Mentor Areas
- Biomedical informatics
- Algorithms and methods for large-scale biological data mining
- Modeling and analysis of high-throughput sequencing and large-scale functional genomic datasets
- Applied machine learning
Description:
Students joining this project will have an opportunity to work on developing and using state-of-the-art algorithms, tools and application programming interfaces for mining large-scale genomics, genetics data using distributed computing platforms, including cloud-based, high-performance cluster (HPC), and Apache Spark-based computing environments.
A sample of ongoing projects includes scalable, high-throughput functional genomic data mining algorithms and API, 3D genome interaction and chromatin structure data (Hi-C, Capture-C, etc) processing, modeling, visualization and analysis, integrating and mining of massive collections of datasets across 4D Nucleome, ENCODE, Epigenomics and other resources, information extraction and mining of biomedical literature.
Preferred Qualifications
Successful candidates will meet the following requirements:
1. Currently enrolled in computer science, applied mathematics, statistics or equivalent program
2. Have completed coursework in data structures, algorithms, programming languages
3. Must have excellent programming skills in Python, scripting language (bash), and Linux command line tools
4. Should be capable of working independently and with our lab team
Project Website
Learn more about the researcher and/or the project here. https://www.med.upenn.edu/apps/faculty/index.php/g275/p8758131
Details:
Preferred Student Year
Second-Year, Junior, Senior
Academic Term
Fall, Spring, Summer
I prefer to have students start during the above term(s).Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.