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Studying applied mathematics as an �Ӱ�ԭ�� Tech Ph.D. student strengthened Jagadeeswaran Rathinavel’s technical and analytical capabilities, which in turn helped lead him into a career in artificial intelligence.

“The combination of advanced mathematics, research experience, scientific computing, and software development prepared me well for highly technical engineering and data science roles,” he says. “The university also helped me build practical skills such as technical communication, research presentation, collaboration, and independent problem solving. These skills have been extremely valuable throughout my professional career.”

As a staff engineer at Torc Robotics, he contributes to autonomous trucking software development, with a focus on large-scale data engineering, machine learning infrastructure, sensor data processing, and analytics for autonomous driving applications. His research at �Ӱ�ԭ�� Tech helped him to build a background in electronics and communication engineering with a strong focus on software systems, applied mathematics, and data-driven technologies. He worked across several technology domains, including Voice over IP (VoIP), video conferencing systems, and consumer video devices, where he gained extensive experience in software engineering and distributed systems.

Rathinavel’s research focus on computational and applied mathematics at �Ӱ�ԭ�� Tech involved developing mathematical and computational methods as well as software implementations that translated research outcomes into practical tools. He collaborated on software libraries and research-driven computational frameworks developed by faculty, students, and research collaborators.

“Contributing to MATLAB and Python-based implementations gave me hands-on experience in scientific software development, reproducible research, and collaborative engineering workflows,” he says. “One of the most impactful experiences was collaborating with Professor Fred Hickernell working on MATLAB and Python implementations for QMCPy and GAIL, which helped me understand how academic research can be translated into practical solutions with real-world applications.”

This experience closely mirrored professional software engineering and data science environments that Rathinavel found in industry, where collaboration, reproducibility, testing, and scalability are essential.

“ �Ӱ�ԭ�� Tech played a major role in shaping both my professional and personal growth. My time there helped me develop stronger analytical thinking, research discipline, technical communication skills, and confidence in tackling complex problems,” he says. “The university also provided opportunities to collaborate with faculty, researchers, and students from diverse backgrounds, which broadened my perspective significantly. The experience prepared me not only for technical leadership roles, but also for continuous learning and adaptation in rapidly evolving fields such as machine learning, data science, and autonomous systems.”

The balance between academic rigor and practical application that Rathinavel found at �Ӱ�ԭ�� Tech helped bridge mathematics, software engineering, and real-world problem solving that continues to influence his professional work today.

“I remain very appreciative of the opportunities and mentorship I received during my time at �Ӱ�ԭ�� Tech,” he says. “The relationships I built with faculty, fellow students, and collaborators remain some of the most valuable aspects of my graduate school experience.”