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Mehran Kazemi

Machine Learning Researcher

Vancouver, Canada

smkazemi [at] cs [dot] ubc [dot] ca

77871072NINE1


Education

PhD in Computer Science
Learning with Relational and Graph Data
University of British Columbia
Prof. David Poole
2014 - Current
MSc in Computer Science
Relational Logistic Regression
University of British Columbia
Prof. David Poole
2012 - 2014
BSc in Computer Science
Image Restoration using Chaotic Neural Nets
Amirkabir University of Technology
Prof. Saeed Shiry Ghidary
2008 - 2012

Work Experience

Lead Machine Learning Scientist (part-time)
TalentSnap
August 2017 - Current

Machine Learning Contractor
Telus
July 2016 - August 2017

Intern (NSERC ENGAGE Grant)
Curatio
September 2014 - March 2015

Guest Lectures

Tensor Factorization for Knowledge Graph Completion
UBC-CPSC 522
February, 2018
Lifted Probabilistic Inference
UBC-CPSC 532
March, 2017
An Introduction to TensorFlow
UBC StaRAI Reading Group
November, 2016


Publications

RelNN: A Deep Neural Model for Relational Learning
Kazemi, S.M. and Poole, D.
Association for Advancements of Artificial Intelligence (AAAI)
Find on GitHub
February, 2018
SimplE Embedding for Link Prediction in Knowledge Graphs
Kazemi, S.M. and Poole, D.
arXiv:1802.04868 [stat.ML]
Find on GitHub
February, 2018
Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models
Kazemi, S.M. and Poole, D.
Frontiers in Robotics and AI
February, 2018
Comparing Aggregators for Relational Probabilistic Models
Kazemi, S.M. , Fatemi, B., Kim, A., Peng, Z., Tora, M.R., Zeng X., Dirks, M. and Poole, D.
UAI Workshop on Statistical Relational AI (StaRAI)
August, 2017
Domain Recursion for Lifted Inference with Existential Quantifiers
Kazemi, S.M. , Kimmig, A., Van den Broeck, G. and Poole, D.
UAI Workshop on Statistical Relational AI (StaRAI)
August, 2017
New Liftable classes for first-order probabilistic inference
Kazemi, S.M. , Kimmig, A., Van den Broeck, G. and Poole, D.
Neural Information Processing Systems (NIPS)
December, 2016
Why is Compiling Lifted Inference into a Low-Level Language so Effective?
Kazemi, S.M. and Poole, D.
IJCAI Workshop on Statistical Relational AI (StaRAI)
Find on GitHub
July, 2016
Knowledge Compilation for Lifted Probabilistic Inference: Compiling to a Low-level Language (short paper)
Kazemi, S.M. and Poole, D.
Principles of Knowledge Representation and Reasoning (KR)
Find on GitHub
April, 2016
A Learning Algorithm for Relational Logistic Regression: Preliminary Results
Fatemi, B., Kazemi, S.M. and Poole, D.
IJCAI Workshop on Statistical Relational AI (StaRAI)
July, 2016
Lazy Arithmetic Circuits
Kazemi, S.M. and Poole, D.
AAAI Workshop on Beyond-NP
February, 2016
Population Size Extrapolation in Relational Probabilistic Modelling
Kazemi, S.M. Buchman, D., Kersting, K., Natarajan, S. and Poole, D.
Scalable Uncertainty Management (SUM)
September, 2014
Relational Logistic Regression
Kazemi, S.M. Buchman, D., Kersting, K., Natarajan, S. and Poole, D.
Principles of Knowledge Representation and Reasoning (KR)
July, 2014
Elimination Ordering in Lifted First-Order Probabilistic Inference
Kazemi, S.M. and Poole, D.
Association for Advancements of Artificial Intelligence (AAAI)
July, 2014