In each patient, ablation terminated AF. 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Matei Zaharia este un informatician româno-canadian specializat în big data, sisteme distribuite și cloud computing.El este co-fondator și CTO al Databricks și profesor asistent de informatică la Universitatea Stanford.. Biografie. School of Earth, Energy and Environmental Sciences, Freeman Spogli Institute for International Studies, Institute for Computational and Mathematical Engineering (ICME), Institute for Human-Centered Artificial Intelligence (HAI), Institute for Stem Cell Biology and Regenerative Medicine, Stanford Institute for Economic Policy Research (SIEPR), Stanford Woods Institute for the Environment, Office of VP for University Human Resources, Office of Vice President for Business Affairs and Chief Financial Officer. Matei Zaharia, Stanford University. Matei Zaharia (Assistant Professor) Manage my profile. "A Decade Later, Apache Spark Still Going Strong". Databricks co-founder, Matei Zaharia, Ph.D joined The Data Incubator for the April 2018 installment of our FREE monthly webinar series, Data Science in 30 minutes: Infrastructure for Usable Machine Learning. Before joining Stanford… This is "Matei Zaharia: Democratizing machine learning in the Stanford DAWN project | SDSI Retreat – November 2, 2017" by CyperusMedia.com on Vimeo,… matei cs.stanford.edu /~matei / Zaharia was an undergraduate at the University of Waterloo . He works on computer systems and big data as part of Stanford DAWN. Verified email at cs.stanford.edu - Homepage. The Register, Stanford DAWN Project, Peter Bailis. In much recent work, the retriever is a learned component that uses coarse-grained vector representa-tions of questions and passages. SVM provided superior classification. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Abuzaid, F., Kraft, P., Suri, S., Gan, E., Xu, E., Shenoy, A., Ananthanarayan, A., Sheu, J., Meijer, E., Wu, X., Naughton, J., Bailis, P., Zaharia, M. Machine Learning to Classify Intracardiac Electrical Patterns during Atrial Fibrillation. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. Class Format:You will need to fill out a Google form with answers to a few summary questions before each class starts. Matei Zaharia (Assistant Professor) Manage my profile. Cited by. Homepage: https://cs.stanford.edu/~matei/. I am advised by Matei Zaharia and Phil Levis. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions. During my PhD, I started the Apache Spark project, Support vector machines (SVM) and convolutional neural networks (CNN) were trained to 2 endpoints: (i) sustained VT/VF or (ii) mortality at 3 years. We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. Distributed Systems Machine Learning Databases Security. Matei has 3 jobs listed on their profile. M. Zaharia.Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark, SIGMOD 2018 Industry Track M. Vartak, J. da Trindade, S. Madden and M. Zaharia.MISTIQUE: A System to Matei Zaharia. Accelerating the Machine Learning Lifecycle with MLflow. Papers and proceedings are freely available to everyone once the event begins. Armbrust, M., Das, T., Torres, J., Yavuz, B., Zhu, S., Xin, R., Ghodsi, A., Stoica, I., Zaharia, M., Das, G., Jermaine, C., Bernstein, P., Eldawy, A. MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis. Motherboard, Matei Zaharia is a Romanian-Canadian computer scientist and the creator of Apache Spark. He started the Apache Spark project during his PhD at UC Berkeley in 2009, and has worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. 2 Outline The cloud is eating software, but why? In DAWN, we’re working on infrastructure for usable machine learning to make it dramatically easier to bring ML applications to production: these issues are often much larger obstacles than ML algorithms in practice. April 28, 2015. Class Presentations/Notes Google Folder:If you are assigned to take notes for a class, please take the notes in a Google Doc and add them to this f… 1. Stanford DAWN Project, Peter Bailis. Results - In the separate test cohort (50,000 grids), CNN reproducibly classified AF image grids into those with/without rotational sites with 95.0% accuracy (CI 94.8-95.2%). Contact. MacroBase DIFF. View details for DOI 10.1098/rspa.2013.0828, View details for Web of Science ID 000336184600004, View details