PhD Defense by Samira Samadi

Event Details
  • Date/Time:
    • Thursday March 5, 2020
      11:00 am - 12:30 pm
  • Location: Klaus 1315
  • Phone:
  • URL:
  • Email:
  • Fee(s):
  • Extras:
No contact information submitted.

Summary Sentence: Human Aspects of Machine Learning

Full Summary: No summary paragraph submitted.

Title: Human Aspects of Machine Learning


Samira Samadi

School of Computer Science

College of Computing

Georgia Institute of Technology


Date:  Thursday, March 5th, 2020

Time:  11:00 am - 12:30 pm (EST)

Location: Klaus 1315



Dr. Santosh Vempala (Advisor, School of Computer Science, Georgia Institute of Technology)

Dr. Adam Kalai (Senior Principal Researcher, Microsoft Research New England)

Dr. Jamie Morgenstern (School of Computer Science & Engineering, University of Washington)

Dr. Vivek Sarkar (School of Computer Science, Georgia Institute of Technology)

Dr. Mohit Singh (School of Industrial & Systems Engineering, Georgia Institute of Technology)



As humans are inevitably being influenced by machine learning algorithms, it is crucial to study the human aspects of these algorithms. In my research, I investigate several ML paradigms from the viewpoint of human usability, security, and fairness.

In the first line of work, I study human usability and security of password strategies — mental algorithms proposed by Blum and Vempala to help people calculate, in their heads, passwords for different websites without dependence on external devices. I present the first usability study of two password strategies: the 3-word strategy and the letter-code strategy. Furthermore, I show that given a limited amount of memorization, there are humanly usable password strategies that achieve the information-theoretic highest security guarantee.

In the second line of work, I investigate different fairness criteria for several machine learning techniques including principal component analysis (PCA) and spectral clustering. I show on real-world data sets that PCA can inadvertently produce low-dimensional representations with different fidelity for two different populations (e.g., lighter- versus darker-skin tone individuals). I define the notion of Fair PCA and present an efficient algorithm for finding a low-dimensional representation of the data which is nearly-optimal for this measure. I conclude by a study of spectral clustering with the constraint that every demographic is proportionally represented in each cluster. For this goal, I develop variants of constrained spectral clustering and show that they help find fairer clusterings on real data. 

Additional Information

In Campus Calendar

Graduate Studies

Invited Audience
Faculty/Staff, Public, Graduate students, Undergraduate students
Phd Defense
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Feb 18, 2020 - 12:28pm
  • Last Updated: Feb 18, 2020 - 12:28pm