I am a Telecommunication Engineering M.Sc. student from Politecnico di Milano. My background spans signal processing, Embedded software development, data science and analytics and software engineering. I have hands-on experience building high-availability architectures, working with embedded hardware, and developing cross-platform applications.
Assembly
Bash
C++
Dart
Flutter
gRPC
HTML
Java
Javascript
Latex
LTspice
Node.js
NodeRed
Python
R
React.js
CSS
Typescript
Matlab
Major : Electrical and Electronics Engineering
Specialization : Data Science and Analytics
CGPA : 8.17 / 10
Thesis Title : Raspberry-Pi Based Fundus Imaging Device
Worked on various medical image processing projects under Prof. Chandra Sekhar Seelamantula, leading to multiple publications in international journals and conferences.
Major : Telecommunication Engineering
Track : Signals and Data Analytics
CGPA : 25.79 / 30
Thesis Title : Semantic Communications Simulation with Sionna
During my internship, I carried out five main tasks:
Master's thesis. Simulated semantic communication via Feature Identification using Nvidia's Sionna physical-layer library and a modular gRPC multi-process architecture.
Three lab challenges: CoAP packet analysis with Scapy, an MQTT data pipeline in Node-RED, and a TinyOS/nesC radio routing protocol simulated on a 7-node WSN with TOSSIM.
Two assignments covering NMO correction and velocity analysis (Report 1), and seismic migration algorithms for subsurface reflector imaging (Report 2).
Modelled antenna gain and power budgets for a UAV pulse radar, then simulated LFM chirp transmission and matched-filter range compression in MATLAB.
Processed UAV radar echoes with range compression and Time Domain Back-Projection (TDBP) to generate focused SAR images, with noise filtering and PSLR evaluation.
Flutter/Dart mobile app that uses geo-fencing to deliver location-based notifications, targeted ads, and real-time ETA predictions for Trenord commuters.
Benchmarked BLE 6.0 ranging (RSSI, PBR, RTT) against UWB DS-TWR, then deployed a quantised TinyML model on a Nordic nRF54L15 for on-device inference.
Interactive 3D ward layout tool built with Vulkan: custom GLSL shaders, drag-and-rotate asset placement, and switchable orthographic/perspective camera views.
1st Place — Multi-sensor fusion pipeline (LiDAR, radar, camera + EKF) for real-time opponent race car tracking at Yas Marina Circuit, developed at Politecnico di Milano.
Cháksu: A Glaucoma-specific Fundus Image Database, Scientific Data (Nature), 2023.
Automated Optic Disc Segmentation Using Basis-Splines-based Active Contour, IEEE Access, 2022.
Automated Segmentation of Common Carotid Artery in Ultrasound Images, IEEE Access, 2022.
A Unified Approach for Automated Segmentation of Pupil and Iris in On-axis Images, Computer Methods and Programs in Biomedicine, 2022.
RaPiD: A Raspberry Pi-based Optical Fundoscope, ISSS Journal of Micro and Smart Systems, 2022.
Automatic Segmentation of Fovea and Macula in Retinal Fundus Images, IEEE TENCON, 2022.
Automatic Optic Cup Segmentation Using Affine Snakes in Gradient Vector Field, IEEE INDICON, 2023.
Segmentation of Left Ventricle in Cardiac MR Images, arXiv, 2022.