Eliya Taleb
Basic Information
I recently graduated from Cornell Tech, Cornell University, with a Master of Engineering in Electrical and Computer Engineering, where my focus was on scalable machine learning systems, probabilistic modeling, and learning under uncertainty. Prior to Cornell, I earned my Bachelor of Science in Mechanical Engineering from Purdue University, with a minor in Artificial Intelligence and Machine Learning.
Personal Introduction
My work focuses on machine learning, engineering systems, and decision-making under uncertainty. I am especially interested in building interpretable and scalable AI systems for complex physical, sequential, and data-driven problems. My background combines mechanical engineering, probabilistic modeling, generative AI, and applied machine learning, with experience in research, design optimization, cloud compliance automation, and probabilistic systems.
Research Interests and Current Work
My research interests include probabilistic modeling, sequential decision-making, scalable machine learning, surrogate modeling, and generative design. I have worked on generative models for aerospace structural data, neural surrogate models for replacing expensive finite element simulations, Hidden Markov Models for sequential gesture recognition, scalable transformer training frameworks, and probabilistic mapping/SLAM. More recently, I developed an automated compliance evidence engine advised by Google Cloud, focused on converting cloud security telemetry into structured, machine-readable evidence artifacts for AI-assisted compliance reasoning.
Selected Achievements
For my Purdue senior design capstone, my team designed and built a regenerative turbo lag reduction system that reduced turbo lag by storing excess boost pressure and reinjecting it during acceleration transients. We used analytical and simulation-based methods, including ANSYS fluid analysis, alongside a physical prototype that measured pressure differences across test conditions to validate the system’s performance and confirm the reduction in turbo lag. The project received Purdue Mechanical Engineering’s Malott Award.
Professional Links
LinkedIn: www.linkedin.com/in/etaleb
