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Welcome to the Laboratory for Hierarchical Anticipatory Learning (HAL Lab). We are a lab at Dalhousie University whose research interests are primarily in the fields of Computational Neuroscience and Machine Learning. Details can be found at the project pages that will be updated frequently.

Current Lab Members

A values lab member.

Dr. Thomas Trappenberg [Prof]
tt@cs.dal.ca http://cs.dal.ca/~tt
Computational Neuroscience, Machine Learning, Cognitive Robotics

Dr. Hossein Parvar [Postdoctoral Fellow]
Machine Learning and Intelligent Systems

Dr. Abraham Nunes [Research Associate]
Computational Psychiatry

Farzaneh Sheikhnezhad Fard [Ph.D]
Cognitive Robotics

Paul Hollensen [Ph.D.]
Machine Learning and Computational Neuroscience

Yoshimasa Kubo [Ph.D.]
Machine Learning and Computational Neuroscience

Stuart Mcilroy [Ph.D.]
Machine Learning and Computational Neuroscience

Ranga Rankaduwa [MSC]
Computational Neuroscience and Synaptic Plasticity

Mike Traynor [MCS]
Machine Learning for Natural Language Processing

Francesco Usai [PhD psych comprehensive]
Deep learning of brain imaging data

Junliang Luo [Honors student]
Deep learning of 3D microscopic data

Peter Lee [Honors student]
Deep learning of medical data

Past Lab Members

Chun Kwang Tan (Master Exchange Student)
Cognitive Robotics

Dr. Hossein Parvar [Adjunct Prof]
Machine Learning and Intelligent Systems

Vignesh Babu [MCS 2015]
Adaptive object tracking, robotics

Hassan Nikoo [MCS 2015]
Deep learning for Time Series

Marjan Zamani [MACS 2015]
Application of Deep Learning

Bassey Etim [MACS 2015]
Application of Deep Learning

Rohan Bhargava [MCS 2014]
Robotics

Michal Lisicki [MCS, 2014]
Image mining, deep learning

Dan Su [MACS, 2014]
Robotic coverage path planing

Karthik Damodaran [MACS, 2014]
Sublingual microcirculation analysis

Rohan Bhargava [MCS, 2014]
Adaptive motion models for AUVs

Patrick Connor [Ph.D 2013]
Biological motivated learning with small data, Basal Ganglia, Reinforcement Learning

Rober Boshra [Honors, 2013]
Machine learning in single trial EEG analysis

Jake Kroeker [Honors, 2013]
Reinforcement Learning

Jason Satel [Ph.D, 2013]
IOR, EEG, Neural Field Theory

Warren Connors [MCS, 2012]
Neural Fields and Cognitive Robotics

Leah Brown [NSERC USRA, 2011]
lbrown@cs.dal.ca
Machine Learning and Robotics

Ian Graven [NSERC USRA, 2011]
Machine Learning and Robotics

Michal Lisicki [RA, 2011]
Machine Learning and Robotics

Paul Hollensen [BCS Honours, 2010]
Restricted Boltzmann Machines

Misha Denil [BCS Honours, 2010]
Machine Learning

Dr. Dominic Standage [Ph.D., 2007]
Mechanisms of short term and long term memory in cortex: neural fields and synaptic plasticity

Bijan Farhoudi [BCS Honours, 2007]
Point attractor networks with resetricted weights

Sajiya Jail [Guest Researcher, 2005-2006]
Spike-timimg dependent plasticity

Matthew Boardman [MCS, 2006]
Extrinsic regularization in parameter optimization for support vector machines

Jesse Rusak [BCS Honours, 2006]
Setup and analysis of a brain-computer interface using support vector machines for single-trial classification

Jason Satel [MCS, 2005]
Motivationally-based learning mechanisms and the dynamics of saccade initiation

Kan Yang [MCS, 2005]
An internet-based electronic voting system with balanced complexity and security

Jie Ouyang [MCS, 2004]
Improved ICAIVS algorithm with mutual information

Patrick Mahaux [MEC, 2004]
Client Profile Model: How to Identify Training Candidates with the least chance of Employment

Daniel Rogers [BCS Honours, 2004]
Exploration of the Techniques, Tools, and Requirements of Electronic Voting Systems

Alicia Grosvenor [MEC, 2004]
Elliptic Curve cryptography: A viable alternative for maintaining security in low precessing and low memory computing environments

Shelagh Gregory [EMEC, 2003]
Should Canada use biometric identifiers if implementing a national identification system?

Dr. Carrie Gates [MSC, 1995]
The application of neural networks to predicating the conductivity of water

George MacLennan [MSC, 1994]
Evolutionary Design of neural networks