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I'm Ran Liu. I completed my PhD in Biomedical Engineering at the Johns Hopkins University in 2021.

I am currently a Postdoctoral Scholar with Dr. Patrick Purdon at Stanford University in the department of Anesthesiology, Perioperative, and Pain Medicine. Our lab was formerly at Massachusetts General Hospital and Harvard Medical School in the department of Anesthesiology, Critical Care, and Pain Medicine. My dissertation was on the application of statistical and machine learning methods for decision support in critical care. Currently, I'm working on research in computational neuroscience and anesthsiology, on treatment optimization in anesthesia and pain management, and on the characterization of consciousness and disease states.

I am broadly interested in the application of dynamical systems modeling, machine learning, and artificial intelligence methods to problems in medicine. In particular, I am interested in brain function, consciousness, and cognition, and in the intersection of machine learning and model-based methods.

https://scholar.google.com/citations?user=bshgBtkAAAAJ

Ran Liu's Projects

amsterdamumcdb icon amsterdamumcdb

AmsterdamUMCdb - Freely Accessible ICU database. Please access our Open Access manuscript at https://doi.org/10.1097/CCM.0000000000004916

clustering_manuscript icon clustering_manuscript

Code associated with the paper, "Spectral Clustering of Risk Score Trajectories Stratifies Sepsis Patients by Clinical Outcome and Interventions Received"

compilers-cs-328 icon compilers-cs-328

SIMPLE to ARMv6 compiler written for the course 600.328 at JHU: http://gaming.jhu.edu/~phf/2016/spring/cs328/

machinelearning icon machinelearning

Application of online neural network learning to ballistic targetting. Final project for 600.475 at JHU

pain_prediction_pops icon pain_prediction_pops

This is the official code repository for the manuscript "Development and prospective validation of postoperatve pain prediction from preoperative EHR data using ICD-10 and CPT attention-based set embeddings."

shockalert-documented icon shockalert-documented

Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU

te-cde icon te-cde

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)

tictactoec icon tictactoec

C command line implementation of Tic Tac Toe, with minimax solver

time2vec-pytorch icon time2vec-pytorch

Reproducing the paper: "Time2Vec: Learning a Vector Representation of Time" - https://arxiv.org/pdf/1907.05321.pdf

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