olga

 Olga Papaemmanouil

Senior Associate Dean of Academic Affairs
School of Arts and Sciences
 
Professor
Department of Computer Science

Brandeis University

Email: {opapaemm} @brandeis.edu
Phone: +1-781-736-2716 / 1-781-736-3453

Fax: +1-781-736-2741

Address: Department of Computer Science,  MS 018
415 South St, Waltham, 02454, MA, USA


Office
Volen 214, Irving Presinential Enclave 74-101D

 

Research interests

My research interests are in the general area of data management and distributed systems with a recent focus on using machine learning  for systems and data management problems, such as query optimization, data exploration, query performance prediction and resource provisioning and workload management.


News
  • November 2021: I gave a keynote talk AIDB@NeurIPS: "Towards AI-Native Query Optimization"
  • June 2021: I gave a mini keynote talk @SIGMOD 2021: "Towards AI-Native Query Optimization"
  • June 2021: Round Table discussion on "Women in DB" @SIGMOD2021.
  • April 2021: Participated as panelist in the "Women in Tech" panel @Brandeis University.
  • February 2021: I am introducing a new course on Data Science in Spring 2021: COSI 143b "Data Management for Data Science".
  • January 2021: Panelist @CIDR 2021 Panel for "ML for Databases".
  • February 2020: My course on Distributed Databases is featured on Vertica's blog
  • January 2020: Our work is picked up by Towards Data Science: Using Reinforcement Learning to Produce Better Join Ordering Strategies
  • November 2019: Our work was featured in Brandeis Innovation Spotlight: Revolutionizing Data Science
  • September 2019: We are presenting  two papers on machine learning + databases and one demonstration on automatic data distribution  in VLDB 2019 in LA.
  • March 2019: Gave a talk on "Deep Learning Meets Query Optimization" at Vertica/Microfocus.
  • March 2019: I am co-chair of the Big Data Visual Exploration and Analytics Workshop at EDBT 2019.
  • January 2019: Our work on using deep reinforcement learning for query optimization is picked up by the Morning Paper
  • January 2019: Our work on deep learning for query performance predictions receives an Amazon Research Award.
  • May 2019: Ryan Marcus graduated - he is moving to MIT for his postdoc.
  • May 2019: Podcast by my student Ryan Marcus on his PhD thesis: Can Machine Learning Solve the Challenges of Cloud Computing
  • September 2018: New NIH grant with Steven Van Hooser (Prof of Neuroscience) on the design of data exploration applications for neuroscientists.
  • August 2018: New NSF grant on using machine learning for distributed data management.
  • March 2018: Gave a talk at the Data Science Colloquium at George Mason University on "Cost Management of Cloud Databases via Machine Learning"
  • March 2018: I am co-chair of the Big Data Visual Exploration and Analytics Workshop at EDBT 2018.
Awards and Funding

My research has been funded by the following sources:
  • Amazon Research Award on Query Performance Modeling via Deep Learning (2019)
  • Huawei Technologies, Research Innovation Award (2018) 
  • NIH Award (Brain Initiative) on "Data interface and apps for systems neurophysiology and imaging" (2018)
  • NSF Award on Automatic Learning-based Services for Distributed Data Management Systems (2018-2021)
  • Huawei Technologies, Research Innovation Award (2017)
  • NSF Career Award on Extensible Performance Management for Cloud Data Services (2012-2018)
  • NSF Award on Development Environment for Query Optimizer Engineering (20012-2016)
  • NSF Award on Interaction History Management Services and their Applications to Evidence-based Practice of Healthcare (2010-2013)

Students (PhD)
  • Yifan Zhang (2019-today)
  • Chi Zhang (2018-today)
  • Ryan Marcus (First position: postdoc at MIT, 2019)
  • Zhan Li (First employment: Oracle, 2017)
  • Kyriaki Dimitriadou (First employment: Amazon, 2017)

