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
Eren Alay (PhD student), Bharat Srikishan (PhD student), Louis Gomez (PhD student), Elena Korshakova (PhD student), Aishat Toye (PhD student), Yiheng Shen (PhD student), Jim Pleuss (PhD student), Bethel Hall (PhD student), Michelle Morrone (MS student), Leigha Tierney (undergraduate researcher).
Our mascot is the Honey Badger, for obvious reasons.
Recent courses
Fall 2021 Causal Inference [CS-582]
Spring 2021 Health Informatics [CS-544]
Current Funding
James S. McDonnell Foundation Scholar Award, NSF Smart & Connected Health grant, NSF III grant, NIH R01s
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
- 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.
- July 2020 New Papers: Tell Me Something I Don't Know: How perceived knowledge influences the use of information during decision making at CogSCi 2020
Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data at MLHC
Investigating potentials and pitfalls of knowledge distillation across datasets for blood glucose forecasting at Knowledge Discovery in Healthcare Data, Workshop on BG Forecasting
- February 2020 New Paper: How Causal Information Affects Decisions