Teaching

The Pennsylvania State University

• Fall 2023

  CSE 543: Computer Security

  This course provides a graduate-level introduction to computer security. Students successfully completing this class will have a broad understanding of cybersecurity and rudimentary skills in security research. Students will also be able to evaluate works in academic and commercial security. The course begins with a tutorial of the basic elements of software security, cryptography, cryptanalysis, and systems security, and continues by covering a number of seminal papers and monographs in a wide range of security areas.

• Spring 2023

  CSE 597: Security and Privacy of Machine Learning

  Today we see applications of machine learning almost everywhere we look - in the domains of autonomous driving, medical diagnosis, fraud detection, etc. While the use of machine learning is increasing in our day-to-day lives, these techniques also pose significant threats to security and data privacy. This course will explore recent academic research at the intersection of machine learning, security, and data privacy that demonstrates the risks adversaries pose to machine learning systems. The research papers explored in this course would cover attacks on machine learning systems as well as defense techniques to mitigate such attacks. At the end of this course, students will (1) acquire a solid background on recent developments in the area of security and privacy of machine learning, (2) be able to identify the security and privacy threats by rigorously analyzing systems that leverage machine learning, and finally, (3) be motivated to conduct research in this emerging area.

Dartmouth College

• Fall 2021

  COSC 55: Security and Privacy

  This course provides an introduction to the theory and application of computer security and privacy. Students will develop the skills necessary to formulate and address the security needs of enterprise and personal environments. The course will begin by describing the goals and mechanisms of security as motivated by recent incidents in the real world. The topics will cover cryptography, authentication, authorization, software security, software vulnerabilities, access control, malware/intrusion detection, web security, database security, privacy, AI security, and other emerging topics. A detailed list of lecture contents, assignments, and due dates (subject to change as semester evolves) will be available on the course website.

• Fall 2021

  COSC 89.27/189: Security and Privacy of Machine Learning

  Today we see applications of machine learning almost everywhere we look - in the domains of autonomous driving, medical diagnosis, fraud detection, etc. While the use of machine learning is increasing in our day-to-day lives, these techniques also pose significant threats to security and data privacy. This course will explore recent academic research at the intersection of machine learning, security, and data privacy that demonstrates the risks adversaries pose to machine learning systems. The research papers explored in this course would cover attacks on machine learning systems as well as defense techniques to mitigate such attacks. At the end of this course, students will (1) acquire a solid background on recent developments in the area of security and privacy of machine learning, (2) be able to identify the security and privacy threats by rigorously analyzing systems that leverage machine learning, and finally, (3) be motivated to conduct research in this emerging area.

• Winter 2021

  COSC 89.27/189: Security and Privacy of Machine Learning

  Today we see applications of machine learning almost everywhere we look - in the domains of autonomous driving, medical diagnosis, fraud detection, etc. While the use of machine learning is increasing in our day-to-day lives, these techniques also pose significant threats to security and data privacy. This course will explore recent academic research at the intersection of machine learning, security, and data privacy that demonstrates the risks adversaries pose to machine learning systems. The research papers explored in this course would cover attacks on machine learning systems as well as defense techniques to mitigate such attacks. At the end of this course, students will (1) acquire a solid background on recent developments in the area of security and privacy of machine learning, (2) be able to identify the security and privacy threats by rigorously analyzing systems that leverage machine learning, and finally, (3) be motivated to conduct research in this emerging area.

Purdue University

• Spring 2015

  Problem Solving and Object-Oriented Programming (CS 18000)

  Teaching Assistant

• Fall 2014

  Problem Solving and Object-Oriented Programming (CS 18000)

  Teaching Assistant

Bangladesh University of Engineering & Technology

• Fall 2013

  Assembly Language Programming (CSE 214)

  Lecturer

• Fall 2013

  Structured Programming Language Sessional (CSE 106)

  Lecturer

• Spring 2013

  Digital Systems Design Sessional (CSE 404)

  Lecturer

• Spring 2013

  Compiler Sessional (CSE 310)

  Lecturer

Ahsanullah University of Science & Technology

• Fall 2013

  Computer Graphics Sessional (ARC 4204)

  Lecturer

• Fall 2013

  Computer Fundamentals (ARC 1130)

  Lecturer