Flying kite at British seaside

Dmitry (Dima) Petrov

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🪁 Hi, I'm Dima — a spatial AI researcher and engineer.

I currently work at Foundation EGI, where we automate labor-intensive CAD tasks such as technical drawings and process planning.

I recently defended my PhD (thesis presentation) at the College of Information and Computer Sciences, University of Massachusetts Amherst, where I worked with Evangelos Kalogerakis at the intersection of 3D vision, graphics and AI. My research focused on generative 3D AI — methods that reconstruct and generate 3D shapes with complex geometry and topology while giving fine-grained, interpretable control over their structure and form. Earlier, I worked at the intersection of machine learning and computational neuroscience, on automated quality control of brain segmentation and the analysis of structural connectomes.

Before my PhD I earned a Master's in Data Science with Leonid Zhukov and a Bachelor's in Mathematics with Alexander Kolesnikov, both at the Higher School of Economics (HSE).

Publications
Vision, Graphics & AI
NewGEOPARD: Geometric Pretraining for Articulation Prediction in 3D Shapes
Pradyumn Goyal, Dmitry Petrov, Sheldon Andrews, Yizhak Ben-Shabat, Hsueh-Ti Derek Liu, Evangelos Kalogerakis
ICCV 2025
NewShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts
Dmitry Petrov, Pradyumn Goyal, Divyansh Shivashok, Yuanming Tao, Melinos Averkiou, Evangelos Kalogerakis
CVPR 2025
GEM3D: Generative Medial Abstractions for 3D Shape Synthesis
Dmitry Petrov, Pradyumn Goyal, Vikas Thamizharasan, Vladimir G. Kim, Matheus Gadelha, Melinos Averkiou, Siddhartha Chaudhuri, Evangelos Kalogerakis
SIGGRAPH 2024 (conference track)
ANISE: Assembly-based Neural Implicit Surface rEconstruction
Dmitry Petrov, Matheus Gadelha, Radomír Měch, Evangelos Kalogerakis
IEEE Transactions on Visualization and Computer Graphics, 2023
Cross-Shape Attention for Part Segmentation of 3D Point Clouds
Marios Loizou*, Siddhant Garg*, Dmitry Petrov*, Melinos Averkiou, Evangelos Kalogerakis (*equal contribution)
Symposium on Geometry Processing, 2023
Neuroscience

Note: I no longer actively work in this area — my research has since moved to vision, graphics and AI. These works are listed for completeness.

Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
Dmitry Petrov, Boris A Gutman, Shih-Hua Julie Yu, Kathryn Alpert et. al
International Workshop on Machine Learning in Medical Imaging (MLMI MICCAI), 2017
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz, Boris Gutman, Daniel Moyer, Julio Villalon, Neda Jahanshad, Paul Thompson
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2017
Structural Connectome Validation Using Pairwise Classification
Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz, Boris Gutman, Daniel Moyer, Julio Villalon, Neda Jahanshad, Paul Thompson
IEEE International Symposium on Biomedical Imaging (ISBI), 2017
Boosting connectome classification via combination of geometric and topological normalizations
Dmitry Petrov, Yulia Dodonova, Leonid Zhukov, Mikhail Belyaev
International Workshop on Pattern Recognition in Neuroimaging, 2016
Classification of structural brain networks based on information divergence of graph spectra
Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz, Boris Gutman, Daniel Moyer, Julio Villalon, Neda Jahanshad, Paul Thompson
IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016
Kernel Classification Of Connectomes Based On Earth Mover's Distance
Yulia Dodonova, Mikhail Belyaev, Anna Tkachev, Dmitry Petrov, Leonid Zhukov
Workshop on Brain Analysis using Connectivity Networks (BACON MICCAI), 2016
Feature engineering and dimensionality reduction for structural connectome classification
Dmitry Petrov
Master's thesis, National Research University Higher School of Economics, 2016
Experience
Service
Mentoring
  • 2020–2024 UGRAD Research Volunteers, UMass CICS, Amherst, MA — mentored undergraduate students on machine/deep learning and data science topics. A recurring CICS program; I took part six times.
  • 2020–2021 PhD Applicant Support, UMass Amherst, Amherst, MA — mentored prospective international PhD applicants as part of the PhD applicant support program.
  • 2019 Eureka! & Women in Engineering volunteer, Amherst, MA — led "Creative Computing with Scratch" and "LED Programming" workshops for high-school girls from Holyoke as part of Eureka!, a program addressing the gender gap in STEM, and at the Women in Engineering Career Day.
Reviewing

Conferences: CVPR (2023–2026), ICCV (2023–2025), SIGGRAPH (2025–2026), NeurIPS (2024–2026), ICML (2025–2026), ICLR (2025), ECCV (2024, Outstanding Reviewer), WACV (2024–2025), Eurographics (2025).

Journals: TPAMI (2022–2023), TVCG (2022–2025).

Past life (journalism & copywriting)

Before tech, I spent almost a decade in journalism and copywriting. I started in journalism before university — as an FMCG reporter at the business weekly SmartMoney, then a reporter and later editor at the daily newspaper Trud. Later, alongside my undergrad, I freelanced as a copywriter and editor in the advertising and custom-publishing departments of Harvard Business Review Russia and Sanoma Independent Media. I genuinely loved that work. (Fun fact: Trud — "Labor" — once held the Guinness record for the world's highest-circulation newspaper, peaking at over 21 million copies in 1990.) As the space for independent journalism in Russia kept shrinking, I moved to math and computer science — probably the right call, since most of the outlets I wrote for are now gone or state-dependent, though I still miss it sometimes. Earlier still, I dabbled in online marketing for software that ran on Pocket PCs and pre-iPhone smartphones — because why not.

Some of my old material (in Russian): sostav.ru.

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