About

Hello, I am a final year Computer Science Ph.D. student in Computational Biology Department at Carnegie Mellon University. My research interest lies in machine learning in cancer genomics, tumor phylogenetics, and computational healthcare.

I have been working with Prof. Russell S. Schwartz. In my first two years at CMU, I collaborated with Dr. William W. Cohen and Prof. Xinghua Lu. Prior to that, I worked with Prof. Jianyang Zeng during my undergrad. I hold a master's degree in machine learning from CMU and a bachelor's degree in automation (double major in economics) from Tsinghua University. You can find my CV here. I am fortunate to be supported by the CMLH Fellowship in Digital Health in 2019-2020.

我的中文名是陶一锋.

Research: Machine Learning in Cancer Genomics

Cancer proceeds from the accumulation of genomic alterations, and develops into heterogeneous cell populations in an evolutionary process. Therefore, the prognoses of cancer patients, such as survival profile, metastasis, and drug response, are encoded by the large-volume genome data. Our research focuses on the personalized medicine of cancer with machine learning and phylogenetic models:

• Reliable phenotype inference of cancer through well-designed interpretable machine learning models. By leveraging the power of large scale genomic data and external biomedical knowledge base, we have been working on deep learning models for the accurate inference of cancer phenotypes, including transcriptome expression levels (Genomic Impact Transformer; GIT), transcription factor activities, and drug resistance (Contextual Attention-based Drug REsponse; CADRE). We addressed the interpretability of models through techniques such as attention mechanism to identify driver mutations and critical biomarkers.
• Revealing intra-/inter-tumor heterogeneity and mechanism of tumor progression via robust deconvolution and phylogenetic algorithms. We formulated the deconvolution of bulk tumor molecular data mathematically as a biologically inspired matrix factorization problem, and proposed a neural network (Neural Network Deconvolution; NND) and then an improved hybrid optimizer (Robust and Accurate Deconvolution; RAD) to solve the problem robustly and accurately. We developed and applied a Minimum Elastic Potential (MEP) algorithm to reconstruct the evolutionary trajectory from the unmixed clones. Our ongoing projects focus on the integration of single-cell data for finer resolution of clone deconvolution and phylogeny inference (FISH-Deconv).
• Improving prognostic prediction of cancer by incorporating machine learning and evolutionary methods. Clinicians traditionally focused on the pathological features and driver-level genomic profiles to facilitate the treatment. However, it is possible that critical clones, instead of the bulk tumor as a whole, affect the prognoses. We explored the questions by integrating both the evolutionary mutational features, driver-level features, and clinical features to improve the prognostic prediction of cancer. We developed an L0-regularized Cox regression model (Phylo-Risk), and found that the evolutionary features account for roughly 1/3 of all the available features, depending on cancer types and sequencing techniques.

Misc

Reviewer

• Reviewer for AAAI Trustworthy AI for Healthcare (AAAI-TAIH), 2021
• Secondary reviewer for International Conference on Research in Computational Molecular Biology (RECOMB), 2021
• Reviewer for Pacific Symposium of Biocomputing (PSB), 2020
• Reviewer for Journal of the American Medical Informatics Association (JAMIA), 2020
• Reviewer for Frontiers of Engineering Management (FEM), 2020
• Reviewer for International Conference on Control, Automation and Systems (ICCAS), 2020
• Secondary reviewer for RECOMB Computational Cancer Biology (RECOMB-CCB), 2020
• Reviewer for PLOS Computational Biology, 2019
• Reviewer for International Journal of Production Research (IJPR), 2019
• Secondary reviewer for International Conference on Intelligent Systems for Molecular Biology (ISMB), 2019
• Secondary reviewer for International Conference on Research in Computational Molecular Biology (RECOMB), 2019
• Secondary reviewer for Workshop on Algorithms in Bioinformatics (WABI), 2019
• Secondary reviewer for IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018

Teaching

• Instructor of Introduction to Machine Learning at Northeastern University (China), 2019

Teaching Assistant

• 10725: Convex Optimization by Pradeep Ravikumar and Aarti Singh
• 10708: Probabilistic Graphical Models by Kayhan Batmanghelich

Courses

• 10715: Advanced Introduction to Machine Learning by Barnabás Póczos
• 10705: Intermediate Statistics by Siva Balakrishnan
• 10702: Statistical Machine Learning by Larry Wasserman
• 10725: Convex Optimization by Ryan Tibshirani and Javier Peña
• 10708: Probabilistic Graphical Models by Eric Xing
• 02750: Automation of Biological Research by Christopher James Langmead
• 02710: Computational Genomics by Jian Ma and Maria Chikina
• 02730: Cell and Systems Modeling by Jim Faeder, Robin Lee and Jianhua Xing
• PT749: Introduction to Computational Structural Biology by Timothy Lezon and Carlos Camacho
• 03730: Advanced Genetics and Genome Editing Biotechnology by John Woolford and Stephanie Wong-Noonan
• 02760: Laboratory Methods for Computational Biologists by Joshua Kangas, Emily Furbee and Joseph Ayoob