Stephen Macke

Software Engineer @ Databricks   stephen.macke [AT] databricks [DOT] com   Github     LinkedIn     Google Scholar   Curriculum Vitae

About

I'm a software engineer at Databricks, where I work on computational notebooks and other systems for AI and machine learning at scale. I was previously at Meta where I worked in a largely similar problem space. I graduated with a PhD from the Data and Information Systems laboratory at UIUC, where I was fortunate to be advised by Aditya Parameswaran. I also spent the last wonderful year of my PhD visiting the UC Berkeley RISELab.

My interests are around interactive computing, especially as applied to large-scale machine learning and and other analytics. I'm grateful to have had my graduate study funded by an NSF Graduate Research Fellowship.

Notice to hedge fund recruiters: Thank you if you reached out. I read all emails but am unfortunately unable to respond to all of them. I'm currently not open to relocating away from Bellevue, WA, though I'm happy to consider remote or hybrid positions, should those ever become an option for candidates based in the pacific northwest.

Publications

• PAPER Bolt-on, Compact, and Rapid Program Slicing for Notebooks
  Shreya Shankar*, Stephen Macke*, Sarah Chasins, Andrew Head, Aditya Parameswaran. To appear at VLDB 2023. (*equal contribution)
  

• PAPER Fine-Grained Lineage for Safer Notebook Interactions
  Stephen Macke, Hongpu Gong, Doris Jung-Lin Lee, Andrew Head, Doris Xin, Aditya Parameswaran. 47th International Conference on Very Large Data Bases (VLDB), Copenhagen, Denmark, Aug. 2021.
  

• PAPER Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees
  Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya Parameswaran, Ronitt Rubinfeld. 37th IEEE International Conference on Data Engineering, April 2021.
  

• PAPER Automating State Management in Computational Notebooks (Extended Abstract)
  Stephen Macke. Conference on Innovative Data Systems Research (CIDR), Jan. 2021.
  Gong show winning entry

• PAPER Towards Scalable Dataframe Systems
  Devin Petersohn, Stephen Macke, Doris Xin, William Ma, Doris Jung-Lin Lee, Xiangxi Mo, Joesph Gonzalez, Anthony Joseph, Joseph Hellerstein, Aditya Parameswaran. 46th International Conference on Very Large Data Bases (VLDB), Tokyo, Japan, Sept. 2020.
  

• PAPER A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead
  Doris Jung-Lin Lee*, Stephen Macke*, Doris Xin*, Angela Lee, Silu Huang, Aditya Parameswaran. IEEE Data Engineering Bulletin, June 2019. (*equal contribution)
  

• PAPER Helix: Holistic Optimization for Accelerating Iterative Machine Learning
  Doris Xin, Stephen Macke, Litian Ma, Jialin Liu, Shuchen Song, Aditya Parameswaran. 45th International Conference on Very Large Data Bases (VLDB), Los Angeles, USA, Aug. 2019.
  

• PAPER Lifting the Curse of Multidimensional Data with Learned Existence Indexes
  Stephen Macke, Alex Beutel, Tim Kraska, Maheswaran Sathiamoorthy, Derek Zhiyuan Cheng, Ed H. Chi. ML for Systems Workshop at NeurIPS, Montreal, CA, Dec. 2018.
  

• PAPER Adaptive Sampling for Rapidly Matching Histograms
  Stephen Macke, Yiming Zhang, Silu Huang, Aditya Parameswaran. 44th International Conference on Very Large Data Bases (VLDB), Rio de Janeiro, Brazil, Aug. 2018.
  

• PAPER Helix: Accelerating Human-in-the-loop Machine Learning (Demo)
  Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran. 44th International Conference on Very Large Data Bases (VLDB), Rio de Janeiro, Brazil, Aug. 2018.
  

• PAPER Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities
  Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran. DEEM Workshop at SIGMOD Int'l Conf. on Management of Data, Houston, USA, June 2018.
  

• PAPER Entropy-Based Subword Mining for Word Embeddings
  Ahmed El-Kishky, Frank Xu, Aston Zhang, Stephen Macke, Jiawei Han. SCLeM Workshop at 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), New Orleans, USA, June 2018.
  

• PRE-PRINT Towards a Theory of Data-Diff: Optimal Synthesis of Succinct Data Modification Scripts
  Tana Wattanawaroon, Stephen Macke, Aditya Parameswaran. ArXiv preprint.