Perouz Taslakian

Perouz Taslakian

Research Scientist in Machine Learning

Samsung Electronics

Biography

I am a Research Scientist in machine learning at the Samsung AI Center in Montreal.

My research at Samsung focuses on developing machine learning techniques to address challenges that come up in wireless communication networks. These challenges range from detecting anomalies in network structures to understanding the causal structure of network events.

Previously, I was a Research Scientist and Research Lead at Element AI (currently Service Now), where I conducted fundamental research in machine learning, specializing in the area of graph learning and causality. I was concurrently the research lead of the Human Decision Support Program, whose goal was to develop decision-making models in a setting where data is volatile, relations are ambiguous, and past is not a good predictor of the future. The program effectively combined time-series analysis with graph and causal representation learning.

I obtained my PhD in Computer Science from McGill University. My academic research focused on theoretical and algorithmic aspect of discrete structures, with problems on coloring and guarding line arrangements, aligning necklaces, deflating polygons, flipping linkages, and proving things about of geometric graphs. Among my own papers, one of my favourites is perhaps Transversals in Trees, mainly because I had a lot fun working with Vašek, Luc, and Victor. But also because it shrank my Erdős number to 2, and gave me a flaming grade on Vašek’s course The mathematics of Paul Erdős.

I used to be a professor and chair of the BS in Computational Sciences Program at the American University of Armenia.

For about four years, I ran a local meetup group called All-Girl Hack Night, whose purpose was to bring together, support and empower women in IT.

Download my resumé.

Interests
  • Learning from graph-like data
  • Relational Reasoning
  • Causal Discovery
Education
  • PhD in Computer Science, 2009

    McGill University

  • MSc in Computer Science, 2004

    Concordia University

  • BSc in Computer Science, 1998

    Haigazian University

Publications

(2022). Typing assumptions improve identification in causal discovery. Causal Learning and Reasoning (CLeaR).

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(2020). Knowledge Hypergraphs: Prediction Beyond Binary Relations. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20.

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(2019). Context-aware visual compatibility prediction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR-19.

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(2017). Efficient multi-robot coverage of a known environment. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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(2016). The four bars problem. Nonlinearity.

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Conference Organization

GCP 2022

GCP 2022

Armenian Workshop On Graphs, Combinatorics, Probability and their Application to Machine Learning.

GroundedML 2022

GroundedML 2022

Workshop on Anchoring Machine Learning in Classical Algorithmic Theory, at the Inter. Conference on Learning Representations (ICLR).

GCP 2019

GCP 2019

Armenian Workshop On Graphs, Combinatorics, Probability and their Application to Machine Learning.

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