Ali Zahid

Worked at the intersection of classical and quantum machine learning to design and deploy production-grade AI systems for industrial and scientific use cases. Engineered scalable ML and QML pipelines supporting proprietary workflows for global partners including Saudi Aramco and Schrödinger Inc., spanning molecular modeling, materials science, and scientific simulation. Focused on translating advanced research, graph-representation learning, transformers, generative models, and quantum-enhanced algorithms, into reliable, high-performance systems aligned with real hardware and business constraints

Established and led the Data Science department, managing cross-functional teams delivering enterprise-scale analytics for collaborations with Columbia University and global pharmaceutical partners including Novo Nordisk, Genentech, Eli Lilly, GSK, Regeneron, and Novartis. Architected cloud-native data and MLOps platforms on GCP, overseeing large-scale ETL, governance, and orchestration for genotype–phenotype processing. Designed end-to-end bioinformatics and ML pipelines for whole-exome and whole-genome sequencing, enabling high-throughput GWAS, eQTL analysis, and differential gene expression to accelerate clinical target discovery

Taught undergraduate level courses across materials science, nanotechnology, thermodynamics, and statistical mechanics

Conducted experimental and computational research in thin-film technologies, plasma diagnostics, and oxide materials. Worked on plasma immersion ion implantation, high-oxygen-pressure sputtering, MBE growth, and advanced thin-film characterization, integrating diagnostics, data analysis, and materials modeling

Performed multiscale materials modeling research within the Microstructure Physics and Alloy Design group, focusing on crystal plasticity and structure–property relationships in metallic systems. Contributed to simulation workflows for microstructural evolution and deformation behavior, linking theoretical modeling with experimental characterization

Conducted research at the interface of neutron science, thin-film physics, and plasma diagnostics. Investigated complex oxide heterostructures using advanced spectroscopy and scattering techniques, while optimizing high-oxygen-pressure sputtering processes through optical emission diagnostics and materials characterization

Engaged in industrial materials and process development for brazed Ag–SnO₂ contact materials, focusing on synthesis, joining processes, and microstructural optimization. Performed metallographic and phase analysis to evaluate joint quality and reliability, contributing to the refinement of brazing parameters for electrical contact applications

Conducted materials research on engine bore coating systems, evaluating coating integrity and performance through destructive and non-destructive testing methodologies. Investigated interfacial microstructure, adhesion strength, and failure mechanisms to support durability assessment and materials selection for automotive powertrain components

Contributed to applied R&D in advanced functional and sintered magnetic materials, focusing on the development of nano-structured hard magnetic alloys. Worked on alloy design, powder metallurgy routes, and process optimization to enhance coercivity and magnetic density, supported by microstructural and phase characterization to establish structure–property relationships for industrial applications

Oxynitride Glasses | Spark Plasma Sintering | Sol Gel Process for Single Crystal Synthesis

- Quantum search algorithms - Quantum operators on a quantum bot - Entanglement and basics quantum protocols - Probabilistic bits, coin-flipping, biased coins, probabilistic states, and probabilistic operators, correlation - Quantum programs as circuits: registers, gates, measurements, execution, and reading the outcomes, quantum state and operators

The Coding School’s Qubit by Qubit’s 2020-2021 Introduction to Quantum Computing program is offered in partnership with IBM Quantum. Semester 1: foundational math and programming concepts necessary for quantum computing, including important linear algebra concepts. Semester 2: quantum mechanics, quantum information and computation, and quantum algorithms along with using Qiskit and IBM Quantum Experience to run quantum simulations on real quantum hardware.

Physics | Chemistry | Mathematics | English | Urdu

Physics | Chemistry | Mathematics | Biology | Advanced Mathematics | English | Urdu