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

We are pleased to announce the Undergraduate Data Science and AI Scholars program for supporting students in Spring 2026. The Data Science and AI Scholars program welcomes undergraduate scholars from multiple disciplines, including non-STEM majors, who will examine all aspects of data and its impact on society. This year, scholars will have the opportunity to be teaching fellowsor research fellows. Scholars will be able to work on research projects in Climate Science, Deepfake detection, and other data science-related projects. 

Students selected through this effort will be part of the Data Science and AI Scholars program, which will be run in partnership withthe Center for Women in Technology (CWIT), the Information Systems Department (IS), and the Center for Social Science Scholarship (CS3). 

Selected scholars will be assigned to respective affiliations (CWIT/IS or CS3) depending on the student's major, and the scholars' expectations per affiliation are as follows:

Center for Women in Technology (CWIT) Responsibilities:

Research Fellows

• Scholars who will work as research fellows will work on a supervised research project and be mentored by faculty. Scholars will be paid $20.00/hour for specific tasks, for 5 - 10 hours per week.

• Scholars will attend at least two events in Spring 2026 and/or participate in research initiatives and professional development for their leadership growth and mentoring. Mentors will coordinate this at CWIT. 

• Scholars will need to register as CWIT Affiliates to participate in the 2 required events.

• Scholars will be required to enroll in a Zero-credit Practicum course on research experience (PRAC 98C) through the UMBC Career Center.

Teaching Fellows 

• Scholars will work as teaching fellows and peer mentors for undergraduate students in IS 296 - Foundations of Data Science, and support students throughout the course.. The scholars will devote time to mentoring and leadership development efforts for their advancement. The cohort of scholars will meet as a group under the supervision of the IS 296 instructor. Scholars will be paid $20.00/hour for specific tasks, for 5 - 10 hours per week during Spring 2026.

• Scholars who will work as teaching fellows will be expected to be available to assist with classes and also present during lectures. Spring 2026 class schedule is as follows: Mondays 4:30 - 7:00 pm (Instructor: Dr. Karen Chen), Mondays 7:10 - 9:40 pm (Instructor: Zehra Zaidi), and Tuesdays 4:30 - 7:00 pm (Instructor: Dr. Neha Singh)

• Scholars will be required to attend one mentoring meeting per month with the coordinator

• Scholars will be required to enroll in a Zero-credit Practicum course on research experience (PRAC 98C) through the UMBC Career Center.

Center for Social Science Scholarship (CS3) Responsibilities:

Research Fellows

• Scholars who will work as research fellows will work on a supervised research project and be mentored by faculty. Scholars will be paid $20.00/hour for specific tasks, for 5 - 10 hours per week.

• Scholars will attend at least two events in Spring 2026 and/or participate in research initiatives and professional development for their leadership growth and mentoring. Mentors will coordinate this at CS3. 

• Scholars will also be expected to attend one mentoring meeting with the faculty.

• Scholars will be required to enroll in a Zero-credit Practicum course on research experience (PRAC 98C) through the UMBC Career Center.

Background and Skills:

ALL applicants should have knowledge in AT LEAST ONE of the following:

• Analysis of social, behavioral, economic, or geographic data

• Python or one of the data science tools and/or languages (such as R, Rapid Miner, Weka, Orange, Knime, ML on cloud computing)

• Jupyter Notebooks with Python or taken IS 296 in a prior semester.

Application Form:

Interested students should complete the Google Submission Form,OR access the form using the following link: https://forms.gle/FFUAa5RpDNMGYvTY9  by Friday, November 21, 2025.

Questions:

For questions, please email our team at DataScienceAIScholars@umbc.edu

To learn more about potential projects, please check out the following websites: 

• https://www.umces.edu/chesapeake-global-collaboratory 

• https://iharp.umbc.edu

• https://mdata.umbc.edu/deep-fake-detection/

• https://socialscience.umbc.edu/ 

This summer, iHARP PhD candidate Tolulope Ale brought his expertise to Microsoft as an intern.

Here's how Tolu summed up his experience: "This summer, I had a fantastic internship experience, supported by the Microsoft mentorship program, which helped me connect with colleagues at every level. I built an LLM customer-engagement chatbot using Semantic Kernel and Azure AI Foundry, with two key features: Brand Persona (responses tailored to a specific brand's voice/tone) and Conversation Style (Brand Persona, Casual, and Gen Z modes selectable by the user). I demoed the solution to my team, a data science group, leadership, and a broader cross-functional audience (DS, SWE, PM, and PMM), and it was very well received. A standout aspect of the design is its easy configurability for new brand use cases with minimal code changes. I also implemented evaluation for each LLM component and added a latency tracker to monitor performance end-to-end."