SFU's School of Computing Science has several faculty members with expertise in the many aspects of data analytics, data engneering, machine learning, artificial intelligence, computer vision, computer graphics, deep learning, information and network security, and more. Our faculty's expertise spans across the vast spectrum of computer science. 

Click on a faculty member's name to view his or her profile and contact information.


Machine learning is a subfield of computing science that deals with algorithms or models that learn patterns in data and are then applied to new data to predict similar patterns. While machine learning is not new, its application has gained new prominence due to the increasing amounts of data generated today.  With big data, machine learning can yield even more interesting and accurate insights and predictions. But there are also new challenges, such as ensuring that predictions are delivered quickly and efficiently in real time.

Ghassan Hamarneh

Area: Medical Image Analysis (Computational/Artificial Intelligence, Computer Vision, Image Processing)

Anoop Sarkar

Area: Computational Linguistics; Machine Learning; Probabilistic Grammars and Formal language theory

Yasutaka Furukawa

Area: Computer Vision, Deep Learning, Computer Graphics & Computational Photography

Greg Mori

Area: Computer Vision, Machine Learning

Oliver Schulte

Area: Machine Learning, Computational Logic, Computational Decision Theory

Maxwell Libbrecht

Area: Computational Biology and Machine Learning


Computer vision is an area of study that enables computers to see, identify and prcoess images in the same way that human vision does, and then provide approriate output. Computer vision is concerned with the theory behind aritificial systems that extract information from images and then apply these theories and models for the construction of computer vision systems.

Greg Mori

Area: Computer Vision, Machine Learning

Yasutaka Furukawa

Area: Computer Vision, Deep Learning, Computer Graphics & Computational Photography

Ghassan Harmeneh

Area: Medical Image Analysis (Computational/Artificial Intelligence, Computer Vision, Image Processing)

Ze-Nian Li

Area: Computer Vision, Multimedia

Ping Tan

Area: Computer Vision, Computer Graphics, Robotics

Brian Funt 

Area: Computational Color Vision

Mark Drew

Area: Computer Vision


Computer graphics refers to the creation, storage and manipulation of images and models. Algorithms and data structures are used to draw images using computers with the help of programming. Topics under this area of study include modelling, geometry, animation, 3D, 2D, digital images, 3D viewport, real-time rendering, compositing, user interface design, vector graphics, among others.

KangKang Yin  

Area: Computer Animation, Computer Graphics, Humanoid Robotics, Machine Learning, Multimedia Analysis

Manolis Savva

Area: Computer Graphics, Computer Vision, Deep Learning

Angel Chang

Area: Natural Language Processing, Artificial Intelligence, Computer Graphics, Computer Vision

Richard (Hao) Zhang

Area: Computer Graphics; Geometric Modelling

Eugene Fiume

Area: Computer Graphics

Ali Mahdavi-Amiri

Area: Computer Graphics, Computer Vision, Artificial Intelligence


Cybersecurity is an interdisciplinary field of study, partly building on classical information security, risk management, situation analysis, data analytics, applied cryptography, cyber forensics and several other areas. While the broader scope is interdisciplinary, the core is fundamentally a computing-based discipline involving technology, people, information, and processes to enable assured operations in the context of adversaries. 

Mohamed Hefeeda

Area: Computer Networks, Multimedia Communications

Uwe Glässer

Area: Cyber Security Analytics, Crime Data Analysis, Situation Analysis

Andrei Bulatov

Area: Constraint Satisfaction, Complexity of Computation

Mohammad Tayebi 

Area: Data Mining, Social Network Analysis

Ouldooz Baghban Karimi

Area: Data & Networks

Brad Bart

Area: Instruction

William (Nick) Sumner

Area: Automated debugging, debugging tools, concurrency & parallelism, program analysis & transformation


Data mining is an area that combines techniques from database, statistics, and machine learning to tackle challenging, real-life and data-intensive problems. The goal is to identify concise summaries that describe the underlying patterns or distributions from which the massive observed data were generated. Such summaries are potentially useful for decision-making and many other purposes.

Martin Ester

Area: Databases and Data Mining

Ke Wang

Area: Databases, Data Mining

Jian Pei

Area: Databases and Data Mining

Jiannan Wang

Area: Database Systems and Big Data


Big data is rapidly moving data towards the edges of the computing spectrum either into the mobile phone or into the data centres of cloud computing. There are many challenges in ensuring rapid availability of data at the required destination while consuming the least energy possible.  

Rob Cameron

Area: Software Engineering Languages; Electronic Publication

Arrvindh Shriraman

Area: Systems architecture, hardware-software interactions, parallelism, sychronization and imporoving software reliability within specialized fields.


Cloud computing is about storing and accessing data and programs over the internet instead of a computer's hard drive. Though the area has seen rapid growth in the last few years, there are challenges relating to the efficiency and effectiveness of cloud services.

Mohamed Hefeeda

Area: Computer Networks, Multimedia Communications

J.C. Liu

Area: Networking and multimedia communications


A large number of big data applications are based on the analysis of naturally occurring data in the natural languages, such as the web or data on Twitter or Facebook. In particular, the challenge of big data is to effectively summarize and visualize vast amounts of "unstructured" information in language.

Fred Popowich

Area: Computational linguistics and logic programming

Anoop Sarkar

Area: Computational Linguistics; Machine Learning; Probabilistic Grammars and Formal language theory


An interdisciplinary branch of science and engineering, this area of study looks into the conception, design, construction and operation of robots - devices that can move and react to sensory input. 

Richard Vaughan

Area: Robotics and Autonomous Systems

Mo Chen

Area:  Robotics and Autonomous Systems


Human-computer interaction (HCI) is a multidisciplinary field of study on the use and design of computer technology, particularly focusing on the interactions between humans and computers. 

Parmit Chilana

Area: Human-Computer Interaction

Angelica Lim

Area: Human Robot Interaction


Bioinformatics is about developing methods and tools to solve problems related to biological data. Computational biology is about applying existing tools to arrive at new insights about living systems. Many of our faculty are involved in developing methods for handling, analyzing, and visualizing big data related to genomics and molecular biology. 

Leonid Chindelevitch

Area: Computational Biology

Martin Ester

Area: Databases and Data Mining