Raghav R. Email and Phone Number

Serving as a Peer Admissions Representative at ASU, assisting prospective students and families while managing office tasks and Salesforce queues. Providing guidance on university life, supporting the Admissions Services team to enhance student interactions, and strengthening customer service skills. Demonstrating the ability to handle pressure effectively while ensuring a positive experience for prospective students and their families.

Contributing to the development and design of a lunar exploration mission concept focused on lunar lava pits for human habitation and resource utilization. Utilizing NX CAD software to create models and simulations, adhering to strict dimensional and mass constraints for lander and rover designs. Conducting environmental analysis to assess potential hazards in lunar lava pits, including structural integrity and radiation levels, supporting mission safety and feasibility studies.

The project addressed composting challenges on campus through engineering solutions. As the budget manager, responsibilities included overseeing costs related to material storage and handling. This role required significant communication and team management skills to ensure the project stayed within budget while meeting objectives. Enhanced abilities in coordinating with team members and managing resources contributed to effective project execution and alignment with sustainability goals.

As the Design and Marketing Head, led the creation of social media content and generated support for the initiative. Collaborated with entrepreneurs worldwide, engaging with passionate individuals who shared similar goals, which enriched the experience and expanded professional networks.

Built and optimized a machine learning model to address a healthcare challenge, utilizing data preprocessing, feature extraction, and model validation for accurate predictions. Enhanced model robustness by tuning algorithms and adjusting parameters to align with industry standards. Applied Python-based machine learning frameworks to develop predictive analytics, collaborating with experts to refine methodologies and ensure practical applicability in healthcare scenarios.