Service and mentoring

Physics of Fluids

AIP Publishing. Reviewing in computational fluid dynamics.

AIAA Aviation Forum 2026

Data-driven methods for CFD modeling.

ASME FEDSM 2027

Computational Fluid Dynamics Technical Committee.

Undergraduate research mentor

Computational Mechanics Laboratory, UMBC

• Mentored Robby Schroeder on a data-driven feedforward neural network framework for predicting dynamic stall in vertical-axis wind turbines, funded by the UMBC Technology Catalyst Fund.
• Guided model architecture design, training strategy and validation against CFD reference data. Lift prediction error fell from 28% to 7.2%, and drag from 40% to 4.2%.
• Supported poster development for COEIT Research Day 2026, which received the Undergraduate Student Award.

NSF research proposal

Submitted. Led technical development of the scientific methodology and contributed to writing for a multi-year proposal integrating physics-informed machine learning with wind energy cyber-physical systems, including solver verification and validation and multi-physics modeling of coupled fluid-electrical systems.

Structure-Preserving Scientific Computing and Machine Learning Summer School and Hackathon

University of Washington, Seattle

• Selected as one of 40 graduate students from across the United States and Canada, with support from the NSF and PIMS.
• Hackathon Project D, Neural ODEs: exploring time integration methods and training strategies, relevant to weather forecasting and nonlinear dynamical systems.
• Lectures, hands-on computational labs and collaborative mini-projects at the intersection of scientific computing and machine learning.

Treasurer, Bangladesh Student Association

UMBC. Managed funds and coordinated events for Bangladeshi graduate students, supporting community engagement.