This project focuses on the development of subject-specific finite element (FE) lumbar spine models (T12–L5) to better assess injury risks in reclined seating during automotive crashes. Current human body models (HBMs) are limited to three body types, and existing crash test protocols do not evaluate lumbar injury despite increasing real-world evidence. To address this, a semi-automated pipeline was built that integrates AI-based CT segmentation (TotalSegmentator in 3D Slicer), refinement in Materialise Mimics, and mesh morphing in MATLAB using rigid ICP, RBF morphing, and non-rigid ICP.

The pipeline preserved the THUMS mesh topology while capturing subject-specific anatomical variability, producing both STL surface meshes and LS-DYNA keyword (.k) files for downstream crash simulations. Applied across twenty subjects, the workflow demonstrated scalability, repeatability, and robustness to differences in CT image quality. The results revealed that transverse processes and pedicles show greater anatomical variation than vertebral bodies, underscoring the importance of subject-specific modeling.

This work highlights a path toward more inclusive, anatomically accurate FE models for crash injury prediction, with potential to support safety system design in future autonomous vehicles with reclined seating configurations.

This project focuses on the parametric modeling of cervical spine geometry to address the limitations of current computational human body models, which represent only three standard body types. Using a dataset of over 800 subjects with diverse characteristics (sex, age, BMI, and stature), the team applied AI-assisted segmentation (TotalSegmentator), post-processing of 314 subjects, and non-rigid ICP mesh morphing to generate anatomically consistent cervical spine geometries.

Statistical shape modeling and regression analysis revealed that factors such as age, gender, stature, and BMI significantly influence cervical spine curvature, height, and overall morphology. The first principal component explained over 40% of variance, primarily reflecting cervical spine size. By quantifying these variations, the project demonstrated how subject diversity can be systematically integrated into human body models.

The outcomes highlight the potential for more inclusive crash simulations and vehicle safety designs that account for anatomical differences across populations, ultimately improving protection for vulnerable groups.

This project, in collaboration with Autoliv India , focused on the strength analysis of seat belt buckle brackets using LS-DYNA with the GISSMO damage model. The buckle bracket was modeled in CATIA, meshed in HyperMesh, and simulated under tensile loading to predict stress, strain, and failure. Laboratory tensile tests validated the CAE results, with GISSMO-enhanced simulations predicting failure at 25.1 kN, closely matching experimental data (~25.9 kN). Unlike conventional models, GISSMO accurately captured ductile failure and element deletion, making simulations more realistic. The project highlights how advanced material modeling improves crashworthiness predictions, reduces prototype costs, and strengthens occupant safety validation.

Team Arion Racing, NMIT is the Formula Student team of Nitte Meenakshi Institute of Technology, where students design, build, and race a formula-style car. The team competes in national-level events such as SAE-SUPRA and Formula Bharat, applying engineering knowledge to real-world motorsport challenges while fostering collaboration, innovation, and leadership among students.

As Lead – Growth & Development, I directed a team across multiple subsystems, focusing on both technical and organizational growth. On the technical side, I contributed to the powertrain subsystem, improving drivetrain efficiency, wheel dynamics, and torque delivery. On the management side, I secured sponsorships, strengthened vendor relations, and organized workshops, open days, and outreach initiatives that expanded the team’s visibility and support base.

This project focused on the fabrication and testing of carbon fiber composites using the vacuum bag moulding process. Carbon fiber, known for its lightweight, high strength, and rigidity, was reinforced with resin in a four-ply layup to create a composite specimen. The work included preparing and curing the composite under vacuum, followed by tensile strength and surface hardness testing, which showed values of 200 MPa and 213.1 HV, respectively. The study highlighted the advantages of carbon fiber in the automotive industry, particularly in weight reduction, crashworthiness, and performance enhancement.

This project presents the design and development of an Arduino-based automatic irrigation controller that uses a soil moisture sensor to optimize water usage in agriculture. The system continuously monitors soil hydration levels, and when the moisture drops below a set threshold, it triggers a submersible water pump through a relay module. Once adequate moisture is restored, the pump is automatically turned off, ensuring efficient irrigation without manual intervention.

The setup was fabricated using an Arduino Nano, single-channel relay, 9V pump, and moisture sensor, with calibration carried out to differentiate between dry and wet soil conditions. By reducing manpower, conserving water, and improving soil and crop yield quality, the system demonstrates a cost-effective and scalable solution for smart irrigation. It also shows potential for extension to greenhouse automation and large-scale farming applications.

This project demonstrates an Arduino-based control system for automotive headlamps. Using an Arduino Nano, dual-channel relay, push-button switches, and high-power LEDs, the system replicates the core functions of a real vehicle headlamp including low beam, high beam, and pass light operation. Inputs from the control panel are processed by the Arduino, which activates the appropriate circuits through relays to safely manage power to the LEDs. The setup effectively models how modern automotive lighting systems function, ensuring efficiency, safety, and reliable switching of headlamp modes.