I'm an award-winning data scientist bridging cheminformatics and metabolomics with a focus on small molecule discovery and mass spectrometry data sciences.
For more details about my work and recognition:
- Read my award news from the Metabolomics Association of North America (MANA).
- View my presentation details here.
I've developed multiple computational pipelines for untargeted mass spectrometry data processing across domains such as metabolomics, lipidomics, exposomics, and environmental studies. My development philosophy emphasizes maximal automation, high precision, multi-platform compatibility, and user-friendly interfaces to minimize extensive lab-based experiments. I also led the development of an AI-powered simulation platform at Aropha, enabling cloud-based bioreactor modeling and predictive analytics as the core of their digital twin system. My broader goal is to advance next-generation AI for chemistry and life science applications.
AI-Powered Digital Twin Replicas for Bioreactors at Aropha
At Aropha, I led the development of digital twins for bioreactors which were high-fidelity virtual replicas that enabled accurate performance prediction, real-time process monitoring, and data-driven decision-making in dynamic bioprocess workflows. By integrating state-of-the-art AI with bioprocess engineering, our approach enhanced process control, reduced experimental costs, and accelerated process optimization.
Explore Aropha's AI-powered simulations platform for batch processing and core engine of the digital twins.
At the Integrated Data Science Laboratory for Metabolomics and Exposomics, I developed an end-to-end untargeted metabolomics workflow to efficiently process and annotate large-scale mass spectrometry data. This workflow minimizes reliance on purely data-driven methods by leveraging stable isotopic principles that underpin biochemistry and organic chemistry.
During my doctoral research, I developed computational mass spectrometry pipelines for environmental cheminformatics projects.
- Isotopic Profile Deconvoluted Chromatogram (IPDC):
An algorithm designed to screen complex environmental matrices for unknown contaminants using chemometric methods. The IPDC algorithm was successfully applied in five projects during my PhD.
Explore the project