CS50 Data Science Symposium

The Data Science Institute is sponsoring one of the symposiums at the UBC Computer Science 50th Anniversary Celebration. Come hear CS alumni speak about their research and how it relates to the rapidly growing field of data science. The event is free but please follow the link below to register.

 

Date: Friday, May 11, 2018 (1:00PM - 3:00 PM)

Location: Hugh Dempster Pavilion - Room 310, UBC 

Click here to register for the event.

 

Dr. Sohrab Shah

Canada Research Chair in Computational Cancer Genomics
Dept of Pathology, UBC
Dept of Molecular Oncology, BC Cancer Agency

Genomics, computing and the route to predictive and personalized cancer treatment

Why do patients die of cancer? How can we predict at diagnosis how best to treat a patient? How can studying the genomes of cancer cells help answer these questions? In this talk I will discuss both the routine and leading edge application of genomics toward the goals of studying tumours and making informed treatment management decisions for patients. The high dimensional nature of genomics data requires sophisticated computing methodology to appropriately process and interpret the domain specific attributes of cancer genomics data. I will cover how Bayesian statistical modeling and machine learning methods applied in a cancer genomics context have advanced our understanding of how cancers initiate and change over time. Furthermore, I will demonstrate how specific properties of cancer genomes, inferred through machine learning methods can be used to stratify patients into clinically relevant groups as indicators of specific treatment management decisions. I will conclude with a forward looking view of the everyday role genomics and data science in the treatment and disease management of cancer patients.

Bio:

Dr. Shah received a PhD in computer science from UBC in 2008 and was appointed as a Principal Investigator to The BC Cancer Agency and the University of British Columbia in 2010. He holds the Canada Research Chair in Computational Cancer Genomics, and is the recipient of both a Michael Smith Foundation for Health Research Career Investigator Award and a Terry Fox Research Institute New Investigator Award. His research focuses on understanding how tumours evolve over time through integrative approaches involving genomics and computational modeling. Dr. Shah has pioneered computational methods and software for inference of mutations in cancer genomes as well as deciphering patterns of cancer evolution which have been widely disseminated internationally. He has a track record of developing novel, innovative Bayesian statistical models, algorithms, and computational approaches to analyze large, high dimensional genomics and transcriptomic data sets, from both patient tumours and model systems (a list of published tools can be found at http://shahlab.ca/projects/). This includes advancing molecular profiling of cancer cells at single cell resolution. Dr. Shah has been at the forefront of studying tumor evolution in breast, ovary and lymphoid malignancies. Dr. Shah’s work is supported by numerous funding agencies and his research has been published in Nature, Nature Genetics, Nature Methods and the New England Journal of Medicine.

Dr. Raj Chari

Director, Genome Modification Core, Frederick National Lab for Cancer Research

Emerging opportunities for large scale data analysis in eukaryotic genome engineering

The CRISPR/Cas9 system has provided an unprecedented ability to edit the DNA of simple and complex eukaryotic genomes. As the technology has evolved and different capabilities have been established, the scale at which genetic perturbations can be performed have also increased. This has necessitated a need for high throughput and sophisticated computational and statistical approaches. In this talk, I will discuss different applications of data science in the context of genome editing and engineering. I will start with how utilizing such approaches has enhanced our understanding of sequence determinants of CRISPR/Cas9 activity. Subsequently, I will discuss the movement towards multiplexed genetic perturbations to understand complex genetic phenotypes. Finally, I will conclude with the movement of pairing genome wide perturbations and single cell RNA sequencing and the current challenges and opportunities in this space.

Bio:

Raj is currently the director of the Genome Modification Core at the Frederick National Lab for Cancer Research. Raj did his undergraduate and graduate training at the University of British Columbia. He has two Bachelor of Science degrees, first in biochemistry and then in computer science. He then went on to obtain his PhD with Dr. Wan Lam at the BC Cancer Research Centre where his thesis work revolved around computational approaches in cancer genomics for the identification of diagnostic and therapeutic markers for lung cancer. From there, he did a postdoctoral fellowship with Dr. George Church at Harvard Medical School where he focused on molecular technology development in genome engineering. Prior to joining the Frederick National Lab, he was working at Juno Therapeutics in the single cell sequencing space in the context of immuno-oncology. He is an avid sports fan who also loves to travel and explore the outdoors.

Dr. Heidi Lam

Senior Software Engineer, Google

Building tools to support analysis

Data analysis is difficult due to the large volume and complexity of most data sets. Over the years at Google, I have helped build tools to support analysts in Ads Quality and Search Quality, and now, in Machine Perception. In this talk, I shall share my thoughts and experiences in building these tools.

Bio:

Heidi Lam obtained a Ph.D. in computer science from the University of British Columbia in 2008 under the supervision of Dr. Tamara Munzner. Heidi joined Google Inc. upon graduation, where she helped build tools to support analysis in Ads Quality, Search Quality, as well as for Fusion Tables users. In 2015, she joined the research team in Tableau Software to better understand analysis questions. Recently, Heidi returned to Google and is now part of the Machine Perception team.