William Lin

My time as a research engineering intern at Google DeepMind has come to a close! As someone whose world revolves around networked, computer, and distributed systems, I never imagined I'd land on my dream team – the model partitioning team! This summer, I worked on PartIR's automatic model partitioning capabilities(https://lnkd.in/egp5Ygvy), diving deeper into distributed computing and exploring applications in reinforcement learning. It was definitely a challenging project, but I picked up so many invaluable skills, from software engineering to research in an industry setting. I’d like to give a huge thank you to Sami Alabed, Masha Samsikova, and the entire model partitioning team for making me feel so welcome and helping me successfully complete my project! I’m also thrilled that my final presentation was well received (and even got some laughs!), helping many at GDM understand a complex topic. Outside of my project, I had the chance to meet so many fascinating people across different teams and, of course, the other interns! A big thanks to the GDM Academy organizers, Charlotte Colson and Claudia Pope, for their incredible support in running such a fantastic program while I was balancing my thesis. And to Lucia Lopez Rivilla and Ioana Mihăilescu, former GDM Academy interns, for helping me find my way around the company! This summer wasn’t without its challenges, though. I finally addressed the anxiety issues I’d been struggling with since my teenage years, as they were impacting my studies and relationships. Unfortunately, I wasn’t able to attend my final year exams as I had hoped. However, I'm so grateful to UCL's Computer Science team and Faculty of Engineering for giving me the flexibility to extend my studies by a year, without penalty, and extending my scholarship. I couldn’t be more thankful for their support! All in all, this experience has solidified my determination to pursue research. In my third year, I discovered my passion for networked systems, and during my time at AMD, I knew I wanted to work on distributed systems. Now, after my time at GDM, I’m confident that distributed and networked systems are my future – and with the added experience of AI, I feel more prepared to navigate the machine learning-filled computer science landscape. Here’s to a (hopefully) restful but equally exciting year ahead! See you soon, Morgane 🦆

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This semester, I worked with Jiayue (Angela) Sun, Sophia Wang, and Colin Wright in using Python to program an IRobot Create3. We were able to give the robot instructions on how to navigate a maze, detect objects, stop when hit, and explore/map given regions. The way we went about this problem was analogous to how humans solve mazes: explore new areas, and if it doesn’t work, remember not to go back! Such an amazing way to apply our newfound Python skills to the real world!

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Hey all, I've just published a new blog post on reinforcement learning, covering its basics, applications, and potential uses. Check out my latest article on Neuraforge to learn more! In this series, I'll be sharing insights and discussions on AI and machine learning. Interested in staying updated? Subscribe to my newsletter (link in comments below) for exclusive content and updates on my latest posts. #ReinforcementLearning #AI #MachineLearning

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