Postdoctoral Researcher
Princeton Neuroscience Institute
Princeton University

Research Focus:
Cognitive Neuroscience
Computational Neuroscience
Machine Learning

mci (at) princeton (dot) edu
Google Scholar

Travel and Presentations

2020


Jan 9-10 Williams College
Williamstown, MA
talk
Feb 28 BioE Colloquium
Princeton, NJ
talk
Jun 19-24 VSS 2020
St. Pete Beach, FL
talk
Jul 29-Aug 1 CogSci 2020
Toronto, ON
talk


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

marius cătălin iordan
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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
I use applied machine learning to study how visual and semantic knowledge is learned, organized, and modulated by attention in human behavior, in the human brain, and in convolutional neural networks. I take an interdisciplinary approach that takes advantage of a diverse array of experimental techniques: psychophysics, neural networks (CNNs), neuroimaging (fMRI), and real-time neurofeedback.

                             


news

07/2020. Presenting at the Cognitive Science Society (CogSci) 2020 Annual Meeting, Neural Network Models of Cognition Affinity Group:
talk: Context Matters: Recovering Human Semantic Structure from Machine-Learning Analysis of Text.

06/2020. Our team was awarded a Research Grant from the GRAMMY Museum Foundation to investigate the neural hierarchy of audio-motor integration during natural music performance.
Co-PI, $19,758 (33% share). PI: Elise Piazza, Princeton University, co-PI: Uri Hasson, Princeton University.

06/2020. Presenting at the Vision Sciences Society (VSS) 2020 Annual Meeting:
talk: Creating Visual Categories Using Closed-Loop Real-Time fMRI Neurofeedback.

12/2019. Presenting at the Real-Time Functional Imaging and Neurofeedback (rtFIN) 2019 Conference:
poster: Creating Visual Categories Using Closed-Loop Real-Time fMRI Neurofeedback.

11/2019. PREPRINT: Incorporating semantic context into the training procedure of word embedding models improves prediction of empirical similarity judgments and feature ratings. Available on arXiv:
Context Matters: Recovering Human Semantic Structure from Machine-Learning Analysis of Text.

10/2019. 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.

05/2019. Presenting at the Vision Sciences Society (VSS) 2019 Annual Meeting:
poster: Using Closed-Loop Real-Time fMRI Feedback to Induce Neural Plasticity & Influence Perception.
11/2018. 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.

07/2018. Presenting at the Cognitive Science Society (CogSci) 2018 Annual Meeting:
talk: Feature Ratings and Dimension-Specific Similarity Explain Distinct Aspects of Semantic Similarity.

12/2017. 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.