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, and I more recently spent a year on sabbatical in the psychology department of University College London.
I've written an academic book, Causality, Probability, and Time, and another for a wider audience, Why: A Guide To Finding and Using Causes. I'm the editor of Time and Causality Across the Sciences.
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
Louis Gomez (PhD student), Elena Korshakova (PhD student), Aishat Toye (PhD student), Yiheng Shen (PhD student), Jim Pleuss (PhD student), Bethel Hall (PhD student), Joy Choi (MS student), Jared Donnelly (undergraduate researcher), Ethan Kleschinsky (undergraduate researcher).
Our mascot is the Honey Badger, for obvious reasons.
Recent courses
Spring 2023 Health Informatics [CS-544]
Fall 2021 Causal Inference [CS-582]
Current Funding
NSF Smart & Connected Health, III and PAC grants, NIH R01s, NIH U54 project award
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
- May 2023 We are organizing the Heuristics and Causality in the Sciences conference at UCL!
- April 2023 Two papers accepted to CogSci, examining how beliefs interact with new information and how to assess the utility of causal models.
- September 2022 New paper using routinely collected ICU data to classify consciousness in neurological ICU patients. Summary of our work, and full article in Neurocritical Care.
New paper on where people get their health info in JMIR Form Res.
- April 2022 We received a new NSF grant (collaborative with Jessecae K. Marsh at Lehigh) to study misplaced beliefs using computation and cognitive science.
- January 2022 We are thrilled to be part of the NIH's new nutrition for precision health program and AI for nutrition center. With $1.3 million in funding we are bringing causal inference to nutrition [more]
- June 2021 Our NIH R01 was renewed for another four years! We will continue our work on consciousness in neuro ICU and expand to studying neonatal ICU over the next four years.