MASc Seminar: Metasploit for Cyber-Physical Security Testing with Real-Time Constraints
Candidate: Sulav Shrestha
Date: August 21, 2023
Time: 11:30 am
Location: remote attendance
Supervisor(s): Sebastian Fischmeister
Candidate: Sulav Shrestha
Date: August 21, 2023
Time: 11:30 am
Location: remote attendance
Supervisor(s): Sebastian Fischmeister
Candidate: Islam Mohamed Mahmoud Nasr
Date: August 16, 2023
Time: 10:00am
Location: E5 4047
Supervisor(s): Fakhri Karray
Candidate: Jonathan Chung
Date: August 15, 2023
Time: 3:00 pm
Location: remote attendance
Supervisor(s): Arie Gurfinkel
Candidate: Xiaomeng Lei
Date: August 10,2023
Time: 1:00pm
Location: remote attendance
Supervisor(s): Mahesh Tripunitara
Candidate: Alex Liu
Date: August 8, 2023
Time: 3:00 pm
Location: EIT 3145
Supervisor(s): Werner Dietl
Candidate: Ridham Dave
Date: August 3, 2023
Time: 12:00pm
Location: remote attendance
Supervisor(s): Sebastian Fischmeister
You are invited by the Department of Electrical and Computer Engineering and the IEEE Electronics Packaging Society (EPS) Student Chapter at University of Waterloo to attend a distinguished lecture:
Speaker: Professor John A. Rogers, McCormick School of Engineering, Northwestern University
Date: September 13, 2023
Time: 12:00pm
Location: PSE-7303/7363
Name: Zhiping Cai
Date: Monday April 3rd 2023
Time: 3:00pm - 4:00pm (EST)
Location: EIT 3145
Supervisor: Prof. Werner Dietl
Attending faculty members: Prof. Arie Gurfinkel, and Prof. Mahesh Tripunitara
Title: UniFlow: A CFG-based Framework for Pluggable Type Checking andType Inference
Abstract:
Name: Huixin Jin
Date: Thursday 23rd of March 2023
Time: 4:00pm to 5:00pm (EST)
Location: E5-4047
Supervisor: Slim Boumaiza
Attending faculty member: Prof. Raafat Mansour
Title: Millimeter-wave 39 GHz 4x4 Phased Antenna Array with Embedded Near-field Probing Antenna for Performance Enhancement
Abstract:
Name: Sheikh Nooruddin
Date: Wednesday, March 22nd 2023
Time: 10AM - 11AM
Location: online
Supervisor(s): Fakhri Karray, Ali Elkamel, Mark Crowley
Title: On the Design of Efficient Deep Learning Methods for Human Activity Recognition in Resource Constrained Devices
Abstract: