micha l matuszak, phd

Micha l Matuszak, PhD

ContactInformation

140 Pasito Terrace Mobile (USA): +1 650–390–7622Sunnyvale, CA 94086 Mobile (PL): +48 696–459–522USA E-mail: mmatuszak85@gmail.com

Summary Software Developer and Machine Learning researcher. Experienced with Signal Processing and custom hard-ware with substantial work experience in Deep Learning and research experience in Probabilistic GraphicalModels (Bayesian and Gaussian Networks), Spiking Neural Networks and Scale–free Graphs. Developedmany large scale supercomputer simulations on CPUs and GPUs with NVIDIA CUDA technology.

ProfessionalExperience

Software Engineer 04.2016 – presentContractor at Google ATAP, Mountain View, CA, USAWorking on Project Soli which is using radar to enable new types of touchless interactions. The Soli sensorcan track sub–millimeter motions at high speed and accuracy. I’m a member of the Soli Machine Learningteam. I’ve working on validating the data, building Data Pipelines and creating, training and testingvarious ML models.

Consultant 01.2018 – presentSenior Software Engineer 07.2016 – 12.2017Software Engineer 07.2015 – 06.2016Contractor at Mobica Ltd., Bydgoszcz, Poland and San Jose, USAWorking for the largest engineering companies in the world (undisclosed names) in their USA and Italybranches.

Assistant Professor 2014 – 09.2015Senior Lecturer 2013 – 09.2014Postdoc 2013 – 09.2014Adam Mickiewicz University, Poznan, PolandWe investigated combined effects of stochasticity and time delays in finite–population three player gameswith two mixed Nash equilibria and a pure one. We show that if basins of attraction of the stable interiorequilibrium and the stable pure one are equal, then an arbitrary small time delay makes the pure onestochastically stable.

Principal Investigator and Project Director 2011 – 09.2015National Science Centre grant at Nicolaus Copernicus University, Torun, PolandThe aim of the project was to model broad classes of temporal patterns of typical behavior with use ofProbabilistic Graphical Models i.e. belief networks (also known as Bayesian networks) and their continuousvariation (Gaussian networks). We proposed, inter alia, strategy optimization in Bayesian influence dia-grams, developed an colour image segmentation algorithm based on introduced class of polygonal Markovfields driven by local activity functions. In Gaussian–network set up, we developed an algorithm for de-termining optimal transition paths between given configurations of systems consisting of many objects.Wedeveloped many large scale simulations on supercomputers and GPUs with NVIDIA CUDA technology tovalidate the results.

Education PhD in Computer Science 10.2009 – 23.05.2013University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Warsaw, PolandStudies in National PhD Programme in Mathematical Sciences for outstanding students– conducted by 8 leading Mathematical and Computer Sciences centers in Poland.

• Dissertation Topic: “Bayesian Networks in Adaptation and Optimization of Behavioral Patterns”

• GPA: 4.77/5.0, Top 5%

MSc in Computer Science 2004 – 2009Nicolaus Copernicus University, Faculty of Mathematics and Computer Science, Torun, Poland

• Thesis Topic: “Application of Monte Carlo Methods in Inference Algorithms in Continuous TimeBayesian Networks”

• GPA: 4.59/5.0, Final grade: 5.0/5.0, Top 5%

ComputerSkills

• Languages: C++, C, Python

• Artificial Intelligence: Machine Learning, Deep Learning, Probabilistic Graphical Models (Bayesianand Gaussian Networks), Statistics

• Libraries: TensorFlow

• Other: Random Graphs, Game Theory, Matrix algorithms, Parallel algorithms

Earlier experience and more detailed information are available in extended version of the resume.