Postdoctoral Fellows

2020 Fellows

 

Jüergen Bernard

Department: Computer Science
Project: Visual analytics support for the HEiDi virtual physician COVID-19 deployment
Bio: Dr. Jürgen Bernard is a postdoctoral research fellow at the Department of Computer Science, University of British Columbia. He received his PhD Degree in Computer Science from the University of Technology in Darmstadt, Germany in 2015. In his dissertation, he studied the exploratory search paradigm for time series data using interactive visual interfaces. His primary research includes the characterization, design, and evaluation of visual-interactive interfaces to combine the strengths of both humans and algorithms in interactive machine learning and data science applications. His problem-driven research focus includes applications like Earth observation, digital libraries, human motion analysis, service and energy network monitoring, political decision-making, music classification, sports data analysis, finance and stock market analysis, as well as medical and patient-related research in particular.

 

 

2019 Fellows

 

Oswald Barral

Department: Computer Science
Project: Leveraging eye-tracking data to improve reliable detection of Alzheimer’s Disease and related patient’s states
Bio: Dr. Oswald Barral is a postdoctoral fellow at the Department of Computer Science, University of British Columbia. His work involves measuring and analyzing user signals (namely physiological signals) for user modeling and advanced human-computer interaction paradigms. His research is now centered in the study of human eye movements applied to the health care domain. He obtained his PhD in Computer Science with distinction from the University of Helsinki in 2018. In his dissertation, he studied implicit interaction paradigms using brain, cardiovascular, and other physiological signals.

Cory Bonn

Department: Psychology
Project: Quantifying individual differences from complex datasets in developmental psychology
Bio: Dr. Bonn is a postdoctoral fellow based in the Centre for Cognitive Development in UBC's Department of Psychology. His research aims to better understand how humans of all ages make inferences about quantities they observe, with a particular focus on developing models of our sense of approximate number. During his time at the DSI, he will develop a set of tools for understanding multivariate measures of brain activity and behavior from challenging subjects such as infants, toddlers, and preschool children, who typically give data in much smaller quantities than adults and tend to have much higher rates of missing or censored data. He completed his PhD at the University of Rochester in Brain and Cognitive Sciences and joined UBC following a postdoc at the Laboratoire Psychologie de la Perception at CNRS/Université Paris Descartes in France.

Ben Sobkowiak

Department: Respiratory Medicine
Project: Using contact networks, administrative, and linked genomic data to understand tuberculosis transmission in BC
Bio: Dr. Sobkowiak is a postdoctoral research fellow working jointly with the BC Centre for Disease Control. His main research focuses on interrogating whole genome sequence data to better understand the spread of infectious disease. He works mainly with bacterial pathogens, notably Mycobacterium tuberculosis, combining genetic and epidemiological data to model outbreaks and reconstruct transmission networks to predict the drivers of transmission through the application of statistical and computational models, including machine-learning methods. He obtained his PhD in Computational Biology from University College London in 2017.

Chendi Wang

Department: Medical Genetics
Project: Using machine learning models for understanding the role of the non-coding genome in brain development and autism
Bio: Dr. Chendi Wang joined the Mostafavi lab as a postdoctoral fellow in Feb 2019. She obtained her PhD in Electrical and Computer Engineering at University of British Columbia in May, 2018. For her PhD Dr. Wang developed machine learning and statistical methods for analysis of multimodal brain imaging data including structural, functional, and diffusion MRI data. She was a research software engineer in industry developing machine learning and deep learning methods for computer vision applications before she joined the Mostafavi lab. Her current research interest is developing statistical and machine learning methods for understand biological and molecular basis of brain development.

 

 

2018 Fellows

 

Kieran Campbell

Department: Statistics
Project: Large-scale Bayesian modelling of drug resistance and evolution in human cancers at single-cell resolution
Bio: Dr. Kieran Campbell is a postdoctoral fellow at the Department of Statistics, University of British Columbia, and the Department of Molecular Oncology, BC Cancer Agency. His research centres around Bayesian statistical modelling of molecular cancer data with a particular focus on understanding why certain cancer cells evade chemotherapy and cause relapse. He gained his DPhil (PhD) from the University of Oxford working on statistical models of single-cell transcriptomics with Chris Yau.

Sébastien Lallé

Department: Computer Science
Project: User modeling and adaptive support for MOOCS
Bio: Dr. Sébastien Lallé is a postdoctoral fellow at the University of British Columbia (UBC). He received his MsC and PhD in Computer Science from the Joseph Fourier University in 2013. In his work he focused on designing user models and personalized support in several interactive computer systems, including intelligent tutoring systems and visualization-based interfaces. His research interests also include user-adapted interaction, intelligent agents, affective computing, and eye-tracking data processing. His current research at UBC is about examining ways to deliver adaptive or personalized interaction in MOOCs (Massive Open Online Courses), in order to improve the learners' achievements and engagement.

