Current Focus

Hello! 👋 I'm currently a Data Scientist at Sephora, where I build recommendation systems and personalization engines that serve millions of customers. My work focuses on next-best-content optimization, offer personalization, and scaling AI systems that directly impact business outcomes.

Beyond my professional role, I'm passionate about exploring the frontiers of AI technology. This blog is where I dive deep into Large Language Models, Reinforcement Learning, Humanoid Robotics, and advanced Recommender Systems - sharing insights, experiments, and thoughts on where AI is heading.

Background & Expertise

I completed my PhD in Computer Science at the University of Cincinnati in 2023, where I specialized in graph neural networks and their applications to complex multimodal datasets. My doctoral research focused on biomedical networks, resulting in several published papers and novel approaches to network analysis.

During my PhD, I also gained valuable industry experience at Seagate, where I investigated Transformers and convolutional approaches for semiconductor soft-sensing data. This work led to innovative solutions for handling imbalanced time-series datasets and bridged the gap between academic research and real-world applications.

Entrepreneurial Journey

Before diving deep into AI research, I was part of founding teams across multiple startups in wellness, advertising, and cloud storage. These experiences taught me the importance of "failure", the real struggle of 0-->1, building products that solve real problems and the challenges of scale from concept to market.

What Drives Me

I'm fascinated by the potential of AI to transform industries and improve lives. Whether it's helping someone discover their perfect product through personalization or envisioning robots that can assist in daily tasks, I'm driven by the intersection of cutting-edge technology and practical impact.

I'm fascinated by the potential of AI to transform industries and improve lives, but what really excites me is something deeper: the patterns of intelligence that nature has been perfecting for eons.

Whether I'm building recommendation systems that help millions find what they need, or exploring how robots might one day work alongside natural systems, I'm always asking: How can we build AI that learns the way life learns? How can we create technology that adapts, balances, and serves the larger web of existence?

Every breakthrough in machine learning feels like an echo of evolution - trial, adaptation, and gradual improvement replayed (the flywheel) at human speed. This perspective guides everything I do, from optimizing algorithms to exploring the frontiers of LLMs and robotics.

I believe the most profound AI won't just solve human problems - it will help us understand and serve the natural intelligence that created us.

Research Profile

Google Scholar

Education

• Ph.D in Computer Science, 2023
  University of Cincinnati
  Dissertation: Graph Neural Networks for Complex Multimodal Systems
• MS in Computer Science, 2018
  University of Cincinnati
  Thesis: Machine learning-based prediction and characterization of drug-drug interactions
• B.Tech in Computer Science, 2013
  Jawaharlal Nehru Technological University

Let’s Connect

I’m always interested in discussing AI, robotics, and the future of technology. Whether you’re a researcher, entrepreneur, or just curious about these fields, I’d love to hear from you.

Reach out: yellajk@mail.uc.edu

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This blog represents my personal views and research interests, not those of my employer.