Arun's Data Analytics (ADA) Lab @ UCSD

Introduction

As the scale, complexity, and variety of data grows (aka Big Data), the use of machine learning (ML) and artificial intelligence (AI) techniques to make sense of, and interact with, such data — collectively called predictive data analytics, statistical data analytics, ML-based data analytics, or simply advanced data analytics (also ADA!) — is increasingly critical for data-driven applications in the enterprise, Web, science, and other domains. Alas, building and deploying ML/AI-powered data analytics applications still involves far too many bottlenecks that slow down the lifecycle of such applications, raise costs, frustrate many application users, and in some cases, make high-quality data-driven decision making almost impossible.

The mission of the ADALab is to democratize advanced data analytics by making it dramatically easier, faster, and cheaper to build and deploy ML/AI-powered data analytics applications throughout their lifecycle.

We are an academic research group headed by Dr. Arun Kumar, and we are a part of the Department of Computer Science and Engineering (CSE) and the Halicioglu Data Science Institute at the University of California, San Diego (UCSD). We are members of CSE's Database Lab and affiliate members of the Artificial Intelligence Group and Center for Networked Systems.

Overview of Our Research

The ADA lifecycle typically revolves around data scientists or ML engineers. Based on conversations with dozens of such data-related professionals, we abstract the ADA lifecyle as follows. After identifying the tasks where ML/AI might benefit their application in terms of business impact or scientific insights, the data scientist steers three main processes, as illustrated below:

  • Data Sourcing: Identify, collect, clean, and organize data in to a form that can be used to train ML models.

  • Model Building: Perform model selection with the data to obtain desired prediction functions.

  • Model Deployment: Integrate trained prediction functions with the application and oversee lifecycle.

 

The ADALab's approach to democratizing advanced data analytics involves accelerating the ADA lifecyle by removing bottlenecks for both the efficiency of the systems and algorithms involved and the productivity of the data practitioners involved.

Towards this grand goal, we synthesize and innovate upon the fields of data management, ML/AI, systems, and human-computer interaction. Our projects target all parts of the ADA lifecycle, and our work spans the whole gamut of building new data systems, algorithms, empirical analysis, and theoretical analysis. All of our systems are released as open source software.

We also enjoy interacting with, and learning from, practitioners — data scientists, ML/software engineers, and domain scientists — and working with them to help them adopt our systems and ideas.

The list of current ADALab projects is here: Projects.

The list of ADALab publications is here: Publications.

For a summary of our current research, you can also read this one-pager, listen to this podcast, or watch this talk video.

Recent ADALab News

  • New! 1/24: Our paper on studying and benchmarking the impact of categorical duplicates on ML, part of the SortingHat project, is accepted to VLDB 2024!

  • New! 11/23: Our paper on Saturn, on an optimized system to run finetuning / transfer learning of multiple large DL models / LLMs is accepted to VLDB 2024!

  • 4/23: Huge congrats to Dr. Supun Nakandala, the first PhD alumnus of the ADALab, on being accorded the 2023 ACM SIGMOD Jim Gray Doctoral Dissertation Award! Supun is the first UCSD student to receive this award and this is the first time this award goes to work in the area of DB for ML / ML systems.

Full list of lab news items here: News.

Members

Faculty

 

Arun Kumar
Associate Professor, CSE and HDSI
Email: arunkk [at] eng [dot] ucsd [dot] edu
Office: CSE 3218

Graduate Students

 

Kabir Nagrecha
PhD, CSE, UCSD
Email: knagrech [at] ucsd [dot] edu

 

Kyle Luoma
PhD, CSE, UCSD
Email: kluoma [at] ucsd [dot] edu
Office: CSE 3232

 

Xiuwen Zheng
PhD, CSE, UCSD
Email: xiz675 [at] eng [dot] ucsd [dot] edu
Office: CSE 3232

Alumni

  • Yuhao Zhang, PhD, CSE, UCSD, 2023. First employment: Databricks.

  • [https:pradyumnasridhara.com/ Pradyumna Sridhara, MS, CSE, UCSD, 2023. First employment: UCSD.

  • Vignesh Nanda Kumar, MS, CSE, UCSD, 2023. First employment: Service Now.

  • Supun Nakandala, PhD, CSE, UCSD, 2022. First employment: Databricks.

  • Vraj Shah, PhD, CSE, UCSD, 2022. First employment: IBM Research Almaden.

  • Liangde Li, MS, CSE, UCSD, 2022. First employment: TigerGraph.

  • Tara Mirmira, MS, CSE, UCSD, 2022. First employment: PhD at UCSD.

  • Advitya Gemawat, BS, HDSI, UCSD, 2021. First employment: Microsoft NERD AI.

  • Side Li, MS, CSE, UCSD, 2021. First employment: Google.

  • Kabir Nagrecha, BS, CSE, UCSD, 2021. First employment: PhD at UCSD.

  • Shaoqing Yi, BS, HDSI and Math, UCSD, 2021. First employment: PhD at UC Berkeley.

  • Kevin Yang, BS, CSE, UCSD, 2020. First employment: MS at UPenn.

  • David Justo, MS, CSE, UCSD, 2019 (Co-advisor: Nadia Polikarpova). First employment: Microsoft.

  • Lingjiao Chen. MS, CS, UW-Madison, 2018 (Co-advisor: Paraschos Koutris). First employment: PhD at Stanford.

  • Side Li. BS, CSE, UCSD, 2018. First employment: Amazon.

  • Anthony Thomas. MS, CSE, UCSD, 2018. First employment: PhD at UCSD.

  • Mingyang Wang. MS, CSE, UCSD, 2017. First employment: Amazon.

Sponsors

We thank the following organizations for their generous support of our research. Any findings or opinions expressed in our research publications or articles are our own and do not necessarily reflect the views of any of these organizations.

 

Past sponsors: Hellman Fellows Fund, NVIDIA, and Opera Solutions.

About our Lab's Name

Apart from being a convenient acronym, it is also a tribute to Ada Lovelace, widely regarded as the first computer programmer. This tribute is part of our lab's commitment to help foster a diverse and inclusive community in computing, as enshrined in the UCSD Principles of Community, for people from all backgrounds, including women, LGBTQ+ people, people of color, and people with disabilities.