Philipp Möhl

Machine Learning Researcher and Engineer
Email: philippmoehl1994[at]gmail.com

CV, GitHub, ResearchGate, LinkedIn

About Me

I am a dedicated Machine Learning Researcher and Engineer, bridging the gap between cutting-edge technology and innovative business solutions. Presently, I am a Research Engineer at Profactor GmbH, contributing to projects that encompass computer vision, reinforcement learning, 3D modeling, robotics, and meta-learning. I collaborate with renowned academic institutions and engage in partnerships with major automotive brands and key industrial stakeholders to foster innovation and drive technological advancements.

Prior to joining Profactor GmbH, I held the position of Senior Machine Learning Engineer at A1 Telekom Austria Group, one of the leading telecommunication companies in Europe. There I orchestrated multiple machine learning projects, from churn prediction and recommendation systems to enhancing customer experiences. Serving as the principal consultant for AI innitiatives, I provided strategic direction and expertise. My academic achievements include an MSc degree in Mathematics from University of Vienna in 2023 and a B.Sc. degree in Mathematics from University of Bonn in 2016.


Projects

InstaGPT   
ML
NLP
  • showcasing capabilities of the large language model ChatGPT, and text-to-image models Dall-E and Stable Diffusion for content creation
  • connected the APIs of ChatGPT, Dall-E, Stable Diffusion and Instagram
  • automated creating soical media posts only based on topics, using customizable prompt engineering
HotelBot   
ML
NLP
  • chatbot for a hotel, answering open questions around reservations and other common issues with machine learning
  • finetuned the rasa open-source chatbot framework on hotel use case
  • uses docker-compose to simultaniously host the chatbot API, an action server and the database backend
Sales Forecasting   
ML
  • Forecasting sales in a small company
  • used machine learning and time-series specific feature engineering
  • web application to show case results and data exploration
ML Lifecylce   
ML
CV
  • build a highly adaptable framework for ML lifecycle development
  • this includes experiment configuration organization, tracking, and selection framework
  • on top a deployment preparation CLI tool supports all standard operations
  • a web application offers frontend for customer testing, admin management for serving and monitoring and includes active learning and data collection
PyHFP   
CV
  • adapted an implementation for primtive-based mesh segmentation in C++
  • binded C++ source code to Python with pybind11
  • added utility functions and distributed the binding as a PyPI package

Publications

Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience
Kapil Deshpande, Philipp Möhl, Alexander Hämmerle, Georg Weichhart, Helmut Zörrer, Andreas Pichler (2022).
in Energies.
Scaling Behavior of Physics Informed Neural Networks for Solving Partial Differential Equations
Philipp Möhl (2023).
in Master's thesis.
code