Sarthak Shirole
@sarthakshirole
Electrical engineering undergraduate focusing on neuromorphic AI and circuit simulation.
IndiaWhat I'm looking for
I am an electrical engineering undergraduate from COEP Technological University with strong academic performance (CGPA 8.9/10, top 5%). I focus on bridging hardware and AI through research in neuromorphic computing and physics-informed machine learning.
My neuromorphic project developed a convolutional spiking neural network using PyTorch and snnTorch, implemented LIF neurons with surrogate gradients, and addressed dead-neuron issues to capture temporal dynamics from event-based vision data.
In scientific machine learning, I built a Physics-Informed Neural Network to accelerate transient circuit simulation, formulating a physics loss based on Kirchhoff’s Voltage Law and validating results against Runge-Kutta solvers with very low error.
I work with Python, C/C++, MATLAB/Simulink, LTSpice and data tools like NumPy, SciPy, PyTorch and visualization platforms. I enjoy reading, music and badminton, and I’m passionate about hardware-software co-design for energy-efficient edge AI.
Experience
Work history, roles, and key accomplishments
Researcher, Scientific Machine Learning
Independent Research
Nov 2025 - Present (7 months)
Developed a physics-informed neural network (Neural-SPICE) to accelerate transient non-linear circuit simulation, achieving a physics residual loss of 5.6e-05 and max current error of 0.0005 A versus Runge-Kutta.
Researcher, Neuromorphic Computing
Independent Research
Nov 2025 - Present (7 months)
Designed and trained a convolutional spiking neural network for event-based gesture recognition, achieving 74.0% Top-1 accuracy and 92.36% sparsity while estimating dynamic energy of 0.0127 μJ per inference on 45nm CMOS benchmarks.
Education
Degrees, certifications, and relevant coursework
COEP Technological University
Bachelor of Technology, Electrical Engineering
2024 -
Grade: 8.9/10.0 (Top 5% of cohort)
Pursuing a Bachelor of Technology in Electrical Engineering with strong academic performance (CGPA: 8.9/10.0, top 5% of cohort).
Tech stack
Software and tools used professionally
Availability
Location
IndiaAuthorized to work in
Job categories
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