Yasser Qureshi
AI Researcher & Engineer driving innovation at the frontier of machine learning and complex systems
about
PhD researcher building interpretable machine learning systems to decode behaviour in biological and real-world environments. I specialise in turning raw, high-dimensional data into actionable insight—whether in public health, neuroscience, or environmental systems.
Deeply passionate about translating breakthrough research into real-world impact, I've led the development of AI-driven solutions across healthcare, sustainability, and beyond. With a strong foundation in software engineering, time-series analysis, and explainable AI, I bridge the gap between academic innovation and practical deployment.
Machine Learning Engineer
Leading the development of multi-modal AI systems to forecast seizure risk using time-series physiological data (heart rate, stress, sleep) from wearables. Built real-time classifiers with continuous learning from user feedback, leveraging signal processing, temporal modeling, and probabilistic forecasting.
PhD Researcher - Explainable Artificial Intelligence for Analysing Mosquito Flight Behaviour
Pioneering machine learning methods to understand mosquito flight behaviour, uncovering insights into insecticide resistance through trajectory analysis.
IBM Extreme Blue Technical Intern
Selected as 1 of 16 interns (< 0.2% acceptance rate) from 10,000+ applicants for IBM's elite Extreme Blue programme, focused on high-impact innovation. Engineered a production-ready 3D digital twin platform for client factory systems and prototyped a digital sustainability card for Google Wallet, impressing senior stakeholders and exceeding client expectations.
Founder - E-commerce Monitoring Saas
Founded and scaled a sneaker e-commerce monitoring platform to nearly $100k ARR, 10,000+ downloads, and a community of 2,500+ active members. Developed a suite of tools to monitor e-commerce sites, providing real-time alerts for restocks and releases.
PaperMatch - AI-Powered Academic Paper Recommender
Built an AI-powered “Tinder for research” that reimagines academic discovery with real-time, feedback-driven paper recommendations.
AI Platform for GC-IMS Data Analysis
Architected and led development of a scalable AI platform for GC-IMS data, enabling real-time biomarker detection across 10+ research studies in healthcare and chemical analysis.
Creator - Sneaker Monitors (Open-Source Project)
Created a widely adopted open-source sneaker monitoring system, scaling to 500+ GitHub stars and influencing a new wave of automation tools in the e-commerce space.
My research sits at the intersection of machine learning, explainable AI, and complex systems. I develop robust, interpretable AI frameworks capable of uncovering structure and insight in high-dimensional, noisy data—pushing the boundaries of what we can extract from real-world signals. My current work pioneers the use of trajectory-based ML to decode mosquito behaviour, revealing patterns critical to vector control and public health.
Beyond entomology, my published work bridges theory and application across domains—from forecasting epileptic seizures using physiological signals to advancing chemical sensing and behavioural modelling. This breadth reflects a core strength: building adaptable, transparent AI systems that generalize across disciplines, with impact that extends far beyond the lab.
