I am a research group leader at the Max Planck Institute for Intelligent Systems (MPI-IS).

My research background is in machine learning and algorithm design with a recent focus on optimizing human ML teaming. I study the interactions between humans and ML and use my findings to design hybrid ML systems that augment humans’ abilities rather than replace them. Most of the questions that I study are inspired by medical applications and public health.

I got my Ph.D. in Computer Science at Georgia Tech under the supervision of Prof. Santosh Vempala.

In my leisure time, I do abstract painting .

For future students: I am a faculty at the International Max Planck Research School (IMPRS-IS) and an associated faculty at the Max Planck ETH Center for Learning Systems (CLS). If you are interested to work with me as a Ph.D. student please apply through these portals. For M.Sc. thesis projects, internships, and postdoc positions please email me your application including your CV and transcripts.

Recent Publications

Sample Efficient Learning of Predictors that Complement Humans

Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, and Samira Samadi

International Conference on Machine Learning [ICML'22] [pdf]

Pairwise Fairness for Ordinal Regression

Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, and Chris Russell

International Conference on Artificial Intelligence and Statistics [AISTATS'22] [pdf][Code]

Socially Fair k-Means Clustering

Mehrdad Ghadiri, Samira Samadi, and Santosh Vempala

ACM Conference on Fairness, Accountability, and Transparency [ACM FAccT '21] [pdf][Code]

[Talk at the ELLIS Workshop on Foundations of Algorithmic Fairness][Talk at IDEAL workshop on Algorithms and their Social Impact]

Human Aspects of Machine Learning

Ph.D. thesis, Georgia Tech, 2020 [pdf]

A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition

Forough Poursabzi-Sangdeh, Samira Samadi, Jennifer Wortman Vaughan, and Hanna Wallach

Fair and Responsible AI workshop [CHI’20][pdf][Article by Georgia Tech][Article by biometricupdate.com]

Multi-Criteria Dimensionality Reduction with Applications to Fairness

Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie Morgenstern, and Santosh Vempala

Conference on Neural Information Processing Systems [NeurIPS'19 (spotlight)] [arXiv][Code][Press]

Guarantees for Spectral Clustering with Fairness Constraints

Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, and Jamie Morgenstern

International Conference on Machine Learning [ICML'19][pdf][code][poster][press][Talk at UMass CICS]

The Price of Fair PCA: One Extra Dimension

Samira Samadi, Uthaipon Tantipongpipat, Jamie Morgenstern, Mohit Singh, and Santosh Vempala

Conference on Neural Information Processing Systems [NeurIPS'18][pdf][code][press][Talk at UMass CICS]

Research Team

Mohammad-Amin Charusaie, PhD student IMPRS-IS

Tom Sühr, PhD student IMPRS-IS

Paris Khayami, Programmer

Amirmehdi Jafari Fesharaki, Intern

Teaching

Fairness in Machine Learning Seminar, Spring 2021

University of Tübingen. With Thomas Grote and Philipp Hennig

Professional Experience

Microsoft Research NYC, Spring 2019

Intern. Hosts: Jenn Wortman Vaughan and Hanna Wallach


Toyota Technological Institute at Chicago, Summer 2018

Intern. Host: Avrim Blum


Sentient Technologies, San Francisco, Summer 2016

Intern. Host: Phil Long


University of Waterloo, 2014-2015

Visiting research associate. Host: Shai Ben-David