👋 Hi, I’m Akash. I work on meeting transcription as a Sr. Applied Scientist at Microsoft. I’m on an R&D team that ships state-of-art speech recognition models to Azure APIs, as well as powers Microsoft products like Copilot-assisted recap of meetings in Teams. In this role I need to be tuned in to fast-evolving research, exercise judgement in connecting user experience with research/technology readiness and keep experimenting until we can ship high-quality models that improve the user experience.
🐋 At the Microsoft global hackathon, I also co-founded OrcaHello, an open-source AI system that monitors and alerts for orcas in the Pacific Northwest. Since going live in September 2020 it has allowed experts to moderate 3 locations, reducing the time needed by ~400x and has flagged over 60 endangered SRKW events so far. Learn more at ai4orcas.net.
In my professional journey so far, being a good teammate and contributing to the design and engineering of products that make magic have been the most enjoyable aspects for me. You can find more details on my CV.
If you’re curious, carry on to read a brief ramble about my unconventional career arc thus far :)
[2018- ] 🤖 Started my tech career at Microsoft. As someone with a non-traditional background, i’ve gotten to pick up industry scale “software and servers” skills, working with large codebases, distributed training, and data pipelines handling hundreds of terabytes. I’ve been fortunate to collaborate with expert PhDs in speech processing and witness the evolution of the whole stack. I’ve also used my analytical eye to help my manager prioritize bottlenecks. Outside of work, I co-founded an award-winning hackathon project that leverages AI to alert for orcas in the Pacific Northwest.
[2016-2018] 🤓 At Stanford's MS&E program I initially aimed to become a PM or data scientist, but was soon drawn to more deeply understand and build technology. I balanced marketing, strategy & design classes with self-assembled technical electives. I delved into math (deep learning, digital signal processing), code (databases, implementing a heap allocator in C), and built a neural text-to-speech system that was appreciated by Prof Richard Socher. They were two intense years, but gave me confidence in my ability to learn and bridge different worlds. I look forward to the dots connecting in the future :)
[2015-2016] 🤘 Took a gap year before Stanford and entered the code world through stints at startups in Bangalore. As a data scientist at Ather Energy, I contributed to an early roadmap for vehicle intelligence features on its pioneering EV. I dipped my feet in the dark arts of deep learning, self-studying the classic CS231n at my roommate’s AI for radiology startup. I met some amazing people, got to witness the magic of crafting great, deep tech products in India, and have been inspired ever since.
[2011-2015] 🧪 In a previous life as a Chemical Engineer at IIT Madras i’ve been at a hydrofracking site with 5000 horsepower pumps, and tinkered with chemicals at a tobacco lab for nicotine extraction. My minor in control systems (linear algebra, stats, signal processing) gave me a foundation for machine learning math. In hindsight, enjoying working on things I can touch and feel likely drew me to deep learning and i’m glad that software is headed in that direction too!