Search
Christopher L.'s Photo

Harvard Biomedical AI PhD
Christopher L.

Your first lesson is backed by our Good Fit Guarantee
Hourly Rate: $100
Contact Christopher

About Christopher

Bio

I’m currently pursuing a PhD in Biomedical Informatics at Harvard Medical School, where my research focuses on applying artificial intelligence to integrate multi-omics data for disease modeling. I graduated summa cum laude from the University of Pennsylvania with a B.S. in Statistics (Wharton) and a B.A. in Mathematics & Computational Biology, complemented by minors in Computer Science and Data Science. To deepen my pedagogical foundation, I earned Penn’s University Certificate in Teaching...

I’m currently pursuing a PhD in Biomedical Informatics at Harvard Medical School, where my research focuses on applying artificial intelligence to integrate multi-omics data for disease modeling. I graduated summa cum laude from the University of Pennsylvania with a B.S. in Statistics (Wharton) and a B.A. in Mathematics & Computational Biology, complemented by minors in Computer Science and Data Science. To deepen my pedagogical foundation, I earned Penn’s University Certificate in Teaching Excellence, which grounded me in evidence-based instructional strategies, learning theories, and inclusive classroom practices.

As Head Teaching Assistant for both Bayesian Statistics and Statistical Inference—courses populated by 18- to 22-year-old undergraduates—I designed and led weekly recitation sessions of 25–30 students. In Bayesian Statistics, I translated concepts like prior selection and Markov chain Monte Carlo into hands-on coding exercises in R and Python, while in Statistical Inference I guided students through deriving likelihood functions, constructing confidence intervals, and hypothesis testing using real biological datasets. I held biweekly office-hour blocks that alternated one-on-one meetings with small-group workshops, employing active-learning techniques (think–pair–share, data-driven case studies) and mid-semester feedback to tailor examples and pacing to diverse mathematical backgrounds.

Across all settings—lecture halls, computer labs, and virtual sessions—I emphasize clear, jargon-free explanations and iterative practice. I bring strong expertise in programming (Python, R, Java), mathematical modeling, statistical theory, and molecular biology to every lesson, pairing technical rigor with supportive mentorship so students build both mastery and confidence.

Read more

Policies

Schedule

Christopher hasn’t set a schedule.

Approved Subjects

Biostatistics

Biostatistics

I am a PhD candidate in Biomedical Informatics at Harvard Medical School, having been accepted by all four of the top-ranked biostatistics PhD programs at Harvard, Johns Hopkins University, the University of Pennsylvania, and the University of Washington. I have served as a Teaching Assistant for courses in Bayesian Statistics and Statistical Inference, where I guided students through probability modeling, hypothesis testing, and the practical implementation of MCMC methods. In my research, I design and apply advanced biostatistical techniques—including hierarchical models, penalized regression, and spatial statistics—to single-cell and spatial transcriptomics data, resulting in multiple first-author publications. I also mentor learners in rigorous experimental design, reproducible analysis pipelines in R and Python, and clear interpretation of complex, high-dimensional datasets.
Machine Learning/ AI

Machine Learning/ AI

I have designed and implemented deep learning architectures—ranging from convolutional neural networks to diffusion models—tailored for single‐cell and spatial transcriptomics, achieving state‐of‐the‐art performance in chromatin accessibility prediction and batch‐effect correction. My work with foundation models and agentic AI has integrated multimodal biomedical data (imaging, single‐cell, spatial omics, EHR) to uncover regulatory cascades and adaptive response signatures. I am proficient in Python and R, leveraging libraries such as PyTorch and TensorFlow to build scalable, explainable AI pipelines for real‐world biological applications. My experience spans model interpretability (e.g., saliency maps, TF‐MoDISco), robust evaluation with validated biomarkers, and translating complex algorithms into reproducible workflows.
Biology
GRE
Statistics
Christopher L.'s Photo

Questions? Contact Christopher before you book.

Still have questions?

Contact Christopher
Hourly Rate: $100
Contact Christopher