A brief introduction.

I am a first year PhD student at CSAIL, MIT studying computer vision, machine learning, and AI under Aude Oliva and Phillip Isola. I previously received a degree in Neuroscience, Physics and Math from Bates College.

I enjoy applying methods from computer science, neuroscience and cognitive science to understand and model how perception and cognition are represented in human and machine. My long-term research goals are focused on promoting a self-propagating convergence of brain & cognitive sciences and machine learning.

Some of my research interests include:

  • Representation Learning
  • Generative Models
  • Video and Event Understanding
  • Embodied Intelligence

Email: alexandonian [at] gmail.com OR andonian [at] mit.edu

CV / Google Scholar / GitHub / Twitter

Community Outreach

Some of the organizations I have worked with in the past.

Industry Experience

Places and teams I hope to work with in the future.

Google Brain/DeepMind Team
Facebook AI Research (FAIR)
MIT-IBM Watson AI Lab


Where I've studied over the years.


Bates College

BS, Neuroscience, Physics, Math
Advisors: Jason Castro, Travis Gould

Summer 2017

Stanford University

Visiting student in NeuroAI Lab
Mentor: Dan Yamins



Principal Research Assistant
Advisor: Dr. Aude Oliva.


A collection of side projects, research studies, and course assignments.


An overview of my research interests, publications, etc.


    Machine Learning
    Computer Vision
    Computational Neuroscience

    Application Areas

    Medical disease detection and diagnosis
    Bioimage informatics
    Computational neuroanatomy

  • Past Groups

  • NeuroAI Lab (2017)
    Department of Computer Science
    Stanford University

    Castro Lab (2017)
    Program in Neuroscience
    Bates College

    Rosen Lab (2013)
    Department of Developmental Biology
    Harvard School of Dental Medicine

    Corkey's Lab (2012)
    Obesity Research Center
    Boston University School of Medicine

  • Recent Publications

  • Temporal Relational Reasoning in Videos. European Conference on Computer Vision (ECCV). 2018.
    A deep learning based method for large-scale classification, registration, and clustering of in-situ hybridization experiments in the mouse olfactory bulb.
    Alex Andonian, Dan Paseltiner, Travis Gould, Jason Castro.
    Journal of Neuroscience Methods. 2018
    Moments in Time Dataset: one million videos for event understanding.
    Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfruend, Carl Vondrick, Aude Oliva.
    Under revision of the IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2018
    N-linked glycosylation of the bone morphogenetic protein receptor type 2 (BMPR2) enhances ligand binding.
    Jonathan W. Lowery, Jose M. Amich, Alex Andonian, Vicki Rosen.
    Cellular and Molecular Life Sciences. 2013.
    Anonymous Submission
    Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Mathew Monfort, Carl Vondrick, Aude Oliva.
    Under review, Computer Vision and Pattern Recognition (CVPR '19). 2018.
    Anonymous Submission
    Bowen Pan, Alex Andonian, Aude Oliva, Bolei Zhou.
    Under review, Computer Vision and Pattern Recognition (CVPR '19). 2018.
    Anonymous Submission
    Mathew Monfort, Kandan Ramakrishnan Alex Andonian, Dan Gutfreund, Aude Oliva.
    Under review, Computer Vision and Pattern Recognition (CVPR '19). 2018.
    • Presentations and Other Papers

      1. A Deep-Learning Pipeline for Studying Olfactory Bulb Molecular Anatomy at Genomic Scale. Alex J. Andonian, Daniel A. Paseltiner, Jason B. Castro. November 2017. Poster
      2. Data Driven Approaches for Investigating Molecular Heterogeneity of the Brain. A. Andonian. Mt. David Summit, Bates College, March 2017.
      3. Informatics Tools for Quantifying Intratumor Heterogeneity in Multiplexed Fluorescence Tissue Data. A. Andonian. Council on Undergraduate Research's Research Experiences for Undergraduates Symposium. National Science Foundation's Atrium, Arlington Virginia. October 2016.
      4. Scalable Informatics Tools for Investigating Intra-Tumor Heterogeneity in Breast Cancer. A. Andonian. Summer Undergraduate Research Symposium, University of Pittsburgh and Duquesne University. July 2016. PDF Code Poster