Lisa Tang

Department: Radiology
Project: Automated diagnosis and prognostication of severity in COPD via deep learning frameworks using multi-modal data
Bio: Dr. Lisa Tang is a postdoctoral fellow presently working closely with experts at the Centre of Lung Heart Innovation, St. Pauls Hospital and UBC. Lisa obtained her PhD and BSc in Computing Science from Simon Fraser University. Her PhD dissertation examined various ways to advance previous methods for the registration of image volumes and sequences using graphical models and discrete optimization. Her research interests include computer vision, machine learning, medical image analyses, and deep learning strategies. She is currently exploring the the use of various deep learning architectures for the staging and prognosis of chronic obstructive pulmonary disease using lung computed tomographic imaging data.

2017 Fellows

 

Zahra Jalali

Department: Medical Genetics
Project: Modeling multiple types of "omics" data to understand the biology of human exposure to pollution and allergens
Bio: Dr. Zahra Jalali received her PhD in Bioinformatics from the South African National Bioinformatics Institute in 2013. The focus of her doctorate research was on the computational identification and characterization of iron regulatory-related proteins in Glossina morsitans. She further continued her academic career as a postdoctoral fellow at the same institute, focusing on the comparative genomics analysis of mycobacterium tuberculosis in identifying putative drug resistance-associated markers. She has recently joined UBC as a postdoctoral fellow to work on the development and implementation of efficient statistical models to integrate multiple omics data types with the aim of identify novel genes and pathways associated with the pathophysiology of respiratory health and lung disease.

Hyeju Jang

Department: Computer Science
Project: Using text analysis for chronic disease management
Bio: Dr. Hyeju Jang received her PhD in Computer Science from Carnegie Mellon University. Her research interests include natural language processing, computational linguistics, discourse analysis, and text mining in various domains. Specifically, her current research is about applying NLP and text mining to the healthcare domain in order to help chronic disease management by processing patient-generated language. Her PhD dissertation focused on computationally modeling metaphor in order to capture how metaphor is used and identify a broader spectrum of predictors from the discourse context that contribute towards its detection.

Halyun Jeong

Department: Mathematics
Project: From heuristics to guarantees: the mathematical foundations of algorithms for data science
Bio: Dr. Jeong received his PhD in Mathematics from the Courant Institute Mathematical Sciences, New York University in 2017. His PhD research interest is about the mathematical signal processing including information theory and analog-to-digital (A/D) conversion, dynamical systems and stochastic processes, and geometry of high-dimensional data sets. In particular, he studied fast phase retrieval algorithms, the quantization of phaseless measurements, and the spectral analysis of an analog-digital conversion algorithm based on Markov chain. Currently, he focuses on the theoretical guarantees of efficient convex and non-convex iterative algorithms.

 

Jean-Sébastien Légaré

Department: Computer Science
Project: A platform for interactive, collaborative, and repeatable genomic analysis
Bio: Dr. Légaré comes from a computer science background and has worked with distributed systems, virtualization, and cloud platforms. For his PhD, he researched novel web-service architectures which could increase user privacy while preserving the business model of providers. He is looking forward to using computer science techniques to accelerate research in the natural sciences; in particular, to allow experiment to be packaged in an auditable and reproducible manner, and to scale existing toolchains to allow faster analysis and interactive queries on huge datasets.

Michael Fernandez Llamosa

Department: Vancouver Prostate Centre
Project: Application of deep learning approaches in modelling cheminformtics data and discovery of novel therapeutic agents for prostate cancer
Bio: Dr. Michael Fernandez is interested in data-driven solutions for the analysis and understanding of complex phenomena in chemical, biochemical and materials systems. He completed a PhD fellowship sponsored by the Japanese government at the School of Computer Sciences and Systems Engineering at Kyushu Institute of Technology, Japan in 2011. For more than a decade, his research has been focused on machine leaning and evolutionary computing strategies to identify structural-property relationships patterns in chemical, biomedical and advanced manufacturing data. He has authored more than 60 research papers, from positions at the Immunology Frontier Research Center, University of Osaka, Japan; the Faculty of Science, University of Ottawa, Canada; and CSIRO Manufacturing Unit, Australia. Currently, as part of the Computational Drug Design Laboratory in Vancouver Prostate Centre, he is implementing deep learning neural networks solutions for the accelerated discovery of new drugs for prostate cancer treatment.

Ezequiel Smucler

Department: Statistics
Project: Data science over graphs, streams, and sequences: From the analysis of fake news to prediction and intervention
Bio: Dr. Ezequiel Smucler received his PhD in Mathematics from University of Buenos Aires in 2016, advised by professor Victor J. Yohai. His research interests include statistics for high-dimensional data, dimension reduction for time series, robust statistics and applications of natural language processing. At the DSI, he is currently working on two projects: ensembling regularized linear models and analysis of fake news