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Amil Khan
Allen Institute | UCSB ECE PhD Student
Amil Khan is a dedicated PhD student in Electrical and Computer Engineering at UCSB with a strong focus on large-scale image and video processing software systems.
With over 5 years of experience in building production-level software in computer vision and machine learning, along with 3+ years in management, Amil brings a wealth of expertise to his research.
Prior to pursuing his PhD, Amil served as the Project Lead for BisQue, an open-source NSF-funded ML platform emphasizing on storing terabytes of imaging data, deploying ML microservices, and enabling up to 5D visualization.
His work revolves around developing novel computer vision algorithms, creating large web-scale distributed systems for Machine Learning, deploying ML microservices with low-latency requirements, expert debugging, and fostering a positive work environment.
Amil Khan's skill set includes expertise in Machine Learning, Computer Vision, PyTorch, TensorFlow, 3D GANs, Large Language Models (LLM), 3D UNET for segmentation, and Large Vision Models.
He is proficient in Distributed Systems, with experience in Kubernetes at a production level and scale, microservices, operators, distributed storage (Minio), stream processing (Kafka, NATS), and job scheduling (Argo).
Throughout his academic and professional journey, Amil has held various roles such as ML Research Engineer, Teaching Assistant, Software Engineering Manager, and Project Management Intern at reputable institutions and organizations.
Amil Khan's diverse background encompasses a Bachelor's degree in Applied Statistics with an English Minor, a Master of Science in Electrical and Computer Engineering, and his ongoing pursuit of a Doctor of Philosophy in the same field at UC Santa Barbara.
For inquiries regarding Amil's impressive work and projects, feel free to reach out for further information.
Highlights
Sharing my slides (that I spent way too much time on) from a talk I gave introducing Diffusion Models. The talk was based on the paper, "Denoising Diffusion Probabilistic Models 1," but includes parts from the original paper, "Deep Unsupervised Learning using Nonequilibrium Thermodynamics 2." Below are some great resources I used to help distill the content of the papers. Hope this helps your understanding of Diffusion Models! 🚀
#ai #machinelearning #datascience
What are Diffusion Models: https://lnkd.in/gNpiaBEV
🔥LinkedIn Family🔥, I am happy to announce I will be pursuing my PhD in Electrical & Computer Engineering at UC Santa Barbara starting this Fall 2022 with funding from the National Science Foundation (NSF) Research Traineeship (NRT) grant. My PhD will be focused on building ML Systems for processing large scale image and video data.
My undergrad GPA wasn't great, I had no publications, yet I wanted to pursue graduate studies. So in my last year of undergrad, I sent out over 200 emails asking professors if I can join their group and gain some research experience. I received ONE reply back from a Professor in Materials Science. She said, "I love your enthusiasm, let's meet in person tomorrow and show me a recent #datascience project you completed." I drove 160 miles, showed her the project, got the OK to join her group, worked with her for a year, joined another group, got hired as a software engineer, published 4 journal papers, took over and rebuilt a large web scale ML Platform, and now PhD Student.
I am happy to connect and answer any questions you may have! I would not be here without the help from my friends, family, and LinkedIn friends that I never met but reached out to for advice!
#machinelearning #ai #computervision #deeplearning #data
