MILA (Montreal Institute for Learning Algorithms)
Email: first dot last, gmail dot com
I started post-secondary education in biology with a focus on health and neuropsychology, but transitioned to a concentration in ecology. Analyzing results for my honour's research in bioremediation, I was introduced to programming for the first time and quickly realized I wanted to do machine learning. I recieved an NSERC scholarship to particpate in a large-scale research project on climate change, and later participated in a number of coding projects and discovered neural networks.
I began an MSc in computer science with Layachi Bentabet, studying biological realism in deep networks. During this time I was awarded a MITACS scholarship to be a machine learning research intern at iPerceptions, exploring semi-supervised learning in predictive models.In November 2015 I completed my MSc, and in January 2016 began a PhD focused on deep learning research at MILA with Christopher Pal.
My primary research is in multimodal data understanding, particularly of video and natural language. I founded and organize a reading group at MILA looking at neuroscience and biology, and am particularly interested in models of memory, time, and causality in deep networks. I'm very interested in the application of ML to environmental management, health, and social welfare.
My CV can be found here.
A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, Christopher Pal. [pdf]
Suprisal-Driven Zoneout. Kamil Rocki, Tomasz Kornuta, Tegan Maharaj. [pdf]
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. David Krueger*, Tegan Maharaj*, Janos Kramar*, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron Courville, Chris Pal. [pdf] [poster] [code]
Prediction and generation of sound with LSTMs: end-of-term project for a deep learning course. [website] (research blog) [code] (based heavily on johnarevalo's code in blocks for RNN-char-prediction, modified to take and generate sound)
S.E.A.N.N. (Software Engineering Artificial Neural Network) group project: Draw a digit and a trained neural network will tell you what probability it assigns to that number being [0-9]. [website] [code]
You can find my blog here.
I started this blog as part of the course requirements for IFT6266 at UdeM.
I was a TA for the following classes during my undergrad and master's:
- CSC211 Introduction to Programming
- CSC103 Interactive Web Page Design
- FIN218 Digital Imaging
- PHY101 Introductory Statistics
- BIO349 Invertebrate Zoology
- ESG226 Oceans I
- BIO110 Genetics
- BIO116 Diversity of Life
I also worked as a tutor at the Computer Science Help Centre at the end of my BSc/beginning of MSc, and at the ITS Helpdesk (troubleshooting and tech support) throughout my BSc.
LSMDC2016 - Fill in the Blank Challenge. Joint 2nd Workshop on Storytelling with Images and Videos (VisStory) at ECCV. 2016/10. [slides]
Zoneout: Regularizing RNNs by randomly preserving hidden activations. Deep Learning Summer School. 2016/08. [slides]
BRAINS (anatomy, structure, function, and evolution). University of Montreal. 2016/06. [slides]
Neuroscience and biology for deep learning. University of Montreal. 2016/04. [slides]
Introducing "neurotransmitters" to an artificial neural network for modular concept learning and more accurate classification. Research week, Bishop's University, Sherbrooke, QC. (1st prize in poster competition) 2014/02.
Intelligent data analysis broadens our understanding of the world (2nd prize in oral competition) 2014/02.