Jiannan Wang
Associate Professor School of Computing Science Simon Fraser University
Postdoc in the AMPLab at UC Berkeley (2015) Ph.D. at THU (2013), B.Sc. at HIT (2008)
Research Areas: Database Systems, Big Data Science
Office: TASC 1 9237
Phone: 1-778-782-4288
Email: jnwang@sfu.ca
8888 University Drive
Burnaby, BC
CANADA V5A 1S6
Open Positions: If you are an SFU undergraduate student and would like to work with me on DataPrep, please email me your resume and transcript with a short statement of research interests.
My lab is part of the SFU Data Science Research Group. Our mission is to speed up data science. We develop innovative technologies and open-source tools for data scientists such that they can turn raw data into actionable insights in a more efficient manner. I recently won an IEEE TCDE Rising Star Award (2018) and a CS-Can|Info-Can Outstanding Early Career Researcher Award (2020).
I am a General Co-chair for VLDB 2023, an Associate Editor for VLDB 2021, and an Associate Editor of Data Science of Frontiers in Big Data (2021 - now). I am the director of the SFU CS Professional Master's Program (2019 - now). Our vision is to train the next generation of technical global leaders in strategically important areas of society. I am also a founding editor-in-chief of the SFU Big Data Science Publication, which has now become one of the most popular Data Science publications on Medium.
News
2022/01/10
2021/08/17
2021/07/26
We are super excited to announce the release of ConnectorX 0.2 (a subproject in dataprep.ai). ConnectorX is the fastest library to load data from DB to DataFrames in Rust and Python. It can accelerate Pandas read_sql by 10x with one line of code. Since its first release, the library has been downloaded by ~12000 times. Please check out our blog post and benchmark results.
2021/07/15
Want to know how to debug an ML model in federated learning? Please check out our recent paper, entitled "Enabling SQL-based Training Data Debugging for Federated Learning", in VLDB 2022!
2021/04/21
I was invited to give two talks in the Thomson Reuters AI@TR Invited Speaker Series on our AutoML-EM and DataPrep projects.
2021/04/08
Congratulations to Brandon Lockhart on his successful M.Sc. thesis defense! In his dissertation, he studies "Explaining Inference Queries with Bayesian Optimization". Learn more about Brandon and his research from this page.
2021/03/18
Congratulations to Dr. Pei Wang on her successful Ph.D. thesis defense! Her thesis is about "Automating Data Preparation with Statistical Analysis", which covers her work on AutoML-EM@ICDE 2021, ActiveDeeper Demo@VLDB 2020, Deeper@SIGMOD 2019, Uni-Detect@SIGMOD 2019, and Deeper Demo@SIGMOD 2018.
2021/03/15
I am thrilled to receive a CS-Can|Info-Can Outstanding Early Career Researcher Award (2020) [SFU News].
2021/03/15
Want to know whether we are ready to deploy learned cardinality models in production database systems? Please check out our recent paper in VLDB 2021!
2021/03/11
Our DataPrep.EDA paper got accepted by SIGMOD 2021!
2021/01/26
I am serving as a General Co-chair for VLDB 2023 @ Vancouver.
2021/01/06
I was invited to give a talk at Databricks to introduce DataPrep: The easiest way to prepare data in Python.
2020/12/17
Congratulations to Xiaoying Wang on her successful M.Sc. thesis defense! In her dissertation she studies "Are We Ready For Learned Cardinality Estimation?".
2020/12/17
A new paper on "Automating Entity Matching Model Development" got accepted by ICDE 2021!
2020/11/12
My Ph.D. students (Pei Wang and Weiyuan Wu) gave a talk at PyData Global 2020, the world's premier data science conference (video).
2020/11/09
We are pleased to announce the launch of DataPrep's brand new website: http://dataprep.ai!
2020/10/01
Receive the "Distinguished PVLDB Review Board Member" award!
2020/09/05
Welcome Danrui Qi (Ph.D.) to join the lab! Congratulate her on receiving the prestigious Graduate Dean's Entrance Scholarship (GDES).
2020/09/01
Promoted to Associate Professor (Tenure)!
2020/05/07
We wrote two blog posts to describe i) how to use DataPrep.EDA to accelerate EDA and ii) why DataPrep.EDA is better than Pandas-profiling.
- DataPrep.EDA: Accelerate your EDA (Towards Data Science, Medium)
- Exploratory Data Analysis: DataPrep.EDA vs Pandas-Profiling (Towards Data Science, Medium)
2020/03/20
We are super excited to announce the release of DataPrep 0.2. DataPrep wants to become "scikit-learn" for data preparation. Since its first release, the library has been downloaded by ~4000 times. This release contains a data connector component to facilitate web data collection and an exploratory data analysis component to enable fast data understanding. More components will be added in future releases.
2020/03/13
Want to know how to debug training data for SQL-ML queries. Please check out our recent paper, entitled "Complaint-driven Training Data Debugging for Query 2.0", in SIGMOD 2020!
