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S. Mehran Kazemi

Senior Machine Learning Researcher

Montreal, Canada

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

77871072NINE1


Education

PhD in Computer Science
Representing and Learning Relations and Properties Under Uncertainty
University of British Columbia
Prof. David Poole
2014 - 2018
MSc in Computer Science
Relational Logistic Regression
University of British Columbia
Prof. David Poole
2012 - 2014
BSc in Computer Science
Amirkabir University of Technology
Prof. Saeed Shiry Ghidary
2008 - 2012

Work Experience

Senior Machine Learning Researcher
Borealis AI
May 2020 - Current

Machine Learning Researcher
Borealis AI
October 2018 - May 2020

Lead Machine Learning Scientist (part-time)
TalentSnap
August 2017 - September 2018

Machine Learning Contractor
Telus
July 2016 - August 2017

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


Publications (Google Scholar Profile)

Out-of-Sample Representation Learning for Knowledge Graphs
Albooyeh, M., Goel, R., and Kazemi, S.M.
EMNLP findings
Find on GitHub
November, 2020
Stay Positive: Knowledge Graph Embedding Without Negative Sampling
Hajimoradlou, A. and Kazemi, S.M.
ICML Workshop on Graph Representation Learning and Beyond
Find on GitHub
July, 2020
Diachronic Embedding for Temporal Knowledge Graph Completion
Goel*, R., Kazemi*, S.M. , Brubaker M., and Poupart, P. (*Equal Contribution)
Association for the Advancements of Artificial Intelligence (AAAI)
Find on GitHub
February, 2020
Representation Learning for Dynamic Graphs: A Survey
Kazemi, S.M. , Goel, R., Jain, K., Kobyzev, I., Sethi, A., Forsyth, P., and Poupart, P.
Journal of Machine Learning Research (JMLR)
2020
Time2Vec: Learning a Vector Representation of Time
Kazemi*, S.M. , Goel*, R., Eghbali*, S., Ramanan, J., Sahota, J., Thakur, S., Wu, S., Smyth, C., Poupart, P. and Brubaker M. (*Equal Contribution)
CoRR abs/1907.05321
2019
SimplE Embedding for Link Prediction in Knowledge Graphs
Kazemi, S.M. and Poole, D.
Neural Information Processing Systems (NeurIPS)
Find on GitHub
December, 2018
RelNN: A Deep Neural Model for Relational Learning
Kazemi, S.M. and Poole, D.
Association for the Advancements of Artificial Intelligence (AAAI)
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
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