Skandhan Karthikeyan

I am an M.S. student in Mechanical Engineering and Applied Mechanics (Mechatronics and Robotics Systems) at the University of Pennsylvania.

I completed my B.Tech in Mechanical Engineering at IIT Madras. During my undergraduate studies, I interned as an Embedded Systems Intern at JSW Steel, where I developed a microcontroller-based diagnostic unit for high-frequency deformation monitoring in the blooming mill. I also worked as a research intern in the Daniels Lab at North Carolina State University, focusing on interparticle force analysis in granular materials using photoelastic disks and the Photoelastic Grain Solver (PeGS). Following my hobby, I was part of the Electronics Club at IIT Madras, where I served as a Controls Engineer and later as a team lead (2023–2024).

Resume  /  LinkedIn  /  GitHub

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What Drives Me:

I am a systems engineer working at the intersection of robotics and applied machine learning. My work spans real‑time software, control architectures, and system integration, aimed at enabling autonomous systems that adapt intelligently. I am interested in developing algorithms and frameworks that bridge the simulation to reality gap and shaping the future of humanity!

Systems I've shaped :

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LiDAR and Camera-based Navigation Algorithms for Lane-keeping in Unstructured roads Skandhan K (Guided by: Dr. Anuj Tiwari)

Classical machine learning based system as a lightweight benchmark for future autonomous driving applications.

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Delay compensation in networked robotic systems
Skandhan K, Shivendra Verma (Guided by: Dr. Anuj Tiwari)

Formulated an observer-based feedback control algorithm to compensate for network delays in mobile, ground robots.

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Photoelastic Grain Solver (PeGS) for larger particles
Skandhan K, Shins K (Guided by: Prof. Ramesh K)

Simplification of evaluation of forces on soft photoelastic disks using efficient, closed-form stress field equations.

Lab-side Leadership :

Course Assistant, TinyML (ESE 3600), University of Pennsylvania (Aug 2025 – Nov 2025).
Helped transition lab workflows from the Arduino IDE to PlatformIO (Espressif IDF) and adapted codebases for the Seeed Studio XIAO ESP32S3 Sense. Reimplemented and tuned embedded inference pipelines for latency-sensitive TinyML assignments.

Tools I Wield :

Software:
MATLAB  |  Python  |  C/C++  |  PyTorch  |  Simulink  |  SQL  |  Unity  |  ROS2  |  Git  |  Linux  |  HTML

Hardware:
FreeRTOS  |  PCB Design (EasyEDA)  |  I2C  |  SPI  |  Bluetooth  |  Microcontrollers (ESP32 & Arduino -class boards)

Others:
Fusion 360  |  AutoCAD  |  Debugging and profiling for embedded systems

Moments That Mattered: