• Build and design models for cellular engineering, materials science, and healthcare life sciences.• Aid in the development of multi-agent-based systems/workflows to improve data interoperability and accelerate discovery within materials science.• Engage in cross-domain collaborations to aid in the design, development, and evaluation of foundation models.

• Implement and evaluate innovative machine learning approaches to identify causal explanations for rare events, like adverse drug reactions, using electronic health record data, publicly available data and knowledge graphs. Workflows written in Python (scikit-learn, SciPy, NumPy, TensorFlow).• Advise and mentor students on algorithm design, knowledge engineering, machine learning pipelines, and test-driven development.• Participate in an international collaboration (academia, industry) tasked with developing tools and conducting studies to advance our understanding of long COVID by analyzing large-scale multimodal data (Python, PySpark, SQL, Palantir).

• Designed, built, and evaluated custom machine learning pipelines to solve a variety of problems within the biomedical domain using multimodal data from publicly available resources, scientific literature, and knowledge graphs. Workflows written in Python (scikit-learn, SciPy, NumPy, Pandas, TensorFlow).• Coordinated, built, and deployed a research database containing >169 million records using SQL, Google BigQuery, and Python.• Advised students on study design and data management and performed analyses (statistics, machine learning) for faculty.

• Established GDPR- and ePrivacy-compliant data protocols to enable use of previous inaccessible customer data for machine learning (patent pending: US20220147654A1).• Applied embedding and hashing algorithms to encode protected email content and prototyped an NLP and machine learning frame-work using Python and Amazon Elastic Compute Cloud (AWS EC2).

Built and tested a composite similarity algorithm in Python to jointly learn representations of patients and vocabulary concepts for supervised classification of rare disease patients (from electronic health records) using scikit-learn and Google BigQuery.

• Scheduled and led bi-weekly lab meetings, designed and maintained lab website and listservs.• Managed 7 federally-funded projects and supervised a team of over 15 volunteers, professional research assistants, and students.• Established data collection and entry protocols and built and maintained several databases (Microsoft Access).• Analyzed data (R, SPSS, SAS) and prepared manuscripts resulting in 8 publications and 17 conference presentations.

• Advised employees on the development, testing, and productization of an empirically validated intervention for high-risk youth.• Trained State Health Department staff on how to implement the interventions.• Helped agencies collect and analyze results from intervention implementations using custom programs (R, SPSS, SAS).

• Prepared and analyzed data for federal grant submissions and manuscripts using R, SPSS, and SAS.• Developed a robust data entry system (Microsoft Access) to monitor projects and enable analyses for presentations and publications.• Programmed questionnaire systems (MediaLab), collected biological samples, conducted clinical interviews, and led interventions.