Nesime Tatbul

Short Bio

Nesime Tatbul is a senior research scientist at Intel's Parallel Computing Lab (PCL) and MIT's Computer Science and Artificial Intelligence Lab (CSAIL). Since 2013, she has been Intel's research lead and industry co-PI for the Intel Science and Technology Center for Big Data (ISTC-BigData), followed by the Data Systems and AI Lab (DSAIL) based at MIT. Previously, she served on the computer science faculty of ETH Zurich in Switzerland, after receiving her PhD & MS degrees in Computer Science from Brown University in USA and her MS & BS degrees in Computer Engineering from the Middle East Technical University (METU) in Turkey.

Her research interests are broadly in large-scale data management systems and modern data-intensive applications, with a recent focus on learned data systems, time series analytics, and observability data management. She is most known for her contributions to stream processing, which include the Aurora/Borealis Systems (now TIBCO StreamBase) and the S-Store System (the first streaming OLTP system).

Nesime is the recipient of a PVLDB Distinguished Associate Editor Award (2023) and an IBM Faculty Award (2008), and a co-recipient of an ACM SIGMOD Research Highlight Award (2022), an ACM SIGMOD Best Paper Award (2021), two ACM SIGMOD Best Demonstration Awards (2005 and 2019), and an ACM DEBS Grand Challenge Award (2011).

She has served on the organization and program committees for various conferences and workshops, including ACM SIGMOD (associate editor in 2011, 2022, and 2024; industrial co-chair in 2014; panels co-chair in 2023), VLDB (demo co-chair in 2019; workshops co-chair in 2020; scalable data science category inaugural co-chair in 2021; associate editor in 2021, 2023, and 2024; industrial co-chair in 2024, program co-chair in 2025), IEEE ICDE (area chair in 2013; industrial co-chair in 2023), ACM DEBS (program co-chair in 2021), SIGMOD/aiDM (program co-chair in 2020 and 2021), SIGMOD/DaMoN (program co-chair in 2023 and 2024), NeurIPS, and NeurIPS/WiML (area chair and mentor in 2019-2022), and on the editorial boards of the ACM SIGMOD Record (2012-2017) and the VLDB Journal (Editor-in-Chief for Americas since 2023) & on the advisory board of the PVLDB (since 2023).

She is an ACM Distinguished Member, an IEEE Senior Member, and an elected member of the VLDB Endowment Board of Trustees (2020-2025).

Research

Recent Projects: DSAIL, Exathlon, Metronome, BigDAWG, S-Store

Selected Publications [ Full List ]

