iHARP is honored to have participated as both a sponsor and presenter at the FAIR in ML, AI Readiness & Reproducibility (FARR) Workshop, held in Washington, D.C., on April 8–9. We want to thank the leadership behind FARR for organizing an amazing and impactful workshop: Christine Kirkpatrick, Julie Christopher, Kevin Coakley, Daniel S. Katz, Douglas Rao, Lynne Schreiber, and Karen Stocks. The workshop was a resounding success, highlighting the critical importance of implementing and promoting data practices that ensure reproducible research across the AI, Data, and ML domains.

Key Highlights from the Workshop:

• Poster Flash Talks: Mostafa Cham, Achala Denagamage, Emam Hossain, Ellie Davidson and Rhoda Nankabirwa presented flash talks on a diverse range of topics, including implementing Open Science workflows, AI-ready and reproducible data, and evaluating the reproducibility of benchmark algorithms.

• The 2nd FAIR HDR ML Challenge: This session, co-led by three HDR centers—iHARP, A3D3, and Imageomics—offered attendees a deep dive into the complexities of managing a multi-faceted ML competition guided by FAIR principles. This year’s theme, Scientific Modeling out of Distribution (Scientific-Mood), featured distinct datasets curated by each institution to reflect their specific research areas.During the session, awards were presented to the winners of each individual sub-challenge as well as the overall grand prize winner.  

Winner: iHARP would like to extend a huge congratulations to Dony Darmawan Putra for taking first place sub-challenge on its challenge: Predicting Coastal Flooding Events. 

We would like to acknowledge and thank the leadership team who designed and ran iHARP’s challenge: Dr. Josephine Namayanja, Dr. Ratnaksha Lele, Dr. Aneesh Subramanian, Dr. Bayu Adhi Tama, and Dr. Vandana Janeja. 

Our gratitude also goes to our dedicated support team who worked tirelessly behind the scenes: Sai Vikas Amaraneni, Emam Hossain, Dr. Maloy Kumar Devnath, and Subhankar Ghosh.

Being part of a mission focused on Findable, Accessible, Interoperable, and Reusable (FAIR) practices is vital to our work. These standards ensure that iHARP’s research breakthroughs remain accessible and maintain a lasting impact on the scientific community.

Akila successfully defended on Monday, March 9, 2026

Throughout her time with us, Akila's dedicated contributions have made her an invaluable member of both the iHARP and UMBC communities. This significant achievement is a true testament to her scholarly rigor, hard work, and immense talent.

Congratulations, Akila, on this incredible milestone! We have been honored to witness your growth and look forward to your future successes. The entire iHARP community wishes you the very best as you embark on your next great adventure!

• Dr. Jianwu Wang, Chair/Advisor (UMBC)
• Dr. Vandana Janeja, Co-Chair (UMBC)
• Dr. Houbing Song (UMBC)
• Dr. James Foulds  (UMBC)
• Dr. Donald K Perovich (Dartmouth College)
• Dr. Nicole Schlegel (NOAA)

Rapid and accelerating Arctic climate change poses significant challenges for artificial intelligence (AI) systems, primarily due to severe data scarcity, inherent nonlinearities, and complex spatiotemporal interactions within the ocean–ice–atmosphere system. Conventional deep learning approaches rely heavily on large data volumes and often lack physical consistency. Consequently, purely data-driven models may produce physically implausible predictions and offer limited interpretability, thereby reducing their utility for scientific discovery and decision-making. To enable reliable and trustworthy sea ice prediction, physics-embedded learning architectures are required to leverage domain-specific priors that encode known physical laws, constraints, and causal mechanisms.

This dissertation presents a physics-integrated deep learning framework for modeling and analyzing the time-series evolution of Arctic sea ice. The proposed framework systematically combines physical knowledge with data-driven learning to enhance predictive performance, interpretability, and scientific validity. Specifically, this work introduces three complementary strategies for embedding physical constraints and governing principles directly into the learning process, each addressing a distinct scientific challenge in Arctic climate research.

First, a physics-informed deep learning model is developed for sea ice thickness prediction by explicitly incorporating thermodynamic constraints and governing energy-balance laws into the loss function. This approach ensures that model predictions respect known physical relationships while remaining flexible enough to learn from sparse observational data. Second, the dissertation introduces physics-encoded neural network architectures that embed established physical relationships directly into the model structure. These architectures enable the inference of latent physical parameters from noisy, incomplete proxy data, facilitating physically meaningful representation learning. Third, knowledge-guided temporal causal models are formulated to quantify the causal impact of sea ice variability on coupled oceanic and atmospheric processes. By incorporating time-varying treatments, causal structural constraints, and physics-based priors, these models provide interpretable estimates of causal effects that are consistent with established physical mechanisms rather than spurious correlations.

Across all evaluation settings, the results demonstrate that physics-integrated models consistently outperform conventional deep learning baselines. Overall, this work highlights the critical role of physics-integrated machine learning in advancing predictive capability, causal understanding, and trustworthiness in climate and Earth system modeling.

Tolulope (Tolu) successfully defended on Tuesday, November 18, 2025. 

Tolu's dedicated contributions have made him an invaluable member of the iHARP and UMBC community.  Following 3 successful internships with NASA GESTAR II, Amazon, and Microsoft, Tolu has secured a full-time Microsoft Data Scientist position commencing in 2026., This significant achievement is a testament to his hard work and talent.

Congratulations, Tolu, on this incredible accomplishment! We look forward to your continued growth and success. The entire iHARP community wishes you great success in your next adventure!

• Dr Vandana Janeja, UMBC, Advisor/Chair  
• Dr. Jianwu Wang,  UMBC
• Dr. Patricia (Patti)  Ordóñez, UMBC
• Dr. Nicole Schlegel,  NOAA
• Dr. Sudip Chakraborty, iHARP/ UMBC,
• Dr. Ratnaksha Lele, iHARP/ UMBC

Maloy successfully defended on Tuesday, November 11, 2025. Maloy has been an invaluable and dedicated member of the iHARP and UMBC community. We look forward to seeing what he does next!

Congratulations to Maloy on all of his hard work!!! iHARP wishes him great success in his next adventure!

• Dr. Vandana P. Janeja, Chair/Advisor, Department of Information Systems, University of Maryland, Baltimore County
• Dr. Sudip Chakraborty, Co-Chair/Co-Advisor, iHARP, University of Maryland, Baltimore County
• Dr. James Foulds, Department of Information Systems, University of Maryland, Baltimore County
• Dr. Jianwu Wang, Department of Information Systems, University of Maryland, Baltimore County
• Dr. Md Osman Gani, Department of Information Systems, University of Maryland, Baltimore County
• Dr. Aneesh Subramanian, Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder

1st ACM SIGSPATIAL International Workshop on Polar Data Science (PolDS 2025)