I am an Assistant Professor of Artificial Intelligence at the University of Nevada, Reno, where I lead MAGI Lab.
I develop reliable and interpretable AI methods for visual, multimodal, scientific, and socially meaningful data, with applications in medical AI, geometric intelligence, and AI for Good.
Methods for AI and Geometric Intelligence
(ma·jai)
MAGI Lab studies how structure, geometry, and multimodal data can make AI systems more useful for science, medicine, and society.
Interpretable, clinically informed learning for pathology and human health.
Learning across images, language, and time series to connect complementary information.
Geometry and topology for structure-aware representations, with applications in science and AI for Good.
Connecting scientific data, domain knowledge, and AI modeling to support discovery.
Interested in building thoughtful AI methods for meaningful problems? We would love to hear from you.
I am looking for highly self-motivated Ph.D., graduate, and undergraduate students with a strong commitment to research.
Email Ankita →Computer Science and Engineering
William N. Pennington Engineering Building
Reno, NV 89557
Research milestones, new collaborations, and stories from MAGI.
Nevada Today · September 17, 2026
Our DOE Genesis Mission project, Data to Decision, explores evidence-traceable AI reasoning to support safety assessments for long-term nuclear waste repositories.
Nevada Today · April 1, 2026
AI models for RoboHydra sensor data support research to help sheep producers.
Nevada Today · March 6, 2026
UNR research on reliable AI for breast cancer, medical text, and sleep disorders.
Nevada Today · January 24, 2025
Featuring Ankita Shukla as a plenary speaker on AI for science and AI for Good, with applications in human health and wildlife conservation.
At MAGI Lab, we develop AI methods that learn from structure, connect information across modalities, and support reasoning that can be examined and evaluated. Our work brings together geometric learning, trustworthy AI, and scientific applications, with a broader commitment to AI for Good.
How can AI systems reason over complex information while remaining grounded in evidence?
We study language models, retrieval-augmented generation, and agentic workflows that help people work with complex scientific and technical information. We are interested in connecting model outputs to supporting sources, evaluating reasoning, and making limitations visible to the experts who use these systems.
How can AI connect scientific data and domain knowledge to support discovery and decision-making?
Scientific problems require more than predictions: they require reasoning across measurements, models, literature, and domain constraints. We develop methods to support these workflows in collaboration with domain experts. Our DOE Genesis Mission project, Data to Decision, explores evidence-traceable AI reasoning for nuclear waste repository safety assessments; related work investigates AI-piloted critical mineral recovery from spent batteries.
How can complementary sources of information help AI better represent clinically meaningful patterns?
We develop learning methods for medical images, clinical text, and time series, with an emphasis on interpretable representations and clinically informed modeling. In computational pathology, our work explores how multimodal experts and concept-guided learning can support pediatric brain tumor classification and make model behavior easier to inspect.
How can the structure of data guide what a model learns?
Geometry and topology provide tools for representing shape, relationships, and variation in data. We use these ideas to develop structure-aware representations, implicit neural models, and knowledge-distillation methods for visual and time series data. Our goal is to build learning methods whose representations reflect meaningful properties of the underlying problem.
People
Principal Investigator
I am an Assistant Professor in the Computer Science and Engineering Department at University of Nevada Reno, USA. Before joining UNR, I was a Postdoctoral researcher in the Geometric Media Lab, working with Prof. Pavan Turaga at Arizona State University.
My research interests lie in deep learning and machine learning approaches for visual and time series data, topological data analysis, differential geometry, and geometry driven approaches for learning. From the applications point of view, I focus on AI for Science and AI for Good, specifically towards wildlife conservation and human health applications.
Current MAGI Lab members.
Ph.D. Student
Ph.D. Student
Ph.D. Student · Co-Advised
Co-advised with Prof. Ji H. Yoon.
Current MAGI Lab members.
Master's Student
Master's Student
Master's Student
Master's Student
Future MAGI Lab members.
Undergraduate researchers, honors students, and project contributors will be listed here.
Former lab members.
Former students, visitors, and collaborators will be listed here as the lab grows.
