Shravan Suresh

Automating Java-based backend systems and AI infrastructure to streamline operations and enhance system efficiency.

Contributing to the enhancement of Life Cycle Management (LCM) processes for Electronic Health Record (EHR) environments.

Developed a Python-based synthetic data generator leveraging GANS, VAEs, diffusion models, and transformers, producing over 10,000 ITAR-compliant synthetic engine cycles from 21-sensor aerospace datasets.

Preserved real-world engine behavior with less than 0.03 correlation error between real and synthetic sensor relationships, validated via PCA, covariance analysis, and distributional metrics.

Engineered conditional generative models parameterized by Mach, altitude, and throttle settings, enabling controlled simulation of diverse flight regimes for PHM algorithm research and testing.

Applied Generative AI and predictive modeling to enhance analysis of over 10 early-phase clinical trials, optimizing insights from small-sample, high-dimensional datasets.

Leveraged LLMs to generate 5-10 clinically relevant subgroup hypotheses per trial, significantly improving focus for enrichment analysis.

Trained GANS, VAEs, and diffusion models in Python to create over 1,000 synthetic patient records, preserving endpoint relationships and augmenting limited trial data.

Conducted extensive market and user research, including 50+ customer interviews and surveys, identifying 3 key opportunities for AI integration projected to improve user efficiency by 25%.

Assisted in prototyping and usability testing of 2 AI-driven tools, achieving an average 80% positive usability score from pilot users.

Supported cross-functional strategy sessions, aligning technical feasibility with customer needs and contributing to a 15% faster roadmap iteration cycle.

Assisted in scientific research under Dr. Usmani, analyzing over 3,000 records using computational methods to identify social trends related to injustice and inequality.

Built a robust data-driven framework for generating evidence-based assessments on complex societal issues, including injustice and inequality.