🧠 Machine Learning |Gen AI | 🏎️ F1 Fanatic | 📚 Research Enthusiast

I am Aman Srivastava, a proactive 4th-year BTech student at IIT BHU. With a keen interest in Generative AI and large language models (LLMs), I am currently exploring how these cutting-edge technologies can be applied across various domains.

Feel free to reach out to discuss anything tech, ML, or F1-related! 🤖🏁

For further discussions or inquiries, please feel free to reach out via email at [email protected] or by phone at +91 9625167658.

📅 Duration: May'23 - Jul'23

🔧 Description:

• Data Preprocessing and Modeling: Leveraged Python and R to curate and preprocess intricate driver behavior datasets. Constructed predictive models employing Random Forest, SVM to discern and categorize instances of distracted driving behavior accurately.

• Interdisciplinary Collaboration: Engaged in synergistic collaboration with cross-functional experts to iteratively enhance research methodologies. Contributed technical acumen to fine-tune algorithmic parameters and streamline feature engineering processes.

• Technical Reporting and Visualization: Employed advanced data visualization techniques to distill multifaceted research findings into comprehensive reports. Translated intricate machine learning outcomes into coherent visual representations, making them comprehensible to technical peers and non-technical stakeholders alike.

This immersive research internship at IIT Hyderabad provided me with a transformative experience. Exploring the intricate dynamics of "Texting-Induced Distraction on Driving Behavior" using R programming was intellectually enriching. Through meticulous data analysis, I unveiled the nuances of distracted driving's impact, contributing to road safety awareness. The research journey bolstered my analytical skills and deepened my understanding of human behavior within the context of technology and transportation. It solidified my commitment to leveraging data-driven insights for meaningful societal contributions.

📅 Duration: Jan'23 - May'23

🔧 Description:

Methodology and Analysis: Applied Dynamic Mode Decomposition (DMD) incorporating Koopman modes to analyze time-dependent fluid flow data. Computed Koopman operator eigenvalues (phi values) to reveal intrinsic temporal patterns in the flow dynamics.

Algorithm Implementation: Developed custom MATLAB scripts to implement DMD algorithms, integrating Koopman analysis.Utilized matrix decomposition techniques to extract Koopman modes and corresponding phi values.

Insights and Applications: Gained insights into the temporal behavior of fluid flows, revealing transient phenomena and recurring patterns. Contributed to understanding turbulence dynamics, vortical structures, and interactions within the flow field.

Fig. 1 Mathematical Equation

Fig. 2 Procedure

Fig. 3 Spatial Modes for r=9

📅 Duration: Jan ’22- Present

🔧 Description:

• Nosecone Refinement and Aero Efficiency: Collaborated on optimizing the nosecone design to enhance aerodynamic performance. Employed SolidWorks and ANSYS for meticulous design and airflow analysis, achieving optimal drag reduction and increased downforce.

• Ground Effect Augmentation - Innovative Underplates: Led the innovation of underplates, integrating vortex generators and boundary layer control devices. Leveraged SolidWorks and ANSYS to enhance ground effect, significantly boosting vehicle stability and cornering prowess.

• Simulation-Driven Design Expertise: Showcased adeptness in simulation-driven design using SolidWorks and ANSYS. Translated theoretical concepts into high-performance reality by conducting structural and fluid flow analyses, guaranteeing optimal performance under race conditions.

This multifaceted experience exemplifies my proficiency in utilizing advanced tools for innovative aerodynamic solutions in the competitive realm of high-performance racing.

Fig. 1 Pressure contour for the final nose cone mode	Fig. 2 Nosecone made on Solidworks
Fig. 3 Velocity contour for the final nose cone model	Fig. 4 Front Wing