Trainees

Justin is working on probing the structure of the electric double layer at the electrochemical interface to enable challenging oxidation reactions through electrolyte engineering.

Risha uses first-principles calculations to investigate how local atomic and electronic strucutres dictate redox behavior. Her work aims to enable high-capacity battery cathodes capable of reversible multi-electron storage.

Anushka is working on understanding nucleation in salt systems by combining machine learning potentials with enhanced sampling methods.

Michelle is working with the MDT Enterprise Technologies group to build ML models that help support material equivalency evaluations ensuring biostable and biocompatible choices are made in line with global regulatory bodies.

Paulo is aiming to apply fundamental data science concepts to chemical engineering problems. His goal is to optimize workflow in industry through the application of machine learning and data infrastructure.

Audrey is working on synthesizing a new scaffold for high throughput synthesis of cationic polymer micelles for nucleic acid delivery. She will also use molecular dynamics to model binding affinity of these systems.

Nathan is developing a method to accelerate the analysis of XPS spectrum with the aid of generative models. This work will be integrated with predictive time-series models to provide valuable manufacturing insights.

Andrew is exploring large-scale data analysis and chemical engineering approaches to optimize fermentation processes and improve their scalability and efficiency in the food industry.  

Youcheng is using molecular dynamics simulations to investigate the structural dynamics and stability of adeno-associated virus (AAV) capsids, with the goal of identifying molecular features that can guide capsid engineering.

Dallin is working to understand electrochemical systems with first-principles modeling. A primary area of interest is gaining insight into how rapid alternating polarity (rAP) is able to promote the feasibility and selectivity of reaction pathways that have faced limitations in the past such as the Kolbe reaction.

Zichen is developing topology-informed deep learning models for predicting charge density in solid-state materials, with plans to extend this approach to generative materials design. 

John is working on developing models for predicting biological performance from polymeric drug delivery vehicles to improve experimental throughput in support of nucleic acid therapies. 

Skyler is tailoring oxidative electrochemical reaction environments using metal oxide strain and electrolyte engineering. 

Amin is developing neural-operator surrogates (e.g., DeepONet/FNO) for dynamic optimization, mapping problem specs directly to full optimal control trajectories. His work enables real-time recipe generation for chemical processes with fast, resolution-invariant predictions. 

Winston applies deep learning, with a focus on generative models, to accelerate rare-event sampling and materials discovery. His work aims to overcome scaling limits in molecular dynamics simulation and computational materials design. 

Tim is studying molecular dynamics simulations using different sampling schemes to understand the underlying mechanism and properties in salt nucleation.