ADA Lab @ UCSD

Peer-reviewed Publications

• How do Categorical Duplicates Affect ML? A New Benchmark and Empirical Analyses
  Vraj Shah, Thomas Parashos, and Arun Kumar
  VLDB 2024 | Paper PDF | TechReport | Code and Data coming soon

• Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads
  Kabir Nagrecha and Arun Kumar
  VLDB 2024 | Paper PDF | TechReport | Code and Docs Release

• Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines
  Yuhao Zhang and Arun Kumar
  VLDB 2023 | Paper PDF | TechReport | Code Release | Blog post

• Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE)
  Paul R. Hibbing et al. (12 authors)
  International Journal of Obesity 2023 | Paper PDF

• Database-Aware ASR Error Correction for Speech-to-SQL Parsing
  Yutong Shao, Arun Kumar, and Ndapandula Nakashole
  IEEE ICASSP 2023 | Paper PDF

• CHAP-child: An open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children
  Jordan A. Carlson et al. (15 authors)
  International Journal of Behavioral Nutrition and Physical Activity 2022 | Paper PDF | Code, Models, and Documentation

• Structured Data Representation in Natural Language Interfaces
  Yutong Shao, Arun Kumar, and Ndapandula Nakashole
  IEEE Data Engineering Bulletin 2022 (Invited) | Paper PDF

• CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data from Hip-Worn Accelerometers in Adults Aged 35+
  John Bellettiere et al. (14 authors)
  Journal for the Measurement of Physical Behaviour 2022 | PDF | Code, Models, and Documentation

• VLDB Scalable Data Science Category: The Inaugural Year
  Arun Kumar, Alon Halevy, and Nesime Tatbul
  ACM SIGMOD Record 2022 | Paper PDF

• Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets
  Supun Nakandala and Arun Kumar
  SIGMOD 2022 | Paper PDF | TechReport | Code Release

• VLDB Panel Summary: “The Future of Data(base) Education: Is the Cow Book Dead?”
  Zachary Ives, Johannes Gehrke, Jana Giceva, Arun Kumar, and Rachel Pottinger
  ACM SIGMOD Record 2021 | Paper PDF

• Some Damaging Delusions of Deep Learning Practice (and How to Avoid Them)
  Arun Kumar, Supun Nakandala, and Yuhao Zhang
  KDD 2021 Deep Learning Day | Extended Abstract PDF | Talk slides | Talk video

• Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning
  Side Li and Arun Kumar
  VLDB 2021 | Paper PDF | TechReport | Talk video | Code Release

• Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches
  Yuhao Zhang, Frank McQuillan, Nandish Jayaram, Nikhil Kak, Ekta Khanna, Orhan Kislal, Domino Valdano, and Arun Kumar
  VLDB 2021 | Paper PDF | TechReport | Talk video | Code release

• Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration
  Liangde Li, Supun Nakandala, and Arun Kumar
  VLDB 2021 Demo | Paper PDF | TechReport | Video

• Towards A Polyglot Framework for Factorized ML
  David Justo, Shaoqing Yi, Lukas Stadler, Nadia Polikarpova, and Arun Kumar
  VLDB 2021 (Industrial Track) | Paper PDF | TechReport | Talk video | Code coming soon

• Towards Benchmarking Feature Type Inference for AutoML Platforms
  Vraj Shah, Jonathan Lacanlale, Premanand Kumar, Kevin Yang, and Arun Kumar
  ACM SIGMOD 2021 | Paper PDF | TechReport | Talk Videos: Short Talk Long Talk | Data, Code, and Pre-trained Models on GitHub | Python library

• Automation of Data Prep, ML, and Data Science: New Cure or Snake Oil?
  Arun Kumar
  ACM SIGMOD 2021 Panel | Paper PDF

• The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study
  Mikael Anne Greenwood-Hickman, Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan Carlson, Paul R. Hibbing, Jingjing Zou, Andrea Z. LaCroix, Arun Kumar, and Loki Natarajan
  Medicine and Science in Sports and Exercise Journal, 2021 | Paper PDF | Code, Models, and Documentation

• Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification
  Supun Nakandala, Marta Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan Carlson, Andrea LaCroix, Sheri Hartman, Dori Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan
  Journal for the Measurement of Physical Behaviour, 2021 | Paper PDF and BibTeX | Code, Models, and Documentation