2020/01/03
Want to know how to detect data errors for machine learning applications? Please check out our recent paper, entitled "SCODED: Statistical Constraint Oriented Data Error Detection", in SIGMOD 2020!
2019/09/22
I am honored to be invited to serve as an Associate Editor for VLDB 2021.
Recent Publications [DBLP] [Google Scholar]
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Xiaoying Wang*, Weiyuan Wu*, Jinze Wu, Yizhou Chen, Nick Zrymiak, Changbo Qu, Lampros Flokas, George Chow, Jiannan Wang, Tianzheng Wang, Eugene Wu, Qingqing Zhou.
ConnectorX: Accelerating Data Loading From Databases to Dataframes.
VLDB 2022 , Sydney, Australia
(* Equally Contributed).
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Jinglin Peng, Bolin Ding, Jiannan Wang, Kai Zeng, Jingren Zhou.
One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees.
SIGMOD 2022 , Philadelphia, PA, USA
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Lampros Flokas, Weiyuan Wu, Yejia Liu, Jiannan Wang, Nakul Verma, Eugene Wu.
Complaint-Driven Training Data Debugging at Interactive Speeds.
SIGMOD 2022 , Philadelphia, PA, USA
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Yejia Liu*, Weiyuan Wu*, Lampros Flokas, Jiannan Wang, Eugene Wu.
Enabling SQL-based Training Data Debugging for Federated Learning.
VLDB 2022 , Sydney, Australia (code)
(* Equally Contributed).
-
Jinglin Peng, Weiyuan Wu, Jing Nathan Yan, Danrui Qi, Jeffrey M. Rzeszotarski, Jiannan Wang.
User Interfaces for Exploratory Data Analysis: A Survey of Open-Source and Commercial Tools.
IEEE Data Eng. Bull. 2022
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Brandon Lockhart, Jinglin Peng, Weiyuan Wu, Jiannan Wang, Eugene Wu.
Explaining Inference Queries with Bayesian Optimization.
VLDB 2021 , Copenhagen, Denmark (code)
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Xiaoying Wang*, Changbo Qu*, Weiyuan Wu*, Jiannan Wang, Qingqing Zhou.
Are We Ready For Learned Cardinality Estimation? (Best EA&B Paper Award)
VLDB 2021 , Copenhagen, Denmark (slides, code, news)
(* Equally Contributed).
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Jinglin Peng*, Weiyuan Wu*, Brandon Lockhart, Song Bian, Jing Nathan Yan, Linghao Xu, Zhixuan Chi, Jeffrey M. Rzeszotarski, Jiannan Wang.
DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python.
SIGMOD 2021, Xi'an, Shaanxi, China. [code, reddit, blog post 1, 2]
(* Equally Contributed).
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Pei Wang, Weiling Zheng, Jiannan Wang, Jian Pei.
Automating Entity Matching Model Development.
ICDE 2021, Chania, Greece.
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Liang Zhao, Qingcan Li, Pei Wang, Jiannan Wang, Eugene Wu.
ActiveDeeper: A Model-based Active Data Enrichment System.
VLDB 2020, Tokyo, Japan (Demo) [video].
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Weiyuan Wu, Lampros Flokas, Eugene Wu, Jiannan Wang.
Complaint-driven Training Data Debugging for Query 2.0.
SIGMOD 2020, Amsterdam, The Netherlands.
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Jing Nathan Yan, Oliver Schulte, MoHan Zhang, Jiannan Wang, Reynold Cheng.
SCODED: Statistical Constraint Oriented Data Error Detection.
SIGMOD 2020, Amsterdam, The Netherlands.
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Weiyuan Wu, Lampros Flokas, Eugene Wu, Jiannan Wang.
Towards Complaint-driven ML Workflow Debugging.
MLOps 2020, Austin, TX, USA.
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Ruochen Jiang*, Changbo Qu*, Jiannan Wang, Chi Wang, Yudian Zheng.
Towards Extracting Highlights From Recorded Live Videos: An Implicit Crowdsourcing Approach.
ICDE 2020, Dallas, TX, USA. (Short Paper) [Project Page]
(* Equally Contributed)
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Pei Wang, Ryan Shea, Jiannan Wang, Eugene Wu.
Progressive Deep Web Crawling Through Keyword Queries For Data Enrichment.
SIGMOD 2019, Amsterdam, The Netherlands.
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Mohamad Dolatshah*, Mathew Teoh*, Jiannan Wang, Jian Pei.
Cleaning Crowdsourced Labels Using Oracles For Statistical Classification.
VLDB 2019, Los Angeles, California, USA [Technical Report]
(* Equally Contributed)
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Jinglin Peng, Dongxiang Zhang, Jiannan Wang, Jian Pei.
AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics.
SIGMOD 2018, Houston, TX, USA.
