About Me

I am an Assistant Professor of Computer Science at Texas Christian University (TCU), specializing in explainable artificial intelligence (XAI), deep learning, and rare event prediction. I earned my Ph.D. in Computer Science from Georgia State University, where my research focused on the development of interpretable deep learning methods for data-intensive applications, including AI systems for high-stakes applications such as space weather forecasting. My research centers on the integration of explainable AI, continual learning, and space weather forecasting. My doctoral work introduced novel full-disk and active region-based solar flare prediction models, significantly enhancing forecasting capabilities, particularly in near-limb regions that are traditionally difficult to model due to projection effects. I also developed ensemble models that integrate time series and image-based inputs to improve prediction accuracy and granularity. In addition to improving predictive performance, my research introduced techniques for analyzing explanation consistency, investigating multi-granular explanations as proxies for localized predictions, and conducting quantitative evaluations of explanations through physically meaningful features, revealing systematic patterns that offer insight into model behavior.

Building on this prior work, I served as a Scientific Researcher with the NASA-partnered Frontier Development Lab (FDL-X 2024), where I was responsible for the design and implementation of a continual learning framework for forecasting geomagnetic perturbations. This effort contributed to the development of two machine learning pipelines, DAGGER++ and SHEATH, designed to meet operational forecasting requirements, where I led the continual learning component of DAGGER++ and supported the redevelopment of the SHEATH model to enable integration of real-time solar and geomagnetic data for dynamic forecasting.

At TCU, I will lead research efforts in interpretable and adaptive machine learning, with a focus on continual learning methods that allow AI systems to evolve alongside changing data environments. I look forward to mentoring students and building collaborative initiatives in trustworthy AI, with applications spanning space weather forecasting and other domains where explainability, adaptability, and reliability are essential.

Research Interests

  • Explainable Deep Learning
  • Pattern Recognition
  • Image Processing & Analysis
  • Continual Learning
  • Space Weather Forecasting

Education

Jan 2021 – Aug 2025
Ph.D., Computer Science — Georgia State University, Atlanta, GA, USA
Advisor: Dr. Berkay Aydin • Thesis: “Explainable Deep Learning For Prediction Of Rare Events”
Jan 2021 – Aug 2024
M.S., Computer Science — Georgia State University, Atlanta, GA, USA
Advisor: Dr. Berkay Aydin
Nov 2013 – Aug 2017
B.E., Computer Engineering — Tribhuvan University, IOE, ERC, Dharan, Nepal

Work & Teaching Experience

Jan 2021 – Jul 2025
Research Assistant, Data Mining Lab — Georgia State University
Interpretable/explainable deep learning for prediction of rare events (solar flares); ML methods for solar physics and space weather.
Jun 2024 – Aug 2024
Scientific Researcher — Frontier Development Lab (Trillium Technologies) in partnership with NASA, Google Cloud, NVIDIA
Led ML and continual learning for data-driven prediction of geomagnetic perturbations at ground stations.
May 2019 – Dec 2020
Research Project Coordinator — Research & Innovation Unit, Himalaya College of Engineering (HCOE)
Supervised research projects of undergraduate students in computer and electronics engineering.
Jan 2024 – Apr 2024
Instructor/Teaching Fellow — Georgia State University
CSC 4780/6780 & DSCI 4780, Fundamentals of Data Science (UG & Grad).
Apr 2020 – Dec 2020
Lecturer — Tribhuvan University, Himalaya College of Engineering
Spring 2020: Artificial Intelligence (BCT Juniors); Discrete Mathematics (BEX Sophomores).
Apr 2018 – Mar 2020
Assistant Lecturer — Tribhuvan University, Himalaya College of Engineering
Fall 2019: Artificial Intelligence (B.Sc. CSIT Juniors); Computer Programming (BCE Freshmen). Spring 2019: Artificial Intelligence (BCT Juniors); Discrete Mathematics (BEX Sophomores). Fall 2018: Computer Programming (BCE Freshmen). Spring 2018: Discrete Mathematics (BEX Sophomores); Numerical Methods (BCT Sophomores).
Sep 2018 – Dec 2020
Instructor / Co‑founder — Line Academy, Kupondole, Lalitpur, Nepal
Computer Programming in C and Fortran.
Dec 2017 – Apr 2018
Part‑time Instructor — Tribhuvan University, KEC, Kalimati, Lalitpur, Nepal
Fall 2017: Computer Programming (BCT Freshmen).