C. Anneser, N. Tatbul, D. Cohen, Z. Xu, P. Pandian, N. Laptev, R. Marcus, "AutoSteer: Learned Query Optimization for Any SQL Database", PVLDB 16(12), August 2023. [code] [bibtex]
F. Kossmann, Z. Wu, E. Lai, N. Tatbul, L. Cao, T. Kraska, S. Madden, "Extract-Transform-Load for Video Streams", PVLDB 16(9), May 2023. [code] [tech report] [bibtex]
P. Negi, Z. Wu, A. Kipf, N. Tatbul, R. Marcus, S. Madden, T. Kraska, M. Alizadeh, "Robust Query-Driven Cardinality Estimation under Changing Workloads", PVLDB 16(6), February 2023. [code] [bibtex]
S. Madden, J. Ding, T. Kraska, S. Sudhir, D. Cohen, T. Mattson, N. Tatbul, "Self-Organizing Data Containers", Conference on Innovative Data Systems Research (CIDR'22), Chaminade, CA, USA, January 2022. [talk video] [bibtex]
F. Solleza, A. Crotty, S. Karumuri, N. Tatbul, S. Zdonik, "Mach: A Pluggable Metrics Storage Engine for the Age of Observability", Conference on Innovative Data Systems Research (CIDR'22), Chaminade, CA, USA, January 2022. [talk video] [bibtex]
V. Jacob, F. Song, A. Stiegler, B. Rad, Y. Diao, N. Tatbul, "Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series", PVLDB 14(11), July 2021. (PVLDB Reproducibility Badge) [code] [tech report] [bibtex]
P. Negi, R. Marcus, A. Kipf, H. Mao, N. Tatbul, T. Kraska, M. Alizadeh, "Flow-Loss: Learning Cardinality Estimates That Matter", PVLDB 14(11), July 2021. [code] [bibtex]
R. Marcus, P. Negi, H. Mao, N. Tatbul, M. Alizadeh, T. Kraska, "Bao: Making Learned Query Optimization Practical", ACM SIGMOD International Conference on Management of Data (SIGMOD'21), Xi'an, Shaanxi, China, June 2021. (Best Paper Award + ACM SIGMOD Research Highlight Award) [code] [talk video] [bibtex]
S. Karumuri, F. Solleza, S. Zdonik, N. Tatbul, "Towards Observability Data Management at Scale", Vision Column, ACM SIGMOD Record 49(4), December 2020. [bibtex]
E. Rezig, A. Brahmaroutu, N. Tatbul, M. Ouzzani, N. Tang, T. Mattson, S. Madden, M. Stonebraker, "Debugging Large-Scale Data Science Pipelines using Dagger", Demonstration, PVLDB 13(12), August 2020. [bibtex]
A. Shanbhag, N. Tatbul, D. Cohen, S. Madden, "Large-Scale In-Memory Analytics on Intel Optane DC Persistent Memory", ACM SIGMOD International Workshop on Data Management on New Hardware (DaMoN'20), Portland, OR, USA, June 2020. [bibtex]
M. Alam, J. Gottschlich, N. Tatbul, J. Turek, T. Mattson, A. Muzahid, "A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions", 33rd Annual Conference on Neural Information Processing Systems (NeurIPS'19), Vancouver, Canada, December 2019. [code] [3min video] [slides] [poster] [bibtex]
R. Marcus, P. Negi, H. Mao, C. Zhang, M. Alizadeh, T. Kraska, O. Papaemmanouil, N. Tatbul, "Neo: A Learned Query Optimizer", PVLDB 12(11), July 2019. [bibtex]
P. Eichmann, F. Solleza, N. Tatbul, S. Zdonik, "Visual Exploration of Time Series Anomalies with Metro-Viz", Demonstration, ACM SIGMOD International Conference on Management of Data (SIGMOD'19), Amsterdam, Netherlands, June 2019. (Best Demonstration Award) [poster] [bibtex]
N. Tatbul, T. J. Lee, S. Zdonik, M. Alam, J. Gottschlich, "Precision and Recall for Time Series", 32nd Annual Conference on Neural Information Processing Systems (NeurIPS'18), Montreal, Canada, December 2018. (Spotlight Paper) [code] [talk video] [talk slides] [poster] [bibtex]
J. Gottschlich, A. Solar-Lezama, N. Tatbul, M. Carbin, M. Rinard, R. Barzilay, S. Amarasinghe, J. B. Tenenbaum, T. Mattson, "The Three Pillars of Machine Programming", ACM SIGPLAN International Workshop on Machine Learning and Programming Languages (MAPL'18), Philadelphia, PA, USA, June 2018. [bibtex]
J. Meehan, C. Aslantas, S. Zdonik, N. Tatbul, J. Du, "Data Ingestion for the Connected World", Conference on Innovative Data Systems Research (CIDR'17), Chaminade, CA, USA, January 2017. [talk slides] [bibtex]
J. Meehan, N. Tatbul, S. Zdonik, C. Aslantas, U. Cetintemel, J. Du, T. Kraska, S. Madden, D. Maier, A. Pavlo, M. Stonebraker, K. Tufte, H. Wang, "S-Store: Streaming Meets Transaction Processing", PVLDB 8(13), September 2015. [code] [bibtex]
N. Dindar, N. Tatbul, R. J. Miller, L. Haas, I. Botan, "Modeling the Execution Semantics of Stream Processing Engines with SECRET", VLDB Journal 22(4), August 2013. [bibtex]
A. Moga, I. Botan, N. Tatbul, "UpStream: Storage-centric Load Management for Streaming Applications with Update Semantics", VLDB Journal 20(6), December 2011. [bibtex]
K. Sheykh Esmaili, T. Sanamrad, P. M. Fischer, N. Tatbul, "Changing Flights in Mid-air: A Model for Safely Modifying Continuous Queries", ACM SIGMOD International Conference on Management of Data (SIGMOD'11), Athens, Greece, June 2011. [bibtex]

