Utkarsh Pratiush

According to our database, Utkarsh Pratiush authored at least 23 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments.

Boris N. Slautin, Utkarsh Pratiush, Yu Liu, Kamyar Barakati, Sergei V. Kalinin

CoRR, May, 2026

PATHFINDER: Multi-objective discovery in structural and spectral spaces.

Kamyar Barakati, Boris N. Slautin, Utkarsh Pratiush, Hiroshi Funakubo, Sergei V. Kalinin

CoRR, April, 2026

AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design.

Utkarsh Pratiush, Huaixun Huyan, Maryam Zahiri Azar, Esmeralda Yitamben, Allen Bourez, Sergei V. Kalinin, Vasfi Burak Ozdol

CoRR, April, 2026

Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy.

Utkarsh Pratiush, Kamyar Barakati, Boris N. Slautin, Catherine C. Bodinger, Christopher D. Lowe, Brandi M. Cossairt, Sergei V. Kalinin

CoRR, March, 2026

Towards Self-Optimizing Electron Microscope: Robust Tuning of Aberration Coefficients via Physics-Aware Multi-Objective Bayesian Optimization.

Utkarsh Pratiush, Austin Houston, Richard Liu, Gerd Duscher, Sergei V. Kalinin

CoRR, January, 2026

2025

DIVIDE: A Framework for Learning from Independent Multi-Mechanism Data Using Deep Encoders and Gaussian Processes.

Vivek Chawla, Boris N. Slautin, Utkarsh Pratiush, Dayakar Penumadu, Sergei V. Kalinin

CoRR, November, 2025

SAM^*: Task-Adaptive SAM with Physics-Guided Rewards.

Kamyar Barakati, Utkarsh Pratiush, Sheryl Sanchez, Aditya Raghavan, Delia J. Milliron, Mahshid Ahmadi, Philip D. Rack, Sergei V. Kalinin

CoRR, September, 2025

Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy.

Yu Liu, Utkarsh Pratiush, Kamyar Barakati, Hiroshi Funakubo, Ching-Che Lin, Jaegyu Kim, Lane W. Martin, Sergei V. Kalinin

CoRR, June, 2025

Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model.

Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, March, 2025

Rewards-based image analysis in microscopy.

Kamyar Barakati, Yu Liu, Utkarsh Pratiush, Boris N. Slautin, Sergei V. Kalinin

CoRR, February, 2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy.

Utkarsh Pratiush, Austin Houston, Kamyar Barakati, Aditya Raghavan, Ralph Bulanadi, Xiangyu Yin, Samuel S. Welborn, Dasol Yoon, Harikrishnan KP, Zhaslan Baraissov, Desheng Ma, Mikolaj Jakowski, Shawn-Patrick Barhorst, Alexander J. Pattison, Panayotis Manganaris, Sita Sirisha Madugula, Sai Venkata Gayathri Ayyagari, Vishal Kennedy, Michelle Wang, Kieran J. Pang, Ian Addison-Smith, Willy Menacho, Horacio V. Guzman, Alexander Kiefer, Nicholas Furth, Nikola L. Kolev, Mikhail Petrov, Viktoriia Liu, Sergey Ilyev, Srikar Rairao, Tommaso Rodani, Ivan Pinto-Huguet, Xuli Chen, Josep Cruañes, Marta Torrens, Jovan Pomar, Fanzhi Su, Pawan Vedanti, Zhiheng Lyu, Xingzhi Wang, Lehan Yao, Amir Taqieddin, Forrest Laskowski, Yu-Tsun Shao, Benjamin Fein-Ashley, Yi Jiang, Vineet Kumar, Himanshu Mishra, Yogesh Paul, Adib Bazgir, Rama Chandra Praneeth Madugula, Yuwen Zhang, Pravan Omprakash, Jian Huang, Eric Montufar-Morales, Vivek Chawla, Harshit Sethi, Jie Huang, Lauri Kurki, Grace Guinan, Addison Salvador, Arman H. Ter-Petrosyan, Madeline Van Winkle, Steven R. Spurgeon, Ganesh Narasimha, Zijie Wu, Richard Liu, Yongtao Liu, Boris N. Slautin, Andrew R. Lupini, Rama K. Vasudevan, Gerd Duscher, Sergei V. Kalinin

Mach. Learn. Sci. Technol., 2025

2024

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry.

