About me
I am a scientist, engineer, and leader, with a diverse set of interests, focusing on the development of algorithmic techniques and solutions that have a concrete and positive impact on the world.
As a scientist, I develop several algorithmic solutions that led to discoveries presented in important venues such as “Nature Communications”, “Nature Immunology” and the “Journal of Experimental Medicine”. My work in single-cell analysis, led to the discovery of previously unknown cell types, thanks to the development of scalable dimensionality reduction approaches. I also lead large-scale research activities, such as the AI4MRI lab between Philips and LUMC. I enjoy working on complex and previously unsolved problems, with a drive for excellence
As an engineer, I implement solutions that are scalable and proven to work well in the real world. In my career, I have covered a diverse set of topics including industrial automation, computer vision, 3D reconstruction and processing, machine learning, deep learning, and artificial intelligence in general. I had success on all of them thanks to a key focus on software excellence and a fast-delivery mindset. As an example, I drove our Philips team to win the fastMRI challenge and convert it into our FDA-approved SmartSpeed product in less than 3 years, while training our staff on hybrid deep learning. Also, my contributions to 3D scanning technology were instrumental in the acquisition of the OpenTechnology startup by FARO.
As a leader, I drive teams to success thanks to a win-win and agile mindset. I led very diverse teams, from very junior to senior teams, from small ones to teams of more than 15 people, and from engineering to research-focused teams. I am experienced in driving teams both in my organization and across organizations. For example, I drive several public-private partnerships leveraging my joint appointment as an assistant professor at TU Eindhoven.
I worked in a diverse set of environments. I have several years of experience in freelancing, startup to scaleup, big tech, the healthcare industry, and publicly funded research institutions. I worked at Google AI with Alexander Mordvintsev, the creator of Google’s DeepDream, where I was recognized as one of the best research efforts in 2018. I currently work at Philips Research, focusing on the development of robust Artificial-Intelligence solutions for healthcare. I also have a joint appointment as an assistant professor at TU Eindhoven, focusing on human-centered AI, and I collaborate with several research groups across the world.
More information, and links to several news and achievements can be found on my LinkedIn page
Selected Publications
A detailed list of my publications can be found on my Google Scholar page or on Semantic Scholar
- N. Pezzotti, S. Yousefi, M. Elmahdy, J. Van Gemert, C. Schuelke, M. Doneva, T. Nielsen, S. Kastryulin, B.P.F. Lelieveldt, M. Van Osch, E. De Weerdt, M. Staring An adaptive intelligence algorithm for undersampled knee MRI reconstruction. IEEE Access, 2020. PDF.
- N. Pezzotti, J. Thijssen, A. Mordvintsev, T. Höllt, B. van Lew, B.P.F. Lelieveldt, E. Eisemann, A. Vilanova. GPGPU Linear Complexity t-SNE Optimization. Transaction on Visualization and Computer Graphics (Proceedings of IEEE VIS 2019), 2019. Preprint, Demo, Google AI post
- V. van Unen*, T. Hollt*, N. Pezzotti*, N. Li, M. J.T. Reinders, E. Eisemann, A. Vilanova, F. Koning, B. P.F. Lelieveldt. Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types. Nature Communications, 2017. Website PDF
- N. Pezzotti, T. Höllt, J. van Gemert, B.P.F. Lelieveldt, E. Eisemann, A. Vilanova. DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks. Transaction on Visualization and Computer Graphics (Proceedings of IEEE VIS 2017), 2018. PDF, Video
- N. Pezzotti, T. Höllt, B.P.F. Lelieveldt, E. Eisemann, A. Vilanova. Hierarchical Stochastic Neighbor Embedding. Computer Graphics Forum (Proc. of EuroVis), 2016. PDF, Suppl. Mat., Video, Slides
- T. Höllt, N. Pezzotti, V. van Unen, F. Koning, E. Eisemann, B.P.F. Lelieveldt, A. Vilanova. Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets. Computer Graphics Forum (Proc. of EuroVis), 2016. PDF
