PARTH DHARMENDRA PRAJAPATI

Led ML-based pattern recognition research on Indus Valley historical scripts, leveraging deep learning models to achieve high accuracy in symbol classification.

Implemented a Transformer-based language translation model for AI curriculum technical demonstrations, significantly improving student comprehension and engagement.

Designed and optimized path optimization algorithms for GIS systems, enhancing visualization capabilities and improving emergency response route efficiency by an estimated X%.

Authored comprehensive technical specifications for model frameworks, ensuring high reproducibility and facilitating iterative development cycles.

Led comprehensive course operations for an enterprise systems program, coordinating cross-functional teams and managing academic delivery for 59 students.

Evaluated assignments and delivered timely, feedback-driven assessments, significantly enhancing learning outcomes and maintaining high-quality standards.

Conducted ERP simulations, performing rigorous testing and validation of assignments while reinforcing complex enterprise architecture concepts for students.

Designed and implemented advanced ML algorithms for large-scale time-series sensor data, enabling precise activity recognition across distributed wearable sensors.

Developed production-ready Python modules for multi-class activity feature extraction using sophisticated signal processing techniques, integrated into the core product pipeline.

Built a robust threshold-based calibration model across distributed body segment sensors, successfully reducing error rates to under 10%.

Engineered and refined prompts for LLM-based applications, significantly improving response consistency, evaluation quality, and downstream accuracy by an estimated X%.

Collaborated effectively within a cross-functional Agile team, delivering well-documented, maintainable Python code on tight delivery schedules.

Assisted in delivering core AI curriculum to 30+ students, grading assignments and facilitating open lab sessions for hands-on learning and practical application.

Performed thorough testing and debugging of AI assignments, resolving student queries to ensure strong conceptual clarity and maximize engagement.

Provided individualized support to students, clarifying complex AI concepts and fostering a collaborative learning environment.