data science lifecycle)  2012-2018 IBM Research – Almaden, USA  Declarative large-scale machine learning  Optimizer and runtime of Apache SystemML  2011 PhD TU Dresden, Germany  Cost-based optimization Algebra for Large-Scale Machine Learning. PVLDB 2016] [Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Large-Scale Machine Learning. VLDB Journal 2018 27(5)] [Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large-Scale Machine Learning. Commun.

#2 “Big Data”  MR/Spark: BigBench, HiBench, SparkBench  Array Databases: GenBase  #3 Machine Learning Systems  SLAB, DAWNBench, MLPerf, MLBench, AutoML Bench, Meta Worlds, TPCx-AI Experiments and text Experiments and Result Presentation [Matthias Boehm et al: SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. CIDR 2020] 17 706.015 Introduction to Scientific Interpretation [Matthias Boehm et al: On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. PVLDB 11(12) 2018] 19 706.015 Introduction to Scientific Writing – 03 Experiments

feedback and recommendations, widen own horizon  Lots of similarities to code reviews in OSS  Learning by What NOT to Do  Accept if no time to review  The Goldilocks Method (examples, proofs, theoretical