Summary
Shakir Khurshid is a data scientist and machine learning engineer with 8 years of experience building production-ready AI solutions across healthcare, railways, advertising, and fashion. He combines deep learning research (ResNet, encoder-decoder architectures, Bayesian neural nets) with pragmatic engineering—delivering real-time LIDAR vegetation detection, ultra-fast image recolorization to 4K, and ML validation pipelines for vaccine production. Shakir has repeatedly improved operational metrics (accuracy gains of 18–25%, inference latencies down to 0.01s, and cost reductions up to 60%) by integrating advanced models with efficient cloud deployments on AWS. Trained at Sapienza with a strong academic foundation and hands-on research at IISc, he blends rigorous evaluation methods (applicability domains, anomaly detection) with product-focused automation. Based in Milan, he favors end-to-end ownership from data collection to deployment and often finds non-obvious gains by rethinking model triggering and resource utilization.
7 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Computer Science, 110/110 with Honors (110L), Master of Science - MS, Computer Science, 110/110 with Honors (110L) at Sapienza Università di Roma
Bachelor of Technology - BTech, Computer Science and Engineering, Bachelor of Technology - BTech, Computer Science and Engineering at University of Kashmir
English, Italian, Kashmiri