Current Work (In progress)

My primary research interest is in precision medicine in neuroscience, with a focus on aligning target discovery and biomarker development for preclinical trials.

Target Discovery

I am primarily interested in leveraging biostatistical and machine learning approaches to integrate molecular (single-cell RNA, spatial transcriptomics, proteomics, human genetics) and clinical data to prioritize novel targets and uncover mechanistic insights by generating and testing hypotheses that draw on multi-omic biosignatures, brain biology, genetic evidence, and biobank resources.

Clinical Biomarkers

My research interest in clinical biomarkers focuses on method development for the in-silico identification and analysis of fluid and imaging markers for disease heterogeneity, progression, and treatment response, supporting patient selection, target engagement, and pharmacodynamic evaluation using data from clinical trials and biobanks.

Past Work

Does Type 2 diabetes increase the risk of Alzheimer’s disease (AD) dementia?

Using TMT phosphoproteome profiling and statistical analysis of postmortem human prefrontal cortex samples from 191 deceased older adults with and without diabetes and pathologic AD, we examined the crosstalk between the pathophysiological processes of DM and AD.

Paper: Alzheimers Dement 2025 Feb

How do we prioritize gene targets to study the genetic architecture of complex traits?

To prioritize target genes at associated loci, we propose a combinatorial likelihood scoring formalism (Gene Priority Score [GPScore]) based on measures derived from 11 gene prioritization strategies and the physical distance to the transcription start site. With GPScore, we prioritize the 30 most probable target genes underlying the adiponectin-associated variants in the cross-ancestry analysis, including well-known causal genes (e.g., ADIPOQ, CDH13) and additional genes (e.g., CSF1, RGS17)

Paper: HGG Advances 2024

How do cells adapt to environmental and genetic challenges affecting protein homeostasis?

Using a multilevel statistical analysis and transposon mutagenesis, we tried to understand the relationship between genetic perturbations and environmental stresses in protein homeostasis of Caulobacter crescentus

Paper: PNAS Nexus 2025

How do we estimate changes in transposon insertions attributable to gene-environment interaction?

We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertions attributable to gene-environment changes in this genetic interaction study without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count information to test for genes that show a statistically significant change in transposon counts.

Paper: PLOS Computational Biology 2022