Multidisciplinary researcher with extensive experience in computational/software engineering and biological sciences. Pioneered a novel computational method leveraging Hybrid NN to infer dynamics from experimental omics data. Developed Genetic Design Automation tools that processed over 10,000 experimental data points and achieved 95% prediction accuracy in dynamic modeling. Authored 32 publications, including 8 in high-impact journals.
Developed and enhanced Python packages for simulating, inferring, and predicting microbial community interactions. Designed and published MIMIC—a package integrating mechanistic models (gLV, VAR, CR) with Bayesian inference to simulate microbial dynamics (Bioinformatics, 2024 :contentReference[oaicite:0]{index=0}). Initiated development of hybrid Neural Networks using gray box modeling and Physics-Informed Neural Networks (PINNs) to infer microbial interactions with improved interpretability and accuracy.
Developed MIMIC, a Python package for simulating, inferring, and predicting microbial community dynamics; published in Bioinformatics
Implemented Gaussian Process regression for imputing missing time-series data in microbial abundance datasets
Applied Bayesian inference with MCMC sampling to recover interaction matrices, achieving 87.48% stability across posterior samples
Integrated multi-omic data to enhance model robustness and ecological insight
Initiated development of hybrid Neural Networks using gray box modeling and PINNs for inferring microbial interactions, improving model interpretability and predictive performance
Streamlined CI/CD pipelines for scalable and reproducible computational biology workflows
Developed Genetic Design Automation tools and mathematical models for synthetic biology. Employed computational methods for genetic circuit simulation and design in a collaborative, interdisciplinary setting. Enhanced iBioSim functionalities resulting in a 15% improvement in simulation accuracy and developed robust models that forecasted gene expression with 95% accuracy. Collaborated with international teams using cloud-based tools and authored 8 peer-reviewed publications during tenure.
Enhanced iBioSim functionalities for genetic circuit modeling
Developed robust mathematical models for genetic design with 95% forecasting accuracy
Collaborated with international research teams using cloud-based tools
Authored 8 peer-reviewed publications during tenure
Optimized simulation workflows resulting in a 15% improvement in accuracy
Organized a monthly seminar series under the Alan Turing Institute's banner to leverage AI in advancing biological system engineering. Unites experts from computer science, biology, and engineering to address global challenges in sustainable manufacturing, healthcare innovation, and environmental impact.
Organized talks, workshops, hackathons, and DIY projects to promote innovation in science, engineering, and synthetic biology.
Coordinated the annual Biology Week to promote science careers among high school students.
Awarded to pursue a master’s degree in the United States starting Fall 2017.
Recognized for excellence in research and communication under the 'Beca Estímulo' scholarship.
Supported research and development tasks in genetics and ecology.
An analysis of how model complexity impacted the robustness of synthetic genetic circuits.
Described the implementation and features of SBOL version 3.1.0 for standardized data exchange in bioengineering.