Rohith K Bobby
India
Machine learning & backend developer, bug magnet, and occasional genius.
My Philosophy
If I can't explain it to myself, I won't trust the machine to get it right, and I won't ask someone else to rely on it. The craft is in making things sturdy enough to survive confusion.
Core Toolkit
Experience & Education
Machine Learning Intern
03/2023 – 12/2023IHRD/SBCID Kerala
- Automated newspaper analysis (OCR, Summarization), slashing processing time by 75%.
- Enhanced case identification accuracy by 20% through NLP-driven keyword extraction.
- Engineered an AI translation pipeline boosting Indic language accuracy by 25% (BLEU), empowering multilingual communication for >20 officials.
Research Intern
03/2022 – 03/2023Aris4D
- Pioneered a deep learning model for petri dish analysis, achieving 95% accuracy in predicting antibiotic effectiveness.
- Reduced antibiotic selection time by 30% in pilot studies.
- Cut model training time by 12% using computer vision for feature extraction.
Bachelor of Technology, Computer Science
11/2022 – PresentCollege of Engineering Kallooppara
Building theoretical and practical foundations in computer science and engineering.
Projects
goKyber
A High-Performance and Practical Post-Quantum Encryption Framework implemented in Go.

Malang
Malang is... well, let's just say it's a unique programming language. Born out of sheer defiance and an unwavering commitment to proving a point, Malang is my way of showing that I did pay attention in my compiler design course—whether my professor appreciates the outcome or not.

Brain Tumor Segmentation & Classification
- Leveraged ResU-Net architecture to achieve ~90% segmentation & classification accuracy, surpassing conventional CNNs by 15%.
- Significantly reduced false positives (10%), enhancing potential diagnostic precision.

Image Search with Words
- Developed a natural language interface for intuitive image library search.
- Optimized embedding and search algorithms for near-instantaneous results.
Diffusion MNIST
- Implemented a Denoising Diffusion Probabilistic Model (DDPM) from foundational principles.
- Successfully generated coherent MNIST digits purely from random noise, exploring controlled generative AI.

Get In Touch
Open to interesting projects and collaborations. Feel free to reach out.