The inaugural 2026–2028 BRITE cohort brings together Doctor of Philosophy (Ph.D.) students from chemistry, pharmaceutical sciences, and engineering.

2026–2028 Cohort

Anastasia Ignashkina

  • Ph.D. program: Pharmaceutical Sciences
  • Faculty mentor: Hossam Hammouda
  • Research: Seeks small-molecule modulators of currently undruggable proteins involved in multiple myeloma bone disease, toward precision anticancer therapeutics. Her work links high-throughput and DNA-encoded library screening, mass spectrometry, and biophysical validation by surface plasmon resonance and microscale thermophoresis with an Artificial Intelligence (AI) enabled drug-discovery platform, in which predictions guide experiments and results refine the next round of predictions. Cell-based assays and organoid models connect binding to biological function.

Petros Koster

  • Ph.D. program: Chemistry
  • Faculty mentor: Michael Ross
  • Research: Uses a peptide-library approach to learn how short, intrinsically disordered peptides control the binding of metabolites to nanoparticles, with the goal of predictive design rules for responsive biomaterials. Target analytes include carbon dioxide (CO2, lactate, disease-relevant cations such as sodium, calcium, and magnesium, and pH. Later stages of the work aim at functional hydrogels, cellular delivery, and medical device applications.

Stephanie Sheehan

  • Ph.D. program: Pharmaceutical Sciences
  • Faculty mentor: Soumita Das
  • Research: Studies how the microbial sensor for Engulfment and Cell Motility (ELMO) protein 1 communicates with endolysosomal pathways to sustain chronic inflammation. Her work combines protein–protein interaction studies, plate-based functional assays in wild-type and ELMO1-knockout macrophages, and patient-derived gut organoid models, with the aim of identifying biomarkers and druggable targets for precision immunomodulation.

Timothy Wallace

  • Ph.D. program: Electrical and Computer Engineering
  • Faculty mentor: Hengyong Yu
  • Research: Develops computed tomography (CT) reconstruction and image-restoration methods that combine physics-based modeling and machine learning, including deep-learning restoration of low-dose CT. His long-term goal is reliable reconstruction methods that improve clinical imaging.


Disclaimer

Supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) under award T32GM165287. The content is the responsibility of the program and does not necessarily represent official NIH views.