wang Postdoc: University of Chicago Data Science Institute (2023) Research Interests : Broad: Machine Learning, Optimization, AI for Science Specific: Bayesian optimization, Bandit algorithms, Active learning, Experimental design, Generative models, AI for drug discovery Applications: Binding affinity prediction, Drug screening, Policy optimization Recent Article Trends : His 2024-2025 publications focus on extending Bayesian optimization theory under practical constraints, quantum-accelerated bandit methods, and multi-objective optimization for drug discovery. Earlier works include private learning frameworks and human-in-the-loop systems. Scientific Awards : 2025: SUNY IITG/OER Impact Grant Professional Activities : Organized NeurIPS workshops on AI for Drug Discovery (2023, 2025), co-organizing INFORMS sessions, and serving on program committees for ICML, NeurIPS, ICLR, and AAAI. He has given invited talks at institutions including University of Chicago, UC Santa Barbara, and Genentech. Teaching : Teaching courses like Numerical Methods (CSI 401) and Machine Learning (CSI 436/536) with syllabi spanning 2024-2025 semesters.

David Cookeمشاهده پروفایل

پژوهشگر

• Computational Solid State Chemistry
• Biomineralization
• Surface Science
• +۳ مورد دیگر

Dr David Cooke is a Subject Area Leader in Chemistry & Chemical Engineering at the Department of Physical and Life Sciences, School of Applied Sciences, University of Huddersfield. He is a member of the Structural, Molecular and Dynamic Modelling Centre and Associate Member of multiple research centers including the Centre for Functional Materials and Catalysis Research Centre. Background: PhD in Computational Solid State Chemistry (Prof SC Parker, University of Bath) Postdoctoral Research: University of Bath and Materials Science department at the University of Cambridge Research Interests: Computational Solid State Chemistry, mineral surface modeling, biomineralization processes, crystal growth, and surface defect analysis. His work aligns with UN Sustainable Development Goals for environmental and health applications. Publications: 15 recent works focus on cerium oxide interfaces, plutonium hydration, oxide-polymer composites, and defect dynamics using Density Functional Theory and Molecular Dynamics. Key collaborations include M. Molinari, L. Gillie, and S. Parker. Scientific Activities: Active in research output since 2001 (50+ publications) 5 supervised works 35+ research activities including oral presentations

Panayiotis Papadopoulosمشاهده پروفایل

استاد

• Computational Mechanics
• Solid Mechanics
• Biomechanics
• +۱ مورد دیگر

Panayiotis Papadopoulos is a Professor and the Byron and Elvira Nishkian Chair in Structural Engineering at the University of California, Berkeley. He serves as Director of the CoE Aerospace Engineering Programs and contributes to the Computational Solid Mechanics Lab. Education: Ph.D. in Civil Engineering, University of California, Berkeley (1991) M.S. in Civil Engineering, University of California, Berkeley (1987) Dipl. in Civil Engineering, Aristotle University, Thessaloniki, Greece (1986) Research Interests: Professor Papadopoulos specializes in computational mechanics, solid mechanics, biomechanics, and applied mathematics. His work bridges theoretical modeling with advanced numerical methods, focusing on multiscale analysis, thermomechanical coupling, and material failure mechanisms. Publication Trends: His recent research emphasizes multiscale finite element methods, thermomechanical analysis of deformable solids, and biomechanical modeling. Key themes include contact mechanics, phase transformations in shape-memory alloys, and computational approaches for microstructural analysis. Scientific Awards: Byron and Elvira Nishkian Chair in Structural Engineering Labs and Teams: He leads the Computational Solid Mechanics Lab, which develops advanced numerical frameworks for material behavior under complex thermomechanical conditions.

• Materials Science
• Nanotechnology
• Physics
• +۳ مورد دیگر

Ville Jantunen is a Researcher and Supervisor in the Doctoral Programme in Materials Research and Nanosciences. His research focuses on atomistic simulations of materials under extreme conditions, including ion irradiation effects, defect evolution in fusion materials, and nanoparticle dynamics. He is actively involved in the SPATEC project (2022–2026), funded by the Academy of Finland, which explores time and spatial dependence of cascade damage in materials under pulsed ion beams. Key research areas include computational materials science, radiation effects in nanomaterials, and predictive modeling of electronic/atomic phenomena. His work spans both fundamental and applied aspects, with contributions to quantum technology through spin-qubit array studies and fusion energy via tungsten defect analysis. Collaborations include international teams on nanoparticle shape transformation mechanisms and kinetic Monte Carlo simulations. He has organized educational outreach activities like the LEGO lab workshop at Helsinki Natural Science Lyceum (2018), demonstrating engagement in science communication. Publications emphasize interdisciplinary approaches, combining computational methods with experimental insights to address challenges in nanotechnology, fusion materials, and radiation physics.