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Pei Wang, Yongjun He, Ryan Shea, Jiannan Wang, Eugene Wu.
Deeper: A Data Enrichment System Powered by Deep Web.
SIGMOD 2018, Houston, TX, USA. (Demo) (system, video).
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Chengliang Chai, Ju Fan, Guoliang Li, Jiannan Wang, Yudian Zheng.
Crowd-Powered Data Mining.
KDD 2018, London, UK (Tutorial).
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Guoliang Li, Jiannan Wang, Yudian Zheng, Ju Fan, Michael J Franklin.
Crowdsourced Data Management: Hybrid Human-Machine Data Management.
Springer 2018 (Book).
Students
Current Students and Postdocs
- Jinglin Peng (PH.D., 2017-)
- Changbo Qu (Ph.D., 2018-)
- Young Wu (Ph.D. 2019-)
- Danrui Qi (PH.D., 2020-)
- Xiaoying Wang (PH.D., 2021-)
- Chunyu Chen (PH.D., 2021-)
- Andy Zhang (PH.D., 2021-)
- Saad Ahmad (M.Sc., 2021-)
- DataPrep Team (Undergrad/M.Sc., 2019-)
Former Students
- Pei Wang, Ph.D., 2016-2021 (Next Huawei Canada Senior Engineer)
- Lydia Zheng, M.Sc., 2019-2021 (Next Workday Software Application Developer)
- Yi Xie, M.Sc., 2019-2021 (Next Amazon Web Services Software Engineer)
- Brandon Lockhart, M.Sc., 2019-2021 (Next Zafin Data Engineer)
- Yejia Liu, Visiting Student, 2020-2021 (Next UC Riverside Ph.D. Student)
- Qingcan Li, Undergrad RA, 2019-2020 (Next Google Software Engineer)
- Ruochen Jiang, M.Sc., 2018-2019 (Next OSU Ph.D. Student)
- Young Wu, M.Sc., 2017-2019 (Next SFU Ph.D. Student)
- Xi Yang, Undergrad RA, 2018-2019 (Next Alibaba Software Engineer)
- Liang Zhao, Postdoc 2018-2019 (Next Assistant Professor at Xi'an Jiaotong University)
- Shubham Laddha, Visiting Student 2019
- Danrui Qi, Visiting Student 2019
- Song Bian, Visiting Student 2019 (Next CUHK M.Sc. Student)
- Xiao Li, Undergrad RA, 2019
- Mohamad Dolatshah, M.Sc. 2016-2018
- Mathew Teoh, Undergrad RA 2016-2017 (Next Data Scientist at Quora)
- Yongjun He, Visiting Student 2017 (Next SFU M.Sc. Student)
- Nathan Yan, Visiting Student 2017 (Next Cornell Ph.D. Student)
Teaching
- CMPT 354: Database Systems [Fall 2018]
- CMPT 843: Traditional vs. Modern Database Systems [Spring 2019, Spring 2018, Spring 2017]
- CMPT 884: Human-in-the-loop Data Management [Fall 2016]
- CMPT 732: Big Data Programming I [Fall 2016]
- CMPT 733: Big Data Programming II [Spring 2020, Spring 2019, Spring 2018, Spring 2017, Spring 2016]
Awards
- VLDB Best Experiments, Analysis & Benchmark Paper Award (2021)
- CS-Can|Info-Can Outstanding Early Career Researcher Award (2020)
- PVLDB Distinguished Review Board Member Award (2020)
- IEEE TCDE Rising Star Award (2018)
- IEEE/WIC/ACM Web Intelligence (WI) Best Paper Award (2017)
- ACM SIGMOD Best Demonstration Award (2016)
- China Computer Federation (CCF) Distinguished Dissertation Award (2013)
- Google Ph.D. Fellowship (2011)
Professional Activities
Program Committee
- SIGMOD: 2022, 2021, 2020, 2019 (Core PC), 2018, 2017, 2016, 2016 (Demo)
- VLDB: 2022, 2021 (Associate Editor), 2020, 2018, 2018 (Demo), 2017 (Demo)
- KDD: 2021, 2020, 2019, 2018
- ICDE/TKDE poster: 2021, 2017, 2016
- SoCC: 2020
- SIGIR: 2020
- ICDCS: 2020
- WWW: 2017
- SDM (2018)
- CIKM (2017)
- HCOMP: 2019, 2016
- DASFAA: 2019, 2019 (demo)
- WAIM: 2018, 2017, 2016, 2015, 2014
- APWeb: 2017, 2016
Chairing
- VLDB 2023 General Co-chair
- IEEE ICDE 2022 PhD Symposium Chair
- SIGMOD 2017 Registration Chair
- WISE 2017 Publicity Co-Chair
Others
- SFU Big Data Academic Advisory Committee Member (2017 - now)
- SFU KEY / Vancity Relationship Steering Committee Member (2017 - now)