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John Twipraham Debbarma

B.Tech + M.Tech (Dual Degree) CSE, IIT Gandhinagar · Graduating 2027. Building at the intersection of machine learning, robotics and the arts.

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Computer Science

Internship · 7 months · May – Dec 2025

Defence-Tech R&D · SDAL

Seven months in the UAV Department at Solar Defence and Aerospace — GPR, SAR, and a LiDAR + mmWave radar project for autonomous drones (obstacle avoidance and SLAM) that I led from scratch.

LiDARmmWave RadarSLAMSARGPRROS

Context

Research Associate (Software Engineer Intern) — R&D Division, UAV Department, Solar Defence and Aerospace Limited, Nagpur. Seven months: May 2025 – December 2025, taken in place of a semester on campus.

This page is a resume-faithful overview. Project specifics are held under NDA; what follows is the level of detail the resume itself uses.

What I worked on

I collaborated with multidisciplinary engineering teams on confidential defence projects, in a regulated environment.

Ground Penetrating Radar (GPR)

Developed a GPR system targeting subsurface detection and mapping. GPR uses radar pulses to image structures below the surface — buried objects, voids, soil-layer transitions. The work involved signal acquisition, processing pipelines and producing interpretable outputs for downstream use.

Synthetic Aperture Radar (SAR)

Contributed to a SAR project for high-resolution imaging. SAR synthesises a large virtual aperture from a moving platform, producing image-quality returns from radar at scales useful for reconnaissance and mapping.

LiDAR + mmWave radar for autonomous drones

Led this project from scratch: integrating LiDAR and mmWave radar for real-time obstacle detection, avoidance and SLAM on autonomous drones used in disaster management and emergency response. LiDAR gives dense, sharp geometry; mmWave radar punches through smoke, dust and rain — together they bracket failure modes that either sensor would hit alone.

The fused stack drives the drone's near-field collision-avoidance loop. I owned the project end to end, including its integration with the other teams' work.

What I came away with

  • Hands-on experience inside the defence-industry quality and documentation discipline — the standards, the review cadence, the traceability expectations.
  • Real-time perception across complementary sensor modalities, with the constraints (latency, weight, power) that flying platforms actually impose.
  • Speed on unfamiliar ground: I learnt QML from scratch and completed my project within the first month.
  • A working appreciation for how the research-to-deployment pipeline runs inside an R&D division, end to end.

Performance

  • 11/10 in one performance quadrant, for outstanding execution and impact.
  • 10/10 in the remaining three.
  • Graded by IIT Gandhinagar professors — ranked 1st in the entire batch.
  • Earned praise from the company, and my manager named me “Most popular person in the office”.

Stack & domain

C++ · Python · QML · ROS 2 (Humble, Jazzy) · LiDAR drivers · mmWave radar signal chains · sensor fusion and SLAM · embedded compute on the airframe.

Note

Anything more specific than the above is intentionally absent. The internship covered work I can't publicly describe further; what's here matches the resume and respects the agreement I signed.

Related

Browse more case studies or check the source.

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