M. Akdere, U. Cetintemel, N. Tatbul, "Plan-based Complex Event Detection Across Distributed Sources", International Conference on Very Large Data Bases (VLDB'08), Auckland, New Zealand, August 2008. [bibtex]

N. Tatbul, U. Cetintemel, S. Zdonik, "Staying FIT: Efficient Load Shedding Techniques for Distributed Stream Processing", International Conference on Very Large Data Bases (VLDB'07), Vienna, Austria, September 2007. [talk slides] [bibtex]

N. Tatbul, S. Zdonik, "Window-aware Load Shedding for Aggregation Queries over Data Streams", International Conference on Very Large Data Bases (VLDB'06), Seoul, Korea, September 2006. [talk slides] [bibtex]

Y. Ahmad, B. Berg, U. Cetintemel, M. Humphrey, J. Hwang, A. Jhingran, A. Maskey, O. Papaemmanouil, A. Rasin. N. Tatbul, W. Xing, Y. Xing, S. Zdonik, "Distributed Operation in the Borealis Stream Processing Engine", Demonstration, ACM SIGMOD International Conference on Management of Data (SIGMOD'05), Baltimore, MD, USA, June 2005. (Best Demonstration Award) [overview poster, game poster, photos] [bibtex]

D. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J. Hwang, W. Lindner, A. Maskey, A. Rasin, E. Ryvkina, N. Tatbul, Y. Xing, S. Zdonik, "The Design of the Borealis Stream Processing Engine", Conference on Innovative Data Systems Research (CIDR'05), Asilomar, CA, USA, January 2005. [talk slides] [bibtex]

N. Tatbul, U. Cetintemel, S. Zdonik, M. Cherniack, M. Stonebraker, "Load Shedding in a Data Stream Manager", International Conference on Very Large Data Bases (VLDB'03), Berlin, Germany, September 2003. [talk slides] [bibtex]

D. Abadi, D. Carney, U. Cetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, S. Zdonik, "Aurora: A New Model and Architecture for Data Stream Management", VLDB Journal 12(2), Special Issue on Best Papers of VLDB 2002, August 2003. [bibtex]

	Nesime Tatbul Senior Research Scientist, Intel Labs and MIT Data Systems and AI Lab (DSAIL) MIT Data Systems Group Email: tatbul@csail.mit.edu, LinkedIn: tatbul, Twitter: @tatbul

