About

A brief introduction.


I am currently doing research in computational neuroscience and computer vision at CSAIL, MIT under Dr. Aude Oliva. I previously received a degree in Neuroscience, Physics and Math from Bates College.

I enjoy applying machine learning to problems in biology and medicine while primarily advancing engineering knowledge. This academic year, I have successfully pursued a joint neuro-physics thesis investigating the molecular organization of the mouse olfactory system. In particular, I have analyzed in-situ hybridization data provided by the Allen Brain Institute to quantify spatial patterning and clustering of gene expression across the OB's mitral cell layer, for a large fraction of the genome. My long-term research goals are focused on promoting a self-propagating convergence of neural science and machine learning.

Some of my general interests are:

  • Deep Learning
  • Computer Vision
  • Neuroscience
  • Artificial Intelligence

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



Education

Where I've studied over the years.

2013-2017

Bates College

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

Summer 2017

Stanford University

Visiting student in NeuroAI Lab
Mentor: Dan Yamins

2017-Present

CSAIL, MIT

Principal Research Assistant
Advisor: Dr. Aude Oliva.

Projects

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

Research

An overview of my research interests, publications, etc.

    Interests

    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, Bolei Zhou, Alex Andonian Sarah Adel Bargal, Tom Yan, Kandan Ramakrishnan, 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