MichalMatuszak, PhD

Contact 140 Pasito Terrace Mobile (USA): +1 650–390–7622 Information Sunnyvale, CA 94086 Mobile (PL): +48 696–459–522 USA E-mail: mmatuszak85@.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 Graphical Models (Bayesian and Gaussian Networks), Spiking Neural Networks and Scale–free Graphs. Developed many large scale supercomputer simulations on CPUs and GPUs with NVIDIA CUDA technology.

Professional Software Engineer 04.2016 – present Experience Contractor at ATAP, Mountain View, CA, USA Working on Project Soli which is using radar to enable new types of touchless interactions. The Soli sensor can track sub–millimeter motions at high speed and accuracy. I’m a member of the Soli Machine Learning team. I’ve working on validating the data, building Data Pipelines and creating, training and testing various ML models.

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

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

Principal Investigator and Project Director 2011 – 09.2015 National Science Centre grant at Nicolaus Copernicus University, Torun, Poland The aim of the project was to model broad classes of temporal patterns of typical behavior with use of Probabilistic Graphical Models i.e. belief networks (also known as Bayesian networks) and their continuous variation (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 Markov fields 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.We developed many large scale simulations on supercomputers and GPUs with NVIDIA CUDA technology to validate the results.

Education PhD in Computer Science 10.2009 – 23.05.2013 University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Warsaw, Poland Studies 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 – 2009 Nicolaus Copernicus University, Faculty of Mathematics and Computer Science, Torun, Poland • Thesis Topic: “Application of Monte Carlo Methods in Inference Algorithms in Continuous Time Bayesian Networks” • GPA: 4.59/5.0, Final grade: 5.0/5.0, Top 5%

Computer • Languages: C++, C, Python Skills • Artificial Intelligence: Machine Learning, Deep Learning, Probabilistic Graphical Models (Bayesian and 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.