Goal-oriented machine learning researcher and engineer with 6 years of commercial experience in transforming AI-powered R&D projects from idea to implementation stage.
Author and co-author of 10 scientific publications accepted into renowned journals and conferences.
Research interests include 3D computer vision, generative modeling, and medical imaging.
Experience
Led R&D projects in the areas of medical image segmentation and fluid simulation, coordinated datasets preparation, collected feedback from medical experts, and participated in patent filling process, as well as supervised mid and junior-level engineers.
Wrocław University of Science and Technology
Teaching Assistant
Feb 2019 - Sep 2022
https://www.pwr.edu.pl
Prepared and taught bachelor-level courses on topics such as machine learning and approximating differential equations. Helped with thesis supervision of bachelor and master-level students.
Co-led and was involved in research projects that resulted in 3 major scientific publications, including acceptance to ICML 2020.
Led and co-led several commercial projects that included discovering client needs, performing feasibility studies, defining project scope for PoC and MVP milestones, and conducting R&D work in areas of deep learning and computer vision.
Involved in journal-published research project in bioinformatics & machine learning areas.
In commercial projects worked on implementation, testing, and documentation as well as providing reports to clients.
Worked in Test Automation team. Created tools to automate the job of manual testers, wrote & refactored Python libraries, management the SVN code repository.
Education
Wrocław University of Science and Technology
Ph.D. in Computer Science Machine Learning
2018 - 2022
- Cum laude
- Area of research: Deep learning, neural networks, representation learning, 3D computer vision
- Thesis ‘‘Representation learning on point cloud data with deep neural networks’’
B.Eng. & M.Sc. in Computer Science
2013 - 2018
- Specialization in Artificial Intelligence
- Thesis ‘‘Adaptation of BiGAN models for semi-supervised learning’’
Publications
- Zamorski, M., Stypułkowski, M., Karanowski, K., , Trzciński, T., Zięba, M. (2022). Continual learning on 3D point clouds with random compressed rehearsal. Computer Vision and Image Understanding (CVIU)
- Stypułkowski, M., Kania, K., Zamorski, M., Zięba, M., Trzciński, T., Chorowski, J. (2021). Conditional Invertible Flow for Point Cloud Generation. Pattern Recognition Letters
- Spurek, P., Winczowski, S., Tabor, J., Zamorski, M., Zięba, M., Trzciński, T. (2020). Hypernetwork approach to generating point clouds. International Conference on Machine Learning (ICML)
- Zamorski, M., Zięba, M., Świątek, J. (2020). Generative Modeling in Application to Point Cloud Completion. International Conference on Artificial Intelligence and Soft Computing (ICAISC)
- Zamorski, M., Zięba, M., Świątek, J. (2020). Comparison of Aggregation Functions for 3D Point Clouds Classification. Intelligent Information and Database Systems (IIDS)
- *Zamorski, M. *, Zięba, M., Klukowski, P., Nowak, R., Kurach, K., Stokowiec, W., Trzciński, T. (2020). Adversarial Autoencoders for Compact Representations of 3D Point Clouds. Computer Vision and Image Understanding (CVIU)
- Stypułkowski, M., Zamorski, M., Zięba, M., Chorowski, J. (2019). Conditional Invertible Flow for Point Cloud Generation. NeurIPS 2019 Workshop on Sets and Partitions
- Zamorski, M., Zdobylak, A., Zięba, M., Świątek, J. (2019). Generative Adversarial Networks: recent developments. International Conference on Artificial Intelligence and Soft Computing (ICAISC)
- Zamorski, M., Zięba, M. (2019). Semi-supervised learning with Bidirectional GANs. Intelligent Information and Database Systems (IIDS)
- Klukowski, P., Augoff, M., Zamorski, M., Gonczarek, A., Walczak, M. J. (2018). Application of Dirichlet process mixture model to the identification of spin systems in protein NMR spectra. Journal of biomolecular NMR
Selected Activity
ML in PL Conference
2022
- Presented poster ‘Continual learning on 3D point clouds with random compressed rehearsal‘.
ML in PL Conference (prev. PL in ML)
2019
- Gave the oral presentation ”Adversarial Autoencoders for Compact Representations of 3D Point Clouds”
- Co-conducted a full-day workshop ”Flow-Based Generative models”.
PL in ML - Polish View on Machine Learning Conference
2018
- Co-conducted a full-day workshop ”Practical Aspects of Generative Models” for 30 attendees as a part of one of the biggest Polish machine learning conferences.
TEDx Wrocław
2014, 2015, 2016
- Volunteer for TEDx Wrocław main events. Worked in an international group. Responsible for cooperating with the audio engineer and continuous service of a conference rooms sound systems.
AIESEC Wrocław University of Technology
2014 - 2015
- Leader of the team organizing work & travel in Lower Silesia region for foreign student volunteers.