Gates Hillman Complex, 4902 Forbes Ave
Pittsburgh, PA 15213
Check out my research on contextualized.ml, a statistical machine learning toolbox for estimating powerful biological models at per-patient or per-cell resolution, enabling truly personalized modeling for healthcare and precision diagnostics.
My research interests span meta-learning, multi-task learning, graphical models, and biological modeling. I primarily work on adaptive and sample-efficient machine learning systems that respond to new challenges by using knowledge gathered across many different environments. I’m particularly interested in using these tools to study heterogeneous diseases, like cancer and COVID-19, where disease behavior and pathological mechanisms vary from patient to patient. Meta-learning helps us understand how contexts like genetics and environmental factors create patient-specific disease behaviors and pathologies.
I also enjoy rock climbing, guitar, learning languages, my cats Mimi and Goma, and my rats Boogie and Disco. I’ve lived in Austin TX, Seattle WA, and Pittsburgh PA and I call them all home.
|Nov 16, 2022||Check out my talk from CSHL Biological Data Science this past week: Contextualized Graphical Models Reveal Sample-Specific Transcriptional Networks for 7000 Tumors|
|Oct 20, 2022||Our extended abstract Sample-Specific Contextualized Graphical Models Using Clinical and Molecular Data Reveal Transcriptional Network Heterogeneity Across 7000 Tumors has been selected for a talk at CSHL Biological Data Science and was also accepted to ML4H and GLIndA.|
|Apr 30, 2022||Our study on the variability of the effects COVID-19 treatment effectiveness is published in JBI.|
|Apr 26, 2022||The first version of contextualized.ml, our open-source machine learning toolbox for personalized modeling, has been released!|
|Nov 3, 2021||MultiDAG: Multi-task learning of order-consistent causal graphs is in NeurIPS.|
- arXivNOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters