Superfloat
↗AI-first datatype and quantization pipelines for hardware acceleration. Optimizing neural networks for efficient power and speed.
- C++
- CUDA
- Python
- Quantization
Building and deploying agentic AI systems and high-throughput inference pipelines.
Computer Science graduate with extensive experience in AI/ML engineering, specializing in deep learning, LLM development, and AI safety research.
Proven track record in building and deploying agentic AI systems and high-throughput inference pipelines. I love involving myself in multi-domain and multi-omic research, pushing the boundaries of Generative AI and Edge Computing.
Architected and deployed agentic AI systems for autonomous threat detection and response. Developed "Agentic Studio" for automated remediation and implemented RL algorithms for cybersecurity.
Investigated LLM hallucination rates in long-context models. Designed experiments to quantify evidence distortion and analyzed safety implications of deception-like behaviors.
Led development of multimodal LLMs for Malayalam education. Launched "Adapt AI", driving 50% MAU growth and achieving #1 ranking in educational apps in Kerala.
Developed ensemble forecasting models (LSTM, RNN, XGBoost) for Cochin Port traffic prediction with 92% accuracy.
Led autonomous Martian rover development for the European Rover Challenge. Built autonomous drone systems using MATLAB and Raspberry Pi.
AI-first datatype and quantization pipelines for hardware acceleration. Optimizing neural networks for efficient power and speed.
RTL design + GDSII layout for a quad-core Q1.15 GPU built for high-throughput inference-only Superfloat workloads.
Uncensored Flux image generation model achieved through surgical unlearning techniques to remove baked-in safety filters.
Advanced Malayalam text-to-speech model based on conditional VAE architecture for natural speech synthesis.
Reverse engineering Gemini's SynthID detection
PyTorch, TensorFlow, JAX, CUDA, LLMs, Computer Vision, RAG Systems, AI Safety
Python, C/C++, Triton, Verilog, React, Node.js, FastAPI, SQL
AWS, GCP, Docker, Modal, Pinecone, Qdrant, Git, ROS 2
Team Horizon is a multi-disciplinary rover building team from CUSAT. Participated in the European Rover Challenge (ERC) in Poland (2023 & 2024), achieving world rank 11th.
Actively organised and hosted hackathons - CodeCrypt. Hosted sessions on Blockchain, AI/ML, and Research.
Engaged in community services, cleanup drives, and led technical bootcamps for social impact.
Regularized Hybrid Deep Learning for DDoS Attack Prediction in Software-Defined IoT Networks. Published in IEEE ICPC2T, 2024.
Novel Text2ASMR framework for generating Autonomous Sensory Meridian Response Audio. Preprint, 2024.
IoT and ML for Information Management: A Smart Healthcare Perspective. Springer Book Chapter, 2024.
Time Series Prediction of Container Freight Through Cochin Port Using Machine Learning Models. CTSEM, 2025.
An Ensembled Deep Learning Model to Improve Diagnosis of Dementia Stages from Brain MRI Images. IEEE ICSCC, 2025.
AI Research Methodologies in Cardiology. Springer Book Chapter 6, 2026 (In Press).
An exploration of abliteration and orthogonalization to remove LLM built-in refusal mechanisms.
Advanced surgical unlearning techniques for Flux.1 Dev, refining the abliteration methodology.
A deep dive into uncensoring the GPT-OSS-20B model using residual stream ablation techniques.
A comprehensive walkthrough of Large Language Models, from transformer blocks to attention mechanisms.
Insights from the Mumbai summit featuring Jensen Huang and Mukesh Ambani on India's technological leap.
A developer's guide to building decentralized real-time communication apps with Huddle01.
Open for collaborations, hackathons, and coffee chats.