iHARP at FAIR in ML, AI Readiness & Reproducibility (FARR) Workshop

Fri, 17 Apr 2026 16:08:30 -0400
iharp

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.