Yongjoo Park

For More Information

• Yongjoo's personal website

Education

• Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor, 2017
• M.S. in Computer Science, University of Michigan, Ann Arbor, 2013
• B.S. in Electrical Engineering, Seoul National University, 2009

Biography

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. At UIUC, he is part of Data and Information Systems (DAIS) Research Lab. Also, Yongjoo is a co-founder and Chief Scientist of Keebo, Inc., a start-up company he co-founded based on his Ph.D. research. Yongjoo's research interest is in building intelligent data-intensive systems using statistical and Artificial Intelligence techniques. Yongjoo obtained a Ph.D. in Computer Science and Engineering from the University of Michigan, Ann Arbor in 2017. His dissertation received the 2018 SIGMOD Jim Gray Dissertation runner-up award.

Academic Positions

• Assistant Professor, Department of Computer Science, University of Illinois at Urbana–Champaign, Jan. 2021 - Present

Other Professional Employment

• Chief Scientist, Keebo, Inc., Sep. 2022 - Present
• Co-founder and CTO, Keebo, Inc., Aug. 2019 - Aug. 2022

Research Interests

• Systems for analytics and machine learning
• A.I. Data-intensive Systems

Articles in Conference Proceedings

• Zhaoheng Li, Pranav Gor, Rahul Prabhu, Hui Yu, Yuzhou Mao, and Yongjoo Park “ElasticNotebook: Enabling Live Migration for Computational Notebooks.” VLDB’24: 50th International Conference on Very Large Data Bases, 2024. (2024 Acceptance Rate Unavailable, Average Rate: 18.6%)
• Supawit Chockchowwat, Wenjie Liu, and Yongjoo Park. “AirIndex: Versatile Index Tuning Through Data and Storage.” SIGMOD’24: Proceedings of the ACM on Management of Data, Santiago, Chile, 2024. (2024 Acceptance Rate Unavailable; Average Rate: 20%)
• Ipoom Jeong, Jiaqi Lou, Yongseok Son, Yongjoo Park, Yifan Yuan, and Nam Sung Kim. “LADIO: Leakage-Aware Direct I/O for I/O-IntensiveWorkloads.” CAL’23: IEEE Computer Architecture Letters, 2023. (short) (Average Acceptance Rate: 20%)
• Supawit Chockchowwat, Zhaoheng Li, and Yongjoo Park. “Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility.” DEEM’23: In Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning, Seattle, USA, 2023 (Acceptance Rate: N/A)
• Barzan Mozafari, Radu Alexandru Burcuta, Alan Cabrera, Andrei Constantin, Derek Francis, David Grömling, Alekh Jindal, Maciej Konkolowicz, Valentin Marian Spac, Yongjoo Park, Russell Razo Carranzo, Nicholas Richardson, Abhishek Roy, Aayushi Srivastava, Isha Tarte, Brian Westphal, and Chi Zhang. “Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning.” SIGMOD’23: In Companion of the 2023 International Conference on Management of Data, 2023. (industry track) (Acceptance Rate: N/A)
• Zhaoheng Li, Xinyu Pi, Yongjoo Park. S/C: Speeding Up Data Materialization with Bounded Memory. ICDE 2023 (research): 39th International Conference on Data Engineering, Anaheim, CA, USA, 2023. (Acceptance Rate: 30.6%)
• Nikhil Sheoran, Supawit Chockchowwat, Arav Chheda, Suwen Wang, Riya Verma, Yongjoo Park. A Step Toward Deep Online Aggregation. SIGMOD 2023 (research): Proceedings of the ACM on Management of Data, Seattle, USA, 2023. (Acceptance Rate: 27.2%) Awarded “Artifacts Available”, “Artifacts Evaluated”, and “Results Reproduced” badges
• Supawit Chockchowwat, Wenjie Liu, Yongjoo Park. Automatically Finding Optimal Index Structure. AIDB Workshop at VLDB 2022 (research): 4th International Workshop on Applied AI for Database Systems and Applications, Sydney, Australia, 2022. (Acceptance Rate: N/A)
• Sophia Yang, Yongjoo Park, and Abdussalam Alawini. The Effects of Teaching Modality on Collaborative Learning: A Controlled Study. FIE 2022 (research): The Frontiers in Education, Uppsala, Sweden, 2022. (Acceptance Rate: 55%)
• Supawit Chockchowwat, Chaitanya Sood, Yongjoo Park. Airphant: Cloud-oriented Document Indexing. ICDE 2022 (research): 38th International Conference on Data Engineering, Kuala Lumpur, Malaysia, 2022. (Acceptance Rate: 27%)
• Johes Bater, Yongjoo Park, Xi He, Xiao Wang, Jennie Rogers. SAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations. PVLDB 2020 (research): 46th International Conference on Very Large Data Bases. Tokyo, Japan (Online due to COVID-19), 2020. (Acceptance Rate: 24.8%)
• Yongjoo Park, Shucheng Zhang, Barzan Mozafari. QuickSel: Quick Selectivity Learning with Mixture Models. SIGMOD’20 (research): ACM SIGMOD/PODS International Conference on Management of Data. Portland, OR, USA, 2020. (Acceptance Rate: 26.9%)
• Yongjoo Park, Jingyi Qing, Xiaoyang Shen, Barzan Mozafari. BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees. SIGMOD’19 (research): ACM SIGMOD/PODS International Conference on Management of Data. Amsterdam, The Netherlands, 2019. (Acceptance Rate: 20.5%)
• Yongjoo Park, Barzan Mozafari, Joseph Sorenson, Junhao Wang. VerdictDB: Universalizing Approximate Query Processing. SIGMOD’18 (research): ACM SIGMOD/PODS International Conference on Management of Data. Houston, TX, USA, 2018. (Acceptance Rate: 19.5%)
• Wen He, Yongjoo Park, Idris Hanafi, Jacob Yatvitskiy, Barzan Mozafari. Demonstration of VerdictDB, the Platform-Independent AQP System. SIGMOD’18 (demo): ACM SIGMOD/PODS International Conference on Management of Data. Houston, TX, USA, 2018. (Acceptance Rate: N/A)
• Yongjoo Park, Amhad Shahab Tajik, Michael Cafarella, Barzan Mozafari. Database Learning: Toward a Database System that Becomes Smarter Over Time. SIGMOD’17 (research): ACM SIGMOD/PODS International Conference on Management of Data. Chicago, IL, USA, 2017. SIGMOD Travel Award. ( Acceptance Rate: 19.6%)
• Yongjoo Park. Active Database Learning. CIDR’17 (abstract): The biennial Conference on Innovative Data Systems Research. Chaminade, CA, USA, 2017. (Acceptance Rate: N/A)
• Yongjoo Park, Michael Cafarella, Barzan Mozafari. Visualization-Aware Sampling for Very Large Databases. ICDE’16 (research): IEEE 32nd International Conference on Data Engineering. Helsinki, Finland, 2016. (2016 Acceptance Rate Unavailable; Average Rate: 19.1%)
• Yongjoo Park, Michael Cafarella, Barzan Mozafari. Neighbor-Sensitive Hashing. PVLDB’15 (research) for VLDB’16: 42nd International Conference on Very Large Data Bases. New Delhi, India, 2016. (Acceptance Rate: 35.5%)
• Michael Anderson, Dolan Antenucci, Victor Bittorf, Matthew Burgess, Michael Cafarella, Arun Kumar, Feng Niu, Yongjoo Park, Christopher Ré, Ce Zhang. Brainwash: A Data System for Feature Engineering. CIDR’13 (vision): The biennial Conference on Innovative Data Systems Research. Asilomar, CA, USA, 2013. (Acceptance Rate: N/A)

