John Zedlewski (@zstats) profile picture

Accelerating data science and access @NVIDIA Formerly RAPIDS, DL for self driving cars, medical imaging, health data, econometrics, and kernel hacking.

Berkeley
Joined May 2011
Photo by John Zedlewski (@zstats)Photo by John Zedlewski (@zstats)Photo by John Zedlewski (@zstats)Photo by John Zedlewski (@zstats)
John Zedlewski retweeted
I once presented some compiler tech to Ian Buck. He raised a deep technical question that had nagged me but no one else had asked My 1st VP Chris Lamb sold me on NVIDIA by whiteboarding what became our whole C++ strategy. I met my 2nd VP, Tim Costa, at a C++ committee meeting
I have a particularly nasty case of cancer. When I was hospitalized again at my lowest point last year, I told Jensen. He immediately reached out to contacts at Stanford, who were able to cut through red tape, and get me into a clinical trial for which I've turned out to be a perfect fit. I think everyone can have their own views about recent events. But any claims that Jensen did not really care about his staff is simply inaccurate. I, personally, owe him more than I can express. I think the best tribute we can pay to Jensen's legacy is to drive NVIDIA's continued success and push the whole industry forward, by showing we've learned the lessons from years of working here. We will compare ourselves against Speed of Light. The mission will be the boss. And we will not be scared to take smart, bold risks and fail fast with ruthless intellectual honesty. But if there is one management story I remember the most, it's a particular Jensen grilling from my time in autonomous vehicles that stayed with me. Luckily I was not in the hot seat for this one. Team A didn't want to use Team B's framework, because they thought it was too slow. Jensen was surprised - it's just applying a bunch of optimized CUDA kernels in sequence, how could it be slow? So he asked the executive from team B to draw the entire data flow sequence from their framework on the whiteboard and highlight every memcpy. Sure enough, there were a bunch of unnecessary memcpy's surrounding each pipeline stage. Everyone in the room could see why it wasn't getting to SoL and a path to improve things. No arguing about fiefdoms or appeals to authority - the data got us all on the same page. The lesson was not (just) that unnecessary memory copying is bad. It's that executives should not be afraid to probe into details if something doesn't add up, and leaders need to be technical enough and honest enough to engage in real time and not turn every meeting into "I'll get back to you in a week." If that means counting memcpy's with a billionaire CEO, so be it. My best executive meetings at NVIDIA have been when I'm fully prepared to present every detail of my team's work, and my worst failures have come when I realized I couldn't go deep enough to really solve the problem. But the thing that gives me confidence is that Jensen was never the only one to live this philosophy. Anyone who has presented to an SVP at NVIDIA knows they are at risk for getting grilled on fundamental constraints and technical details. I have been part of amazing conversations with both recent and old timer leaders where we were able to get to the core of a problem in an hour rather than a month, because we had the right people in the room and were not afraid to go deeper. The company is filled with supremely talented people who have been trained for 3 decades to work at the highest level, and you can see it in what we produce every day. I'm so proud to be an NVIDIAn, and I can't wait to show off what we ship in the coming months and years.
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I have a particularly nasty case of cancer. When I was hospitalized again at my lowest point last year, I told Jensen. He immediately reached out to contacts at Stanford, who were able to cut through red tape, and get me into a clinical trial for which I've turned out to be a perfect fit. I think everyone can have their own views about recent events. But any claims that Jensen did not really care about his staff is simply inaccurate. I, personally, owe him more than I can express. I think the best tribute we can pay to Jensen's legacy is to drive NVIDIA's continued success and push the whole industry forward, by showing we've learned the lessons from years of working here. We will compare ourselves against Speed of Light. The mission will be the boss. And we will not be scared to take smart, bold risks and fail fast with ruthless intellectual honesty. But if there is one management story I remember the most, it's a particular Jensen grilling from my time in autonomous vehicles that stayed with me. Luckily I was not in the hot seat for this one. Team A didn't want to use Team B's framework, because they thought it was too slow. Jensen was surprised - it's just applying a bunch of optimized CUDA kernels in sequence, how could it be slow? So he asked the executive from team B to draw the entire data flow sequence from their framework on the whiteboard and highlight every memcpy. Sure enough, there were a bunch of unnecessary memcpy's surrounding each pipeline stage. Everyone in the room could see why it wasn't getting to SoL and a path to improve things. No arguing about fiefdoms or appeals to authority - the data got us all on the same page. The lesson was not (just) that unnecessary memory copying is bad. It's that executives should not be afraid to probe into details if something doesn't add up, and leaders need to be technical enough and honest enough to engage in real time and not turn every meeting into "I'll get back to you in a week." If that means counting memcpy's with a billionaire CEO, so be it. My best executive meetings at NVIDIA have been when I'm fully prepared to present every detail of my team's work, and my worst failures have come when I realized I couldn't go deep enough to really solve the problem. But the thing that gives me confidence is that Jensen was never the only one to live this philosophy. Anyone who has presented to an SVP at NVIDIA knows they are at risk for getting grilled on fundamental constraints and technical details. I have been part of amazing conversations with both recent and old timer leaders where we were able to get to the core of a problem in an hour rather than a month, because we had the right people in the room and were not afraid to go deeper. The company is filled with supremely talented people who have been trained for 3 decades to work at the highest level, and you can see it in what we produce every day. I'm so proud to be an NVIDIAn, and I can't wait to show off what we ship in the coming months and years.
