I’m Sera, an AI researcher and engineer who thrives at the intersection of data, systems, and human understanding. My work focuses on building transparent, reproducible, and reliable AI systems that blend analytical depth with human-centered design. From interpretable ML models to agentic AI prototypes, I aim to make machine intelligence measurable, ethical, and meaningful.
My work bridges data-centric ML and applied AI systems. I’ve developed interpretable models for ICU patient mortality prediction, phenotyping clinical cohorts, and anomaly detection pipelines in enterprise data systems. Recently, I’ve been building agentic AI and RAG systems that combine reasoning with structured analytics.
I believe in rigor over hype, every model, experiment, or agent I build must be measurable, traceable, and communicable. My focus remains on aligning machine intelligence with human decision-making contexts.
At Cognizant, I spent over four years designing and delivering enterprise-grade software solutions for legacy systems in development, data and integration environments. I worked extensively with DB2, IMSDB, and large-scale aviation data systems to optimize performance, ensure data consistency, and derive business-critical insights.
Beyond engineering, I learned how robust systems and clear data pipelines shape organizational intelligence. I led components of data migration projects, mentored junior developers, and coordinated cross-functional collaboration between business and engineering teams, experiences that grounded my later transition into applied AI and machine learning.
During my postgraduate studies, I specialized in Artificial Intelligence, focusing on data efficiency, interpretability, and multimodal learning. My research has examined ethical AI in higher education system, patient stratification, and the robustness of self-supervised learning in health diagnostics.
These experiences shaped my approach: bridging research-grade depth with real-world deployability; always emphasizing clarity, ethics, and empirical grounding.
I’ve led teams through high-stakes data analysis projects and collaborative AI initiatives. From leading our team during the Grand Challenge Innovation Award to first-authoring and presenting research on AI bias in digital marketing, I’ve learned that meaningful innovation requires both vision and execution discipline.
Whether optimizing legacy data pipelines or exploring multi-omic representations of disease, my goal remains the same, to create systems that combine analytical precision, ethical design, and human-centered impact.