About me

I'm an Associate Professor in the Computer Science department at Stevens Institute of Technology.

After completing my PhD in Computer Science in 2010 at NYU, I spent two years as a postdoctoral Computing Innovation Fellow at Columbia University, in the Department of Biomedical Informatics. Before that I was an undergraduate at NYU in Computer Science and Physics.

I've written an academic book, Causality, Probability, and Time, and another for a wider audience, Why: A Guide To Finding and Using Causes, that was published in 2015.


HAIL logo
Health and AI Lab website

We are primarily motivated by trying to improve human health, through the development of artificial intelligence methods. Most of these problems come back to the question of why things happen or how they change, so we focus on causal inference and time series data. We look at both clinical data as well as data generated outside of hospitals and aim to support both medical providers and patients in their decision making. Key application areas include stroke and diabetes. We are also working on devices that can automatically measure food intake, using body-worn sensors.

Lab members

Min Zheng (PhD student), Mark Mirtchouk (PhD student), Tolga Yavuz (PhD student), Selcuk Karakas (PhD student), Dana McGuire (Undergraduate researcher), Kyle Bernardes (Undergraduate researcher), Dylan DiGeronimo (Undergraduate researcher).

Our mascot is the Honey Badger, for obvious reasons.

Recent courses

Fall 2018 Causal Inference [CS-582]
Spring 2018 Health Informatics [CS-544]

Current Funding

James S. McDonnell Foundation Scholar Award, NSF CAREER Award, NIH R01

samantha.kleinberg@stevens.edu
(email is my preferred contact method)

Lab twitter: @HealthAI_Lab

Current Openings

We're hiring postdocs and seeking undergrad and grad students. Join us!

News

  • September 2018 I've been promoted to Associate professor, with tenure!
  • July 2018 New paper: Automated Identification of Causal Moderators in Time-Series Data published at the KDD Causal Discovery Workshop
  • March 2018 Time and Causality across the Sciences, an edited volume based on TaCitS 2017, is under contract with Cambridge University Press
  • November 2017 New paper: Multi-Scale Change Point Detection in Multivariate Time Series accepted to the NIPS Time Series Workshop
  • August 2017 Two new papers: Replicability, Reproducibility, and Agent-based Simulation of Interventions accepted to AMIA 2017

    Recognizing Eating from Body-Worn Sensors: Combining Free-living and Laboratory Data accepted to IMWUT, and to be presented at UbiComp 2017