Stephen Macke

Ph.D. Candidate Database and Information Systems Laboratory Department of Computer Science University of Illinois at Urbana-Champaign   smacke [AT] illinois [DOT] edu   Github     LinkedIn     Google Scholar   Curriculum Vitae

About

I'm a Ph.D. student in the Data and Information Systems laboratory at UIUC. I am currently visiting UC Berkeley, where I'm fortunate to be advised by Aditya Parameswaran. My research is broadly focused on improving user workflows for machine learning and other analytics tasks, particularly by bringing ideas from the database community to enforce rigor and structure in such workflows, but without compromising interactivity. I'm grateful to have had my graduate study funded by an NSF Graduate Research Fellowship.

Publications

• PRE-PRINT Fine-Grained Lineage for Safer Notebook Interactions
  Stephen Macke, Hongpu Gong, Doris Jung-Lin Lee, Andrew Head, Doris Xin, Aditya Parameswaran. Under revision for VLDB 2021, Aug. 2020.

• PRE-PRINT Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees
  Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya Parameswaran, Ronitt Rubinfeld. To appear at ICDE 2021.

• 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, Aug. 2020.

• PAPER A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead
  Doris Jung-Lin Lee*, Stephen Macke*, Dorix 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, Nov. 2018.

• 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, Sept. 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.