Atlanta, Georgia, United States
I am a GT MechE Masters student. My passion is bridging computational physics and intelligent systems to solve real engineering problems. In the Flow Physics and Computational Science Lab, I develop CFD simulations using Basilisk for multiphase flow analysis. This work helps researchers understand thermal and fluid dynamics in ways that would take months to prototype. In the SRL Lab, I build machine learning systems (meta learning) that learn from physics. When these systems work, they reduce iteration costs and showcase predictive maintenance. My impact is direct: better thermal designs, faster autonomous systems, smarter products. I am fascinated by how rigorous physics and modern machine learning intersect. I want to work with companies developing thermal management solutions, autonomous systems, and ML driven products. I am drawn to problems where my computational foundation and coding skills combine to create something that matters. I started understanding thermal systems and never stopped asking why. That rigor became my lens for everything. Now I'm extending that thinking into machine learning and autonomous systems, recognizing that the most elegant engineering happens when physics meets intelligence. Beyond the lab, I love playing tennis, building personal projects that make my life easy:) and watching Formula 1.
Working in the GT-AX Lab under Prof. Seung-Kyum Choi on developing an LLM-enabled robotic manipulation framework that integrates NLP task grounding, vision-based object localization, motion planning, and failure-recovery classifiers for adaptive pick-and-place execution, with a long-term goal of incorporating world-model/VLA-based reasoning for self-improving robotic behavior.
Modeled secondary breakup of surfactant-laden droplets in the Flow Physics and Computational Science (FPCS) Lab under Prof. Suhas, applying multiphase CFD to quantify the effects of surfactant transport, interfacial tension gradients, and Marangoni stresses on droplet deformation and breakup dynamics. • Developed 3D multiphase CFD models in Basilisk using phase field method & adaptive mesh refinement to predict droplet breakup under surfactant effects, delivering propulsion system design insights that reduced experimental testing requirements by 50%. • Implemented parallelized simulations, using MPI on HPC cluster infrastructure with up to 800K elements, executing batch jobs for parametric studies while maintaining thorough data logs for interfacial analysis, reducing computational time by 30%. • Automated data processing pipelines using Shell scripting, Python to extract and visualize large datasets from batch simulation jobs.
• Supervised 160+ undergraduate students across 3 mechanical engineering courses (Manufacturing Process (ME F219), Advanced Manufacturing Process (ME F315), Prime Move and Fluid Machines (ME F341)) designing assessments and facilitating comprehension. • Designed and evaluated course assessments to monitor & support student comprehension and academic progress effectively. • Conducted engaging doubt-clearing sessions, providing personalized support and clarification to enhance student learning outcomes.
I did my undergraduate thesis on the topic "Influence of Biomechanical Parameters of Circulating Tumour Cells using MicrofluidicBio-mimicking Blood Vessels". • Designed and fabricated microfluidic channel systems using soft lithography techniques for real-time analysis of circulating tumor cells under laminar flow conditions. • Cultured and characterized HuH7 cell lines, optimizing flow rates and biocompatibility for experimental validation. • Developed image acquisition and computational analysis framework to quantify cell biomechanical properties and deformation patterns, bridging experimental platforms with data-driven insights.
• Directed team of 25+ members to establish India’s first ISHRAE student chapter, fostering HVAC industry engagement. • Led development and secured ₹1 lakh in funding for a PCM-based milk container, validating real world impact through dairy partner testing • Curated high-impact competition and technical talk at annual college Techfest, spotlighting HVAC innovations
Worked under Prof Ng Yin Kwee to infuse organic nanoparticles with n-tetradecane creating a Phase Change Material (PCM) that keeps the vaccines cool for upto 146 hours between 2- 8 degree celsius. • Leveraged in-depth knowledge of PCMs to enhance passive energy thermal systems for more efficient vaccine transportation through thermal model development and optimization. • Achieved 40% longer cooling and 25% cost reduction via ANSYS-based transient analysis and hardware validation of prototype. • Translated complex thermal physics results for stakeholder presentations that directly informed design modification decisions. • Developed a Physics Informed Neural Network (PINN) that embeds heat transfer physics into learning, cutting simulation time by 80% while accurately predicting transient PCM thermal behavior for rapid design optimization.