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WELCOME TO ESTER LAB
MACHINE LEARNING AND DATA MINING RESEARCH GROUP
SIMON FRASER UNIVERSITY
Principal Investigator
Principal Investigator
Welcome! My name is Martin Ester, and I am a Professor of Computing Science at Simon Fraser University in Burnaby, British Columbia. I got my Diplom (M.Sc.) in Computer Science from University of Dortmund, Germany, in 1984 and my Ph.D. in Computer Science from ETH Zurich, Switzerland, in 1990.
I am a co-director of the Databases and Data Mining Laboratory and member of the Omics Data Science Research Cluster. My research interests are in Data Mining and Machine Learning, in particular causal pattern discovery, transfer learning, network analysis, and recommender systems. Much of my research is driven by bio-medical applications such as patient stratification and drug response prediction. Our methods are typically based on Probabilistic Graphical Models and Deep Neural Networks.
Since my move to Vancouver in 2001, I am enjoying "Beautiful BC" and especially the many hiking trails around the Vancouver area. Our campus lies in the middle of the "wilderness" of the Burnaby Mountain conservation area, and I am commuting to and from work on my mountain bike.
NEWS
NEWS
Recent Updates
Recent Updates
Jun. 2024. Congratulations to Ali Arab on successfully defending his Ph.D. Thesis, titles, Knowledge Discovery: from Correlation to Causation.
Sep. 2023. Congratulations to Amirreza Kazemi on successfully defending his MSc Thesis, titled Deep Representation Learning for Continuous Treatment Effect Estimation!
Sep. 2023. Welcome our new Ph.D. student, Ali Izadi!
Mar. 2023. Congratulations to Dr. Raquel Aoki on successfully defending her Ph.D Thesis, titled Causal Inference for Computational Biology!
Jan. 2023. Congratulations to Atia Hamidizadeh on successfully defending her MSc Thesis, titled Semi-Supervised Junction Tree Variational Autoencoder for Molecular Graphs!
Aug. 2022. The Ester Lab would like to congratulate Dr. Oliver Snow on successfully defending his Ph.D. Thesis, titled Interactive Machine Learning for Scarce Molecular Datasets!
Jun. 2022. The Ester Lab would like to congratulate Lai Wei on successfully defending his MSc Thesis, titled Combining Graph Attention Mechanism and PageRank to Learn Graph-level Representations!
Apr. 2022. The Ester Lab would like to congratulate Dr. Mehrdad Mansouri on successfully defending his Ph.D. Thesis, titled Causal Discovery from High-dimensional Observational Data!
Jobs Updates
Jobs Updates
Feb 2024 - Congrats to Amirreza Kazemi for her new position as an Machine Learning Engineer at Huawei, Montreal Office - Canada
Jun 2023 - Congrats to Atia Hamidizadeh for her new position as an Associate Machine Learning Researcher at Huawei, Montreal Office - Canada
Sep 2022 - Congrats to Lai Wei for his new position as Software Engineer at Amazon, Vancouver Office - Canada.
Jan 2022 - Congrats to Atia Hamidizadeh for her new position as an intern at Borealis AI, Toronto Office - Canada.
Sep 2021 - Congrats to Lai Wei on his new position as an intern at Huawei, Toronto Office - Canada.
Sep 2021 - Congrats to Hossein Sharifi for his new position as an AI4Life Resident at Novartis - Switzerland.
Aug 2021 - Congrats to Oliver Snow for his new position as a Machine Learning Researcher at Terramera, Vancouver Office - Canada.
Jul 2021 - Congrats to Jialin Liu for his new position as a Software Engineer at Huawei, Vancouver Office - Canada.
May 2021 - Congrats to Raquel Aoki for her new position as a Research Intern at Google Brain, Cambridge Office - US.
Recent Publications - Last 24 months
Recent Publications - Last 24 months
[Paper] Oliver Snow, Amirreza Kazemi, Forum Bhanshali, Alyas Nasiri, Annett Rozek, and Martin Ester. "Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks", Journal of Chemical Information and Modeling, 2024
[Paper] Yuzhen Mao, Martin Ester, and Ke Li. "IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs", International Conference on Learning Representations (ICLR), 2024.
[Poster] Shuman Peng, Parsa Alamzadeh, and Martin Ester. "Better Calibration Error Estimation for Reliable Uncertainty Quantification", Workshop on Interpretable ML in Healthcare at International Conference on Machine Learning (ICML), 2023.
[Poster] Atia Hamidizadeh, Tony Shen and Martin Ester. "Semi-Supervised Junction Tree Variational Autoencoder for Molecular Graphs", Workshop on Deep Learning on Graphs: Method and Applications, AAAI 2023.
