My research contributions span machine learning, natural language processing, speech recognition, human-computer interaction, and distributed systems. I am not actively publishing new research, but I am always open to collaboration.
While not actively performing academic research, I am interested in the intersection of AI/ML with finance, private equity, software engineering and the overall impact on the entire entrepreneurship ecosystem.
Michael Brenndoerfer
In Progress • 2025
A comprehensive guide to language AI that bridges the gap between fundamental concepts and cutting-edge applications. From the early symbolic foundations to modern transformer architectures, this book provides practical insights for practitioners working with language models, NLP systems, and AI applications.
Read onlineErik Edwards, Michael Brenndoerfer, Amanda Robinson, Najmeh Sadoughi, Gregory Finley, Maxim Korenevsky, Nico Axtmann, Mark Miller, David Suendermann-Oeft
SPECOM 2018 (20th International Conference on Speech and Computer) • 2018
Gregory Finley, Erik Edwards, Wael Salloum, Amanda Robinson, Najmeh Sadoughi, Nico Axtmann, Maxim Korenevsky, Michael Brenndoerfer, Mark Miller, David Suendermann-Oeft
SPECOM 2018 (20th International Conference on Speech and Computer) • 2018
Najmeh Sadoughi, Gregory Finley, Erik Edwards, Amanda Robinson, Maxim Korenevsky, Michael Brenndoerfer, Nico Axtmann, Mark Miller, David Suendermann-Oeft
SPECOM 2018 (20th International Conference on Speech and Computer) • 2018
Gregory Finley, Erik Edwards, Amanda Robinson, Najmeh Sadoughi, James Fone, Mark Miller, David Suendermann-Oeft, Michael Brenndoerfer, Nico Axtmann
INTERSPEECH 2018 • 2018
Gregory Finley, Wael Salloum, Najmeh Sadoughi, Erik Edwards, Amanda Robinson, Nico Axtmann, Michael Brenndoerfer, Mark Miller, David Suendermann-Oeft
NAACL-HLT 2018 (Industry Track) • 2018
Gregory Finley, Erik Edwards, Amanda Robinson, Michael Brenndoerfer, Najmeh Sadoughi, James Fone, Nico Axtmann, Mark Miller, David Suendermann-Oeft
NAACL-HLT 2018 (Demonstrations) • 2018
Casey Dugan, Aabhas Sharma, Michael Muller, Di Lu, Michael Brenndoerfer, Werner Geyer
INTERACT 2017 (IFIP Conference on Human-Computer Interaction) • 2017
Michael Muller, Casey Dugan, Michael Brenndoerfer, Megan Monroe, Werner Geyer
CSCW 2017 (ACM Conference on Computer-Supported Cooperative Work and Social Computing) • 2017
Michael Brenndoerfer
LinkedIn Article • 2021
Michael Brenndoerfer
LinkedIn Article • 2021
Michael Brenndoerfer, Stefan Palombo, Vinitra Swamy
Distill Literature Review • 2018
Michael Brenndoerfer, by Jessie Ying
Berkeley Master of Engineering (Medium) • 2018
Michael Brenndoerfer, edited by Maya Rector
Berkeley Master of Engineering (Medium) • 2018
Learn how to build agentic workflows with LangChain and LangGraph.
An in-depth look at what happens to money during market crashes, how wealth is redistributed, and the mechanisms behind market recovery.
Understand the mathematical foundations of LLM fine-tuning with clear explanations and minimal prerequisites. Learn how gradient descent, weight updates, and Transformer architectures work together to adapt pre-trained models to new tasks.
A comprehensive guide to choosing the right approach for your LLM project: using pre-trained models as-is, enhancing them with context injection and RAG, or specializing them through fine-tuning. Learn the trade-offs, costs, and when each method works best.
Learn the foundational concepts of LLM workflows - connecting language models to tools, handling responses, and building intelligent systems that take real-world actions.
Learn how to use Monte Carlo simulation to model and analyze stock market returns, estimate future performance, and understand the impact of randomness in financial forecasting. This tutorial covers the fundamentals, practical implementation, and interpretation of simulation results.
A comprehensive guide to understanding AI agents, their building blocks, and how they differ from agentic workflows and agent swarms.
A deep dive into how MCP makes tool use with LLMs easier, cleaner, and more standardized.
An exploration of why setting temperature to zero doesn't eliminate all randomness in large language model outputs.
This is a test notebook for testing the admin editing functionality.
Get notified when I publish new articles on data and AI, private equity, technology, and more.