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John Zedlewski retweeted
Today, I'm excited to share that the full HeavyAI platform is now open source under the Apache 2.0 license: our GPU-accelerated database, HeavyDB, along with our rendering engine and visualization frontend. github.com/heavyai/heavydb Nearly 15 years ago, I started building what became HeavyAI to answer questions beyond the reach of existing tools. Working with Tarek Masoud at Harvard Kennedy School, and collaborating closely with @HarvardCGA, we tried to analyze hundreds of millions of geolocated tweets to better understand the Arab Spring in Egypt. I'd find that my queries on CPU would take hours or days, only in many cases to run out of memory, and so using the Nvidia 560 Ti under my desk, I taught myself CUDA and started to explore GPU-acceleration as a way allow me to explore the data interactively and at scale. I was fortunate to continue that work with @SamuelMadden in @MIT_CSAIL's database lab. It soon became clear that many others shared the same frustration: they had the data, but couldn't interactively explore it. That led me to found MapD (Massively Parallel Database) in 2013. The name changed over the years, first to OmniSci and then HeavyAI, but the goal stayed the same: help people get answers from their data at the "speed of curiosity." We built a SQL engine that ran on NVIDIA GPUs and CPUs, powered by an LLVM-based query compiler, and a rendering engine that made massive datasets explorable through interactive visualization, particularly for geospatial analytics. None of this would have happened without the team who built it, the customers who pushed us, and the partners and investors who believed in us. Special thanks goes to @JensenHuang and the @nvidia team, who saw the potential of accelerated data processing early on, and to @NEA, Tiger Global, @VanedgeCapital, @GVteam, @VZVentures, @InQTeI, and @DcodeCapital. And to our customers across telco, financial services, energy, utilities, and the public sector: thank you for trusting us with your hardest data problems. Now that the HeavyAI team and I are at NVIDIA, we're more convinced than ever that accelerated computing will play a central role in the future of data processing. We're working with partners across the ecosystem to help make that happen. We're already seeing GPU acceleration become part of mainstream analytics platforms alongside their existing CPU engines. That said, I still think specialized platforms like HeavyAI have an important role to play, for example where interactive visualization and geospatial analytics need to work together at scale. Try the platform, explore the code, and tell us what you build with it. I started this project to answer questions I couldn't easily explore with existing tools. With the full platform now open source, I'm excited to see what people build with it.
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John Zedlewski retweeted
Our general-purpose coding agent just scored 100% on the ARC-AGI-3 interactive reasoning benchmark. NVIDIA AVO completed all 183 levels across all 25 public environments, figuring out what to do with no instructions, explicit rules, or stated goals.
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Your AI is bottlenecked by your data. Our Dell AI Data Platform: 12x faster vector indexing, 3x faster processing with Lightning FS (the fastest parallel file system in the world), feeding GPUs at 150 GB/s per rack. 🚀 dell.com/en-us/blog/ai-at-sc…
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John Zedlewski retweeted
Nvidia just crossed Google as the biggest org on @huggingface with 3,881 team members on the hub. I'm officially calling it: Nvidia is the new American king of open-source AI!
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John Zedlewski retweeted
Quoting Jensen: "All of these platforms are processing DataFrames. This is the ground truth of business. This is the ground truth of enterprise computing. Now we will have AI use structured data. And we are going to accelerate the living daylights out of it." Polars DataFrames are at the core of the AI revolution. youtube.com/watch?v=jw_o0xr8…
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John Zedlewski retweeted
Truly awesome to see Jensen in his GTC keynote present @nvidia's joint partnership with @IBM to use cuDF to accelerate open source Velox and the Presto SQL engine in watsonx, showcasing how @Nestle achieves 5X faster queries for 83% less cost. Amazing collaboration between the Nvidia and IBM teams to make this a reality, and we're just getting started.
