CV

Education

Ph.D in Computer Science, Khoury College of Computer Sciences, 2025 (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.

Bayesian statistical modeling reveals missing value mechanisms in label-free Mass Spectrometry-based proteomics experiments

June 08, 2023

Talk at ASMS 2023, Houston, TX

Computational causal inference methods and single cell proteomics enable the estimation of interventions from purely observational experiments

October 05, 2023

Talk at ASMS Asilomar 2023, Monterey, CA

Single cell mass spectrometry (MS)-based proteomics coupled with suitable experimental design enables the application of causal inference methods to observational experiments.

October 06, 2023

Talk at iSCMS 2023, Provo, UT

The Promises and Perils of MS Machine Learning

October 15, 2023

Panel at ASMS Asilomar 2023, Monterey, CA

Service and leadership

Committees

Human Glycoproteomics Initiative - 2nd study, 2022 - Present, https://www.hupo.org/page-1757437

Peer Reviewer

Bioinformatics - 2022
Molecular & Cellular Proteomics - 2022

Student Mentor

Mentoring multiple Masters in Computer Science and Data Science students, 2022-present

Github Repositories

Personal: https://github.com/devonjkohler/ Laboratory: https://github.com/Vitek-Lab/

Devon Kohler

CV

Education

Research experience

Work experience

Technical Knowledge

Publications

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

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

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

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

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

Talks

MSstatsPTM: an R/Bioconductor software for detecting quantitative changes in post-translational modifications

Explorations of causal probabilistic programming approaches for rule-based models of biological signaling pathways

MSstatsShiny: A Multipurpose UI for Reproducible Analysis of Quantitative Proteomic Experiments

Single-cell mass spectrometry-based proteomics enables causal inference in observational studies

Analysis of Quantitative MS-based Proteomics Experiments using MSstats and MSstatsTMT

Data Independent Acquisition: After the Acquisition

Bayesian statistical modeling reveals missing value mechanisms in label-free Mass Spectrometry-based proteomics experiments

Computational causal inference methods and single cell proteomics enable the estimation of interventions from purely observational experiments

Single cell mass spectrometry (MS)-based proteomics coupled with suitable experimental design enables the application of causal inference methods to observational experiments.

The Promises and Perils of MS Machine Learning

Teaching

Algorithms

R Fundamentals and Best Practices for Mass Spectrometry Data Analysis

Statistical Inference in Computer Science

Quantitative Proteomics: Case Studies

Statistical considerations: Statistical methods & Experimental Design

Design and Analysis of Quantitative Proteomics Experiments with TMT Labeling: case studies using FragPipe and MSstatsTMT

Quantitative Proteomics: Case Studies

Quantitative proteomics: strategies and tools to probe biology

Bioinformatics

Design and analysis of quantitative proteomics experiments: statistical methods and case studies with MSstats

Experimental design and statistical analysis of targeted proteomics data

Experimental Design and Biostatistics (Lecture + Tutorial)

Service and leadership

Github Repositories