2026
Xuan Wang, Zhongling Xu, Gopi Kannedhara, Joakim Nguyen, Jian Yu, Jinrui Fang, Abdurrahmaan Baghdadi, Tianlong Chen, Awais Naeem, Chandra Krishnan, Edward Castillo, Andrew H. Song, Ankita Shukla, Ying Ding, Nicholas Konz, Hairong Wang.
arXiv:2605.24399
2026
Joakim Nguyen, Jian Yu, Jinrui Fang, Nicholas Konz, Tianlong Chen, Sanjay Krishnan, Chandra Krishnan, Ying Ding, Hairong Wang, Ankita Shukla.
ICHI 2026; arXiv:2604.21060
2026
Jian Yu, Joakim Nguyen, Jinrui Fang, Awais Naeem, Zeyuan Cao, Sanjay Krishnan, Nicholas Konz, Tianlong Chen, Chandra Krishnan, Hairong Wang, Edward Castillo, Ying Ding, Ankita Shukla.
arXiv:2603.01547
2026
Mayukh Debnath, Ankita Shukla.
WACV 2026 Workshops, LENS: Learning and Exploitation of Latent Space Geometries, pp. 1326-1333
2025
Gunner Stone, Youngsook Choi, Alireza Tavakkoli, Ankita Shukla.
International Conference on Machine Learning and Applications (ICMLA), 2025, pp. 778-783
2025
Utkarsh Nath, Rajhans Singh, Ankita Shukla, Kuldeep Kulkarni, Pavan K. Turaga.
International Journal of Computer Vision, 133(5): 2967-2995
2025
Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan K. Turaga.
Neurocomputing, 645: 130408
2025
Eun Som Jeon, Sinjini Mitra, Jisoo Lee, Omik M. Save, Ankita Shukla, Hyunglae Lee, Pavan K. Turaga.
IEEE Internet of Things Journal, 12(16): 34406-34420
2025
Ankita Shukla, Sandeep Kumar, Amrit Singh Bedi, Tanmoy Chakraborty, Pooja Singh, Sandeep Chatterjee, Gullal S. Cheema.
1st Workshop on Multimodal Models for Low-Resource Contexts and Social Impact; Shared Task on Machine Translation into Tribal Languages
2024
Eun Som Jeon, Hongjun Choi, Ankita Shukla, Yuan Wang, Hyunglae Lee, Matthew P. Buman, Pavan K. Turaga.
Engineering Applications of Artificial Intelligence, 130: 107719
2024
Eun Som Jeon, Hongjun Choi, Ankita Shukla, Yuan Wang, Matthew P. Buman, Hyunglae Lee, Pavan K. Turaga.
EPJ Data Science, 13(1): 77
2024
Ankita Shukla, Rishi Dadhich, Rajhans Singh, Anirudh Rayas, Pouria Saidi, Gautam Dasarathy, Visar Berisha, Pavan K. Turaga.
Frontiers in Computer Science, 6
2024
Baaz Jhaj, Ankita Shukla, Pavan K. Turaga, Michael N. Kozicki.
AI-SIPM 2024
2023
Eun Som Jeon, Hongjun Choi, Ankita Shukla, Pavan K. Turaga.
Neurocomputing, 518: 466-481
2023
Eun Som Jeon, Hongjun Choi, Ankita Shukla, Yuan Wang, Matthew P. Buman, Pavan K. Turaga.
IEEE Transactions on Instrumentation and Measurement, 72: 1-14
2023
Tripti Shukla, Paridhi Maheshwari, Rajhans Singh, Ankita Shukla, Kuldeep Kulkarni, Pavan K. Turaga.
CVPR Workshops 2023
2023
Rajhans Singh, Ankita Shukla, Pavan Turaga.
CVPR 2023
2023
Rajhans Singh, Ankita Shukla, Pavan Turaga.
DLGC, CVPR 2023
2023
Hongjun Choi, Eunsom Jeon, Ankita Shukla, Pavan Turaga.
WACV 2023
2022
Nicholas Ho, John Kevin Cava, John Vant, Ankita Shukla, Jake Miratsky, Pavan Turaga, Ross Maciejewski, Abhishek Singharoy.
