Postdoctoral Researcher
Princeton Neuroscience Institute
Princeton University
Research Focus:
Cognitive Neuroscience
Computational Neuroscience
Machine Learning
mci (at) princeton (dot) edu
Twitter: @MCatalinIordan
Google Scholar
| 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 |
| Oct 26-30 | Neuromatch 3.0 Zoom talk |
| 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 |
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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.
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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 artificial 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.
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news
10/2020. PREPRINT: New way to provide humans with conceptual knowledge by directly sculpting activity patterns in the brain using fMRI, real-time neurofeedback, and machine learning! Available on bioRXiv: Sculpting New Visual Concepts into the Human Brain. 
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. |