Nikan Doosti

Through my research, I like to produce practical and accessible tools to be used by scientists, content creators, engineers, and end-users as a connecting media between different domains. I plan to do this by devising self-supervised physics-aware deep learning method. I am interested in applications of AI (mostly deep learning) in computer graphics, science, and generally digital twin. Currently, I am looking for an unique PhD opportunity and I plan to continue my research career in industry!

I am a MSc Computer Engineering graduate from Iran University of Science and Technology (IUST) and I am working as a machine learning engineer. Previously, I did a research internship in Artificial Intelligence aided Design and Manufacturing (AIDAM) group at Max Planck Institut für Informatik (MPI-INF). I worked on Structural Topology Optimization (TO) via Deep Learning (DL) under supervision of Dr. Vahid Babaei (AIDAM group leader) and in collaboration with Prof. Julian Panetta (Assistant Professor at University of California, Davis). We published this work in ACM Symposium on Computational Fabrication. This opportunity allowed me to realize the importance and applicability of AI in conjunction with science, and interdisciplinary work. Prior to that, I was mainly focused on classic digital image processing and data driven deep computer vision, and data science. In the past of couple of years I grew interest in applications of AI in medicine and computational neuroscience but I was not lucky enough to professionally work on it.

In my spare time, I play video games, read about history, and I like comedy (standup). On top of that, I find the idea and practice of open source amazing and I use Github as my main social media app.

news

Oct 23, 2022	I defended my master’s thesis with full grade at Iran University of Science and Technology.
Apr 24, 2022	I joined Nahal Gasht as a full-time machine learning engineer.
Mar 4, 2022	I gave a talk at Toronto Geometry Colloquium about my SCF21 publication.
Oct 1, 2021	We published and presented a peer-reviewed paper in SCF21 conference!

selected publications

Topology Optimization via Frequency Tuning of Neural Design Representations

Nikan Doosti, Julian Panetta, and Vahid Babaei

In Proceedings of the 6th Annual ACM Symposium on Computational Fabrication 2021

Abs Bib HTML PDF Code Poster Slides

Structural topology optimization seeks to distribute material throughout a design domain in a way that maximizes a certain performance goal. In this work, we solve the topology optimization problem by parameterizing the designs via recently introduced coordinate-based neural networks. Specifically, we show that networks with Fourier feature mapping can achieve state-of-the-art performance. Our method enables the realization of a range of designs using a single mesh via tuning the frequency content of the solutions independently of the finite element discretization grid. This frequency control offers attractive properties, such as mesh-independent results and sub-pixel filtering that leads to appropriate designs for upsampling. We demonstrate our method on the compliance minimization problem, optimizing for the stiffest possible structure within a weight budget for a prescribed set of loads.
@inproceedings{10.1145/3485114.3485124, author = {Doosti, Nikan and Panetta, Julian and Babaei, Vahid}, title = {Topology Optimization via Frequency Tuning of Neural Design Representations}, year = {2021}, isbn = {9781450390903}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3485114.3485124}, doi = {10.1145/3485114.3485124}, booktitle = {Proceedings of the 6th Annual ACM Symposium on Computational Fabrication}, articleno = {1}, numpages = {9}, keywords = {neural networks, generative design, Topology optimization}, location = {Virtual Event, USA}, series = {SCF '21} }