Maxim A. Ziatdinov

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 46

Top Topics

Gaussian Process

Electron Microscopy

Active Learning

Top Venues

Mach. Learn. Sci. Technol.

Publications

Ayana Ghosh, Maxim A. Ziatdinov, Sergei V. Kalinin
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings. CoRR (2024)
Maxim A. Ziatdinov
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data. CoRR (2024)
Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov, Sergei V. Kalinin
Multimodal Co-orchestration for Exploring Structure-Property Relationships in Combinatorial Libraries via Multi-Task Bayesian Optimization. CoRR (2024)
Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
Deep kernel methods learn better: from cards to process optimization. Mach. Learn. Sci. Technol. 5 (1) (2024)
Arpan Biswas, Sai Mani Prudhvi Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities. CoRR (2024)
Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning. CoRR (2024)
Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu, Gerd Duscher, Maxim A. Ziatdinov, Sergei V. Kalinin
Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS. CoRR (2024)
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim A. Ziatdinov, Craig Bridges
Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems. CoRR (2023)
Gayathri Saranathan, Martin Foltin, Aalap Tripathy, Maxim A. Ziatdinov, Ann Mary Justine Koomthanam, Suparna Bhattacharya, Ayana Ghosh, Kevin Roccapriore, Sreenivas Rangan Sukumar, Paolo Faraboschi
Towards Rapid Autonomous Electron Microscopy with Active Meta-Learning. SC Workshops (2023)
S. V. Venkatakrishnan, Chris M. Fancher, Maxim A. Ziatdinov, Rama K. Vasudevan, Kyle Saleeby, James Haley, Dunji Yu, Ke An, Alex Plotkowski
. Mach. Learn. Sci. Technol. 4 (2) (2023)
Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore, Ben Blaiszik, Ayana Ghosh, Maxim A. Ziatdinov, Anees Al-Najjar, Christina Doty, Sarah Akers, Nageswara S. V. Rao, Joshua C. Agar, Steven R. Spurgeon
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy. CoRR (2023)
Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin
. Mach. Learn. Sci. Technol. 4 (4) (2023)
Maxim A. Ziatdinov, Chun Yin Wong, Sergei V. Kalinin
. Mach. Learn. Sci. Technol. 4 (4) (2023)
Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim A. Ziatdinov, Rama K. Vasudevan
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy. CoRR (2023)
Yongtao Liu, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin
Explainability and human intervention in autonomous scanning probe microscopy. Patterns 4 (11) (2023)
Arpan Biswas, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
. Mach. Learn. Sci. Technol. 4 (1) (2023)
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim A. Ziatdinov, Craig Bridges
Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems. SMARTCOMP (2023)
Yongtao Liu, Anna N. Morozovska, Eugene A. Eliseev, Kyle P. Kelley, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials. Patterns 4 (3) (2023)
Arpan Biswas, Maxim A. Ziatdinov, Sergei V. Kalinin
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis. CoRR (2023)
Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments. CoRR (2023)
Sheryl Sanchez, Yongtao Liu, Jonghee Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Mahshid Ahmadi
Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra. Adv. Intell. Syst. 5 (5) (2023)
Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
Deep Kernel Methods Learn Better: From Cards to Process Optimization. CoRR (2023)
Ayana Ghosh, Sergei V. Kalinin, Maxim A. Ziatdinov
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space. CoRR (2023)
Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Hiroshi Funakubo, Maxim A. Ziatdinov, Sergei V. Kalinin
Experimental discovery of structure-property relationships in ferroelectric materials via active learning. Nat. Mach. Intell. 4 (4) (2022)
Maxim A. Ziatdinov, Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Sergei V. Kalinin
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning. CoRR (2022)
Maxim A. Ziatdinov, Ayana Ghosh, Chun Yin Wong, Sergei V. Kalinin
AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy. Nat. Mac. Intell. 4 (12) (2022)