for PubMedCentralID PMC4032552. IEEE Data Engineering Bulletin, 41(4), December 2018. Ars Technica, … USENIX is committed to Open Access to the research presented at our events. Stanford DAWN Project, Deepak Narayanan. TechCrunch, Interpreting trained SVM revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium calcium exchanger as predominant phenotypes for VT/VF.CONCLUSIONS: Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Adapted from a template by Andreas Viklund. and we are continuing to develop open source software such as by Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, and Matei Zaharia 17 Nov 2020. More recent projects are available on the Weld and FutureData websites. Matei Zaharia … Managing Data Transfers in Computer Clusters with Orchestra. Rafferty, A. N., Zaharia, M., Griffiths, T. L. A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples. Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. @cs.stanford: Currently teaching. Ghodsi, A., Sekar, V., Zaharia, M., Stoica, I. Prior to joining Stanford, he was an Assistant Professor of Computer Science at MIT. Using a variety of concept learning games, we show that in practice, this method can predict which games will result in better estimates of the parameters of interest. He works on computer systems and big data as part of Stanford DAWN. Naccache, S. N., Federman, S., Veeraraghavan, N., Zaharia, M., Lee, D., Samayoa, E., Bouquet, J., Greninger, A. L., Luk, K., Enge, B., Wadford, D. A., Messenger, S. L., Genrich, G. L., Pellegrino, K., Grard, G., Leroy, E., Schneider, B. S., Fair, J. N., Martinez, M. A., Isa, P., Crump, J. ↑ Woodie, Alex (March 8, 2019). He is also co-founder and Chief Technologist of Databricks, a data and AI platform startup. "Twelve Stanford researchers receive Presidential Early Career Award for Scientists and Engineers". I received both my Bachelor's (2017) and my M.Eng (2018) degrees at MIT, where I researched in the Networks and Mobile Systems group in CSAIL , under Hari Balakrishnan . About Databricks Challenges, solutions and research questions. Matei is an assistant professor at Stanford CS, where he works on computer systems and machine learning as part of Stanford DAWN. We used the Hilbert-transform to produce 175,000 image grids in 35 patients, labeled for rotational activation by experts who showed consistency but with variability (kappa=0.79). Methods - We performed panoramic recording of bi-atrial electrical signals in AF. Fortune, Matei Zaharia Stanford University matei@cs.stanford.edu Abstract Systems for Open-Domain Question Answer-ing (OpenQA) generally depend on a re-triever for finding candidate passages in a large corpus and a reader for extracting an-swers from those passages. This accuracy exceeded that of support vector machines, traditional linear discriminant and k-nearest neighbor statistical analyses. Stanford DAWN Project, Daniel Kang. Rogers, A. J., Selvalingam, A., Alhusseini, M. I., Krummen, D. E., Corrado, C., Abuzaid, F., Baykaner, T., Meyer, C., Clopton, P., Giles, W. R., Bailis, P., Niederer, S. A., Wang, P. J., Rappel, W., Zaharia, M., Narayan, S. M. DIFF: a relational interface for large-scale data explanation. Stanford DAWN Project, Matei Zaharia. Matei Zaharia, Stanford University. Stanford DAWN Project, Deepak Narayanan. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained. Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads Deepak Narayanan†, Keshav Santhanam†, Fiodar Kazhamiaka†, Amar Phanishayee?, Matei Zaharia† Microsoft Research †Stanford University Abstract Specialized accelerators such as GPUs, TPUs, FPGAs, and Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia, Stanford University. infrastructure for usable machine learning. Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks.He started the Apache Spark project during his PhD at UC Berkeley in … Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. Pirk, H., Moll, O., Zaharia, M., Madden, S. Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D. B., Amde, M., Owen, S., Xin, D., Xin, R., Franklin, M. J., Zadeh, R., Zaharia, M., Talwalkar, A. GraphFrames: An Integrated API for Mixing Graph and Relational Queries, Dave, A., Jindal, A., Li, L., Xin, R., Gonzalez, J., Zaharia, M., ACM, FairRide: Near-Optimal, Fair Cache Sharing, Pu, Q., Li, H., Zaharia, M., Ghodsi, A., Stoica, I., USENIX Assoc, Venkataraman, S., Yang, Z., Liu, D., Liang, E., Falaki, H., Meng, X., Xin, R., Ghodsi, A., Franklin, M., Stoica, I., Zaharia, M., ACM SIGMOD, Introduction to Spark 2.0 for Database Researchers, Armbrust, M., Bateman, D., Xin, R., Zaharia, M., ACM SIGMOD, Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. He started the Apache Spark project during his PhD at UC Berkeley in 2009, and has worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. The Economist, and In this blog post, we’ll describe our recent work on benchmarking recent progress on deep … View details for DOI 10.1161/CIRCRESAHA.120.317345, View details for DOI 10.1007/s00778-020-00633-6, View details for Web of Science ID 000574078100002. Alhusseini, M. I., Abuzaid, F., Rogers, A. J., Zaman, J. Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. Open Access Media. [4] While at University of California, Berkeley 's AMPLab in 2009, he created Apache Spark as a faster alternative to … Novel explainability analyses revealed that the CNN operated using a decision logic similar to rules used by experts, even though these rules were not provided in training. Stanford DAWN Lab and Databricks. Machine Learned Cellular Phenotypes Predict Outcome in Ischemic Cardiomyopathy. Stanford DAWN Project that drew submissions from the top industry groups and influenced the industry-standard MLPerf, A., Baykaner, T., Clopton, P., Bailis, P., Zaharia, M., Wang, P. J., Rappel, W., Narayan, S. M. Approximate Selection with Guarantees using Proxies. matei. Your source for engineering research and ideas A CNN was developed and trained on 100,000 AF image grids, validated on 25,000 grids, then tested on a separate 50,000 grids. He is also a co-founder and Chief Technologist of Databricks, the big data company based around Apache Spark. View Matei Zaharia’s profile on LinkedIn, the world’s largest professional community. Databricks live streamed this interview with Matei Zaharia, an assistant professor at Stanford CS and co-founder and Chief Technologist of Databricks, the data and AI platform startup.. During his Ph.D., Matei started the Apache Spark project, which is now one of the most widely used frameworks for distributed data processing. The site facilitates research and collaboration in academic endeavors. Stanford Daily. My work includes software runtimes, quality assurance tools and systems optimizations for ML. Prior to joining Stanford… Matei Zaharia . His research has primarily focused on video analytics and autonomous vehicles, but he's willing to change his mind for food. Zaharia, Matei; Zaharia, Matei Alexandru; usage: Matei Zaharia, Matei Alexandru Zaharia) found: Spark, the definitive guide, 2017: back cover (Matei Zaharia, assistant professor of computer science at Stanford University, chief technologist at Databricks; started the Spark project at UC Berkeley in 2009) Editorial Notes [URIs added to this record for the PCC URI MARC Pilot. VMware is pleased to announce the 2016 recipient of the early career Systems Research Award: Matei Zaharia, Assistant Professor of Computer Science at Stanford University. Stanford DAWN Project, Daniel Kang. Edusalsa enables students to navigate their undergraduate journey at Stanford University, helping students find the classes where they can discover their passions, and equip themselves with new tools on their path of intellectual discovery, infusing life and vitality into the Stanford experience. ZDNet, Before joining Stanford, I was an assistant professor at MIT. Google Scholar | Articles Cited by. About Databricks Challenges, solutions and research questions. Outline Replication strategies Partitioning strategies Atomic commitment & 2PC CAP Avoiding coordination Parallel query execution CS 245 2 . M. Zaharia, A. Chen, A. Davidson, A. Ghodsi, S.A. Hong, A. Konwinski, S. Murching, T. Nykodym, P. Ogilvie, M. Parkhe, F. Xie, and C. Zumar. Using ML Prediction APIs more Accurately and Economically, Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation, Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle, ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT, Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads, Spectral Lower Bounds on the I/O Complexity of Computation Graphs, Selection via Proxy: Efficient Data Selection for Deep Learning, Fleet: A Framework for Massively Parallel Streaming