Research Activities
Session Chair
-
2024 Pittsburgh, PA, USA
Data Sciences and Related Methods
Mathematics of Materials 2024, Society for Industrial and Applied Mathematics
Minisymposium Organizer
-
2024 Vancouver, BC, Canada
Machine learning algorithms for accelerating material characterization, discovery, design, and manufacturing processes
World Congress of Computational Mechanics -
2024 Pittsburgh, PA, USA
Accelerating analysis and design of complex materials via novel numerical methods and machine learning techniques
Mathematics of Materials 2024, Society for Industrial and Applied Mathematics -
2024 Pittsburgh, PA, USA
Machine learning's role in uncovering insights from heterogeneous materials data
Mathematics of Materials 2024, Society for Industrial and Applied Mathematics -
2023 El Paso, TX, USA
Integrating machine learning and numerical methods to accelerate engineering design
Mechanistic Machine Learning and Digital Engineering for Computational Science, Engineering and Technology
Talks
Guest Lectures
-
2024 Mumbai, MH, India
Machine learning algorithms for Inverse design.
Deparment of Mechanical Engineering, Indian Institute of Technology, Bombay
Invited Talks
-
2024 Kitakyushu, Japan
Mulitmodal machine learning with small datasets for process strcture property modeling
International Workshops on Advances in Computational Mechanics -
2024 College Station, Texas, USA
Overcoming challenges of scarce and multimodal data in material design with machine learning
BIRDSHOT Center seminar, Texas A&M University -
2024 Oak Ridge, TN, USA
Enabling Material Discovery: Harnessing Multimodal Machine Learning Algorithms for Inverse Design.
Mathematics in Computation (MiC) seminar, Oak Ridge National Laboratory -
2022 Ohio, USA
Predicting stress hotspots in polycrystalline materials from microstructural features using deep learning
MIrACLE seminar, Air Force Research Laboratory -
2021 Providence, RI, USA
Predicting stress hotspots in polycrystalline materials from microstructural features using deep learning
Crunch seminar, Department of Applied Mathematics, Brown University -
2021 Los Alamos, NM, USA
Predicting stress hotspots in polycrystalline materials from microstructural features using deep learning
Physics and Chemistry of Materials Group Seminar, Los Alamos National Laboratory -
2021 Berkeley, CA, USA
Predicting stress hotspots in polycrystalline materials from microstructural features using deep learning
Computational Biosciences Group Seminar, Lawrence Berkeley National Laboratory
Contributed Talks
-
2023 El Paso, NM, USA
Spatio-temporal super-resolution of dynamical systems using physics-informed deep-learning
Mechanistic Machine Learning, and Digital Engineering for Computational Science, Engineering and Technology -
2023 El Paso, NM, USA
Predicting microstructure from physical vapor deposition process conditions using machine learning.
Mechanistic Machine Learning, and Digital Engineering for Computational Science, Engineering and Technology -
2023 Albuquerque, NM ,USA
Modeling process structure property relationships in Mo thin films from multi-modal data using machine learning
U.S. National Congress on Computational Mechanics -
2023 Livermore, CA ,USA
Spatio-temporal super-resolution of dynamical systems using physics-informed deep-learning
Machine Learning/Deep Learning Workshop, Sandia National Laboratory -
2023 San Diego, CA, USA
Bayesian optimization-assisted sputter deposition of Molybdenum thin films
International Conference on Metallurgical Coatings and Thin Films -
2023 San Diego, CA, USA
Analyzing latent dimensional representations of microstructure evolution
The Minerals, Metals, and Materials Society -
2021 San Diego, CA, USA
Predicting microstructure from physical vapor deposition process conditions using machine learning.
Mechanistic Machine Learning and Digital Engineering for Computational Science, Engineering and Technology -
2020 Pittsburgh, PA, USA
Predicting Stress Hotspots Inside Microstructures Using Deep Learning
Materials Science & Technology conference
Poster Presentations
-
2020 Pittsburgh, PA, USA
Identifying microstructural features that drive stress hot-spots using a data mining approach
NextManufacturing Center Virtual Membership Meeting & Research Expo -
2019 Los Angeles, CA, USA
Identifying microstructural features that drive stress hotspots using a data mining approach
Engineering Mechanics Institute Conference