Joseph
Jonathan
Fernandes
Final-year CE student at GEC Goa. Production embedded C at Visteon. Built real-time ISL recognition at 98.33% accuracy and a secure cross-platform terminal assistant in C.
Who I am
About
From production ECU code
to real-time sign language recognition.
Final-year Computer Engineering student at GEC Goa with an AI/ML Honors specialization. Completed an embedded systems internship at Visteon Technical & Services Centre in 2025, working on production AUTOSAR ECU modules. A Pre-Placement Offer has been accepted.
Projects span embedded C through applied AI. The Vฤksetu ISL recognition system achieves 98.33% accuracy across 300 sign classes at 60+ FPS CPU inference. CmdBridge maps natural-language intent to native OS APIs in C with 94% test coverage.
GATE qualified in both CSE and DA. 47 NPTEL courses at top recognition tiers โ Domain Scholar, Superstar, Megastar, and Evangelist.
Recognition
Achievements
HackAura 2025 โ 1st place, national-level hackathon (30 teams)
Infofest 2025 โ 1st place competitive programming, Goa University
HackIndia Spark 3 2025 โ Top 7 of 80+ teams (Goa)
GATE qualified โ CSE and DA (both 2025 and 2026)
NPTEL โ 47 courses at top recognition tiers: Domain Scholar (Programming & Data Science), Superstar, Megastar, Evangelist, Discipline and many more
Technix Quiz โ co-organized state-level technical quiz (2023, 2025,2026)
Quizzing & Debating โ 9x 1st-place finishes at major intercollegiate events (NIT Goa, Goa University), state-level RBI Quiz finalist, and represented GEC at the Goa Vidhan Sabha
CodeChef rating 1025 ยท HackerRank badges in C, Python, SQL, Java
Odoo Hackathon โ National Finalist (NMIT Bangalore), selected from 200+ teams in the online qualifiers
Open Source: 15+ merged pull requests to major repositories, including 14 PRs to public-apis (300k+ stars)
Background
Education
Computer Engineering (AI/ML Honors)
Government Engineering College, Goa
What I've built
Featured Projects
Work spanning AI/ML, embedded systems, full-stack web, and systems programming.
Vฤksetu
Core Contributor
Vฤksetu is a real-time Indian Sign Language recognition system. Its core Sign-to-Text component uses MediaPipe skeletal landmarks with a hybrid BiGRU + Spatial Graph Neural Network, trained on 93,000+ gesture sequences across 300 classes โ 98.33% accuracy, 97.84% macro F1. The model was compressed from 4.2 MB to 1.05 MB via INT8 ONNX quantization for 60+ FPS CPU inference at ~6.22ms/frame, with the training pipeline migrated to HDF5 for 391ร faster data loading.
CmdBridge
Solo
Cross-platform terminal assistant that maps natural-language user intent to native OS APIs instead of executing raw shell commands, eliminating a class of command-injection vulnerabilities. Features secure intent parsing with explain-before-execute validation. 94% automated test coverage across 211 unit and integration tests using AddressSanitizer, UndefinedBehaviorSanitizer, and CI. Supports Windows, Linux, and macOS.
CrowdSense
Full-stack, backend-focused ยท Team of 4
Disaster response teams need real-time situational awareness from social media during crises. CrowdSense applies NLP and anomaly detection โ Z-score and EWMA algorithms โ to live social streams, extracts locations via Named Entity Recognition, maps incidents with Leaflet, and dispatches SMS alerts via Twilio. Backend-focused in a team of 4.
Collatz Sequence Analyzer
Solo
A 21-module multithreaded C++ research platform analyzing the Collatz conjecture across 50 million integers. Multithreading and path caching deliver a 3.3x speedup (11.7s -> 3.6s). Includes a reproducible benchmarking and regression pipeline with R-squared fit up to 0.999995 via statistical modeling in R. Full CMake build system.
HackGuard
Risk-scoring, not verdicts: an AI-powered hackathon integrity platform that analyzes submitted GitHub repos against declared hackathon time windows. It surfaces evidence (commit timestamps, massive code dumps) for judges to review. Built with a philosophy that it never accuses, but intelligently flags anomalies for human review.
Multi-Sensor Fusion ADAS
Designed and implemented a low-cost, multi-sensor embedded safety system (ADAS) on an Arduino Uno. Fuses biometric driver-condition monitoring (MQ-3 alcohol sensor, dual IR sensors) with environmental sensing (HC-SR04 ultrasonic, SW-420 vibration, LDR). Features a custom cumulative weighted risk-scoring algorithm with predictive collision detection and non-blocking multi-actuator response.
Other Notable Projects
NASA Space Apps Challenge (state-level) โ AI exoplanet analysis with RAG chatbot.
Space exploration platform with live NASA APIs and multi-agent AI. Coders Club Hackathon 2025.
Knowledge graph task scheduler with MeTTa-based reasoning. HackIndia Spark 3 2025 โ top 7 of 80+ teams.
Computer vision waste classifier with gamified eco-points. Global AI Buildathon.
Multi-threaded network security scanner with vulnerability assessment and GUI. Solo.
Interactive Streamlit web app showcasing diverse image enhancement algorithms, filters, and computer vision techniques.
A human-centered Streamlit app for calculating CGPA with semester-level credit control and partial semester support.
Modular C++ inventory system with file-based persistence, demonstrating OOP best practices, memory safety, and clean architecture.
A smart CLI to-do list built in C++ utilizing a Min Heap for priority-based task management.
On GitHub
GitHub Activity
What I work with
Skills
Languages
9Frameworks & Libraries
6ML & AI Libraries
5Databases
3Tools & Concepts
8Where I've worked
Experience
Software Engineering Intern โ Embedded Systems
Joined a 7-person embedded team working on production ECU modules for automotive systems. Work focused on AUTOSAR compliance, static analysis, and test coverage across production-grade C/C++ code.
Key Contributions
- 100% branch, statement, and function coverage across 14 AUTOSAR modules using VectorCAST
- Analyzed 100K+ MISRA-C / CERT-C / static warnings with a 99%+ deviation approval rate
- Proposed fixes for ~5% of Klocwork warnings flagged for the next production release
- Wrote Python scripts to automate Excel-based warning analysis workflows
- Created internal documentation and Teams resources adopted by the broader team
Technologies
Get in touch
Contact
I am joining Visteon as a Software Engineer, but I'm always open to discussing open source, research, or interesting tech. Direct email is fastest.
Let's connect
Available for
- Open source collaborations
- Research opportunities
- Hackathons & Events
- Technical discussions