Hoffman Lab: Meet the team

The Hoffman Lab at the Princess Margaret Cancer Centre and the University of Toronto develops machine learning techniques to better understand chromatin biology. These models and algorithms transform high-dimensional functional genomics data into interpretable patterns and lead to new biological insight. A key focus of the lab is to train a new generation of computational biologists.

Principal Investigator

Dr. Michael Hoffman

Michael Hoffman creates predictive computational models to understand interactions between genome, epigenome, and phenotype in human cancers. He implemented the genome annotation method Segway, which simplifies interpretation of large multivariate genomic datasets, and was a linchpin of the NIH ENCODE Project analysis. He is a principal investigator at Princess Margaret Cancer Centre and Assistant Professor in the Departments of Medical Biophysics and Computer Science, University of Toronto. He was named a CIHR New Investigator and has received several awards for his academic work, including the NIH K99/R00 Pathway to Independence Award, and the Ontario Early Researcher Award. Michael enjoys kickball (or "soccer baseball" as it is called in Canada), agritourism, the Marvel Cinematic Universe, and making ice cream (favorite flavor: maple walnut).

Postdoctoral Researchers

Dr. Gergely Pap

Gergely earned his PhD in Deep Learning from the University of Szeged. His interests include genomics and neural network architectures. He has been involved in implementing new model structures for novel DNA representations to classify binding sites of transcription factors. Recently, he has been researching and pursuing new machine learning techniques to answer questions in computational biology. In his free time, he enjoys playing chess (but refuses to properly learn an opening) and developing/playing computer games.

Graduate Students

Coby Viner

Coby is broadly interested in the intersection of algorithms and computational genomics. He previously worked on analyzing transcription factor binding sites with Shannon information theory and developed Veridical, a method to computationally validate mRNA splicing mutations. Since then, he has worked on modelling the effects of DNA modifications, like methylation, on transcription factor binding. He is currently developing new graph-based methods for integrating DNA accessibility and methylation data. Coby currently holds an NSERC Canada Graduate Scholarship (CGS-D) and previously held a CGS-M. He has also won best oral presentation awards at various conferences. Outside of the lab he enjoys going skiing, playing squash, and trying various Chinese teas.

Mickaël Mendez

Mickaël develops machine learning techniques to integrate the diversity of publicly available next generation RNA sequencing data and characterize cell type specific transcriptional patterns.

Luomeng Tan

Luomeng’s thesis work focuses on methodology development on biological sequencing data and optimization. She previously worked on analyzing transcription factor binding sites using CUT&RUN sequencing data and optimizing the analysis pipeline. She now works on the prediction of histone modification from DNA methylation data.

Mary Agopian

Mary is interested in the application of computational medicine approaches to the expansive field of immunology. Her thesis focuses on designing internal controls for an immune sequencing method, with the goal of developing a robust and quantifiable tool to be used in the field. She received her H.B.Sc. in Microbiology and Immunology from McGill University. Outside the lab, you can find her running a yoga session, performing ballet, and reading.

Yahan Zhang

Yahan is interested in applying machine learning techniques to predict gene regulation, with a special focus on cell-free DNA. She earned her H.B.Sc. in Computer Science and Biology from McGill University. Beyond her work in the lab, she finds joy in reading, exercising, and hiking.


Eric Roberts

  • Technical Analyst

Eric Roberts is a Technical Analyst at the University Health Network for the Princess Margaret Cancer Centre. He is the lead developer in the Hoffman Lab for tools used by researchers in areas of semi-automated genome annotation (Segway) and efficient genomic data storage (Genomedata). Previously Eric has worked on building control systems for sensory deprivation tanks using microcontrollers, game development for the PlayStation Vita and PlayStation 3, and safety-critical video drivers for embedded systems. Eric is always interested in learning and applying new computational methods to epigenomic datasets and has a wide range of programming interests including functional programming, distributed systems, and embedded systems.

Natalia Mukhina

  • Lab Administrator

Natalia provides administrative support to Dr. Michael Hoffman and the Hoffman’s Lab. She obtained her MA in Health Studies at Queen’s University (Canada), with a specialization in Health Communications and Cancer Studies. Natalia is a member of the Association of Health Care Journalists and is interested in creating engaging content about cancer - a complex, sensitive, and often challenging topic to discuss. She runs a blog - "Research News" - for the Breast Cancer Society of Canada.

Visiting Scientists

Dr. Linh Huynh

Linh is a postdoctoral fellow studying the three-dimensional organization of mammalian genomes. He is also interested in integrating multi-omic data to study gene regulation. Linh obtained his B.E. in computer science and engineering at Ho Chi Minh City University of Technology in 2008, and his PhD in computer science at UC Davis in 2016.

Undergraduate Students

Annie Lu

Annie is a second-year student at the University of Toronto studying data science and computer science. Her work in the lab focuses on integrating chromatin long-range interactions to improve gene set enrichment analysis. Annie has been awarded the Lester B. Pearson International Scholarship and the Samuel Beatty Scholarship. Her interests outside the lab include writing, jogging, collecting postcards, and translating articles for the Chinese edition of Scientific American.

Lab Alumni