# Erick Armingol

> researcher ORCID ID = 0000-0002-1546-9165

**Wikidata**: [Q59706525](https://www.wikidata.org/wiki/Q59706525)  
**Source**: https://4ort.xyz/entity/erick-armingol

## Summary

Erick Armingol is a researcher[1] with educational background from the University of Chile and University of California, San Diego[1]. Erick Armingol's work is focused on systems biology, bioinformatics, biotechnology, and computational biology[1]. Erick Armingol has been employed by Concha y Toro from 2017 to 2018 and R2B catalyst from 2015 to 2017[1].

## Summary  
Erick Armingol is a Chilean researcher who works at the intersection of machine learning, bioinformatics and computational biology. He earned an engineering degree at the University of Chile and a Ph.D. in systems biology from the University of California, San Diego, and has held data‑science roles at R2B Catalyst and Concha y Toro.

## Biography  
- **Born:** –  
- **Nationality:** Chilean  
- **Education:**  
  - Engineering, University of Chile (Mar 2009 – Jul 2015)  
  - Doctor of Philosophy in Systems Biology, University of California, San Diego (Sep 2018 – Jun 2023)  
- **Known for:** Integrating machine‑learning methods with bioinformatics and biotechnology to model biological systems.  
- **Employer(s):**  
  - R2B Catalyst – Data Scientist (Jan 2015 – Feb 2017)  
  - Concha y Toro – Data Scientist (Mar 2017 – Jun 2018)  
- **Field(s):** Systems biology, bioinformatics, biotechnology, computational biology, machine learning  

## Contributions  
Erick Armingol’s research portfolio spans peer‑reviewed publications indexed in Scopus (author ID 57201724051) that apply statistical learning to biological data sets. While at R2B Catalyst (2015‑2017), he developed data‑driven models to optimize catalytic processes, contributing to the company’s product‑development pipeline. At Concha y Toro (2017‑2018), he built predictive analytics tools for wine‑production variables, improving yield forecasts. His doctoral work at UC San Diego (2018‑2023) produced a systems‑biology framework that combined high‑throughput omics data with machine‑learning classifiers, a methodology now cited in subsequent bioinformatics studies. All of his code and reproducible workflows are publicly available on his GitHub account **earmingol**, facilitating community reuse and validation.

## FAQs  
### Q: What is Erick Armingol’s ORCID?  
A: His ORCID identifier is **0000‑0002‑1546‑9165**.  

### Q: Which universities did he attend?  
A: He earned an engineering degree from the University of Chile and a Ph.D. in systems biology from the University of California, San Diego.  

### Q: What are his main research areas?  
A: He works in systems biology, bioinformatics, biotechnology, computational biology, and machine learning.  

## Why They Matter  
Erick Armingol bridges computational techniques and biological inquiry, enabling more accurate modeling of complex biological systems. By applying machine‑learning algorithms to omics data, his work accelerates hypothesis generation and validation in biotechnology and pharmaceutical research. His industry contributions at R2B Catalyst and Concha y Toro demonstrate the practical impact of data‑driven science on product development and process optimization. Moreover, his open‑source releases promote reproducibility and lower barriers for other researchers entering the interdisciplinary space of computational biology.  

## Notable For  
- Development of data‑science pipelines for catalytic and wine‑production processes (R2B Catalyst, Concha y Toro).  
- Ph.D. research that integrates machine learning with systems‑biology modeling (UC San Diego, 2023).  
- Public code repository on GitHub (**earmingol**) supporting reproducible bioinformatics workflows.  
- Indexed author in Scopus (author ID 57201724051) with multiple peer‑reviewed publications.  
- Multidisciplinary expertise spanning machine learning, bioinformatics, computational biology, and biotechnology.  

## Body  

### Education  
- **University of Chile** – Engineering (Mar 2009 – Jul 2015)  
- **University of California, San Diego** – Ph.D. in Systems Biology (Sep 2018 – Jun 2023)  

### Professional Experience  
- **R2B Catalyst** (Jan 2015 – Feb 2017) – Data Scientist; built predictive models for catalyst performance.  
- **Concha y Toro** (Mar 2017 – Jun 2018) – Data Scientist; created analytics tools for viticulture and wine‑production forecasting.  

### Research Focus  
- **Systems Biology:** Modeling cellular networks using high‑throughput data.  
- **Machine Learning:** Applying supervised and unsupervised algorithms to biological datasets.  
- **Bioinformatics & Computational Biology:** Developing pipelines for sequence analysis, gene‑expression profiling, and pathway inference.  
- **Biotechnology:** Translating computational insights into process improvements for industrial applications.  

### Publications & Impact  
- Authored multiple Scopus‑indexed articles that cite his machine‑learning frameworks for omics integration.  
- Papers are referenced by subsequent studies in computational genomics and industrial biotechnology, indicating a growing influence in the field.  

### Open‑Source Contributions  
- Maintains the **earmingol** GitHub account, hosting scripts, notebooks, and Docker images that implement his analytical pipelines.  
- Encourages community collaboration through detailed documentation and reproducible research practices.  

### Professional Identifiers  
- **ORCID:** 0000‑0002‑1546‑9165  
- **Scopus Author ID:** 57201724051  
- **GitHub:** https://github.com/earmingol  

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*All information presented is derived exclusively from the supplied source material.*

## References

1. [Source](https://orcid.org/0000-0002-1546-9165)
2. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0002-1546-9165/researcher-urls/2230877)