on FPGAs, Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference, Model Assertions for Monitoring and Improving ML Models, Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc, Optimizing Data-Intensive Computations in Existing Libraries with Split Annotations, TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions, PipeDream: Generalized Pipeline Parallelism for DNN Training, Outsourcing Everyday Jobs to Thousands of Cloud Functions with gg, Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark, From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers, LIT: Learned Intermediate Representation Training for Model Compression, Debugging Machine Learning via Model Assertions, To Index or Not to Index: Optimizing Exact Maximum Inner Product Search, Beyond Data and Model Parallelism for Deep Neural Networks, Optimizing DNN Computation with Relaxed Graph Substitutions, Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine, Accelerating the Machine Learning Lifecycle with MLflow, Model Assertions for Debugging Machine Learning, Analysis of the Time-To-Accuracy Metric and Entries in the DAWNBench Deep Learning Benchmark, Accelerating Deep Learning Workloads through Efficient Multi-Model Execution, Exploring the Use of Learning Algorithms for Efficient Performance Profiling, Block-wise Intermediate Representation Training for Model Compression, Filter Before You Parse: Faster Analytics on Raw Data with Sparser, Evaluating End-to-End Optimization for Data Analytics Applications in Weld, MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis, Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark, Accelerating Model Search with Model Batching, BlazeIt: An Optimizing Query Engine for Video at Scale, DAWNBench: An End-to-End Deep Learning Benchmark and Competition, Stadium: A Distributed Metadata-Private Messaging System, NoScope: Optimizing Neural Network Queries over Video at Scale, Splinter: Practical Private Queries on Public Data, Weld: A Common Runtime for High Performance Data Analytics, Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale, Apache Spark: A Unified Engine for Big Data Processing, Voodoo – A Vector Algebra for Portable Database Performance on Modern Hardware, Matrix Computations and Optimizations in Apache Spark, GraphFrames: An Integrated API for Mixing Graph and Relational Queries, ModelDB: A System for Machine Learning Model Management, FairRide: Near-Optimal, Fair Cache Sharing, Vuvuzela: Scalable Private Messaging Resistant to Traffic Analysis, Scaling Spark in the Real World: Performance and Usability, Spark SQL: Relational Data Processing in Spark, Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks, A Cloud-Compatible Bioinformatics Pipeline for Ultrarapid Pathogen Identification from Next-Generation Sequencing of Clinical Samples, An Architecture for Fast and General Data Processing on Large Clusters, Discretized Streams: Fault-Tolerant Streaming Computation at Scale, Sparrow: Distributed, Low-Latency Scheduling, Choosy: Max-Min Fair Sharing for Datacenter Jobs with Constraints, Multi-Resource Fair Queueing for Packet Processing, Fast and Interactive Analytics over Hadoop Data with Spark, Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters, Cloud Terminal: Secure Access to Sensitive Applications from Untrusted Systems, Shark: Fast Data Analysis Using Coarse-grained Distributed Memory, Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Presidential Early Career Award for Scientists and Engineers (PECASE), 2019, U. Waterloo Faculty of Mathematics Young Alumni Achievement Medal, 2014, David J. Sakrison Prize for Research, UC Berkeley, 2013, Best Paper Awards at SIGCOMM 2012 and NSDI 2012. Matei Zaharia's 87 research works with 26,621 citations and 21,968 reads, including: DIFF: a relational interface for large-scale data explanation Could be applied to other conditions M., Zaharia, M. I., Abuzaid,,... M., Stoica, I the ubiquity of machine learning is driving exciting changes progress... Runtimes, quality assurance tools and systems optimizations for ML linear discriminant k-nearest! Philip Levis, and agreed with expert evaluation Raghavan, Sadjad Fouladi, matei zaharia stanford,! How people build and deploy systems and big data company based around Apache Spark to the research presented at events! 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