• Cerebro: A Layered Data Platform for Scalable Deep Learning
  Arun Kumar, Supun Nakandala, Yuhao Zhang, Side Li, Advitya Gemawat, and Kabir Nagrecha
  CIDR 2021 (Vision paper) | Paper PDF and BibTeX | Talk video

• Cerebro: A Data System for Optimized Deep Learning Model Selection
  Supun Nakandala, Yuhao Zhang, and Arun Kumar
  VLDB 2020 | Paper PDF and BibTeX | Errata | TechReport | Talk videos: Youtube Bilibili | Blog post | SAIS Talk video | Source code and documentation

• Panorama: A Data System for Unbounded Vocabulary Querying over Video
  Yuhao Zhang and Arun Kumar
  VLDB 2020 | Paper PDF and BibTeX| TechReport | Talk slides | Talk videos: Youtube Bilibili | Blog post | Source code on GitHub

• Understanding and Benchmarking the Impact of GDPR on Database Systems
  Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, and Vijay Chidambaram
  VLDB 2020 | Paper PDF | TechReport | Webpage | Talk videos: Youtube Bilibili

• Query Optimization for Faster Deep CNN Explanations
  Supun Nakandala, Arun Kumar, and Yannis Papakonstantinou
  ACM SIGMOD Record 2020 | Paper PDF and BibTeX
  ACM SIGMOD Research Highlights Award

• Incremental and Approximate Computations for Accelerating Deep CNN Inference
  Supun Nakandala, Kabir Nagrecha, Arun Kumar, and Yannis Papakonstantinou
  ACM TODS 2020 | Paper PDF and BibTeX
  Invited Paper

• Vista: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale
  Supun Nakandala and Arun Kumar
  ACM SIGMOD 2020 | Paper PDF and BibTeX | TechReport | Blog post | Talk Video | Code

• SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data
  Vraj Shah, Side Li, Arun Kumar, and Lawrence Saul
  ACM SIGMOD 2020 | Paper PDF and BibTeX| TechReport | Blog post | Dataset on Drive

• Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations
  Supun Nakandala, Arun Kumar, and Yannis Papakonstantinou
  ACM SIGMOD 2019 | Paper PDF and BibTeX | TechReport | Blog post | Talk Video
  Honorable Mention for Best Paper Award

• Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra
  Side Li, Lingjiao Chen, and Arun Kumar
  ACM SIGMOD 2019 | Paper PDF and BibTeX | Code and Data on Github

• Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
  Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Jeffrey Naughton, Jignesh Patel, and Xi Wu
  ACM SIGMOD 2019 | Paper PDF | TechReport | Code on GitHub

• Model-based Pricing for Machine Learning in a Data Marketplace
  Lingjiao Chen, Paraschos Koutris, and Arun Kumar
  ACM SIGMOD 2019 | Paper PDF | TechReport

• Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems
  Supun Nakandala, Yuhao Zhang, and Arun Kumar
  ACM SIGMOD 2019 DEEM Workshop | Paper PDF and BibTeX | TechReport | Blog post

• The ML Data Prep Zoo: Towards Semi-Automatic Data Preparation for ML
  Vraj Shah and Arun Kumar
  ACM SIGMOD 2019 DEEM Workshop | Paper PDF and BibTeX| TechReport | Blog post | Data Prep Zoo Repository on GitHub

• Demonstration of SpeakQL: Speech-driven Multimodal Querying of Structured Data
  Vraj Shah, Side Li, Kevin Yang, Arun Kumar, and Lawrence Saul
  ACM SIGMOD 2019 Demo | Paper PDF and BibTeX | Video

• Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace
  Lingjiao Chen, Hongyi Wang, Leshang Chen, Paraschos Koutris, and Arun Kumar
  ACM SIGMOD 2019 Demo | Paper PDF | Video coming soon

• Demonstration of Krypton: Optimized CNN Inference for Occlusion-based Deep CNN Explanations
  Allen Ordookhanians, Xin Li, Supun Nakandala, and Arun Kumar
  VLDB 2019 | Paper PDF and BibTeX | Video

• Demonstration of Krypton: Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations
  Supun Nakandala, Arun Kumar, and Yannis Papakonstantinou
  SysML 2019 Demo | Paper PDF | Video