Short Bio Nesime Tatbul is a senior research scientist at Intel's Parallel Computing Lab (PCL) and MIT's Computer Science and Artificial Intelligence Lab (CSAIL). Since 2013, she has been Intel's research lead and industry co-PI for the Intel Science and Technology Center for Big Data (ISTC-BigData), followed by the Data Systems and AI Lab (DSAIL) based at MIT. Previously, she served on the computer science faculty of ETH Zurich in Switzerland, after receiving her PhD & MS degrees in Computer Science from Brown University in USA and her MS & BS degrees in Computer Engineering from the Middle East Technical University (METU) in Turkey. Her research interests are broadly in large-scale data management systems and modern data-intensive applications, with a recent focus on learned data systems, time series analytics, and observability data management. She is most known for her contributions to stream processing, which include the Aurora/Borealis Systems (now TIBCO StreamBase) and the S-Store System (the first streaming OLTP system). Nesime is the recipient of a PVLDB Distinguished Associate Editor Award (2023) and an IBM Faculty Award (2008), and a co-recipient of an ACM SIGMOD Research Highlight Award (2022), an ACM SIGMOD Best Paper Award (2021), two ACM SIGMOD Best Demonstration Awards (2005 and 2019), and an ACM DEBS Grand Challenge Award (2011). She has served on the organization and program committees for various conferences and workshops, including ACM SIGMOD (associate editor in 2011, 2022, and 2024; industrial co-chair in 2014; panels co-chair in 2023), VLDB (demo co-chair in 2019; workshops co-chair in 2020; scalable data science category inaugural co-chair in 2021; associate editor in 2021, 2023, and 2024; industrial co-chair in 2024, program co-chair in 2025), IEEE ICDE (area chair in 2013; industrial co-chair in 2023), ACM DEBS (program co-chair in 2021), SIGMOD/aiDM (program co-chair in 2020 and 2021), SIGMOD/DaMoN (program co-chair in 2023 and 2024), NeurIPS, and NeurIPS/WiML (area chair and mentor in 2019-2022), and on the editorial boards of the ACM SIGMOD Record (2012-2017) and the VLDB Journal (Editor-in-Chief for Americas since 2023) & on the advisory board of the PVLDB (since 2023). She is an ACM Distinguished Member, an IEEE Senior Member, and an elected member of the VLDB Endowment Board of Trustees (2020-2025). Research Recent Projects: DSAIL, Exathlon, Metronome, BigDAWG, S-Store Selected Publications [ Full List ] C. Anneser, N. Tatbul, D. Cohen, Z. Xu, P. Pandian, N. Laptev, R. Marcus, "AutoSteer: Learned Query Optimization for Any SQL Database", PVLDB 16(12), August 2023. [code] [bibtex] F. Kossmann, Z. Wu, E. Lai, N. Tatbul, L. Cao, T. Kraska, S. Madden, "Extract-Transform-Load for Video Streams", PVLDB 16(9), May 2023. [code] [tech report] [bibtex] P. Negi, Z. Wu, A. Kipf, N. Tatbul, R. Marcus, S. Madden, T. Kraska, M. Alizadeh, "Robust Query-Driven Cardinality Estimation under Changing Workloads", PVLDB 16(6), February 2023. [code] [bibtex] S. Madden, J. Ding, T. Kraska, S. Sudhir, D. Cohen, T. Mattson, N. Tatbul, "Self-Organizing Data Containers", Conference on Innovative Data Systems Research (CIDR'22), Chaminade, CA, USA, January 2022. [talk video] [bibtex] F. Solleza, A. Crotty, S. Karumuri, N. Tatbul, S. Zdonik, "Mach: A Pluggable Metrics Storage Engine for the Age of Observability", Conference on Innovative Data Systems Research (CIDR'22), Chaminade, CA, USA, January 2022. [talk video] [bibtex] V. Jacob, F. Song, A. Stiegler, B. Rad, Y. Diao, N. Tatbul, "Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series", PVLDB 14(11), July 2021. (PVLDB Reproducibility Badge) [code] [tech report] [bibtex] P. Negi, R. Marcus, A. Kipf, H. Mao, N. Tatbul, T. Kraska, M. Alizadeh, "Flow-Loss: Learning Cardinality Estimates That Matter", PVLDB 14(11), July 2021. [code] [bibtex] R. Marcus, P. Negi, H. Mao, N. Tatbul, M. Alizadeh, T. Kraska, "Bao: Making Learned Query Optimization Practical", ACM SIGMOD International Conference on Management of Data (SIGMOD'21), Xi'an, Shaanxi, China, June 2021. (Best Paper Award + ACM SIGMOD Research Highlight Award) [code] [talk video] [bibtex] S. Karumuri, F. Solleza, S. Zdonik, N. Tatbul, "Towards Observability Data Management at Scale", Vision Column, ACM SIGMOD Record 49(4), December 2020. [bibtex] E. Rezig, A. Brahmaroutu, N. Tatbul, M. Ouzzani, N. Tang, T. Mattson, S. Madden, M. Stonebraker, "Debugging Large-Scale Data Science Pipelines using