GeezerVison

I’m a full-time graduate researcher in ADDLab, currently working on integration of data-driven deep learning in additive manufacturing (metal 3D printing) process for defect detection and prevention. This research is conducted under supervision of Prof. Jan Akmal, in collaboration with Electro Optical Systems EOS Finland Oy at Aalto Design Factory, Aalto University.

I have built foundation and experience in DL/ML, Physical-Simulation, Computational Design, and Digital Fabrication; especially enthusiastic about expanding into Drug Design and Healthcare, and in general, applicability of AI in conjunction with science & engineering.

Previously, I had a chance to join the Artificial Intelligence Aided Design and Manufacturing (AIDAM) group at the Max Planck Institute for Informatics (MPI-INF) as an research intern, doing my master’s thesis. During my time there, I focused on Computational Fabrication, specifically, Physics-Informed Deep Learning for Structural Optimization 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. Also, I worked as a machine learning engineer, building decision-making systems for business optimization. In my bachelor’s, my work centered around classical digital image processing, deep computer vision, and data science.

Just to state the obvious, I am not doing AI because it’s trendy thing; I was working on ML/DL back in 2017 in my third-year of bachelor’s even though no ML course was part of my curriculum. So yeah, I am truly passionate about it, and I don’t like using it for building TikTok filters even though there’s good amount of money in it. I’m here to solve core problems that people will notice when the downstream consequences and products are being used.

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.

I’m always open to collaborate and having a discussion around research, from ideation to dissemination, industry challenges, and even setting up a local server to play Factorio/DoTA2/CS2 and so on! So do not hesitate to contact me!

1. SCF

   

   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, Virtual Event, USA, 2021

   Abs DOI 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}
   }