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. Davide Chicco

Davide Chicco obtained his Bachelor of Science and Master of Science degrees in computer science at Università di Genova (Genoa, Italy), respectively in 2007 and 2010. He then started the PhD program in computer engineering at Politecnico di Milano (Milan, Italy), where he graduated in spring 2014. He has also spent a semester as visiting research scholar at University of California Irvine. Since September 2014, he has been a postdoctoral researcher at the Princess Margaret Cancer Centre and guest at University of Toronto. His research topics focus upon machine learning algorithms applied to bioinformatics.

Dr. Samantha L Wilson

Sam is a postdoctoral fellow studying DNA methylation signatures in cell-free DNA. She is interested in using machine learning approaches to develop predictive models in the context of both pregnancy (cell-free placental DNA; pre-term birth) and cancer (circulating tumour DNA). Sam completed her B.Sc Honours in Genetics at The University of Western Ontario in 2012 (and refuses to call it Western University), and her PhD in Medical Genetics at The University of British Columbia in 2017. Her PhD dissertation focused on DNA methylation profiles of placentas from pregnancies complicated by preeclampsia (a maternal hypertensive disorder) and intrauterine growth restriction (poor fetal growth). During her PhD, Sam was awarded the Elsevier Trophoblast New Investigator award (IFPA 2016) and the University of British Columbia's 4 year doctoral fellowship. While not in the lab, Sam enjoys hiking, playing soccer, spending time at the cottage and cooking yummy things.

Graduate Students

  • Coby Viner

  • PhD Student in Computer Science
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. 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 and playing squash, despite being bad at both. He also enjoys playing chess, even when Sam beats him in four moves.

Mehran Karimzadeh

Mehran is interested in using machine learning to predict and interpret the epigenome. Currently he is working on predicting transcription factor binding by learning from the transcriptome. He is also interested in using accurate predictive models of transcription factor binding to identify driver non-coding somatic mutations in cancer. Mehran completed his B.Sc. in biology at University of Tehran in 2012 and his M.Sc. in Human Genetics and Bioinformatics at McGill University in 2015. Mehran has been awarded the Ontario Graduate Scholarship, Frank Fletcher Memorial Fund, and the Medical Biophysics Excellence Award. His interests outside the lab include the art of making good coffee, photography, chess, swimming, and jogging.

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.

Danielle Denisko

Danielle works on improving transcription factor motif elucidation in ChIP-seq datasets. She is also interested in exploring 3D chromatin organization and local DNA shape. Previously, she obtained her Hon. B.Sc. in biological physics (specialist) and mathematics (minor) at the University of Toronto. She was awarded a NSERC Canada Graduate Scholarships-Master’s upon commencing her graduate studies. In her free time, Danielle sings soprano in the Hart House Chorus, edits videos for the MBP Podcast Series, and volunteers for scoliosis awareness and education initiatives. She is also very enthusiastic about astronomy, so don’t hesitate to ask her about local stargazing events!

Francis Nguyen

Francis applies machine learning to make sense of the overwhelming amounts of data biologists have access to. He looks at improving techniques that interpret the epigenome of cells so that they require less data, saving time, money, and input sample for researchers down the line. By matching against well-studied cell lines, he aims to build a predictor that can more easily interpret data from uncharacterized samples, identifying interesting regions even when only small amounts of the sample of interest can be assayed. Outside of the lab, he enjoys Dance Dance Revolution, video game development, and attending various hackathons.

Rachel Chan

Rachel's research is broadly centred on the use of machine learning methods to elucidate new biological insights from epigenomic data. Rachel obtained her BASc in Engineering Physics with minor in Honours Mathematics from The University of British Columbia (UBC) in 2018. Previously at UBC, she worked on creating embedded systems for optogenetic experiments on Drosophila, on reaction-diffusion systems for modelling single-cell wound repair and other biochemical systems, and on a numerical model for myelinated axons in MRI that takes into account the diffusion-mediated effects of water molecules in the brain. Outside of the lab, she enjoys a variety of video games ranging from Terraria to Dark Souls, and befriending every cat she meets.


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. Lee Zamparo

  • Visiting Postdoctoral Research Fellow
  • Postdoctoral Research Fellow at Memorial Sloan Kettering (New York, USA)
Lee is a post-doctoral fellow in computational biology at Memorial Sloan Kettering Cancer Center, and a visiting scientist at the Hoffman Lab at the Princess Margaret Cancer Centre. His primary training is in machine learning and computational biology, with over nine years experience in the field. During his PhD, Lee worked on quantifying the effect of genetic deletions on DNA damage phenotypes in yeast cell image data. As a post-doctoral fellow in Christina Leslie's lab, he has worked on a statistical model to call significant interactions in HiC data, a better guide-RNA design tool for CRISPR, and deep learning models to predict chromatin accessibility and transcription factor (TF) occupancy from sequence.

Summer Students

Flora (Yufang) Liu

  • Summer Student
  • B.Sc. Student Computer Science, University of Toronto
Flora is broadly interested in dynamics of systems across different disciplines and scales, ranging from sub-cellular molecular systems, self identity systems to socioeconomic systems. During the time in Hoffman Lab, Flora mainly worked on developing a proteomics pipeline to identify alternatively translated peptides in CPTAC breast cancer data sets. She entered the University of Toronto as a life science student, switched to computer science program in second year and is still trying to figure out where to focus afterwards. Flora was awarded St. Michael in-course scholarship , University of Toronto Scholar and Medical Biophysics summer studentship. Outside of schools and labs, Flora enjoys cooking, reading and traditional Chinese calligraphy. She also routinely participate in various sports like yoga, jogging and swimming.

Matthew Mcneil

  • Summer Student
  • B.Sc. Student Biochemistry, University of Toronto
Matthew is a fourth year student at the University of Toronto studying biochemistry and statistics, graduating in the spring of 2019. His work in the lab focuses primarily on updating Segway's API to make it more modular allowing for the implementation of novel machine learning algorithms. Outside of the lab he is a member of the UofT Varsity Rowing and Nordic Skiing teams.

Winnie Xu

  • Summer Student
  • B.Sc. Student Computer Science, University of Toronto
Winnie is a rising sophomore at the University of Toronto where she studies Computer Science, Statistics, and Math. Having pursued different research projects on the side since she was in high school, she has been awarded multiple top honors at national student research competitions and is keen on continuing to satiate her passions in machine learning/AI, biotechnology, and entrepreneurship. Beyond her academics and extracurriculars, Winnie’s enjoys training in ballet and contemporary dance, rooftopping, travelling, travelling, and standing desks.

Lab Alumni