MSc in Applied Immunology by Advanced Study | University of Toronto
Immunology | Computational Biology | Inflammatory Bowel Disease | Liver Transplantation
Using multi-omics, machine learning, and computational biology to study immune disease.
Pediatric IBD, Liver Transplantation, Multiomics, Immune Profiling, Patient-derived Organoids, Microbiome Platforms, and Computational Biology.
About
Joseph Katz is a current MSc student in Applied Immunology by Advanced Standing at the University of Toronto and SickKids, supervised by Dr. Eytan Wine. His work sits at the intersection of pediatric inflammatory bowel disease, computational immunology, multi-omics, microbiome platforms, metabolomics, proteomics, machine learning, and translational research. He is interested in connecting immune mechanisms, patient-derived models, and clinical data to better understand inflammatory disease.
Experience
Causal and mechanistic dissection of ultra-processed food-driven gastrointestinal inflammation using proteomic, metabolomic, microbiome, immune, and metagenomic data analysis. ML prediction model creation, patient-derived organoids, barrier physiology, immune profiling, and integrative in vivo models.
Developed a workflow for human liver IMC preprocessing, single-cell segmentation, immune-cell phenotyping, dimensional reduction, neighbourhood analysis, and cell-cell interaction analysis
Designed a Python-based multi-agent system simulating a liver-transplant selection committee, using retrieval augmented generation and clinical donor data to support decision-making.
Studied genetic and molecular risk factors and processed biobank samples of IBD patients through DNA extraction, DNA quantification, ELISA assays, visualization tools, and comparative/regression analyses in Excel and R.
Projects
Integrative research connecting immune profiling, microbiome, serum, proteomic, metabolomic, and clinical data to associate UPF consumption with multiomic changes in pediatric IBD patients. Mechanistic validation in patient-derived organoids and cell culture.
Python and R pipeline for IMC preprocessing, segmentation, phenotyping, dimensional analysis, and immune-environment comparison in liver allograft fibrosis.
Hepatologist, Pathologist, and Transplant Surgeon multi-agent committee used to enhance liver donor selection based on patient clinical profiles.
Skills
Python, R, RStudio, numpy, pandas, Excel, CrewAI, LangChain, IMC Tools, Retrieval Augmented Generation
Data cleaning, visualization, regression analysis, statistical modeling, dimensionality reduction, t-SNE, UMAP, Neighbourhood Analysis, SHAP, XGBoost
Single-cell segmentation, Mesmer, immune-cell phenotyping, multi-omics predictive analysis, cohort analysis
IBD, transplantation, immunogenetics, immune profiling, ELISA, clinical and developmental immunology
ELIZA, DNA isolation, DNA quantification, biosample processing, cell culturing, organoid culture, pipetting, PCR, microscopy
Education
University of Toronto and SickKids, Division of Gastroenterology, Hepatology and Nutrition; Cell and Systems Biology. Supervisor: Dr. Eytan Wine.
University of Toronto. Double major in Physiology and Immunology. Graduated with High Distinction. Immunology Honours Thesis.
Upper Canada College. Advanced specialization in Physics, Math, and Economics.
Presentations / Research Outputs
Poster presentation on the multi-agent liver transplant donor selection project.
Journal club presentation and discussion materials connected to multi-agent AI methods in biomedical research.
Undergraduate honours thesis presentation from the University of Toronto Immunology program.
Achievements
Recognized for achieving a cumulative GPA of 3.50 or higher.
Honored for academic achievement during the 2021-2022 school year.
Awarded to students in the top 10% of academic standing.
Recognized for research comparing naturopathic remedies and antibiotics against E. coli and Staphylococcus aureus growth.
Placeholder for future conferences, fellowships, or presentation awards.
Contact
I am open to research collaborations, computational biology projects, and academic opportunities in immunology, multi-omics, machine learning, and translational medicine.