About Me

I work at the intersection of machine learning, systems, and distributed algorithms. In particular, my research interests include distributed machine learning and edge computing, and how to make them easier. I also work on distributed stream processing, wearable devices, SSD workload analysis and characterization, systems for machine learning, and applied ML for healthcare and systems. Previously, I've worked on clinical gait analysis, datacenter monitoring, and compressing deep neural networks.

I started at the University of Toronto as an Assistant Professor with limited-term appointment (CLTA) in 2018, after receiving my Ph.D. from the Computer Science Department at the Technion - Israel Institute of Technology in 2017, advised by Assaf Schuster and Danny Keren. I received my M.Sc. from the same department in 2013. During my graduate studies I undertook several internships and collaborations with Microsoft Research, as well as major EU FP7 research projects. I also have extensive professional background from my industry days.

Contact

  • Email: mgabel (-at-) cs.toronto.edu
  • Office: BA5211
    Bahen Centre for Information Technology
    40 St. George Street
    Toronto, Ontario M5S 2E4
  • Scarborough office: IC347
    Instructional Centre
    1095 Military Trail
    Toronto, Ontario M1C 1A4

Publications

  • It's Not What Machines Can Learn, It's What We Cannot Teach. G. Yehuda, M. Gabel, and A. Schuster. 37th International Conference on Machine Learning (ICML), 2020.
    [paper] [10 minute video] [supplemental material] [arXiv]
  • Pytheas: Pattern-based Table Discovery in CSV Files. C. Christodoulakis, E. B. Munson, M. Gabel, A. D. Brown, and R. Miller. 46th International Conference on Very Large Data Bases (VLDB), 2020.
    [full text] [10 minute video] [github]
  • Feather: Hierarchical Querying for the Edge. S. H. Mortazavi, M. Salehe, M. Gabel, and E. de Lara. The Fifth ACM/IEEE Symposium on Edge Computing (SEC), 2020. To appear.
  • Incremental Sensitivity Analysis for Kernelized Models. H. Sivan, M. Gabel, and A. Schuster. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2020.
    [full text] [15 minute video] [code]
  • SSD-Based Workload Characteristics and Their Performance Implications. G. Yadgar, M. Gabel, S. Jaffer, and B. Schroeder. ACM Transactions on Storage (ToS). To appear.
  • Online Linear Models for Edge Computing. H. Sivan, M. Gabel, and A. Schuster. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2019.
    [full text]
  • Wearbreathing: Real World Respiratory Rate Monitoring Using Smartwatches. D. Liaqat, M. Abdalla, P. Abed-Esfahani, M. Gabel, T. Son, R. Wu, A. Gershon, F. Rudzicz, and E. de Lara. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT / UbiComp), 3(3), 2019.
    [full text]
  • Taming Momentum in a Distributed Asynchronous Environment. I. Hakimi, S. Barkai, M. Gabel, and A. Schuster. ArXiv abs/1907.11612, 2019.
    [arXiv]
  • Reconfigurable Streaming for the Mobile Edge. A. Tiwari, B. Ramprasad, S. H. Mortazavi, M. Gabel, and E. de Lara. Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications (HotMobile), 2019.
    [full text]
  • WristO2: Reliable Peripheral Oxygen Saturation Readings from Wrist-Worn Pulse Oximeters. C. Phillips, D. Liaqat , M. Gabel, and E. de Lara. ArXiv abs/1906.07545, 2019.
    [arXiv]
  • Anarchists, Unite: Practical Entropy Approximation for Distributed Streams. M. Gabel, D. Keren, and A. Schuster. 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.
    [full text] [3 minute video]
  • One for All and All for One: Simultaneous Approximation of Multiple Functions over Distributed Streams. A. Lazerson, M. Gabel, D. Keren, and A. Schuster. 11th ACM International Conference on Distributed and Event-Based Systems (DEBS), 2017.
    [full text]
  • On the Equivalence of the LC-KSVD and the D-KSVD Algorithms. I. Kviatkovsky, M. Gabel, E. Rivlin, and I. Shimshoni. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 39(2):411-416, 2017.
    [full text]
  • Avoiding the Streetlight Effect: I/O Workload Analysis with SSDs in Mind. G. Yadgar, M. Gabel. 8th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage), 2016.
    [full text]
  • Monitoring Least Squares Models of Distributed Streams. M. Gabel, D. Keren, and A. Schuster. 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015.
    [full text]
  • Latent Fault Detection With Unbalanced Workloads. M. Gabel, K. Sato, D. Keren, S. Matsuoka, and A. Schuster. Event Processing, Forecasting and Decision-Making in the Big Data Era (EPForDM) workshop, held in conjunction with EDBT, 2015.
    [full text]
  • Communication-efficient Distributed Variance Monitoring and Outlier Detection for Multivariate Time Series. M. Gabel, D. Keren, and A. Schuster. 28th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2014.
    [full text]
  • Communication-efficient Outlier Detection for Scale-out Systems. M. Gabel, D. Keren, and A. Schuster. First International Workshop on Big Dynamic Distributed Data (BD3), held at the 39th International Conference on Very Large Data Bases (VLDB), 2013.
  • Full Body Gait Analysis With Kinect. M. Gabel, R. Gilad-Bachrach, E. Renshaw, and A. Schuster. 34th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society (EMBC), 2012.
    [full text]
  • Latent Fault Detection in Large Scale Services. M. Gabel, A. Schuster, R.-G. Bachrach, and N. Bjørner. 42nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2012.
    [full text] [master thesis]

Students

I am co-advising several graduate students from the University of Toronto and the Technion.

I currently do not have any open positions for students or post-docs.

Teaching

  • Winter 2021: CSC B58 - Computer Organization
  • Winter 2020: CSC B58 - Computer Organization
  • Winter 2019: CSC B58 - Computer Organization
  • Fall 2018: CSC 494/2699 - Independent Study: a study of SSD-based workload characteristics
  • Previously in Technion as TA: Intro to Computer Science, Intro to Computer Science - C language, Operating Systems

Service

  • Program Committee, OSDI 2021
  • External Review Committee, ASPLOS 2021
  • Poster Chair, SEC 2020
  • Poster Co-chair, MobiSys 2020
  • Program Committee, ATC 2020
  • Program Committee, ECML PKDD 2020
  • Program Committee, SYSTOR 2020
  • Program Committee, HotEdge 2020
  • Program Committee, EdgeSys 2020
  • Program Committee, ATC 2019
  • Program Committee, CIKM 2018
  • Reviewed for: ATC 2015-2017, KDD 2016-2018, OSDI 2018, SDM 2015-2017, EuroSys 2012, 2015, 2017, IPDPS 2013, BigData 2017, DEBS 2016, EDBT 2016, FAST 2013, HotCloud 2013-2014, ICS 2013, SOCC 2014.

Industry Experience

Before returning to academia, I was a professional software developer for 10 years. I worked on embedded systems, computer graphics, file system and kernel development, DSP development, image processing, and more. My work included coding, algorithm R&D, API design, performance optimization, management roles, and project planning.