Postdoctoral Researcher
Princeton Neuroscience Institute
Princeton University

Research Focus:
Cognitive Neuroscience
Computational Neuroscience
Machine Learning

Princeton Neuroscience Institute
Washington Road
Princeton, NJ 08544

mci (at) princeton (dot) edu

Google Scholar | Research Gate

Travel and Presentations

2019


Jan 15-16 Univ. of Toronto
Toronto, ON
talk
Mar 3-5 Indiana University
Bloomington, IN
talk
Apr 10-12 McMaster University
Hamilton, ON
talk
May 5-7 Univ. of Rochester
Rochester, NY
talk
May 17-22 VSS 2019
St. Pete Beach, FL
poster
Oct 19-23 SfN 2019
Chicago, IL
talk | talk
Nov 15 PDP Seminar
Princeton, NJ
talk
Dec 7-11 rtfIN 2019
Aachen, Germany
poster


2018


Mar 23 PDP Seminar
Princeton, NJ
talk
May 18-23 VSS 2018
St. Pete Beach, FL
talk
June 17-21 OHBM 2018
Singapore
poster
July 25-28 CogSci 2018
Madison, WI
talk
Nov 3-7 SfN 2018
San Diego, CA
poster | poster
Dec 11-13 Pomona College
Claremont, CA
talk

marius cătălin iordan
home        papers        teaching        outreach        cv

about me
I'm a Postdoctoral Researcher at the Princeton Neuroscience Institute, working with Jon Cohen, Ken Norman, and Nick Turk-Browne (Yale University). I earned my Ph.D. in Computer Science from the Vision Lab at Stanford University, co-advised by Fei-Fei Li and Diane Beck (University of Illinois). Before that, I received my B.A. from Williams College in Computer Science, Mathematics, and Cognitive Science.
research interests
my research program uses applied machine learning to study the mechanisms of
visual and semantic categorization in human behavior and in the human brain.

We rely on vision more than on any other sensory modality to interact with and make sense of the world. My work uses applied machine learning to investigate how visual and semantic information is learned, organized into categories, and actively modulated in our behavior and in our brains.
I take an interdisciplinary approach to this problem that involves developing advanced computational tools applicable to a diverse array of experimental techniques, including psychophysics, functional neuroimaging (fMRI), and real-time neurofeedback.

       

news

PREPRINT: Our work showing that incorporating semantic context into the training procedure of word embedding models improves prediction of empirical relationships between concepts is available on arXiv:
Context Matters: Recovering Human Semantic Structure from Machine-Learning Analysis of Text.

Dec. 2019: Presenting at the Real-Time Functional Imaging and Neurofeedback (rtFIN) 2019 Conference:
poster: Creating Visual Categories Using Closed-Loop Real-Time fMRI Neurofeedback.

Oct. 2019: Presenting at the Society for Neuroscience (SfN) 2019 Annual Meeting:
talk: Uncovering the Neural Underpinnings of Semantic Similarity Judgments.
talk: Contextually-Specific Word Embedding Models Improve Prediction of Human Semantic Relationships.

May 2019: Presenting at the Vision Sciences Society (VSS) 2019 Annual Meeting:
poster: Using Closed-Loop Real-Time fMRI Feedback to Induce Neural Plasticity & Influence Perception.
Nov. 2018: Presenting at the Society for Neuroscience (SfN) 2018 Annual Meeting:
poster: Using Closed-Loop Real-Time fMRI Neurofeedback to Induce Neural Plasticity & Influence Perception.
poster: Why We Struggle to Multitask: Converging Evidence from Modeling, Behavior, and Neuroimaging.
Our work also received a Trainee Professional Development Award from the Society for Neuroscience.

Jul 2018: Presenting at the Cognitive Science Society (CogSci) 2018 Annual Meeting:
talk: Feature Ratings and Dimension-Specific Similarity Explain Distinct Aspects of Semantic Similarity.

May 2018: Presenting at the Vision Sciences Society (VSS) 2018 Annual Meeting:
talk: Inducing Neural Plasticity and Perceptual Similarity Using Real-Time fMRI Feedback.

Dec. 2017: Presenting at the Real-Time Functional Imaging and Neurofeedback (rtFIN) 2017 Conference:
poster: KL-Evidence: A Novel Multivariate Method for Differentiating Representations.
Our work also received a Travel Award and a Best Poster Award from the rtFIN Program Committee.

Aug. 2017: Our work showing that vocal timbre is a discriminative feature between infant-directed and adult-directed speech was accepted for publication in Current Biology:
Mothers Consistently Alter Their Unique Vocal Fingerprints to Communicate With Infants.