Abhivir Singh
AI & ML at Imperial College London
Final-year MEng Computing (AI & ML) student at Imperial. I work on deep learning for medical imaging, build ML systems, and occasionally trade derivatives. Currently writing my thesis on AI-guided fetal ultrasound analysis.
About
I'm studying at Imperial College London, having transferred from IIT Delhi after my first year. My focus is on machine learning research — particularly deep learning for medical applications and natural language processing.
Before university, I was a KVPY Fellow, qualified the Regional Math Olympiad, and scored 9.0/9.0 on the ENGAA. I've received multiple Gold Medals for academic performance. Outside of research, I'm interested in quantitative finance, systems programming, and building things that work well.
Featured Work
See all work →AI for Fetal Heart Ultrasound
2024 — PresentOngoing MEng thesis at Imperial College London. Developing MobileUNet for real-time fetal heart segmentation, AutoFHR for automated heart rate estimation, and L-FUSION for landmark-guided probe navigation. Targeting deployment on portable ultrasound devices for low-resource settings.
Automated ML Platform ↗
Summer 2025Built an end-to-end automated machine learning platform at Fetch.ai using LangChain agents for intelligent pipeline orchestration. Implemented stacked ensemble methods with automated hyperparameter tuning, feature engineering, and model selection.
AI-Powered Text-to-Speech for Ads
Winter 2024/25Developed production voice synthesis at DeepSearch Labs using MaskCycleGAN-TTS for voice conversion and VITS for end-to-end speech synthesis. Enabled personalised ad audio generation at scale.
GPT-2 from Scratch
Summer 2024Implemented GPT-2 (124M parameters) from scratch as part of Eureka Labs. Built the full transformer decoder architecture including multi-head self-attention, positional encoding, and byte-pair encoding tokenizer. Trained on OpenWebText.
Reading
See more →Vaswani et al.
The paper that started the transformer revolution. Still worth re-reading every few months.
Rich Sutton
A short essay arguing that general methods leveraging computation are ultimately the most effective in AI research.
Martin Kleppmann
The best reference for understanding distributed systems, databases, and the infrastructure behind modern applications.