Patents

• Alekh Jindal, Barzan Mozafari, Yongjoo Park, David Wolfgang Grömling, Brian Westphal, and Alan D Cabrera. “Managed tuning for data clouds.” (US Patent 11,693,857, 2023)"
• Alekh Jindal, Barzan Mozafari, Yongjoo Park, Brian Westphal, Shi Qiao, Matthew Larson, Advait Abhay Dixit. Platform Agnostic Query Acceleration (United States Patent 11567936)

Conferences Organized or Chaired

• Publicity Chair, 39th IEEE International Conference on Data Engineering (ICDE 2023)
• Co-chair, SIGMOD 2022 Student Research Competition
• Co-chair, SIGMOD 2021 Student Research Competition
• Publicity Chair, ACAIA workshop 2017 (http://dbgroup.eecs.umich.edu/acaia/)

Teaching Honors

• Teaching Excellence, Fall 2023 (2024)
• Teaching Excellence, Fall 2022 (2023)

Research Honors

• UIUC Engineering Council Outstanding Advising Award. (2021)
• 2018 ACM SIGMOD Jim Gray Dissertation Award Runner-up (2018)
• ACM SIGMOD Student Travel Award. (2017)

Other Honors

• 2021 Engineering Council Outstanding Advising Award (February 2021 )