- N. Pezzotti, B.P.F. Lelieveldt, L. van der Maaten, T. Höllt, E. Eisemann, and A. Vilanova. Approximated and User Steerable tSNE for Progressive Visual Analytics. Transaction on Visualization and Computer Graphics (Presented at IEEE VIS 2016), 2016. PDF, Suppl. Mat., Video I, Video II, Video III
Awards
- 2021 - Philips Innovation Award: deep learning methodologies for improved image quality in MRI
- 2021 - Won the IEEE CoG Bot Bowl III competition, developing the first and only machine learning algorithm that is able to win in Blood Bowl
- 2020 - Philips Innovation Award: tools and techniques for Deep Learning and trustworthy AI
- 2020 - Best Scientific Paper Award in Computer Vision - Philips AI Conference
- 2020 - Highest Business Potential Award in Computer Vision - Philips AI conference
- 2020 - Recipient of the Outstanding Achievement Award at Philips Research
- 2019 - Winner of the first FastMRI challenge organized by Facebook AI Research and NYU Langone Health - Results, Philips Research Blog, Recordings
- 2019 - IEEE VGTC 2019 Best Dissertation Award
- 2019 - Dirk Bartz Prize for Visual Computing in Medicine
- 2019 - PhD Cum Laude - Awarded to 5% of PhD candidates in the Netherlands
- 2018 - Portraits of Science 2018 - TU Delft Excellence in Research
- 2006 - Awarded in “Expo Del capitale umano 2006” for academic merit
- 2005 - Silver medal in the Italian selection for the International Olympiad in Informatics (IOI)
News
I’ve started to share updates only via LinkedIn. You can see all updates on my LinkedIn timeline. I also share stuff on Twitter: have a look at Twitter my feed.
Here some older updates:
- 01 Oct 2020 - I joined the program committee of IAAI-21
- 29 Sep 2020 - Speaker at the panel “AI and the Future of Humankind” at the Czech Innovation Week 2020.
- 24 Jul 2020 - Published a white paper in collaboration with Intel on how OpenVINO can be used to optimize and deploy our winning fastMRI model
- 22 Jul 2020 - Certified Product Owner & Product Manager for SAFe
- 16 Jun 2020 - In collaboration with Shi Hu and Max Welling, published a pre-print on the uncertainty quantification of deep ensembles
- 14 May 2020 - I presented a workshop on AI and its influence on the workplace at LCB 2020
- 14 Apr 2020 - Published the pre-print describing our winning model in the fastMRI challenge
- 29 Mar 2020 - I joined the program committee of ECML-PKDD 2020
- 15 Feb 2020 - I received the Outstanding Achievement Award 2020 from Philips
- 14 Jan 2020 - I started a joint appointment as Assistant Professor at Eindhoven University of Technology - Announcement
- 14 Dec 2019 - At NeurIPS, I presented our winning solution in the FastMRI challenge - Recordings
- 01 Dec 2019 - Facebook AI Research announced that our team won all multi-coil tracks in the FastMRI challenge
- 14 Nov 2019 - Published a work on fingerprinting dictionaries via dimensionality reduction
- 24 Oct 2019 - I presented our work on GPGPU linear complexity tSNE at IEEE VIS 2019
- 22 Oct 2019 - I was awarded the IEEE VGTC Best Dissertation Award 2019 at IEEE VIS 2019
- 10 Oct 2019 - The Kickstart-AI initiative was officially announced at the World Summit AI. I am part of the team supporting the creation of joint-appointments between industry and academia.
- 04 Oct 2019 - I joined the program committee of IAAI-20
- 07 May 2019 - Received, together with the Cytosplore team, the Dirk Bartz prize for visual computing in medicine.
- 12 Apr 2019 - I received my PhD Cum Laude with the dissertation Dimensionality-Reduction Algorithms for Progressive Visual Analytics
- 21 Mar 2019 - I gave a talk on Interpretable AI at the Delft-AI Meetup
- 05 Nov 2018 - I joined Philips Research in Eindhoven
- 15 Jan 2019 - My work feature on Google AI’s research effort in 2019
- 10 Jan 2019 - I featured in Portraits of Science 2018, a showcase of research excellence at TU Delft. You can read my interview here.
- 25 Jul 2018 - Published a Distill.pub article on neural network interpretability and generative properties
- 25 Jul 2018 - My work at Google is presented in Nodes, the research blog of TU Delft faculty of computer science
- 7 Jun 2018 - My work on GPGPU tSNE featured on the Google AI research blog
- 2 Feb 2018 - I joined Google AI in Zurich as a research intern
My old blog can be found here