Yoel Zimmermann, Adib Bazgir, Zartashia Afzal, Fariha Agbere, Qianxiang Ai, Nawaf Alampara, Alexander Al-Feghali, Mehrad Ansari, Dmytro Antypov, Amro Aswad, Jiaru Bai, Viktoriia Baibakova, Devi Dutta Biswajeet, Erik Bitzek, Joshua D. Bocarsly, Anna S. Borisova, Andres M. Bran, L. Catherine Brinson, Marcel Moran Calderon, Alessandro Canalicchio, Victor Chen, Yuan Chiang, Defne Circi, Benjamin Charmes, Vikrant Chaudhary, Zizhang Chen, Min-Hsueh Chiu, Judith Clymo, Kedar Dabhadkar, Nathan Daelman, Archit Datar, Matthew L. Evans, Maryam Ghazizade Fard, Giuseppe Fisicaro, Abhijeet Sadashiv Gangan, Janine George, Jose D. Cojal Gonzalez, Michael Götte, Ankur K. Gupta, Hassan Harb, Pengyu Hong, Abdelrahman Ibrahim, Ahmed Ilyas, Alishba Imran, Kevin Ishimwe, Ramsey Issa, Kevin Maik Jablonka, Colin Jones, Tyler R. Josephson, Greg Juhasz, Sarthak Kapoor, Rongda Kang, Ghazal Khalighinejad, Sartaaj Khan, Sascha Klawohn, Suneel Kuman, Alvin Noe Ladines, Sarom Leang, Magdalena Lederbauer, Sheng-Lun Mark Liao, Hao Liu, Xuefeng Liu, Stanley Lo, Sandeep Madireddy, Piyush Ranjan Maharana, Shagun Maheshwari, Soroush Mahjoubi, José A. Márquez, Rob Mills, Trupti Mohanty, Bernadette Mohr, Seyed Mohamad Moosavi, Alexander Moßhammer, Amirhossein D. Naghdi, Aakash Naik, Oleksandr Narykov, Hampus Näsström, Xuan Vu Nguyen, Xinyi Ni, Dana O'Connor, Teslim Olayiwola, Federico Ottomano, Aleyna Beste Ozhan, Sebastian Pagel, Chiku Parida, Jaehee Park, Vraj Patel, Elena Patyukova, Martin Hoffmann Petersen, Luis Pinto, José M. Pizarro, Dieter Plessers, Tapashree Pradhan, Utkarsh Pratiush, Charishma Puli, Andrew Qin, Mahyar Rajabi, Francesco Ricci, Elliot Risch, Martiño Ríos-García

CoRR, 2024

Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments.

Kamyar Barakati, Utkarsh Pratiush, Austin Houston, Gerd Duscher, Sergei V. Kalinin

CoRR, 2024

Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy.

Yu Liu, RogerProksch, Jason Bemis, Utkarsh Pratiush, Astita Dubey, Mahshid Ahmadi, Reece Emery, Philip D. Rack, Yu-Chen Liu, Jan-Chi Yang, Sergei V. Kalinin

CoRR, 2024

Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization.

Aditya Raghavan, Utkarsh Pratiush, Mani Valleti, Richard Liu, Reece Emery, Hiroshi Funakubo, Yongtao Liu, Philip D. Rack, Sergei V. Kalinin

CoRR, 2024

Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope.

Utkarsh Pratiush, Austin Houston, Sergei V. Kalinin, Gerd Duscher

CoRR, 2024

Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation.

Yu Liu, Utkarsh Pratiush, Jason Bemis, Roger Proksch, Reece Emery, Philip D. Rack, Yu-Chen Liu, Jan-Chi Yang, Stanislav Udovenko, Susan Trolier-McKinstry, Sergei V. Kalinin

CoRR, 2024

Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS.

Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu, Gerd Duscher, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2024

Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries.

Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov, Sergei V. Kalinin

CoRR, 2024

Policy Gradient for Reinforcement Learning with General Utilities.

Navdeep Kumar, Kaixin Wang, Utkarsh Pratiush, Kfir Yehuda Levy, Shie Mannor

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

2023

Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy.

Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim A. Ziatdinov, Rama K. Vasudevan

CoRR, 2023

EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations.

Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M. Smedskjaer, Sayan Ranu, N. M. Anoop Krishnan

CoRR, 2023

Discovering mesoscopic descriptions of collective movement with neural stochastic modelling.

Utkarsh Pratiush, Arshed Nabeel, Vishwesha Guttal, Prathosh AP

CoRR, 2023