CV

Education

Northeastern University, Boston, MA, 2018 - Present
- PhD in Computer Science
University of Michigan Engineering Honors College, Ann Arbor, MI, 2014 - 2018
- B.S.E in Computer Science Engineering with a minor in statistics
- Summa Cum Laude

Work experience

January 2019 - Present: Graduate Research Assistant: Northeastern University
- Developed a deep learning model for class prior estimation for positive-unlabeled data using Tensorflow
- Researching heterogeneous graph-based models for compound protein interaction prediction using Pytorch
September 2018 - December 2018: Graduate Research Assistant: Northeastern University
- Developed a deep sequence-to-sequence model using GRUs that forecasts spatiotemporal data
- Achieved prediction accuracy on-par with published methods for next hour traffic speed forecasting in the METR-LA dataset
- Trained model to forecast realistic sequences of human poses using the Human3.6M dataset
May 2017 - April 2018: Research Assistant: University of Michigan
- Implemented a model that risk stratifies hospital patients for Acute Respiratory Distress Syndrome
- Developed a model that beats the current leading method by a significant margin
- Supervisor: Jenna Wiens
June - August 2016: Software Defined Core Network Engineering Intern: Comcast
- Developed a network health dashboard that displays key metrics on Comcast’s service performance and identifies service outages using Python, the Django framework, JavaScript, and JQuery
- Wrote Python scripts that automate the IP address cleanup workflow, managing millions of IPs
May - July 2015: Engineering Analysis Intern: Comcast
- Created visualizations using the D3.js data visualization library that modeled On-Demand data flow that was used by business operations personnel to make decisions on network scaling
- Taught myself advanced JavaScript techniques and the fundamentals of data visualization
July - August 2015: Software Development Intern: Scholly
- Developed an online database management console that allows team members to update scholarships and view database metrics, using MySQL, Python, and Django
- This tool automated Scholly’s database management, improving efficiency across the company

Achievements and Skills

“Most Likely To Have Transformative Scientific Impact” Award - MIDAS Symposium 2017
Tau Beta Pi Engineering Honor Society
Proficient in C++, Python, Matlab, Java, MySQL, Node.js, Angular.js, MongoDB, Express.js

Publications

Machine learning for patient risk stratification for acute respiratory distress syndrome

Zeiberg, Daniel, et al. "Machine learning for patient risk stratification for acute respiratory distress syndrome." PloS one 14.3 (2019): e0214465.

Fast Nonparametric Estimation of Class Proportions in the Positive-Unlabeled Classification Setting

Zeiberg, Daniel, Shantanu Jain, and Predrag Radivojac. "Fast nonparametric estimation of class proportions in the positive-unlabeled classification setting." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 04. 2020.

Classification in biological networks with hypergraphlet kernels

Lugo-Martinez, Jose, et al. "Classification in biological networks with hypergraphlet kernels." Bioinformatics 37.7 (2021): 1000-1007.

Multi-objective prioritization of genes for high-throughput functional assays towards improved clinical variant classification

Chen, Yile, et al. "Multi-objective prioritization of genes for high-throughput functional assays towards improved clinical variant classification." PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023: Kohala Coast, Hawaii, USA, 3–7 January 2023. 2022.

Daniel Zeiberg

CV