Career Profile
-
Computational research scientist with 10+ years of experience in data analysis and 4+ years of experience developing and applying machine learning tools.
Education
Skills & Proficiency
- Github
- Python
- R
- Matlab
- Latex
- sci-kit learn
- keras
- tensorflow
- Machine Learning
- Applied mathematics
- Deep Learning, generative neural networks
- Topology
- Dynamical systems
Experience
-
- Prototype cloud-based computational workflows to analyze genomic, proteomic large-scale datasets
- Train users how to apply the workflows for their own research via online webinars
-
- Advise technical aspects of projects that applied machine learning methods to proteomics data
-
- Performed gene-gene correlation analyses that revealed transcriptional patterns within and between core and accessory genes across P. aeruginosa strain types
- Designed an approach to distinguish between common and context-specific transcriptional signals using a generative neural network to simulate a set of background transcriptome experiments: https://github.com/greenelab/sophie
- Developed and published a python package to simulate a compendium of genome-wide gene expression data using a generative neural network: https://pypi.org/project/ponyo/
- Built an analysis pipeline in python to measure the effect of varying amounts of technical noise in simulated compendia
-
- Designed and implemented a computational analysis workflow to identify candidate Zika-specific epitopes to improve diagnostic tests
- Assisted in the development of a user-constrained computational pipeline to semi-automatically gate flow cytometry data (python)
- Used data mining to identify antigenic regions of Influenza A virus HA protein that could be candidate targets of protective immunity
- Facilitated enhancements to Influenza Research Databases (www.fludb.org) and Virus Pathogen Resource Database (www.viprbrc.org) Bioinformatics Research Center
- Performed research on: Influenza virus, Flaviviruses, Chikungunya virus, MERS-CoV, Arenavirus, RSV, Ebolavirus
Teaching Experience
- Designed structure of the workshop
- Presented about recurrent neural networks and dimensionality reduction methods (PCA, autoencoders)
- Materials: Blog advertisement; workshop recording
- Designed structure of the course
- Wrote weekly HW assignments with built in test cases
- Gave lecture about python pandas
- Assisted students with HW and gave them feedback on assignments
- Course material
- Mentored one undergraduate student through GCB’s Summer in Computational Biology pilot program
- Guided students through their research projects; helped them to decide next steps; Provided technical support
- Tutored and assisted students in lecture content and homework at bi-weekly office hour sessions
- Tutored and assisted students in studying/programming scribbler robots to perform simple tasks
Leadership Experience
- Lead initiatives to help 1st/2nd year graduate students adjust to Penn, Philadelphia, and graduate-school life
- Attended bi-monthly committee meetings to discuss the progress of initiatives and provide feedback on new proposals
- Organized venue, activities, food, and refreshment for social events
- Coordinated with external vendors