Chicago, Illinois, United States
I'm currently a Senior Applied ML Scientist at Grainger. Prior to that, I worked in healthcare and talent technology, building LLM-powered applications and agentic AI systems for production. I completed my postdoctoral research at the University of Chicago, utilizing Python and deep learning to study the effects of schizophrenia on cognitive functions. I hold a PhD in Cognitive Neuroscience, where I used big data, statistical testing, and machine learning to understand human emotion and cognition.
- Developed AI and ML solutions with large language models (LLMs) and natural language processing (NLP) to streamline and automate talent management processes, such as talent search. - Built and deployed RAG-based talent recommendation systems with conversational interfaces using LangChain and vector databases. - Collaborated with cross-functional teams to optimize AI/ML application performance, improving load times and reducing latency for production talent management systems.
- Using Python and deep neural networks (CNNs, RNNs), built models to predict biomarkers associated with schizophrenia, facilitating early-stage clinical diagnostics - Ingested, transformed, and maintained clinical data, ensuring HIPAA compliance and enhancing data quality for classification and regression modeling - Applied statistical techniques (causal inference, time-series analysis, survival analysis) to analyze clinical data, improving understanding of cognitive decline and mental health outcomes
- Developed automated feature extraction pipelines to process biometric data (EEG, fMRI, pupil), session data, unstructured text, and assessment results. - Applied causal inference and Bayesian methods for data quality control and outlier detection, revealing insights into cognitive and emotional health during memory and stress-related tasks. - Built machine learning and deep learning models—including linear regression, logistic regression, XGBoost, CNNs, and RNNs—to analyze behavioral and biometric data, identifying biomarkers related to memory function and stress response. - Created time series models (ARMA, ARIMA) to predict engagement patterns in gamified cognitive assessments app, leveraging UX analysis to optimize app features.
- Teaching Assistant for courses: -- Introductory Psychology -- Sensation and Perception -- Neuroscience of Brain and Behavior -- Advanced Statistical Research Methods -- Mixed Research Methods - Taught discussion sessions to reinforce course concepts and fostered student engagement through interactive learning. - Provided personalized feedback on assignments and supported the instructor with course preparation, grading, and student progress tracking. - Completed the Teaching Assistant Development Program (TADP), enhancing instructional and student engagement skills.