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.
marius cătălin iordan
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about me
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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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research interests
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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 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.
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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. |