Angel A.

Last night, I had the opportunity to return to UCLA to speak in the Philosophy Department’s Being in the World series. It was a full-circle moment. These were the same halls where I first began studying computer science and imagining a career pivot into engineering. We discussed career paths, job markets, AI’s impact on education, and how students can break into tech. But one question stood out: How do you think an undergraduate degree in philosophy has benefitted you both generally and in your field? I’ve thought about that question for years. Generally, philosophy has given me a disciplined way of orienting myself in the world. We are living through profound shifts:  artificial intelligence, ecological destabilization, geopolitical realignment, social fragmentation. Philosophy trains you to examine systems in their totality rather than reducing them to a single domain like economics, politics, or engineering. It provides a scaffolding for moving between disciplines without losing coherence - a space of expansive thought that transcends any particular topic. In my field, philosophy has been equally practical. Software engineering is not fundamentally about writing code. It is about navigating ambiguity, clarifying assumptions, defining terms, and reconciling human intention with technical constraint. At higher levels, engineering becomes the design of logical systems in response to shifting organizational and cultural pressures. The tension between vision and implementation mirrors much older philosophical tensions:  perception and reality, reason and passion, abstraction and instantiation. The questions are ancient. The tooling is new. It has been years since I’ve written on Descartes or Russell, but I remain a philosopher in spirit. Grateful to UCLA Department of Philosophy for the invitation, and especially grateful to the students asking serious questions about their futures.

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🚀 🚀 How I helped build a rocket as a software engineer 🚀 🚀 Full Article: https://lnkd.in/gFavf3eY Last year, Shawn Wei Chew and I had the amazing opportunity to serve as software leads for our school’s rocket team, Rocket Project at UCLA. We wrote code for a hybrid propellant rocket that reached an apogee of 19,200 ft, making it the most successful flight in our team’s history. Coming from a traditional SWE background, we didn’t know what to expect, and from our first outing messing with microcontrollers, multi-threading & communication protocols - we’ve learnt tons along the way So we wrote this article hoping that it would be helpful for prospective club applicants, future batches, or at the very least, interesting to the average reader. Enjoy! Acknowledgements - this article (and rocket) would not have been possible without: • The larger Rocket Project organization, who helped build every single other part of the rocket from scratch that was not code — to me, software is tremendously easier than engineering so I don’t know how they do it LOL • Shawn, my fellow co-software lead and one of my best friends, who helped review and proofread this article. • Angela & Yifan, our amazing electronic leads who helped read this article and put out countless of fires when Shawn and I couldn’t • Our two other software leads who helped build this software system with Shawn and I • My wonderful friends who I roped in to build the web app with us • The electronics team, who were all extremely fun to be around and helped us figure out the hardware side of things so I could just write code

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🎓 AI is reshaping learning at San Jose State, from smarter classrooms to city-wide digital twins driving real-world innovation. Today’s episode of the NVIDIA AI Podcast features Dr. Cynthia Teniente Matson, 31st President of San Jose State University. She discusses: 🧠 How AI is embedded across every discipline, from engineering to social sciences 💡 Digital twins and AI avatars transforming learning and civic engagement 🌎 Preparing students for a rapidly evolving, AI-driven workforce 🎧 Listen to the full episode: https://bit.ly/3Kwt9To

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The life of a STEM PhD student can seem like a mystery to undergraduate students. Last week, I had the opportunity to present a clarifying talk to the UC Irvine chapter of IEEE Eta Kappa Nu, Electrical and Computer Engineering honors society. I set out to answer three questions: 1. What is it like to get a PhD? 2. Why should you do a PhD? 3. How to get the most out of your PhD?     (Things I wish I knew when I started) Hopefully, I've convinced you that there are lots of great reasons to get a PhD in STEM, even if you could already get a competitive engineering job right out of college! Thanks to everyone at IEEE-Eta Kappa Nu Zeta Omega, IEEE-Eta Kappa Nu, and especially to student officers Gavin Nguyen and Rohit De for hosting the talk last week!

