Nikan Doosti

I hold a Master’s degree in Computer Engineering from the Iran University of Science and Technology (IUST) and currently work as a machine learning engineer, specializing in the development of data-driven decision-making systems for business optimization. Previously, I completed a research internship with the Artificial Intelligence Aided Design and Manufacturing (AIDAM) group at the Max Planck Institute for Informatics (MPI-INF). During my time there, I focused on Structural Topology Optimization (TO) using Deep Learning (DL) under the supervision of Dr. Vahid Babaei, the AIDAM group leader, and in collaboration with Prof. Julian Panetta, an Assistant Professor at the University of California, Davis. This opportunity allowed me to realize the importance and applicability of AI in conjunction with science & engineering, and highlighted the exciting possibilities of interdisciplinary research. Before this, my work centered around classical digital image processing, deep computer vision, and data science.

In my spare time, I play video games, read about history, and I like standup comedy. I’m also passionate about open-source projects and consider GitHub my primary social media platform.

Currently, I am actively seeking an exceptional PhD opportunity where I can further explore and contribute to the field of artificial intelligence, particularly in areas that intersect with science and engineering.

news

Jun 2024	I am now actively seeking a PhD position where I can apply my passion for research and further contribute to my field.
Jan 2024	I have resigned from my engineering position and took a couple of months to focus on personal growth and career direction, preparing myself for the next step in my professional journey.
Mar 2023	I have been promoted to the position of Team Leader at Panafor
Oct 2022	I defended my master’s thesis with full marks at Iran University of Science and Technology, one of the most prestigious universities in Iran.
Apr 2022	I joined Panafor as a full-time machine learning engineer.

latest posts

Jan 01, 2024	An empty post as the template

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

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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} }