From Mass Spec to Machine Learning: A Three-Decade Journey Back to Environmental Engineering (Mixed with a HUGE dose of Data Science and Analytics)

Thirty years ago this month, I was analyzing volatile organic compounds in aquatic systems using mass spectrometry as a first-year Environmental Engineering graduate student. After pivoting to Chemistry and pursuing medical school preparation, I've spent the last decade applying computational approaches across pharma, government, and academia. Now I'm returning to my environmental roots through Johns Hopkins' Environmental Health and Engineering program.

Over the past 5 years, my career has evolved at the intersection of chemical informatics, computational toxicology, and health data science. I've developed expertise in:

• QSAR modeling and chemical risk assessment frameworks

• Epidemiological analysis of environmental health disparities

• Machine learning applications in exposure science

I'm particularly drawn to methodological advances in:

Environmental Exposure Modeling: Integrating geospatial data, atmospheric dispersion models, and biomarker analysis to quantify chemical exposures in vulnerable populations

Computational Toxicology Applications: Leveraging AI/ML for high-throughput screening, adverse outcome pathway prediction, and mixture toxicity assessment

Environmental Justice Informatics: Developing data-driven approaches to identify and address disproportionate environmental burdens, particularly in communities like Louisiana's Cancer Alley

Predictive Risk Assessment: Building ensemble models that incorporate chemical properties, environmental fate data, and population vulnerability metrics

The convergence of Environmental Big Data, advanced computing resources, and urgent public health needs creates unprecedented opportunities for this field. We can now integrate:

• High-resolution exposure monitoring datasets

• Omics-based toxicity screening results

• Social determinants and demographic data

This data fusion enables more precise risk characterization and targeted intervention strategies than traditional approaches.

I'm looking forward to future collaborations with researchers developing innovative informatics solutions for Environmental Health challenges. The technical problems that can be solved from chemical mixture risk assessment to exposure pathway modeling, require interdisciplinary expertise spanning chemistry, toxicology, data science, and environmental engineering.

Feel free to connect with me if you're working on:

• Novel computational approaches to environmental risk assessment

• AI applications in chemical safety evaluation

• Data integration methods for exposure science

• Environmental justice research with quantitative components

The field needs professionals who can bridge traditional Environmental Science and Engineering with cutting-edge computational methods. Let's discuss how we can advance the science while addressing critical public health challenges, especially in vulnerable populations.