Dagger", Demonstration, PVLDB 13(12), August 2020. [bibtex] A. Shanbhag, N. Tatbul, D. Cohen, S. Madden, "Large-Scale In-Memory Analytics on Intel Optane DC Persistent Memory", ACM SIGMOD International Workshop on Data Management on New Hardware (DaMoN'20), Portland, OR, USA, June 2020. [bibtex] M. Alam, J. Gottschlich, N. Tatbul, J. Turek, T. Mattson, A. Muzahid, "A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions", 33rd Annual Conference on Neural Information Processing Systems (NeurIPS'19), Vancouver, Canada, December 2019. [code] [3min video] [slides] [poster] [bibtex] R. Marcus, P. Negi, H. Mao, C. Zhang, M. Alizadeh, T. Kraska, O. Papaemmanouil, N. Tatbul, "Neo: A Learned Query Optimizer", PVLDB 12(11), July 2019. [bibtex] P. Eichmann, F. Solleza, N. Tatbul, S. Zdonik, "Visual Exploration of Time Series Anomalies with Metro-Viz", Demonstration, ACM SIGMOD International Conference on Management of Data (SIGMOD'19), Amsterdam, Netherlands, June 2019. (Best Demonstration Award) [poster] [bibtex] N. Tatbul, T. J. Lee, S. Zdonik, M. Alam, J. Gottschlich, "Precision and Recall for Time Series", 32nd Annual Conference on Neural Information Processing Systems (NeurIPS'18), Montreal, Canada, December 2018. (Spotlight Paper) [code] [talk video] [talk slides] [poster] [bibtex] J. Gottschlich, A. Solar-Lezama, N. Tatbul, M. Carbin, M. Rinard, R. Barzilay, S. Amarasinghe, J. B. Tenenbaum, T. Mattson, "The Three Pillars of Machine Programming", ACM SIGPLAN International Workshop on Machine Learning and Programming Languages (MAPL'18), Philadelphia, PA, USA, June 2018. [bibtex] J. Meehan, C. Aslantas, S. Zdonik, N. Tatbul, J. Du, "Data Ingestion for the Connected World", Conference on Innovative Data Systems Research (CIDR'17), Chaminade, CA, USA, January 2017. [talk slides] [bibtex] J. Meehan, N. Tatbul, S. Zdonik, C. Aslantas, U. Cetintemel, J. Du, T. Kraska, S. Madden, D. Maier, A. Pavlo, M. Stonebraker, K. Tufte, H. Wang, "S-Store: Streaming Meets Transaction Processing", PVLDB 8(13), September 2015. [code] [bibtex] N. Dindar, N. Tatbul, R. J. Miller, L. Haas, I. Botan, "Modeling the Execution Semantics of Stream Processing Engines with SECRET", VLDB Journal 22(4), August 2013. [bibtex] A. Moga, I. Botan, N. Tatbul, "UpStream: Storage-centric Load Management for Streaming Applications with Update Semantics", VLDB Journal 20(6), December 2011. [bibtex] K. Sheykh Esmaili, T. Sanamrad, P. M. Fischer, N. Tatbul, "Changing Flights in Mid-air: A Model for Safely Modifying Continuous Queries", ACM SIGMOD International Conference on Management of Data (SIGMOD'11), Athens, Greece, June 2011. [bibtex] M. Akdere, U. Cetintemel, N. Tatbul, "Plan-based Complex Event Detection Across Distributed Sources", International Conference on Very Large Data Bases (VLDB'08), Auckland, New Zealand, August 2008. [bibtex] N. Tatbul, U. Cetintemel, S. Zdonik, "Staying FIT: Efficient Load Shedding Techniques for Distributed Stream Processing", International Conference on Very Large Data Bases (VLDB'07), Vienna, Austria, September 2007. [talk slides] [bibtex] N. Tatbul, S. Zdonik, "Window-aware Load Shedding for Aggregation Queries over Data Streams", International Conference on Very Large Data Bases (VLDB'06), Seoul, Korea, September 2006. [talk slides] [bibtex] Y. Ahmad, B. Berg, U. Cetintemel, M. Humphrey, J. Hwang, A. Jhingran, A. Maskey, O. Papaemmanouil, A. Rasin. N. Tatbul, W. Xing, Y. Xing, S. Zdonik, "Distributed Operation in the Borealis Stream Processing Engine", Demonstration, ACM SIGMOD International Conference on Management of Data (SIGMOD'05), Baltimore, MD, USA, June 2005. (Best Demonstration Award) [overview poster, game poster, photos] [bibtex] D. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J. Hwang, W. Lindner, A. Maskey, A. Rasin, E. Ryvkina, N. Tatbul, Y. Xing, S. Zdonik, "The Design of the Borealis Stream Processing Engine", Conference on Innovative Data Systems Research (CIDR'05), Asilomar, CA, USA, January 2005. [talk slides] [bibtex] N. Tatbul, U. Cetintemel, S. Zdonik, M. Cherniack, M. Stonebraker, "Load Shedding in a Data Stream Manager", International Conference on Very Large Data Bases (VLDB'03), Berlin, Germany, September 2003. [talk slides] [bibtex] D. Abadi, D. Carney, U. Cetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, S. Zdonik, "Aurora: A New Model and Architecture for Data Stream Management", VLDB Journal 12(2), Special Issue on Best Papers of VLDB 2002, August 2003. [bibtex]