Contacts

Phone: (818) 288 8754

Email: [email protected]

LinkedIn: https://www.linkedin.com/in/maximilian-sherr-067112184/

Github: https://github.com/wolfink

Technical Knowledge

C/C++, Python, Java, Rust, PyTorch, JUCE, Max/MSP, Pure Data, Linux/Unix, Microsoft Office Suite

Skills

  • Adaptable to new environments
  • Quickly and creatively solve tasks
  • Strong work ethic

Experience

Research Assistant @ UC Berkeley Center for New Music and Audio Technologies April 2020 to June 2021 Berkeley, CA

  • Analyzed sound for event detection to be used for the purpose of generating orchestrations
  • Determined methods for quantifying the difference between orchestrated sounds and their sources
  • Implemented baseline functions, such as non-negative matrix factorization, to test against solutions using neural networks

Publications

Luke Dzwonczyk, Léo Chédin, Alejandro Saldarriaga-Fuertes, Max Sherr, Hélène-Camille Crayencour,et al. Source Separation Methods for Computer-assisted Orchestration. The 3rd Conference on AI Music Creativity (AIMC 2022), Sep 2022

Projects

JUCE VST3 plugin for modeling string resonance (Virtual Resonators)

  • Created program meant to emulate string resonance using the Karplus-Strong algorithm
  • Designed and built a graphical user interface for the plugin
  • Created unit tests for the various components of the plugin

wl Programming Language

  • Built lexer and parser for a language inspired by C and Lua
  • Designed tests for compiler components

SL (Structuring Language)

  • Built and designed macro expansion system.

SQL-like DSL for a relational database management system (DBMS)

  • Implemented using the Java programming language
  • Built DBMS able to create, load, store, and insert into data tables, among other functionalities
  • Wrote JUnit test cases to verify code functionality

Python Compiler

  • Built a lexer and parser, and organized inputs into an AST
  • Interpreted AST to implement Python functionality (with the additional functionality added for typed variables)

Matrix Optimization

  • Used SIMD instructions, vectorization, and thread programming to improve matrix addition and multiplication

Education

University of California Berkeley, Aug 2020

BA in Computer Science and Music

Cumulative GPA: 3.2

Course Work

Efficient Algorithms and Intractable Problems, Programming Languages and Compilers, Operating Systems and System Programming, Introduction to Machine Learning, Introduction to Artificial Intelligence