Wolfgang Windlمشاهده پروفایل

استاد

• Computational Materials Science
• Atomistic Simulations
• Density-Functional Theory
• +۵ مورد دیگر

Wolfgang Windl is a Professor in the Department of Materials Science and Engineering at The Ohio State University with a joint appointment in Physics. He co-founded Goniotech LLC and previously worked at Motorola as a Principal Staff Scientist. He holds a doctoral degree in physics from the University of Regensburg and completed postdoctoral research at Los Alamos National Laboratory and Arizona State University. His research specializes in computational materials science, focusing on: Atomistic simulations and density-functional theory Machine learning applications in materials design Semiconductor transport and layered materials (e.g., Dirac semimetals) Atom probe tomography and characterization techniques Analysis of his 15 most recent publications (2023-2025) reveals dominant themes: advanced simulations of field evaporation, topological quantum materials (PtTe 2 , PdTe 2 ), and computational frameworks for materials characterization. His work frequently integrates spectroscopy, tomography, and Bayesian methods to study alloys, 2D materials, and additive manufacturing defects. Awards and Honors Fraunhofer-Bessel Research Award (2006) Four Lumley Research Awards Boyer Award for Teaching Excellence (2015) Faculty Diversity Excellence Award (2020) Two Mars Fontana Best Teacher Awards (2006, 2015) ASEE Best Paper & Diversity Awards (2019) He advises 11+ graduate students (7 alumni, 5 current) and leads the Windl Group research team focused on computational materials modeling. His group develops simulation tools for atomic-scale characterization and collaborates with national laboratories.

Johann Guilleminotمشاهده پروفایل

دانشیار

• Computational Mechanics
• Materials Science
• Mechanics of Heterogeneous Materials
• +۸ مورد دیگر

Johann Guilleminot is an Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University. He joined Duke in 2017 after a Maître de Conférences position at Université Paris-Est. His research bridges computational mechanics, materials science, and uncertainty quantification, with applications in additive manufacturing, biomedical implants, and naval systems. Education: M.S. in Theoretical Mechanics, Lille University of Science and Technology (2005) Ph.D. in Theoretical Mechanics, Lille University of Science and Technology (2008) Habilitation in Mechanics, Université Paris-Est (2014) His work focuses on probabilistic methods for heterogeneous materials, stochastic solvers, and scientific machine learning. Recent projects include data-driven uncertainty quantification in molecular dynamics and additive manufacturing simulations, funded by the Army Research Office, NSF, and national laboratories. Scientific Awards: No specific awards listed in the provided text. Lab & Collaborations: Leads the Guilleminot Lab at Duke, collaborating with Sandia National Laboratories and the U.S. Naval Research Laboratory. Research spans atomistic-to-continuum coupling, inverse problems, and stochastic modeling for predictive simulations.

• Materials Science
• Computational Chemistry
• Density Functional Theory
• +۳ مورد دیگر

Michael Herbst is an Assistant Professor (tenure-track) at EPFL, holding a joint appointment in the School of Basic Sciences (SB) and the School of Engineering (STI). He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on error control in atomistic simulations, density-functional theory (DFT), and interdisciplinary computational methods. His work bridges mathematics, materials science, and computer science, emphasizing robust algorithms and Julia-based software development. Herbst holds a PhD from Heidelberg University and has held postdoctoral positions at RWTH Aachen and Inria Paris. He is a core member of the MARVEL and CESMIX research centers. Education: 2018: Dr. rer. nat. (magna cum laude), Heidelberg University 2009–2013: BA and MSci (1st class) in Natural Sciences, University of Cambridge 2008–2009: Studies in Mathematics/Physics, TU Kaiserslautern Research Interests : Herbst's research centers on developing reliable computational methods for materials modeling, including error estimation in DFT, black-box SCF algorithms, and Julia-based tools like the Density-Functional Toolkit (DFTK). His work addresses challenges in high-throughput simulations, numerical stability, and interdisciplinary collaboration across mathematics, physics, and computer science. Grants & Projects : MARVEL Center for Computational Design (EPFL) CESMIX Center for Extreme-Scale Simulations (MIT) EMC² Project (Sorbonne/Inria/École des Ponts) Awards : HGS MathComp PostDoc Fellowship (2018–2021) DAAD Travel Funding (2018) Exploratory Research Space Fund (RWTH Aachen, 2022) Labs & Teams : Head of the MatMat group at EPFL, focusing on error-controlled simulations and open-source software development.