• Data Management in Machine Learning Systems
  Matthias Boehm, Arun Kumar, and Jun Yang
  Synthesis Lectures on Data Management, Morgan & Claypool Publishers (Book), 2019 | PDF | Order hard copy

• Hierarchical and Distributed Machine Learning Inference Beyond the Edge
  Anthony Thomas, Yunhui Guo, Yeseong Kim, Baris Aksanli, Arun Kumar and Tajana Rosing
  IEEE ICNSC 2019 | Paper PDF

• Predicting Eating Events in Free Living Individuals
  Jiayi Wang, Jiue-An Yang, Supun Nakandala, Arun Kumar and Marta M. Jankowska
  eScience 2019 Conference (Poster)

• A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics
  Anthony Thomas and Arun Kumar
  VLDB 2018/2019 | Paper PDF | TechReport | Code and Data

• In-RDBMS Hardware Acceleration of Advanced Analytics
  Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, and Hadi Esmaeilzadeh
  VLDB 2018 | Paper PDF | Addendum

• Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?
  Vraj Shah, Arun Kumar, and Xiaojin Zhu.
  VLDB 2018 | Paper PDF and BibTeX| TechReport | Code and Data

• Materialization Trade-offs for Feature Transfer from Deep CNNs for Multimodal Data Analytics
  Supun Nakandala and Arun Kumar
  SysML 2018 Short paper/poster | Paper PDF

• Towards Linear Algebra over Normalized Data
  Lingjiao Chen, Arun Kumar, Jeffrey Naughton, and Jignesh Patel
  VLDB 2017 | Paper PDF | TechReport | Code and Data

• Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
  Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey Naughton
  ACM SIGMOD 2017 | Paper PDF | TechReport

• Data Management in Machine Learning: Challenges, Techniques, and Systems
  Arun Kumar, Matthias Boehm, and Jun Yang
  ACM SIGMOD 2017 Tutorial | Paper PDF | Slidedeck PDF | Video of tutorial on Youtube

• SpeakQL: Towards Speech-driven Multi-modal Querying
  Dharmil Chandarana, Vraj Shah, Arun Kumar, and Lawrence Saul
  ACM SIGMOD 2017 HILDA Workshop | Paper PDF and BibTeX

• Model-based Pricing: Do Not Pay for More than What You Learn!
  Lingjiao Chen, Paraschos Koutris, and Arun Kumar
  ACM SIGMOD 2017 DEEM Workshop | Paper PDF

• Cerebro: A System to Manage Deep Learning for Relational Data Analytics
  Arun Kumar
  CIDR 2017 Abstract | Paper PDF

• To Join or Not to Join? Thinking Twice about Joins before Feature Selection
  Arun Kumar, Jeffrey Naughton, Jignesh M. Patel, and Xiaojin Zhu
  ACM SIGMOD 2016 | Paper PDF and BibTeX| TechReport | Code and Data

• Materialization Optimizations for Feature Selection Workloads
  Ce Zhang, Arun Kumar, and Christopher Re
  ACM TODS 2016 (Invited) | Paper PDF

• Model Selection Management Systems: The Next Frontier of Advanced Analytics
  Arun Kumar, Robert McCann, Jeffrey Naughton, and Jignesh M. Patel
  ACM SIGMOD Record Dec 2015 Vision Track | Paper PDF

• Demonstration of Santoku: Optimizing Machine Learning over Normalized Data
  Arun Kumar, Mona Jalal, Boqun Yan, Jeffrey Naughton, and Jignesh M. Patel
  VLDB 2015 Demo | Paper PDF | Code and Data

• Learning Generalized Linear Models Over Normalized Data
  Arun Kumar, Jeffrey Naughton, and Jignesh M. Patel
  ACM SIGMOD 2015 | Paper PDF | Code and Data

• Materialization Optimizations for Feature Selection Workloads
  Ce Zhang, Arun Kumar, and Christopher Re
  ACM SIGMOD 2014 | Paper PDF
  Best Paper Award; Invited to ACM TODS 2016

• Distributed and Scalable PCA in the Cloud
  Arun Kumar, Nikos Karampatziakis, Paul Mineiro, Markus Weimer, and Vijay Narayanan
  NIPS BigLearn 2013 | Paper PDF

