CV

Education

Ph.D in Computer Science, Khoury College of Computer Sciences, 2026 (expected)
M.S. in Data Science, Khoury College of Computer Sciences, 2020
B.A. in Economics, Union College, 2013

Research experience

Graduate Researcher, Northeastern University, Boston, MA, 2020-Present

Work experience

Computational Proteomics Intern, Genentech, South San Francisco, CA, Summer 2021
Data Science Graduate Coop, The TJX Companies, Framingham, MA, Winter 2020
Data Analyst/Engineer, Cybba Inc, Boston, MA, 2015-2019

Technical Knowledge

Programming Languages: Python, R, SQL, Julia, Matlab, Git (Github/Bitbucket), BASH
Machine Learning/Statistics: Scikit-learn, Keras, PyTorch, Pyro, XGBoost, lme4
Software: Amazon Web Services (AWS), Docker, Posit Cloud, Linux, Mac OS, Windows

Publications

Recent Developments in MSstats Ecosystem: A collection of statistical methods for general scalable quantitative analysis of proteomic experiments.

**Kohler, D.**, Stankiak, M., & Vitek, O. (2022). Recent Developments in MSstats Ecosystem: A collection of statistical methods for general scalable quantitative analysis of proteomic experiments. HUPO News.

Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications

Malinovska, L., Cappelletti, V., **Kohler, D**. et al. Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications. Nat Protoc 18, 659–682 (2023).

MSstatsPTM: Statistical Relative Quantification of Post-translational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

**Kohler D**, et al. MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics. Mol Cell Proteomics. 2023 Jan;22(1):100477.

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

**Kohler D**, et al. MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments. J Proteome Res. 2023 Feb 3;22(2):551-556.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

**Kohler D**, et al. MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale. J Proteome Res. 2023 May 5;22(5):1466-1482.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

**Kohler, D.**, Staniak, M., Yu, F. et al. An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing. Nat Protoc (2024).

Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach

Mateusz Staniak, Ting Huang, Amanda M Figueroa-Navedo, **Devon Kohler**, Meena Choi, Trent Hinkle, Tracy Kleinheinz, Robert Blake, Christopher M Rose, Yingrong Xu, Pierre M Jean Beltran, Liang Xue, Małgorzata Bogdan, Olga Vitek, Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach, Bioinformatics, Volume 41, Issue 3, March 2025, btaf046, https://doi.org/10.1093/bioinformatics/btaf046.