“Yaser is a fast learner and experienced engineer when it comes to designing and implementing an analytics platform at scale. He is passionate about work and learning new technology. When leading a project, he communicates well and coordinates with all stakeholders, and takes 110% ownership to drive the project to completion.”
About
Building data driven development platforms for physical intelligence.
Activity
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After years of planning and hard work by our leadership team, I am thrilled to announce that Prospiant is moving into a state-of-the-art…
After years of planning and hard work by our leadership team, I am thrilled to announce that Prospiant is moving into a state-of-the-art…
Liked by Yaser Khalighi
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We design cars to move us. What if they could also make us feel at home? Ralph Gilles, Chief Design Officer at @Stellantis, on when technology…
We design cars to move us. What if they could also make us feel at home? Ralph Gilles, Chief Design Officer at @Stellantis, on when technology…
Liked by Yaser Khalighi
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🥷 SuperNinja just got another upgrade with more web-based editors. Try it out today: super.MyNinja.ai AI agents are powerful. But they're usually…
🥷 SuperNinja just got another upgrade with more web-based editors. Try it out today: super.MyNinja.ai AI agents are powerful. But they're usually…
Liked by Yaser Khalighi
Experience
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Education
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Stanford University
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Thesis title: Computational Aeroacoustics of Complex Flows at Low Mach Number
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Licenses & Certifications
Courses
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Acoustics
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Advanced Fluid Mechanics
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Advanced Thermodynamics
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Aeroacoustics
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Controls
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Experimental Methods
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Heat Transfer
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Large Scale Computing
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Microfluidics
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Numerical Methods
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Partial Differential Equations
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Statistical Mechanics
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Turbulence
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Languages
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English
Native or bilingual proficiency
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Persian
Native or bilingual proficiency
Recommendations received
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LinkedIn User
4 people have recommended Yaser
Join now to viewMore activity by Yaser
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Yesterday Tech for Palestine hosted a community gathering in London and it was absolutely amazing 🤩 120+ innovators, activists, founders and…
Yesterday Tech for Palestine hosted a community gathering in London and it was absolutely amazing 🤩 120+ innovators, activists, founders and…
Liked by Yaser Khalighi
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We have demonstrated the world's highest performance 4D imaging mmWave radar using TI's front-end chips. As a system integrator, we are chip…
We have demonstrated the world's highest performance 4D imaging mmWave radar using TI's front-end chips. As a system integrator, we are chip…
Liked by Yaser Khalighi
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My husband David Wood is in Kathmandu, where over 70% of new cars sold in 2024 are electric—second only to Norway globally. Just four years ago…
My husband David Wood is in Kathmandu, where over 70% of new cars sold in 2024 are electric—second only to Norway globally. Just four years ago…
Liked by Yaser Khalighi
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I’m thrilled to share that January AI has been named one of TIME’s Best Inventions of 2025! This recognition is deeply meaningful to me and our…
I’m thrilled to share that January AI has been named one of TIME’s Best Inventions of 2025! This recognition is deeply meaningful to me and our…
Liked by Yaser Khalighi
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If you’re interested in space and live in India - Catalyx is hiring. Also congrats to the team on big first steps!
If you’re interested in space and live in India - Catalyx is hiring. Also congrats to the team on big first steps!
Liked by Yaser Khalighi
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Jousef Murad
🚀 New Blog Series by MathWorks on Physics-Informed Machine Learning (PhiML) 🔹Part 1: What is Physics-Informed Machine Learning? A gentle introduction to the core ideas behind PhiML, including: what it is, how it is different from traditional ML/DL, and why it matters. 🔹Part 2: Methods and Implementation A deeper dive into the techniques and implementation of PhiML methods in MATLAB, complete with MATLAB code examples and a GitHub repo to get you started. 💻 Code on GitHub: https://lnkd.in/eYBX3eGJ 👉 Read the series here: Part 1: https://lnkd.in/ebGUEC8M Part 2: https://lnkd.in/gDRmS4xJ Repost by Mae Markowski #engineering #pinns #ai
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Dr. Sandeep Pandey
#OpenFOAMGPT continues to evolve with the integration of advanced models and improved design. The end-to-end workflow is bringing us closer to achieving full automation with OpenFOAM. Excited to share a new preprint with Xu Chu, where #DeepSeek, #Qwen, and other models were evaluated within an agentic autonomous workflow: https://lnkd.in/ddk-GzTf
11 Comments -
Keunwoo Choi
Data loading is extremely important yet tricky problem when training LLMs and other large models, with multiple nodes and GPUs. It is also the topic of our first blog post by Prescient Design. I explored with Torchdata -- the new version of it, that discarded all the existing, working but sadly a bit unstable Datapipes, by replacing them with Nodes ™️. Turned out it works really well 😉 https://lnkd.in/ehm4eMYi
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Karin Sevegnani
Excited to share two new blog posts I co-authored with my colleague Giuseppe on optimizing Large Language Model (LLM) training with NVIDIA Grace Hopper! 🚀 Together, we dove deep into practical strategies and tools to help you get the most out of your LLM training workflows: 1️⃣ Profiling LLM Training Workflows We walk through how to use NVIDIA Nsight Systems to pinpoint bottlenecks and optimize both CPU and GPU utilization. https://lnkd.in/d5G-MtpB 2️⃣ Advanced Optimization Strategies Explore advanced techniques like CPU offloading, Unified Memory, AMP, and FP8 training to push performance even further. https://lnkd.in/dngHYQDU If you’re working with LLMs on Grace Hopper, we hope these resources help you unlock new levels of efficiency and scalability. #AI #NVIDIA #LLM #GraceHopper #DeepLearning #Optimization
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Graham Jaffe Orr
For the smart ppl out there: a discrete graph-manifold problem relevant to computational geometry and numerical methods... Given a near-Delaunay mesh manifold topology (blue), write an algorithm that identifies non-manifold spanning elements (red). Assume: - given an element, there's a list of nodes comprising the vertices - given a node there's a mapping to adjacent elements - given an element, we can find the immediate neighbor elements - the topological dimensionality can be 1, 2, or 3 (but 2 is most useful) There will bonus points for efficient and parallel. And a gold star.
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