Selected Publications [DBLP] [Google Scholar]
Curriculum Vitae
  • ML-In-Databases: Assessment and Prognosis,  Tim Kraska, Umar Farooq Minhas, Thomas Neumann, Olga Papaemmanouil, Jignesh Patel, Chris Re, Michael Stonebraker, IEEE Bulletin on Data Engineering, March 2021, Vol. 44 No. 1 [pdf]

  • NDI: A platform-independent data interface and database for neuroscience physiology and imaging experiments,  Garca Murillo D, Rogovin O, Zhao Y, Chen S,Wang Z, Papaemmanouil O, Van Hooser SD, preprint 2020 [biorxv]

  • Bu ffer Pool Aware Query Scheduling via Deep Reinforcement Learning, C. Zhang, R. Marcus, O. Papaemmanouil, In Proceedings of the 2nd International Workshop on Applied AI for Database Systems and Applications, VLDB Workshops (aiDB 2020) [pdf]

  • Big Data Exploration, Visualization and Analytics, N. Bikakis, G. Papastefanatos, O. Papaemmanouil, Big Data Research, Volume 18, 2019 [pdf]

  • Neo: A Learned Query Optimizer, Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, Nesime Tatbul. In Proceedings of 45th International Conference on Very Large Databases (VLDB 2019) [pdf]

  • Plan-Structured Deep Neural Networks for Query Performance Prediction, Ryan Marcus, Olga Papaemmanouil. In Proceedings of 45th International Conference on Very Large Databases (VLDB 2019) [pdf]

  • NashDB: Fragmentation, Replication, and Provisioning using Economic Methods (Demonstration), Ryan Marcus, Chi Zhang, Shuai Yu, Geoffrey Kao, Olga Papaemmanouil. In Proceedings of 45th International Conference on Very Large Databases (VLDB 2019) [pdf]

  • Flexible Operator Embeddings via Deep Learning, Ryan Marcus, Olga Papaemmanouil. January 2019, arXiv.org [pdf]

  • Towards a Hands-Free Query Optimizer through Deep Learning, Ryan Marcus, Olga Papaemmanouil, In Proceedings of the 9th Biennial Conference in Innovative Data Systems Research (CIDR 2019) [pdf]

  • Deep Reinforcement Learning for Join Order Enumeration, Ryan Marcus, Olga Papaemmanouil, In Proceedings of the 1st International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, SIGMOD Workshops (aiDM 2018). [pdf]

  • NashDB: An Economic Approach to Fragmentation, Replication and Provisioning for Elastic Databases , Ryan Marcus, Olga Papaemmanouil, Sofiya Semenova, Solomon Garber,  In Proceedings of 37th ACM Special Interest Group in Data Management (SIGMOD 2018). [pdf]

  • A Learning-based Service for Cost and Performance Management of Cloud Databases (Demonstration), Ryan Marcus, Sofiya Semenova, Olga Papaemmanouil, In  Proceedings of 33rd  IEEE International Conference on Data Engineering (ICDE  2017). [pdf]

  • Releasing Cloud Databases from the Chains of Predictions Models. Ryan Marcus, Olga Papaemmanouil. In Proceedings of the 8th Biennial Conference in Innovative Data Systems Research (CIDR 2017). [pdf]

  • Interactive Data Exploration via Machine Learning Models, Olga Papaemmanouil, Yanlei Diao, Kyriaki Dimitriadou, Liping Peng. In Proceedings of IEEE Data Engineering Bulletin (invited paper), Volume 39, Issue 4, pages 21-30, December 2016. [pdf]

  • AIDE: An Active Learning-based Approach for Interactive Data Exploration. Kyriaki Dimitriadou, Olga Papaemmanouil, and Yanlei Diao. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 28, Issue 11, pages 2842 - 2856, November 2016. [pdf]

  • OptMark: A Toolkit for Benchmarking Query Optimizers,  Zhan Li, Olga Papaemmanouil, Mitch Cherniack. In Proceedings of 25th ACM International Conference on on Information and Knowledge Management (CIKM  2016).  [pdf] [long version]