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🎓 #AI is reshaping learning at San José State University, from smarter classrooms to city-wide digital twins driving real-world innovation. Today’s episode of the NVIDIA AI Podcast features Dr. Cynthia Teniente Matson, 31st President of San Jose State University. She discusses: 🧠 How AI is embedded across every discipline, from engineering to social sciences 💡 Digital twins and AI avatars transforming learning and civic engagement 🌎 Preparing students for a rapidly evolving, AI-driven workforce 🎧 Listen to the full episode: https://bit.ly/4mwx9kh #DigitalTwin #Education

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$100k can be low income. Even for a single-person household. Of course this isn’t referring to a small town in the countryside. According to the California Department of Housing and Community Development’s 2025 income levels (linked below): In Marin, San Francisco, and San Mateo counties <$109700 is low income for single-person households. In Santa Cruz county <$111100. And in Santa Clara county <$111700. It’s pretty well known that Silicon Valley and surrounding areas are fairly expensive. And for many people the salaries in tech make up for and reflect that. Still, when the median income in Santa Cruz county (e.g. $92950 for a single-person household) is lower than the low income threshold… It’s pretty astonishing to think about. Accounting for income, is the SF bay area the most costly city in the world for the average resident?

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Recently, while working with a capstone project team of UCI students, I was reminded of an important point about industrial AI. Industrial AI today is often discussed through the lens of large language models such as ChatGPT, Gemini, Claude, or LLaMA. These technologies are impressive and have transformed how we interact with information. But in many industrial environments, real progress in AI depends on something different: 𝐝𝐨𝐦𝐚𝐢𝐧-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. Industrial systems are fundamentally different from the text and images that general-purpose models are trained on. Engineering data appears in forms such as CAD geometry, sensor signals, time-series data, manufacturing processes, and physical simulations. These systems are governed by physics, constraints, and complex interactions between components. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬 𝐦𝐨𝐝𝐞𝐥𝐬 𝐝𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐨𝐟 𝐭𝐡𝐚𝐭 𝐝𝐚𝐭𝐚. For example, learning from CAD models requires geometric and topological reasoning. Predicting equipment behavior often relies on time-series analysis of sensor data. Manufacturing optimization may require graph-based representations of processes and assemblies. In these contexts, domain knowledge is not optional, it is essential. What we are beginning to see is a new layer of AI emerging in industry: models trained specifically on engineering and operational data. These models combine machine learning with physics, system constraints, and domain expertise. Rather than replacing engineering tools, they augment them and helping engineers analyze complex systems, detect patterns across large datasets, and explore design spaces more effectively. Large language models will certainly play a role, particularly as interfaces that help orchestrate workflows or make complex tools easier to access. But the real breakthroughs in industrial AI will likely come from deep integration between machine learning and domain-specific knowledge. In other words, the future of industrial AI will not be built by generic models alone. It will be built by 𝐜𝐨𝐦𝐛𝐢𝐧𝐢𝐧𝐠 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐝𝐞𝐞𝐩 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐨𝐟 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐰𝐢𝐭𝐡𝐢𝐧 𝐞𝐚𝐜𝐡 𝐝𝐨𝐦𝐚𝐢𝐧. #ArtificialIntelligence #IndustrialAI #MachineLearning #DomainSpecificAI #EngineeringAI #DigitalTwin #Manufacturing #CAD #Industry40 #Engineering

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Based on the provided articles, here are 3 promising AI startups with recent funding news: 1. Flint - Raised $5 million in seed funding (October 14, 2025) - Led by Accel with participation from Sheryl Sandberg's fund and Neo - Building an AI platform that autonomously builds and updates websites - Source: https://lnkd.in/dYMYVyWu 2. Leonardo.ai - Acquired by Canva (announced July 29, 2024) - Had previously raised over $38.8 million from investors including Smash Capital, Blackbird, Side Stage Ventures and others - Specializes in generative AI content creation with over 19 million registered users - Source: https://lnkd.in/d3J5DAMA 3. Coco Robotics - While specific funding isn't mentioned, the company announced launching a new physical AI research lab (October 14, 2025) - Partnering with OpenAI and led by UCLA professor Bolei Zhou - Focuses on autonomous last-mile delivery robots - Source: https://lnkd.in/dUbYZDCf

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