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Maxim A. Ziatdinov, Debangshu Mukherjee, Olga Ovchinnikova, Kevin Roccapriore, Andrew R. Lupini, Sergei V. Kalinin
Enabling Autonomous Electron Microscopy for Networked Computation and Steering. e-Science (2022)
Maxim A. Ziatdinov, Yongtao Liu, Sergei V. Kalinin
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning. CoRR (2022)
Nicole Creange, Ondrej Dyck, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
. Mach. Learn. Sci. Technol. 3 (1) (2022)
Arpan Biswas, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach. CoRR (2022)
Addi Malviya-Thakur, Seth Hitefield, Marshall McDonnell, Matthew Wolf, Richard Archibald, Lance Drane, Kevin Roccapriore, Maxim A. Ziatdinov, Jesse McGaha, Robert Smith, John Hetrick, Mark Abraham, Sergey Yakubov, Gregory R. Watson, Ben Chance, Clara Nguyen, Matthew Baker, J. Robert Michael, Elke Arenholz, Ben Mintz
Towards a Software Development Framework for Interconnected Science Ecosystems. SMC (2022)
Maxim A. Ziatdinov, Ayana Ghosh, Sergei V. Kalinin
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process. Mach. Learn. Sci. Technol. 3 (1) (2022)
Sergei V. Kalinin, Rama K. Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim A. Ziatdinov
Microscopy is All You Need. CoRR (2022)
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Maxim A. Ziatdinov, Debangshu Mukherjee, Olga Ovchinnikova, Kevin Roccapriore, Andrew R. Lupini, Sergei V. Kalinin
Enabling Autonomous Electron Microscopy for Networked Computation and Steering. CoRR (2022)
Pravallika Devineni, Panchapakesan Ganesh, Nikhil Sivadas, Abhijeet Dhakane, Ketan Maheshwari, Drahomira Herrmannova, Ramakrishnan Kannan, Seung-Hwan Lim, Thomas E. Potok, Jordan B. Chipka, Priyantha Mudalige, Mark Coletti, Sajal Dash, Arnab Kumar Paul, Sarp Oral, Feiyi Wang, Bill Kay, Melissa R. Allen-Dumas, Christa Brelsford, Joshua R. New, Andy Berres, Kuldeep R. Kurte, Jibonananda Sanyal, Levi Sweet, Chathika Gunaratne, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin, Olivera Kotevska, Jean C. Bilheux, Hassina Z. Bilheux, Garrett E. Granroth, Thomas Proffen, Rick Riedel, Peter F. Peterson, Shruti R. Kulkarni, Kyle P. Kelley, Stephen Jesse, Maryam Parsa
Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. SMC (2021)
Maxim A. Ziatdinov, Chun Yin Wong, Sergei V. Kalinin
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders. CoRR (2021)
Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck, Sergei V. Kalinin, Maxim A. Ziatdinov
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy. CoRR (2021)
Yongtao Liu, Rama K. Vasudevan, Kyle P. Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim A. Ziatdinov
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy. CoRR (2021)
Yongtao Liu, Rama K. Vasudevan, Kyle K. Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim A. Ziatdinov
. Mach. Learn. Sci. Technol. 2 (4) (2021)
Maxim A. Ziatdinov, Sergei V. Kalinin
Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms. CoRR (2021)
Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob D. Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan
Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy. CoRR (2021)
Maxim A. Ziatdinov, Ayana Ghosh, Tommy Wong, Sergei V. Kalinin
AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond. CoRR (2021)
Maxim A. Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, Sergei V. Kalinin
Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries. CoRR (2021)
M. P. Oxley, Junqi Yin, N. Borodinov, Suhas Somnath, Maxim A. Ziatdinov, Andrew R. Lupini, Stephen Jesse, Rama K. Vasudevan, Sergei V. Kalinin
Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening. Mach. Learn. Sci. Technol. 1 (4) (2020)
Rama K. Vasudevan, Maxim A. Ziatdinov, Lukas Vlcek, Sergei V. Kalinin
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality. CoRR (2020)
Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Don D. March, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Thomas P. Karnowski, Maxim A. Ziatdinov, Sergei V. Kalinin
167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation. SC (2018)