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


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


Mar 31 PDP Seminar
Princeton, NJ
May 19-24 VSS 2017
St. Pete Beach, FL
June 28-30 IASL 2017
Bilbao, Spain
Sep 20 CogSci Seminar
Princeton, NJ
Nov 11-15 SfN 2017
Washington, DC
poster | poster
Nov 28-Dec 1 rtFIN 2017
Nara, Japan
Dec 8 PDP Seminar
Princeton, NJ

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
We often come across never before seen objects, scenes, and semantic concepts, such as when driving down an unfamiliar street among unfamiliar cars. However, these unique novel objects don't flummox us, since we interact with them as examples of familiar categories (car), which we have learned and honed across years of experience.
My work uses functional neuroimaging (fMRI), real-time neurofeedback, and machine learning to explore how the human brain builds and learns categories from groups of items, how we use them to generalize old experiences to novel situations, and how to control and influence category representations to improve our interactions with the world.



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.

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.

Nov. 2017: Presenting at the Society for Neuroscience (SfN) 2017 Annual Meeting:
poster: Inducing Neural Plasticity and Perceptual Similarity Using Real-Time fMRI Neurofeedback.
poster: The Importance of "Motherese": Early Drivers of Successful Communication.

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".

May 2017: Presenting at the Vision Sciences Society (VSS) 2017 Annual Meeting:
poster: The Relative Contribution of Features and Dimensions to Semantic Similarity.

Nov. 2016: Presenting at the Society for Neuroscience (SfN) 2016 Annual Meeting:
poster: Sequential Warping of Neural Representations According to Cognitive Principles in Visual Cortex.