• Feature Selection in Enterprise Analytics: A Demonstration using an R-based Data Analytics System
  Pradap Konda, Arun Kumar, Christopher Ré, and Vaishnavi Sashikanth
  VLDB 2013 Demo | Paper PDF

• Hazy: Making it Easier to Build and Maintain Big-data Analytics
  Arun Kumar, Feng Niu, and Christopher Re
  ACM Queue 2013 | Article
  Invited to the Communications of the ACM March 2013

• Brainwash: A Data System for Feature Engineering
  Michael Anderson, Dolan Antenucci, Victor Bittorf, Matthew Burgess, Michael Cafarella, Arun Kumar, Feng Niu, Yongjoo Park, Christopher Re, and Ce Zhang
  CIDR 2013 Vision Track | Paper PDF

• Towards a Unified Architecture for in-RDBMS Analytics
  Xixuan Feng*, Arun Kumar*, Benjamin Recht, and Christopher Re
  ACM SIGMOD 2012 | Paper PDF | TechReport | Code and Data

• The MADlib Analytics Library or MAD Skills, the SQL
  Joseph M. Hellerstein, Christopher Ré, Florian Schoppmann, Daisy Zhe Wang, Eugene Fratkin, Aleksander Gorajek, Kee Siong Ng, Caleb Welton, Xixuan Feng, Kun Li, and Arun Kumar
  VLDB 2012 Industrial Track | Paper PDF

• Probabilistic Management of OCR Data using an RDBMS
  Arun Kumar, and Christopher Re
  VLDB 2012 | Paper PDF | TechReport | Code and Data

Manuscripts and Articles

• Arun Kumar's contribution to “Reminiscences on Influential Papers”
  Pinar Tozun
  ACM SIGMOD Record 2023 | Article PDF

• Design and Evaluation of an SQL-Based Dialect for Spoken Querying
  Kyle Luoma and Arun Kumar
  TechReport

• Hydra: A Data System for Large Multi-Model Deep Learning
  Kabir Nagrecha and Arun Kumar
  TechReport | Code release

• Integrating Cerebro with Ray
  Abhishek Gupta and Rishikesh Ingale
  CSE 234 Project TechReport | Code Release

• Integrating Cerebro with Dask
  Vignesh Nanda Kumar and Pratik Ratadiya
  CSE 234 Project TechReport | Code Release

• Categorical Data Deduplication
  Soham Pachpande and Gehan Chopade
  CSE 234 Project TechReport | Code, Data, and Pre-trained Models on GitHub

• Bringing ML-based Feature Type Inference to OpenML
  Ryan Tran and Victor Zhu
  CSE 234 Project TechReport | Code Release on GitHub | Package on PyPi

• Letter from the Rising Star Award Winner
  Arun Kumar
  IEEE Data Engineering Bulletin, June 2021 | PDF

• Improving Feature Type Inference Accuracy of TFDV with SortingHat
  Vraj Shah, Kevin Yang, and Arun Kumar
  TechReport

• ML/AI Systems and Applications: Is the SIGMOD/VLDB Community Losing Relevance?
  Arun Kumar
  Blog post on the official ACM SIGMOD Blog, 2018 | Webpage

• Advice from PhD to Early Career
  Arun Kumar
  ACM SIGMOD 2018 New Researcher Symposium Talk | Slides

• A Survey of the Existing Landscape of ML Systems
  Arun Kumar, Robert McCann, Jeffrey Naughton, and Jignesh M. Patel
  UW-Madison Technical Report TR1827 | PDF

Theses, and Dissertations

• Simplifying Data Preparation for Machine Learning on Tabular Data
  Vraj Shah. PhD Dissertation. UC San Diego. 2022 | PDF

• Query Optimizations for Deep Learning Systems
  Supun Nakandala. PhD Dissertation. UC San Diego. 2022 | PDF

• Efficient Systems for Advanced Data Analytics
  Liangde Li. MS Thesis. UC San Diego. 2022 | PDF

• Write once, rewrite everywhere: A Unified Framework for Factorized Machine Learning
  David Justo. MS Thesis. UC San Diego. 2019 | PDF

• Learning Over Joins
  Arun Kumar. PhD Dissertation. UW-Madison. 2016 | PDF | Video of job talk at UCSD
  Wisconsin CS 2016 Graduate Student Research Award for best dissertation research