Andrew Wang

A data-driven approach to driving wound healing outcomes Marcella Gomez, University of California, Santa Cruz https://lnkd.in/gAkWZYJT Meet: https://lnkd.in/g4AAWkcK YouTube Stream: https://lnkd.in/gwtqaTmf Talk details: https://lnkd.in/gWn3B3cU Subscribe for talk announcements: https://lnkd.in/g5ciuNuX Abstract: Precision medicine requires an ability to predict the response of an individual to a prescribed treatment regimen a priori. Thus, advancement in the field is challenged by a lack of predictive models and, arguably, a lack of time-series information for a highly dynamic system. Here, we discuss work in wound healing, for accelerating wound closure. We argue that timing of treatments is as critical to consider as the choice of drug or therapy. Due to system size and complexity, data-driven methods need to be explored to develop multi-dimensional quantifiable indicators tracking systemic changes. In this work I discuss how bioelectronic devices enhanced with deep learning can help facilitate real-time sensing and actuation for automated decisions in treatment for wound healing and preliminary work in transcriptomic based classification of wound states. Bio: Marcella M. Gomez is an associate professor at UC Santa Cruz in the department of Applied Mathematics and Associate Dean for Diversity, Equity, and Inclusion for Baskin Engineering. She received her PhD from Caltech in 2015 and a B.S. from UC Berkeley in 2009; both degrees in Mechanical Engineering. Her research interests are in the broad field of bio-control leveraging methods in machine learning and control theory. Applications range from controlling single-cell response to driving complex systems such as wound healing. #modeling #simulation #medicine #healing #trauma #predictivemodeling #datascience #data #woundhealing #biology #bioengineering #systemanalysis #sensing #omics

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Recently, there has been much discussion about Nvidia's CEO's approach of not having 1:1 meetings while leading 60 direct reports, emphasizing the benefits of a flat organizational structure and portraying 1:1s as unnecessary. Like with many trends, the focus has been primarily on these two aspects. Leaders have attempted to replicate this model by increasing the number of direct reports per manager or even eliminating 1:1s altogether. It's refreshing to learn about another side of the story that many are trying to emulate, one where valuing employees and placing them at the center of the organization is crucial for the company's success.

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When the Lights Go Out, the Learning Continues As the Puget Sound area faces power outages, I’ve taken the downtime as an opportunity to reflect and document my journey of learning system architecture. It’s a reminder of the importance of resilience—whether in systems or life. Here’s an article documenting key lessons and strategies I’ve gathered along the way. Welcome to any feedback 😀 #software engineer

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NVIDIA's CEO, Jensen Huang, recently praised Elon Musk, calling him "superhuman" for his incredible work on a powerful new computer at his company, xAI. Musk’s team built this supercomputer, named "Colossus," in just 19 days — a project that usually takes up to four years to complete. Huang admired Musk's strong engineering skills and his talent for bringing the right people and resources together to get things done quickly. This computer, Colossus, will help xAI dive deeper into advanced AI projects, making it possible to work with complex models and learn even more from data. The project is a big step forward for xAI and highlights the strong partnership between Musk’s team and NVIDIA, showing just how much Musk’s fast-paced style is respected by leaders in technology. #ElonMusk #AI #NVIDIA #xAI

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LLMs and LangChain: The Dynamic Duo 🦸‍♀️🤖 Just finished a fun project where we built a Q&A bot using these two powerhouses. It's like having a personal AI assistant that's always ready to answer your burning questions! 🔥 Here's what I learned: LLMs are wizards: They can conjure up answers from thin air (or, uh, the internet). 🧙‍♂️ LangChain is the glue: It holds everything together and makes it easy to use. 🔗 Endless possibilities: This tech can be used for anything from customer support to writing catchy social media posts. 📝 Want to see it live (obvio a test server link 😛 ): https://lnkd.in/dZNAVCSV Note: it's a render link, they will take down the instance because of no use, you can hit the link and wait for two minutes, they will spin up the instance and you can try it out. Want to see the code? Check it out here: https://lnkd.in/dzjh4r47 #AI #MachineLearning #NLP #LangChain #LLM #Python #Developer #FunProject"

