Skip to content

Dr. Argenis A. Arriojas Maldonado

Argenis A. Arriojas Maldonado

Position: Postdoctoral Fellow, Center for Personalized Cancer Therapy
Role: Computational Biologist

Education:

  • Ph.D. in Computational Sciences, University of Massachusetts Boston, 2022
  • M.S. in Physics, Simón Bolívar University, 2014
  • B.S. in Physics, Simón Bolívar University, 2010

Research Interests:

  • Bioinformatics and computational biology
  • Bayesian inference and statistical modeling
  • Gene regulatory networks
  • Machine learning and computer vision
  • Semantic image segmentation and analysis
  • Reinforcement learning
  • Stochastic models of gene expression

Email: [email protected]

Links: LinkedIn | ORCID | GitHub | Google Scholar

Biography

Dr. Argenis Arriojas Maldonado is a Postdoctoral Fellow at the Center for Personalized Cancer Therapy, University of Massachusetts Boston. With a background in physics and computational sciences, his research focuses on developing computational methods and tools for analyzing complex biological data, particularly in the fields of gene regulation, single-cell genomics, and parasitology. His interdisciplinary expertise spans machine learning, Bayesian inference, and mathematical modeling of biological systems.

Research Focus

Dr. Arriojas Maldonado develops innovative computational approaches to analyze multi-omics data and model biological systems. His current research areas include:

  • Time course analysis in single-cell RNA and ATAC-seq datasets
  • Visualization tools for parasite atlases
  • Automated data analysis pipelines for cut-and-run, scRNA, scATAC, and single-cell multi-omics
  • Deep learning models for image segmentation of cellular structures
  • Bayesian inference models for transcription factor activity
  • Stochastic modeling of gene expression regulation

Current Projects

Cell Flow: Interactive Analysis of Single-Cell Omics Data

Developing interactive tools for cell density and trajectory analysis of single-cell omics data, with applications in parasite biology research.

Computational Analysis of Transcriptional Regulation

Working on integrated analysis of CUT&RUN and single-cell knockdown data to reveal hierarchical gene regulation in Toxoplasma gondii.

AI-Enabled Analysis of Mitochondrial Morphology

Developing automated analysis pipelines to assess the impact of chemotherapy on mitochondrial morphology in triple negative breast cancer using transmission electron micrographs.

Selected Publications

  1. Lou J, Rezvani Y, Arriojas A, Wu Y, Shankar N, Degras D, et al. (2024). "Single cell expression and chromatin accessibility of the Toxoplasma gondii lytic cycle identifies AP2XII-8 as an essential ribosome regulon driver." Nature Communications, 15(1):7419.

  2. Arriojas A, Patalano S, Macoska J, Zarringhalam K (2023). "A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data." NAR Genomics and Bioinformatics, 5(4):lqad106.

  3. Arriojas A, Adamczyk J, Tiomkin S, Kulkarni RV (2023). "Bayesian inference approach for entropy regularized reinforcement learning with stochastic dynamics." In: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR, 99-109.

  4. Arriojas A, Adamczyk J, Tiomkin S, Kulkarni RV (2023). "Entropy regularized reinforcement learning using large deviation theory." Physical Review Research, 5(2):023085.

  5. Rezvani Y, Keroack CD, Elsworth B, Arriojas A, Gubbels MJ, Duraisingh MT, et al. (2022). "Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles." PLOS Biology, 20(9):e3001816.

Software and Resources

Honors and Awards

  • Oracle Doctoral Research Fellowship, College of Science and Mathematics, UMass Boston (Fall 2019, Fall 2020, Fall 2021)
  • U54 Program Trainee, UMass Boston Dana-Farber/Harvard Cancer Center U54 Partnership Graduate Research Education Program (2018)