Peer‑reviewed Journal Articles

  1. T. Adeyeha, C. Pandey, and B. Aydin, “Tamag: A python library for transformation and augmentation of solar magnetograms,” SoftwareX, vol. 29, p. 102032, Feb. 2025. doi:10.1016/j.softx.2024.102032
  2. K. Whitman, R. Egeland, I. G. Richardson, …, C. Pandey, et al., “Review of solar energetic particle models,” Advances in Space Research, Aug. 2023. doi:10.1016/j.asr.2022.08.006
  3. C. Pandey, A. Ji, R. A. Angryk, M. K. Georgoulis, and B. Aydin, “Towards coupling full‑disk and active region‑based flare prediction for operational space weather forecasting,” Frontiers in Astronomy and Space Sciences, vol. 9, Aug. 2022. doi:10.3389/fspas.2022.897301

Conference Proceedings

  1. T. Adeyeha, C. Pandey, and B. Aydin, “Large scale evaluation of deep learning‑based explainable solar flare forecasting models with attribution‑based proximity analysis,” in 2024 IEEE International Conference on Big Data (BigData), 2024, pp. 1209–1214. doi:10.1109/BigData62323.2024.10825177
  2. C. Pandey, T. Adeyeha, J. Hong, R. A. Angryk, and B. Aydin, “Advancing solar flare prediction using deep learning with active region patches,” in Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, Springer Nature Switzerland, 2024, pp. 50–65. doi:10.1007/978-3-031-70381-2_4
  3. A. Ji, C. Pandey, and B. Aydin, “Towards hybrid embedded feature selection and classification approach with slim‑tsf,” in Big Data Analytics and Knowledge Discovery, Springer Nature Switzerland, 2024, pp. 91–105. doi:10.1007/978-3-031-68323-7_7
  4. C. Pandey, A. Ji, J. Hong, R. A. Angryk, and B. Aydin, “Embedding ordinality to binary loss function for improving solar flare forecasting,” in 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA), 2024. doi:10.1109/DSAA61799.2024.10722839
  5. C. Pandey, R. A. Angryk, and B. Aydin, “Unveiling the potential of deep learning models for solar flare prediction in near‑limb regions,” in 2023 International Conference on Machine Learning and Applications (ICMLA), IEEE, Dec. 2023. doi:10.1109/icmla58977.2023.00103
  6. J. Hong, C. Pandey, A. Ji, and B. Aydin, “An innovative solar flare metadata collection for space weather analytics,” in 2023 International Conference on Machine Learning and Applications (ICMLA), Dec. 2023, pp. 408–413. doi:10.1109/ICMLA58977.2023.00063
  7. J. Hong, A. Ji, C. Pandey, and B. Aydin, “Enhancing solar flare prediction with innovative data‑driven labels,” in 2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI), IEEE, Nov. 2023. doi:10.1109/cogmi58952.2023.00035
  8. C. Pandey, R. A. Angryk, M. K. Georgoulis, and B. Aydin, “Explainable deep learning‑based solar flare prediction with post hoc attention for operational forecasting,” in Discovery Science, Springer Nature Switzerland, Oct. 2023, pp. 567–581. doi:10.1007/978-3-031-45275-8_38
  9. C. Pandey, A. Ji, T. Nandakumar, R. A. Angryk, and B. Aydin, “Exploring deep learning for full‑disk solar flare prediction with empirical insights from guided grad‑cam explanations,” in 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, Oct. 2023. doi:10.1109/dsaa60987.2023.10302639
  10. C. Pandey, R. A. Angryk, and B. Aydin, “Explaining full‑disk deep learning model for solar flare prediction using attribution methods,” in European Conference on Machine Learning and Knowledge Discovery in Databases: ADS Track (ECML PKDD), Springer Nature Switzerland, Sep. 2023, pp. 72–89. doi:10.1007/978-3-031-43430-3_5
  11. C. Pandey, A. Ji, R. A. Angryk, and B. Aydin, “Towards interpretable solar flare prediction with attention‑based deep neural networks,” in 2023 IEEE Sixth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), IEEE, Sep. 2023. doi:10.1109/aike59827.2023.00021
  12. J. Hong, A. Ji, C. Pandey, and B. Aydin, “Beyond traditional flare forecasting: A data‑driven labeling approach for high‑fidelity predictions,” in Big Data Analytics and Knowledge Discovery (DaWaK), Springer Nature Switzerland, Aug. 2023, pp. 380–385. doi:10.1007/978-3-031-39831-5_34
  13. C. Pandey, R. Angryk, and B. Aydin, “Deep neural networks based solar flare prediction using compressed full‑disk line‑of‑sight magnetograms,” in Information Management and Big Data, Springer International Publishing, 2022, pp. 380–396. doi:10.1007/978-3-031-04447-2_26
  14. C. Pandey, R. A. Angryk, and B. Aydin, “Solar flare forecasting with deep neural networks using compressed full‑disk HMI magnetograms,” in 2021 IEEE International Conference on Big Data (BigData), IEEE, Dec. 2021, pp. 1725–1730. doi:10.1109/bigdata52589.2021.9671322