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This is a question many Data Engineers asked me: How are systems like Apache Druid, Apache Pinot, Clickhouse, etc... different from RisingWave? Aren't they all "real-time" and can't they do "streaming"? There are indeed fundamental differences between these systems and RisingWave. For OLAP Databases, their computation model is still executing in batch. They are "real-time" in a sense that their adhoc queries can execute really quickly. And they can do "streaming" in terms of ingestion, they can quickly stream in records, and run batch computation on them. For RisingWave, it's differentiated, because it does incremental rather than batch computation instead. So we can achieve lower latencies for streaming queries, and spend less compute to get the same results. That being said, streaming systems do not entirely replace OLAP/OLTP systems as well, there are many use cases where both can complement each nicely. Catch the talk to understand more about the fundamental differences, and how these systems can work cohesively. #risingwave #olap #streaming #database #realtime

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Want a preview of what I'm going to be talking about in my keynote for RubyConf? You can catch a glimpse of it here!

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Distinguished Engineers (E9) at Meta make $4m/year. That's 16x the Bay Area median ($255k) Let's break it down [output = input x leverage] We all have 24 hours in a day, so there is a cap on [input] But hey, let's say they manage to have 2x-3x more focused time, so their [input] is 2x-3x There is still 5.3x-8x to go And that's [leverage] Here is the [leverage] they have: 1️⃣ Writing Every piece of text they write is clear to everyone, and they write a lot, for a lot of people 2️⃣ They create something from nothing And that something has clear value to the business 3️⃣ They influence other Engineers around them They are often described as having a "Midas Touch" in terms of their ability to uplift and develop other Engineers around them The best indicator is that the Engineers in their teams get promoted faster 4️⃣ They are well known and have an impact beyond Meta, promoting the brand 5️⃣ They have industry-leading experience in some areas There are rumoured to be <50 E9s at Meta

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Guess what? My little project has finally hatched and is ready to take on the world! 🐣 Azure AI Search is now integrated with Mem0, an intelligent memory layer designed to enhance AI assistants and agents. Using LLM and embedding models, it remembers user preferences, adapts to individual needs, and continuously improves over time. Intelligent memory is rapidly gaining adaption, and the growth of Mem0 on GitHub is a testament to this trend. The project has seen exponential growth, now boasting over 20k⭐! 💥 Mem0 in action: Imagine you have an AI assistant integrated with Mem0. During a conversation, you mention that you love Italian food and prefer warm climates. Mem0 processes this information and stores it as memories like “Enjoys Italian cuisine” and “Prefers warm climates.” Later, when you ask your AI assistant for restaurant recommendations, it recalls these memories and suggests Italian restaurants in warm locations. This personalized response enhances your experience by making the interaction more relevant and tailored to your preferences. Now, suppose your preferences change, and you mention that you now prefer Japanese cuisine and cooler climates. Mem0 automatically finds the related memories and updates them to reflect these new preferences. The memory “Enjoys Italian cuisine” is updated to “Enjoys Japanese cuisine,” and “Prefers warm climates” is updated to “Prefers cooler climates.” Next time you ask for restaurant recommendations, your AI assistant will suggest Japanese restaurants in cooler locations, demonstrating how Mem0 adapts to your changing preferences and continues to provide personalized, relevant responses3. 💫 Creating more intelligent, responsive, and personalized AI systems is now available using Mem0 and Azure AI Search. I’m excited to see the impact it will have on various applications and industries including education, gaming, healthcare and more! 🎇 Big thanks to Sidney Phoon, my GBB colleague, for her support and contribution of adding vector scaling/compression feature. This feature will improve the efficiency and reduce the cost of building AI applications! https://lnkd.in/gp2hqgev https://lnkd.in/gpV-Mhcf https://lnkd.in/gZg86Wbp #AI #Azure #Mem0 #ArtificialIntelligence #MachineLearning #TechInnovation #Personalization #CustomerExperience

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In this world of ongoing layoffs, companies going against the grain to build highly customized in-house tech stacks that don't track to the skills and expertise engineers need for their past and future jobs will find themselves becoming a black hole devoid of talent. The ones who are good enough to leave will, and the ones who aren't will feel stuck because their work isn't advancing their skills in a way that adds to their value as an employee.