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NVIDIA won Deep Research Bench with NVIDIA AI Q and Nemotron 3 models. A blog is under way.
Very proud that a cross NVIDIA effort led to top positions in Deep Research Bench I and II. DRB-I evaluates overall report quality: comprehensiveness, insight, instruction-following, and readability whereas DRB-II is more difficult and tests for information recall, analysis, and presentation. This was led by David Austin and @raja_biswas from my team. I contributed a bit. We will share more ASAP. huggingface.co/spaces/muset-… agentresearchlab.com/benchma…
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Very proud to share that @JiweiLiu, @MJeblick , and @jackyu815 from my team just won DABStep benchmark with an agent that learns from the tasks it solves. We'll share more details ASAP. huggingface.co/spaces/adyen/…
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I love this view, with us (NVARC, with Ivan Sorokin) being on the right efficiency!
Replying to @arcprize
We also verified that GPT-5.2 Pro (High) is SOTA for ARC-AGI-2, scoring 54.2% for $15.72/task (Due to API timeouts, we were unable to reliably verify GPT 5.2 Pro X-High on ARC-AGI-2) All verified GPT-5.2 family scores: arcprize.org/leaderboard
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NVIDIA #CUDA-X libraries are accelerating scientific breakthroughs with the power of #AI. 🧬 NVIDIA cuEquivariance takes protein structure analysis from months to minutes, and RAPIDS-singlecell lets researchers process massive volumes of genomics data in seconds. 🔋 NVIDIA ALCHEMI rapidly accelerates battery and OLED discoveries, turning years of lab work into days. 🌎 NVIDIA Earth-2 and NVIDIA PhysicsNeMo enable realistic weather forecasts in seconds, helping scientists predict disasters with AI-driven precision. Watch here to learn more 👇 or on YouTube ➡️ youtube.com/watch?v=vNhbqYts…
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🎊 Congrats to our NVIDIA cuOpt team who just won the 2025 COIN-OR Cup 🏆, awarded for top #opensource achievements in decision optimization and operations research. cuOpt runs LP, MIP, and VRP workloads on NVIDIA GPUs, delivering fast, scalable results with a robust open codebase. Get started 🖥️ github.com/NVIDIA/cuopt
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John Zedlewski retweeted
HUGE > we have a new #1 on the > MTEB Embedding Benchmark Leaderbord llama-embed-nemotron-8b > beats Gemini, Qwen3, Linq, GTE > fits on ONE RTX 5090 > 4096 dims, 32k ctx > 69.46 avg across tasks > multilingual bi-encoder, zero-shot across retrieval, rerank, STS, classification
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John Zedlewski retweeted
🔥 Join us at #SciPy2025 for our tutorial, "Scaling Clustering for Big Data: Leveraging RAPIDS cuML" with Allison Ding! 🚀 Come learn to implement, optimize, and scale clustering algorithms effectively unlocking faster, deeper insights in your machine learning workflows.
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⚡cuML accelerations for ML workflows for scikit-learn, UMAP and HDBSCAN -- with zero code changes required. cuML is up to 60x faster for UMAP and up to 175x for HDBSCAN over traditional CPU-based implementations. Read the intro to GPU-accelerated UMAP and HDBSCAN workflows: odsc.medium.com/accelerating…
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🔢 Data > ? Memory 🐏 👀 3 solutions 📹 youtu.be/nyXbTVTKVDM
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🎉 Huge congrats to our NVIDIA team “NemoSkills” for winning the AIMO-2 Competition 🏆 on @Kaggle. Their system solved 34 out of 50 problems in just 5 hours using 4 L4 GPUs. 🔢✨⏱️ kaggle.com/competitions/ai-m… How? A powerhouse squad—Christof Henkel, Darragh Hanley, Ivan Sorokin, Benedikt Schifferer, Igor Gitman, Shubham Toshniwal, and Ivan Moshkov—combined expertise in LLM training, data generation, and KGMON insights. Their final test set performance even beat earlier public results, proving they don’t overfit.👏 More details on the final solution are coming soon.💡 @kagglingdieter, @gonedarragh, @igtmn, @shubhamtoshniw6, and @i_vainn
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