Postdoctoral Researcher
Princeton Neuroscience Institute
Princeton University

Research Focus:
Cognitive Neuroscience
Computational Neuroscience
Machine Learning

mci (at) princeton (dot) edu
Twitter: @MCatalinIordan
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
Oct 26-30 Neuromatch 3.0
Zoom
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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instructor

Workshop that trained senior thesis students how to develop a strong rationale for performing effective empirical research. Also included professional development training (time management, effective writing and presentation style, designing compelling figures).

How to Effectively Design, Write, and Present a Neuroscience Honors Thesis
Princeton University. Neuroscience Senior Thesis Workshop
Fall 2020 | 11 students

Discussion-based group tutorial that taught students how to analyze and interpret modern neuroscience literature. Topics focused on semantic and visual categorization in humans and non-human primates, covering multiple modalities (fMRI, ECoG, MEG, DNNs, modeling).


Cognitive and Computational Concerns in Cortical Concept Categorization
Princeton University. Neuroscience Junior Tutorial
Fall 2017 | 11 students | Teaching Effectiveness Rating 4.6/5.0


guest lectures

Multivariate Pattern Separation For Multiple Experimental Techniques
Jul 2020 | Princeton University. Scientific Computing. (co-taught with Elise A. Piazza)
slides

Networks and Hierarchical Processing: Object Recognition in Human and Computer Vision
Dec 2014 | Stanford University. CS 131. Computer Vision and Applications
slides

A Primer on Human Vision: Insights and Inspiration for Computer Vision
Oct 2014 | Stanford University. CS 131. Computer Vision and Applications
slides


course assistant, stanford university

Taught discussion sections, held office hours, assisted students with problem sets and code, graded assignments. Work included Matlab programming and extensive theoretical proofs.


CS 131. Computer Vision and Applications
Instructor: Prof. Fei-Fei Li
Fall 2014 | 50 students

CS 229. Machine Learning
Instructor: Prof. Andrew Ng
Fall 2011 | 460 students


teaching assistant, williams college

Assisted students with coding assignments, held consulting hours, graded student work. Assignments included extensive theoretical proofs and programming in Java, C, C++, Lisp, Smalltalk, and Assembly language.


CS 334. Programming Languages
Instructor: Prof. Morgan McGuire
Spring 2009 | 30 students

CS 334. Programming Languages
Instructor: Prof. Stephen Freund
Spring 2008 | 30 students

CS 361. Theory of Computation
Instructor: Prof. Brent Heeringa
Fall 2007 | 20 students

CS 237. Microarchitecture
Instructor: Prof. James D. Teresco
Fall 2006 | 35 students

CS 361. Theory of Computation
Instructor: Prof. Brent Heeringa
Fall 2008 | 25 students

MATH 211. Linear Algebra
Instructor: Prof. Cesar E. Silva
Spring 2008 | 60 students

MATH 211. Linear Algebra
Instructor: Prof. Theron J. Hitchman
Spring 2007 | 60 students

MATH 211. Linear Algebra
Instructor: Prof. Cesar E. Silva
Fall 2006 | 120 students