Exploring the intersection of human and artificial intelligence
I build at the boundary of biological brains and computational intelligence, advancing neural decoding, training deep neural nets, and AI-driven medical systems. Driven by curiosity and a builder's mindset, I love hardware prototyping, startup ideas, and shipping open-source projects.
The tools and technologies I work with across AI, neurotech, embedded hardware, and the web.
Open-source contributions and hardware prototypes combining digital signal processing, Edge AI, and web technology.
Engineered an end-to-end pipeline to classify 4-class motor imagery (hand/foot movement intents) using the EEGNet architecture. Implemented MNE-Python for ICA-based artifact removal, establishing critical performance baselines in BCI experiments.
Deployed a real-time computer vision detection system on an ESP32-S3 Sense board. Optimized a quantized MobileNet V2 model using the FOMO algorithm, reaching 7 FPS at 143ms latency for industrial parts sorting.
Contributing to an open-source initiative dedicated to preserving and revitalizing endangered Indian languages. Combining NLP, speech recognition, and LLMs to create accessible linguistic analysis tools.
A high-performance, client-side tool for removing sparkle watermarks from images generated by Google Gemini AI. Uses a mathematically precise Reverse Alpha Blending algorithm to restore original pixels with zero quality loss.
Research internships, open-source leadership, and engineering roles.
Leading workshops, mentoring students, and driving open-source culture at Model Engineering College.
Worked at Tencent AI Lab on ML, NLP, and LLM research for open-source AI model development.
Developed the website for a decentralized marketplace built on the TON blockchain.
Research preprints and academic contributions in computational neuroscience and AI.
Abhin Krishna · Computational Neuroscience · Neural Modelling
A rigorous comparative analysis of two foundational computational neuroscience models: the biophysically detailed Hodgkin-Huxley model and the computationally efficient Izhikevich model, evaluated for their accuracy in predicting high-fidelity neural spike trains across varied stimulation conditions.
Selected milestones demonstrating academic rigour, research, and global engagement.
Selected for the prestigious Harvard Project for Asian and International Relations Conference.
Completed computational systems neural circuit modeling training at CNS Lab, IIT Madras.
Presented "PhytoScan", an AI-driven fruit freshness detection device at Money Conclave.
Goethe Zertifikat B1 certified speaker in German language with a score of 82.
I am actively looking for co-founders, research collaborators, and start-up connections. If you want to discuss BCI, neural systems modeling, or start-up ideas, let's talk.