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Friday, May 21, 2021 2:00 pm - 2:00 pm EDT (GMT -04:00)

Seminar • Systems and Networking — Logging Statement Analysis and Automation in Software Systems with Data Mining and Machine Learning Techniques

Please note: This Systems and Networking seminar will be given online.

Sina Gholamian, PhD candidate
Department of Electrical and Computer Engineering, University of Waterloo

Friday, May 28, 2021 12:00 pm - 12:00 pm EDT (GMT -04:00)

PhD Seminar • Cryptography, Security, and Privacy (CrySP) — On Privacy and Confidentiality of Communications in Organizational Graphs

Please note: This PhD seminar will be given online.

Masoumeh Shafieinejad, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Florian Kerschbaum

Tuesday, June 1, 2021 3:00 pm - 3:00 pm EDT (GMT -04:00)

PhD Seminar • Cryptography, Security, and Privacy (CrySP) — Practical Adversarial Attacks on Automatic Speaker Verification and Spoofing Countermeasures Systems

Please note: This PhD seminar will be given online.

Andre Kassis, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Urs Hengartner

Thursday, June 10, 2021 3:00 pm - 3:00 pm EDT (GMT -04:00)

Seminar • Scientific Computing — Sympiler: Transforming Sparse Computations

Please note: This seminar will be given online.

Kazem Cheshmi, Department of Computer Science
University of Toronto

Sparse matrix computations are an important class of algorithms frequently used in scientific simulations such as computer graphics and weather modeling as well as in data analytics codes and machine learning computations. The performance of these simulations relies heavily on the high-efficient implementations of sparse computations. 

Monday, June 14, 2021 9:30 am - 9:30 am EDT (GMT -04:00)

DSG Seminar Series • Efficient Network Embeddings for Large Graphs

Please note: This seminar will be given online.

Xiaokui Xiao, School of Computing
National University of Singapore

Given a graph G, network embedding maps each node in G into a compact, fixed-dimensional feature vector, which can be used in downstream machine learning tasks. Most of the existing methods for network embedding fail to scale to large graphs with millions of nodes, as they either incur significant computation cost or generate low-quality embeddings on such graphs.