  • WiSeDB: A Learning-based  Workload Management Advisor for Cloud Databases, Ryan Marcus, Olga Papaemmanouil. In Proceedings of  the Very Large Databases Endowment (PVLDB 2016). Volume 9, Issue 10, pages 780-791. [pdf]

  • AIDE: An Automatic User Navigation Service for Interactive Data Exploration (Demonstration), Yanlei Diao, Kyriaki Dimitriadou, Zhan Li, Wenzhao Liu, Olga Papaemmanouil, Kemi Peng, Liping Peng. In Proceedings of 41st International Conference on Very Large Databases (VLDB 2015) [pdf]
  • Overview of Data Exploration Techniques (Tutorial), Stratos Idreos, Olga Papaemmanouil, Surajit Chaudhuri. In Proceedings of 34th ACM Special Interest Group in Data Management (SIGMOD 2015). [pdf] [slides] (the slides cover part 1 (User Interaction) and part 2 (Middleware Optimizations))

  • Skew-Aware Join Optimization for Array Databases, Jenny Duggan, Olga Papaemmanouil, Leilani Battle, Michael Stonebraker. In Proceedings of 34th ACM Special Interest Group in Data Management (SIGMOD 2015). [pdf]
  • Explore-by-Example: An Automatic Query Steering Framework for Interactive Data Exploration, Kyriaki Dimitriadou, Olga Papaemmanouil, Yanlei Diao. In Proceedings of 33rd ACM Special Interest Group in Data Management (SIGMOD 2014). [pdf]
  • Contender: A Resource Modeling Approach for Concurrent Query Performance Prediction, Jenny Duggan, Olga Papaemmanouil, Ugur Cetintemel, Eli Upfal. In Proceedings of 17th International Conference on Extending Database Technology (EDBT 2014). [pdf]
  • Devel-Op: An Optimizer Development Environment (Demonstration), Zhibo Peng, Mitch Cherniack, Olga Papaemmanouil. In Proceedings of 30th IEEE International Conference on Data Engineering (ICDE  2014). [pdf]

  • Performance Prediction for Concurrent Database Workloads, Jennie Rogers, Ugur Cetintemel, Olga Papaemmanouil, Eli Upfal. In Proceedings of the 30th ACM Special Interest Group on Management of Data (SIGMOD 2011). [pdf]

Recent Service
  • PC Member: VLDB 2020, CIDR 2020, SIGMOD 2019 (demo), CIDR 2019, VLDB 2019, VLDB 2018, SIGMOD 2018 (Demo), CIDR 2017, VLDB 2016, ICDE 2016, SIGMOD 2016
  • Publicity Chair SoCC 2019
  • PC co-Chair BigVis 2018, BigVis 2019
  • Area Chair ICDE 2017
  • Associate Editor SIGMOD Record (2015-2018)

Teaching
  • COSI 143b: Data  Management for Data Science
  • COSI 132b: Distributed Data Management (former Networked Information Systems)
  • COSI 131a: Operating Systems
  • COSI 228a: Topics in Distributed Systems (seminar)
  • COSI 129a: Introduction to Big Data Analysis
  • COSI 12b: Advanced Programming Techniques

Short Bio

Olga Papaemmanouil is an  Professor in the Department of Computer Science at Brandeis University and Senior Associate Dean of Academic Affairs in the School of Arts and Sciences. She received her undergraduate degree in Computer Science and Informatics at the University of Patras, Greece(1999), a M.Sc. in Information Systems at the University of Economics and Business, Athens, Greece (2001) and a Ph.D. in Computer Science at Brown University (2009). Her research interests are in databases and distributed data management with a recent focus on applying machine learning techniques to solve data management problems. She is the recipient of an Amazon Research Award (2019), an  NSF Career Award (2013), a Best Demonstration Award (SIGMOD 2015), a Paris Kanellakis Fellowship (2002) and multiple NSF, NIH and industrial grants.