MLSB, NeurIPS 2022
2022
D. Lagrois, T. R. Bonnell, A. Shukla, C. Chion.
Journal of Marine Science and Engineering
2022
E. Jeon, A. Som, A. Shukla, K. Hasanaj, M. P. Buman, P. Turaga.
IEEE Internet of Things Journal
2022
V. M. Arujo, Ankita Shukla, C. Chion, S. Gambs, R. Michaud.
Sensors and Artificial Intelligence for Wildlife Conservation
2022
R. Singh, A. Shukla, Pavan Turaga.
2021
Ankita Shukla, R. Anirudh, E. Kur, J. J. Thiagarajan, P. Bremer, B. K. Spears, T. Ma, Pavan Turaga.
Machine Learning for Physical Sciences, NeurIPS 2021
2021
J. K. Cava, J. Vant, N. Ho, A. Shukla, Pavan Turaga, R. Maciejewski, A. Singharoy.
ELLIS Machine Learning for Molecule Discovery Workshop
2021
Ankita Shukla, Pavan Turaga, Saket Anand.
3rd Workshop on Adversarial Learning Methods for Machine Learning and Data Mining, KDD 2021
2020
A. Shukla, Pavan Turaga, Saket Anand.
2020
2019
Ankita Shukla, C. Anderson, G. S. Cheema, P. Guo, S. Onda, D. Anshumaan, S. Anand, R. Farrell.
CVWC, ICCV 2019
2019
Ankita Shukla, Shagun Uppal, Sarthak Bhagat, Saket Anand, Pavan Turaga.
BMVC 2019
2019
Ankita Shukla*, Gullal Singh Cheema*, Saket Anand, Qamar Qureshi, Yadvendradev Jhala.
PRICAI 2019 (*Equal Contribution)
2018
Ankita Shukla, Shagun Uppal, Sarthak Bhagat, Saket Anand, Pavan Turaga.
ICVGIP 2018
2018
Anirudh Som, Kowshik Thopalli, K. N. Ramamurthy, V. Venkataraman, Ankita Shukla, Pavan Turaga.
ECCV 2018
2018
Ankita Shukla*, Gullal Singh Cheema*, Saket Anand, Qamar Qureshi, Yadvendradev Jhala.
arXiv:1811.00743 (*Equal Contribution)
2017
Dhananjay Kimothi, Ankita Shukla, Pravesh Biyani, Saket Anand, James M. Hogan.
SPAWC 2017
2017
Wazir Singh, Ankita Shukla, Sujay Deb, Angshul Majumdar.
Integration, the VLSI Journal
2016
Ankita Shukla, Saket Anand.
ICIP 2016
2015
Ankita Shukla, Saket Anand.
Differential Geometry in Computer Vision, BMVC 2015 (Best Student Paper)
2015
Angshul Majumdar, Ankita Shukla, Rabab Ward.
ICASSP 2015
2015
Ankita Shukla, Angshul Majumdar.
Biomedical Signal Processing and Control
2015
Ankita Shukla, Angshul Majumdar, Rabab Ward.
ICAPR 2015
2015
Ankita Shukla, Angshul Majumdar.
Biomedical Signal Processing and Control
2014
Anupriya Gogna, Ankita Shukla, Hemant Kumar Aggarwal, Angshul Majumdar.
ICIP 2014
2014
Wazir Singh, Ankita Shukla, Sujay Deb, Angshul Majumdar.
EMBC 2014
2014
2013
Ankita Shukla, Angshul Majumdar, Rabab Ward.
IASTED Signal and Image Processing 2013
Teaching
Courses taught in artificial intelligence, large language models, and multimodal AI at the University of Nevada, Reno.
ENG 481/681
Fall 2026
CS 482/682
Fall 2024, Fall 2025
CS 791
Fall 2024
CS 491/691
Spring 2025
Student Corner
A space for the small and big moments in research life: fellowships, first posters, accepted papers, conference travel, internships, and talks that students are proud of.
Bring the update; we will make space for it here.
Student news will appear here as new milestones come in.
Fellowship
We will highlight student fellowships, travel awards, and other competitive research support here.
Poster or Talk
Conference posters, workshop talks, demos, and lab presentations will be collected here.
Paper
We will feature student-led preprints, accepted papers, and collaborative publications here.