[Poster] Lai Wei, Atia Hamidizadeh, Martin Ester. “Combining Graph Attention Mechanism and PageRank to Learn Graph-level Representations”, Deep Learning on Graphs workshop, AAAI 2022.
[Paper] Mehrdad Mansouri, Sahand Khakabimamaghani, Leonid Chindelevitch, Martin Ester. "Aristotle: Stratified Causal Discovery for Omics Data", in ACM Bioinformatics, 2021.
[Paper] Oliver Snow, Hossein Sharifi-Noghabi, Jialin Lu, Olga Zolotareva, Mark Lee, and Martin Ester. "Interpretable Drug Response Prediction using a Knowledge-based Neural Network." In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3558-3568. 2021.
[Paper] Raquel Aoki and Martin Ester. "ParKCa: Causal Inference with Partially Known Causes." Pac Symp Biocomput. 2021
[Paper] Ali Arab, Betty Chinda, George Medvedev, William Siu, Hui Guo, Tao Gu, Sylvain Moreno, Ghassan Hamarneh, Martin Ester, and Xiaowei Song. "A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT." Scientific Reports 10.1 (2020): 1-12.
[Preprint] Shuman Peng, Weilian Song, and Martin Ester. "Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification." arXiv preprint arXiv:2011.00179 (2020).
[Preprint] Hossein Sharifi-Noghabi, Hossein Asghari, Nazanin Mehrasa, and Martin Ester. "Domain Generalization via Semi-supervised Meta Learning." arXiv preprint arXiv:2009.12658 (2020).
[Paper] Mehrdad Mansouri, Ali Arab, Zahra Zohrevand, and Martin Ester. "Heidegger: Interpretable Temporal Causal Discovery." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1688-1696. 2020.
[Paper] Hossein Sharifi-Noghabi, Shuman Peng, Olga Zolotareva, Colin C. Collins, and Martin Ester. "AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics." Bioinformatics 36, no. Supplement_1 (2020): i380-i388.
[Paper] Oliver Snow, Nada Lallous, Martin Ester, and Artem Cherkasov. "Deep learning modeling of androgen receptor responses to prostate cancer therapies." International journal of molecular sciences 21, no. 16 (2020): 5847.
[Preprint] Oliver Snow, Hossein Sharifi-Noghabi, Jialin Lu, Olga Zolotareva, Mark Lee, and Martin Ester. "BDKANN-Biological Domain Knowledge-based Artificial Neural Network for drug response prediction." bioRxiv (2020): 840553.
[Paper] Olga Zolotareva, Sahand Khakabimamaghani, Olga I. Isaeva, Zoe Chervontseva, Alexey Savchik, and Martin Ester. "Identification of Differentially Expressed Gene Modules in Heterogeneous Diseases." bioRxiv (2020).
[Award] SFU COMPUTING SCIENCE PROFESSOR MARTIN ESTER NAMED ROYAL SOCIETY OF CANADA FELLOW [link]
[Paper] Sahand Khakabimamaghani, Dujian Ding, Oliver Snow, and Martin Ester. "Uncovering the subtype-specific temporal order of cancer pathway dysregulation." PLoS computational biology 15, no. 11 (2019): e1007451.
[Paper] Sahand Khakabimamaghani, Yogeshwar D. Kelkar, Bruno M. Grande, Ryan D. Morin, Martin Ester, and Daniel Ziemek. "SUBSTRA: Supervised Bayesian Patient Stratification." Bioinformatics 35, no. 18 (2019): 3263-3272.
[Poster] Jialin Lu and Martin Ester. "An Active Approach for Model Interpretation." arXiv preprint arXiv:1910.12207 (2019). in Human-centric machine learning (HCML2019) workship, co-located with NeurIPS, 2019.
[Poster] Jialin Lu and Martin Ester. "Checking Functional Modularity in DNN By Biclustering Task-specific Hidden Neurons." (2019). in AI Workshop, co-located with NeurIPS, 2019.
[Poster] Raquel Aoki and Martin, Ester. "Bayesian Predictive Model combined with Matrix Factorization for Causal Inference Analysis." in 14th Machine Learning in Computational Biology (MLCB) meeting, co-located with NeurIPS, 2019.
[Paper] Sahand Khakabimamaghani, Salem Malikic, Jeffrey Tang, Dujian Ding, Ryan Morin, Leonid Chindelevitch, and Martin Ester. "Collaborative intra-tumor heterogeneity detection." Bioinformatics 35, no. 14 (2019): i379-i388.
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