Noor Chalhoub
Ph.D. Student
Noor is using GWAS and eQTL data from European and East Asian populations to deduce causal risk SNVs for Type 2 diabetes at the NKX6-3/ANK1 locus. Her work utilizes genome engineering techniques to build synthetic haplotypes with various combinations of potential risk SNVs and integrate them into human iPSCs. The cells containing the synthetic haplotypes will be differentiated into multiple cell types associated with Type 2 diabetes and characterized with snRNA-seq, snATAC-seq, and RT-qPCR.
Jimin Tan
Ph.D. Student
Jimin is interested in understanding biological systems through machine learning modeling. He developed CANVAS, an ML framework to discover novel tumor microenvironments in multiplexed imaging data. He also developed C.Origami, a deep neural network that predicts chromatin conformation and enables in silico genetic screening on 3D genome.
Grant Hussey
Ph.D. Student
Grant is focused on improving PhosphoDisco, a Python-based toolkit to discover co-regulated phosphorylation modules in phosphoproteomic data, and its application on the CPTAC consortium's pan-cancer datasets.
Michelle Hollenberg
Ph.D. Student
Michelle focuses on developing strategies for analyzing multiplexed immunofluorescent images to investigate the tumor microenvironment in endometrial carcinoma. I use deep learning, image analysis, and multiomics tools to identify relevant biomarkers and morphological features that correlate with clinical outcomes, such as recurrence and immunotherapy response.
Efiyenia Kaparos
Ph.D. Student
Fenia is interested in quantifying transposable elements (TEs) in genomic and proteomic datasets and characterizing associated molecular features of TE activity. She is using proteogenomic data analysis to characterize the phenotypic landscape of high LINE-1 retrotransposon activity in our CPTAC and TCGA pan-cancer datasets, as well as support other computational and clinical projects involving human LINE-1.
Madu Nzerem
Ph.D. Student
Bacterial virulence factors can be used as an alternative target to treat/prevent bacterial infections. By representing biological information with deep learning based tools, I am integrating data from tertiary level protein structure and genomic neighborhoods to predict known and potentially novel bacterial virulence factors.
Tianxiao Zhao
Ph.D. Student
Tianxiao develops computational tools for spatial multi-omics studies using deep learning, focusing on spatial data simulation, spatial context reconstruction, and data integration. He is also exploring applications of large language models in molecular biology.
Ze Chen
Ph.D. Student
Ze uses statistical and machine learning methods to explore biology and medicine, and currently he is focusing on understanding aneuploidy in cancer by studying the interactions between cancer cells and immune cells.
Linda Procell
Ph.D. Student
Linda is interested in studying health and disease in women's healthcare and gynecologic disorders through multi-omic analysis with a focus on single-cell and spatial transcriptomics. She is also intrigued by cellular heterogeneity in disease and the structural components of microenvironmental interactions.