Makduma Zahan Badhan is a driven Ph.D. candidate in Civil & Environmental Engineering at the University of Connecticut (UConn), expected to graduate in 2026. Under the mentorship of Dr. Guiling Wang, her research focuses on climate modeling, data analysis, and environmental impact assessments, particularly through the use of the Community Land Model (CLM) and advanced deep-learning techniques. Her work plays a pivotal role in understanding the complex relationships between the carbon, water, and nitrogen cycles in climate models.

Before joining UConn, Makduma served as a lecturer at Presidency University in Bangladesh, where she mentored undergraduate students and deepened her passion for academic research and education. Her diverse experience includes contributing to NSF-funded NRT program Team Terra, where she tackled real-world challenges at the nexus of food, energy, water, and ecosystems. As a graduate research assistant, she has developed a deep-learning-based framework for statistical downscaling of global climate models (GCMs), enhancing the accuracy of climate projections crucial for future risk management.

Makduma’s decision to join UConn stemmed from its strong reputation in environmental engineering and the opportunity to work with distinguished faculty members like Dr. Wang. The interdisciplinary research environment, combined with cutting-edge facilities, has provided her with an ideal platform to explore her academic interests further. She joined the John Lof Leadership Academy (JLLA) to hone her leadership skills, develop professionally, and network with like-minded individuals.

As the Parliamentarian on the JLLA E-Board, Makduma plays a critical role in ensuring that meetings run smoothly, while also participating in the recruitment of new members and assisting with event proposals. Her role has expanded her experience in leadership, event planning, and strategic decision-making. Through this position, she’s gained a greater appreciation for teamwork and collaboration.

Makduma’s current research involves the statistical downscaling of CMIP6 model precipitation data over the U.S. Northeast using deep learning techniques. This project is particularly exciting as it combines cutting-edge machine learning with climate science to produce high-resolution climate projections. These projections are vital for understanding future climate impacts and developing strategies for adaptation in vulnerable regions.

Looking ahead, Makduma plans to continue her research in climate science, focusing on innovative modeling techniques that will help communities better adapt to and mitigate the impacts of climate change. Her ultimate goal is to contribute to the creation of more resilient communities through science-based policies and sustainable practices. Her advice to aspiring students is to stay curious, embrace interdisciplinary learning, and always persevere, as the most innovative solutions often emerge from unexpected collaborations.

JLLA has been instrumental in Makduma’s journey, offering her valuable leadership training and the opportunity to connect with scholars across disciplines. The academy has fostered a supportive environment where she has been able to challenge herself and grow as both a researcher and leader. This experience has solidified her belief in the power of strong leadership in driving positive change, both within academia and in the broader global community.