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With the upcoming holidays you might be looking for a book recommendation. Think about checking out The Nvidia Way by Tae Kim, I read through it during this las rainy Saturday, so let me reflect a bit. The book starts detailing the origin story of Nvidia and how the three co-founders got together. What is fascinating is that they faced big challenges from the start and had a clear divide in expertise, while of course having overlap as well. Next it dives into the first success and subsequent struggles that emerged by getting to siloed and isolated from their customers. And finally into the CUDA and now AI era. Through out the book three things stand out: 1️⃣ Jen-Hsun Huang, commonly referred to as Jensen Huang, is central to Nvidia and keeps his fingers on the companies pulse. 2️⃣ The organization as a whole is very flat and highly focused on merit. 3️⃣ This quote from a Nvidia competitor have they have joined it : "Oh my God, we got here and we thought there was going to be a secret sauce. It turns out it's just really hard work and intense execution on schedules" - meaning people and culture. To keep the pulse of Nvidia Jensen Huang receives hundreds of E-mails from employees across the company with 5 bullet points, that contain what they are working on, what they found interesting, etc. Of these he reads 100 a day to keep track of what is going on. With 60 direct reports as the CEO, that is a number you will not find in other big companies. The book is a real page turner and with just 245 of them an easy read. If you are interested in AI, then I think it is worth it to pick this one up just to get a glimpse at arguably the company shaping the foundation of the current AI boom.

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A new development environment is ostensibly a behavior change. We've thought long and hard about what an AI-native workflow should be, and the dev environment to house it. GitHub Copilot Workspace is our take, where AI is a thought partner supporting developers from the beginning of a task -- where developers tell us they struggle most -- all the way to submitting a pull request. Task -> spec -> plan -> code, is a good candidate for a human-AI collaboration flow because it mirrors how developers organically work on complex tasks, and where they embrace more AI support that is steerable. Now, I can't wait to learn from how developers use Copilot Workspace, and to understand the impact they experience.

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Xian Ke, this is incredible! The speed at which you and Vishal Punjabi leveraged crewAI, Streamlit, and various LLM models to create Finbot, a powerful company analysis tool for investors, was mind-blowing. ICYMI, Finbot is here: https://lnkd.in/eSQD69qA I couldn't help but chuckle when I saw the Finbot name - it reminded me of the Wall Street Bets crowd. Maybe you can get them to yolo their life savings based on Finbot's analysis? (Kidding, of course. Or am I? 💎🙌) But in all seriousness, your enthusiasm and passion for AI and LLMs is contagious. It's engineers like you who are pushing the boundaries of what's possible with AI and paving the way for a brighter future.🏆 I'd love to extend an invitation to any other software engineers from the bootcamp to join our Devs Do AI Discord server. It's a great place for engineers without an ML / AI background to connect with like-minded devs, share knowledge, and collaborate on LLM projects. DM me for the invite link. 🤝

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Principal Engineer Staff Engineer Distinguished Engineer (my favourite) I’m going to say it and it’s likely a little rhetorical: I dislike all these terms immensely. Please can someone give me the canonical description for what they are supposed to mean? (I know I can use a search engine or AI to do it… I want to know what people think they mean). The industry I work in is in danger of disappearing up its own backside. What’s wrong with: Software engineer Why do we need to differentiate? The only reason I can see is signposting to people with differing levels of experience or different sets of responsibilities. If we need to do that then, I’m not sure our teams are set up correctly in the first place. #software #softwareengineering #softwaredevelopment #productdevelopment

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While there are certainly individual variations in skill and experience when discussing modern software development the story is much more nuanced than pure individual effort.  * software development is a team sport -- individual performance is heavily impacted by the system in which they are asked to operate in.  * we pair skill and experience to this model of understanding performance by expecting people to be able to influence and adapt in direct proportion to their seniority, aka the more senior you are the more responsible you are for the outcomes of your work, the less senior you are the more your manager and management chain are responsible.  * humans want to succeed, we're social animals who derive purpose from demonstrating skill, and working collectively. All of which is to say there is no way you have over 1000 people underperforming at your company without senior leadership deeply implicated in.  If nearly 10% of your company is failing, versus just having the market move against you, you need to fire the CEO.

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