Headshot The personal website and blog of Ryan Marcus, a postdoc at MIT CSAIL. I research applications of machine learning to both traditional and cloud databases.
      
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Portfolio

I'm Ryan Marcus, and I've been a postdoc researcher at MIT for a while now. I study applications of machine learning to systems, especially databases, under the supervision of Tim Kraska. I received my Ph.D. from Brandeis University, where I was advised by Olga Papaemmanouil.

Publications

  • Benchmarking Learned Indexes
    1. Ryan Marcus
    2. Andreas Kipf
    3. Alexander Van Renen
    4. Mihail Stoian
    5. Sanchit Misra
    6. Alfons Kemper
    7. Thomas Neumann
    8. Tim Kraska
    VLDB '21 (pdf)
  • Low Bitrate Compression of Video with Dynamic Backgrounds
    1. Solomon Garber
    2. Ryan Marcus
    3. Antonella DiLillo
    4. James Storer
    DCC '20 (pdf)
  • ARDA: Automatic Relational Data Augmentation for Machine Learning
    1. Nadiia Chepurko
    2. Ryan Marcus
    3. Emanuel Zgraggen
    4. Raul Castro Fernandez
    5. Tim Kraska
    6. David Karger
    VLDB '20 (pdf)
  • CDFShop: Exploring and Optimizing Learned Index Structures Demo.
    1. Ryan Marcus
    2. Emily Zhang
    3. Tim Kraska
    SIGMOD '20 (pdf)
  • Learned Garbage Collection
    1. Lujing Cen
    2. Ryan Marcus
    3. Hongzi Mao
    4. Justin Gottschlich
    5. Mohammad Alizadeh
    6. Tim Kraska
    MAPL @ PLDI '20 (pdf)
  • Cost-Guided Cardinality Estimation: Focus Where it Matters
    1. Parimarjan Negi
    2. Ryan Marcus
    3. Hongzi Mao
    4. Nesime Tatbul
    5. Tim Kraska
    6. Mohammad Alizadeh
    SMDB @ ICDE '20 (pdf)
  • RadixSpline: a single-pass learned index
    1. Andreas Kipf
    2. Ryan Marcus
    3. Alexander van Renen
    4. Mihail Stoian
    5. Alfons Kemper
    6. Tim Kraska
    7. Thomas Neumann
    aiDM @ SIGMOD '20 (pdf)
  • Towards a Hands-Free Query Optimizer through Deep Learning
    1. Ryan Marcus
    2. Olga Papaemmanouil
    CIDR '19 (pdf)
  • Compact Representations of Dynamic Video Background Using Motion Sprites
    1. Solomon Garber
    2. Aaditya Prakash
    3. Ryan Marcus
    4. Antonella DiLillo
    5. James Storer
    DCC '19 (pdf)
  • Park: An Open Platform for Learning-Augmented Computer Systems
    1. Hongzi Mao
    2. Parimarjan Negi
    3. Akshay Narayan
    4. Hanrui Wang
    5. Jiacheng Yang
    6. Haonan Wang
    7. Ryan Marcus
    8. ravichandra addanki
    9. Mehrdad Khani Shirkoohi
    10. Songtao He
    11. Vikram Nathan
    12. Frank Cangialosi
    13. Shaileshh Venkatakrishnan
    14. Wei-Hung Weng
    15. Song Han
    16. Tim Kraska
    17. Mohammad Alizadeh
    18. H. Wallach
    19. H. Larochelle
    20. A. Beygelzimer
    21. F. d Alche-Buc
    22. E. Fox
    23. R. Garnett
    NeurIPS '19 (pdf)
  • AI Meets AI: Leveraging Query Executions to Improve Index Recommendations
    1. Bailu Ding
    2. Sudipto Das
    3. Ryan Marcus
    4. Wentao Wu
    5. Surajit Chaudhuri
    6. Vivek R. Narasayya
    SIGMOD '19 (pdf)
  • Plan-Structured Deep Neural Network Models for Query Performance Prediction
    1. Ryan Marcus
    2. Olga Papaemmanouil
    VLDB '19 (pdf)
  • Neo: A Learned Query Optimizer
    1. Ryan Marcus
    2. Parimarjan Negi
    3. Hongzi Mao
    4. Chi Zhang
    5. Mohammad Alizadeh
    6. Tim Kraska
    7. Olga Papaemmanouil
    8. Nesime Tatbul
    VLDB '19 (pdf)
  • NashDB: Fragmentation, Replication, and Provisioning using Economic Methods Demo.
    1. Ryan Marcus
    2. Chi Zhang
    3. Shuai Yu
    4. Geoffrey Kao
    5. Olga Papaemmanouil
    VLDB '19 (pdf)
  • SOSD: A Benchmark for Learned Indexes
    1. Andreas Kipf
    2. Ryan Marcus
    3. Alexander van Renen
    4. Mihail Stoian
    5. Alfons Kemper
    6. Tim Kraska
    7. Thomas Neumann
    MLForSystems @ NeurIPS '19 (pdf)
  • NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning
    1. Ryan Marcus
    2. Olga Papaemmanouil
    3. Sofiya Semenova
    4. Solomon Garber
    SIGMOD '18 (pdf)
  • Deep Reinforcement Learning for Join Order Enumeration
    1. Ryan Marcus
    2. Olga Papaemmanouil
    aiDM @ SIGMOD '18 (pdf)
  • Releasing Cloud Databases from the Chains of Performance Prediction Models
    1. Ryan Marcus
    2. Olga Papaemmanouil
    CIDR '17 (pdf)
  • A Learning-Based Service for Cost and Performance Management of Cloud Databases Demo.
    1. Ryan Marcus
    2. Sofiya Semenova
    3. Olga Papaemmanouil
    ICDE '17 (pdf)
  • WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases
    1. Ryan Marcus
    2. Olga Papaemmanouil
    VLDB '16 (pdf)
  • Workload Management for Cloud Databases via Machine Learning
    1. Ryan Marcus
    2. Olga Papaemmanouil
    CloudDM @ ICDE '16 (pdf)
  • Techniques for Automated Performance Analysis
    1. Ryan Marcus
    tech. report '14 (pdf)
  • An Efficient Algorithm and Monte Carlo Methods for Inferring Functional Dependencies
    1. Ryan Marcus
    2. Shaughan Lavine
    tech. report '14 (pdf)
  • DP: a Fast Median Filter Approximation
    1. Ryan Marcus
    2. William C. Ward
    tech. report '13 (pdf)
  • MCMini: Monte Carlo on GPGPU
    1. Ryan Marcus
    tech. report '12 (pdf)
  • Developing a Monte Carlo mini-app for exascale co-design
    1. Lawrence J. Cox
    2. Ryan Marcus
    tech. report '11 (pdf)

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