Posters

  1. C. Pandey, R. A. Angryk, and B. Aydin, Towards reliable deep learning models for solar flare prediction, AGU, Authorea Inc., 2024. doi:10.22541/essoar.173457205.58483493/v1
  2. B. K. Jha, C. Pandey, O. Issan, et al., Geo‑cloak: Operational machine learning tool for global geomagnetic field perturbation forecasting, AGU, 2024. poster link
  3. J. Hong, C. Pandey, and B. Aydin, Enhancing solar flare prediction with integrated multi‑wavelength imagery and conformal prediction, AGU24, 2024. abstract
  4. C. Pandey, T. Adeyeha, T. Nandakumar, A. Rafal, and B. Aydin, Insights into deep learning‑based full‑disk solar flare prediction with post hoc explanation and evaluation, EarthCube 2023. doi:10.13140/RG.2.2.34673.97124
  5. C. Pandey, M. K. Georgoulis, B. Aydin, R. A. Angryk, and A. Ji, Exploring heuristics in full‑disk aggregation from individual active region prediction of solar flares, Jul. 2022, p. 3457. doi:10.13140/RG.2.2.34673.97124
  6. C. Pandey, A. Ji, R. Angryk, and B. Aydin, Training and Deployment of Predictive Models for Space Weather Forecasting: An Application on Full‑disk and Active Region‑based Flare Prediction, AGU Fall Meeting Abstracts, SH55A–1825, Dec. 2021. poster link

Technical Skills

  • Programming Language: Python, C, C++, matlab
  • Databases: Mysql, Postgresql
  • Web Development: Html, css, JavaScript, Django
  • Libraries & Framework: Numpy, Pandas, Matplotlib, Scikit‑Learn, Pytorch, Tensorflow, Keras
  • Tools: Git, LaTeX, Docker, Notion, Miro
  • Computing Environment: Google Cloud Platform (GCP), High Performance Computing Environment (HPCE)

Awards

  • Jun 03–07, 2024 — NSF Travel Grant, 11th Community Coordinated Modeling Center (CCMC), NASA, Community Workshop 2024.
  • Jun 27–28, 2023 — Early‑career Travel Award, EarthCube 2023.
  • Jan–Dec, 2022 — Google Cloud Student Research Credit ($1,000).
  • May 2021 – Aug 2022 — Second Century Initiative (2CI) University Doctoral Fellowship, Georgia State University.
  • Jul 2016 – Jun 2017 — 4th Committee President, Association of Computer Engineering Students (ACES), Purwanchal Campus, Dharan, Nepal.
  • Nov 2013 – Aug 2017 — Full Governmental Scholarship on Merit, B.E. in Computer Engineering, Tribhuvan University, Institute of Engineering, Dharan, Nepal.

Service to Profession

  • 2025: Reviewer — 24th International Conference on Machine Learning and Applications (ICMLA); Solar Physics (Springer Nature); PeerJ Computer Science; Journal of Geophysical Research (JGR) – Machine Learning and Computation; Earth and Space Science (AGU); Journal of Circuits, Systems, and Computers (JCSC).
  • 2024: Reviewer — Astronomy and Computing Journal; Program Committee Member — 27th International Conference on Discovery Science (DS); Reviewer — DS 2024; Reviewer — 23rd International Conference on Machine Learning and Applications (ICMLA); External Reviewer — 27th International Conference on Pattern Recognition (ICPR).
  • 2023: Reviewer — 22nd International Conference on Machine Learning and Applications (ICMLA); Session Chair — Session 21B, 22nd ICMLA.