MacroBase DIFF. Home; Explore; Journeys; Feedback; Login; Edusalsa Discover Your Stanford . Cody Coleman, Trevor Gale, Peter Kraft, Deepak Narayanan, Deepti Raghavan. … Interests: I’m interested in computer systems for emerging large-scale workloads such as machine learning, big data analytics and cloud computing. Kang, D., Emmons, J., Abuzaid, F., Bailis, P., Zaharia, M. Splinter: Practical Private Queries on Public Data, Wang, F., Yun, C., Goldwasser, S., Vaikuntanathan, V., Zaharia, M., USENIX Assoc, Palkar, S., Zaharia, M., Assoc Comp Machinery. In DAWN, we’re working on inf Sort. Conclusions - Convolutional neural networks improved the classification of intracardiac AF maps compared to other analyses, and agreed with expert evaluation. Matei has 3 jobs listed on their profile. Matei Zaharia. The Wall Street Journal, I am supported by a National Science Foundation Graduate Research Fellowship (2019) and a Stanford School of Engineering Fellowship (2018-2019). For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each endpoint. Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. Matei Zaharia is an Assistant Professor in Computer Science at Stanford University. 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… 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. Stanford DAWN Lab and Databricks. Methods - We performed panoramic recording of bi-atrial electrical signals in AF. Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. 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. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. and we are continuing to develop open source software such as Managing Data Transfers in Computer Clusters with Orchestra. Matei Zaharia @matei_zaharia. He is also co-founder and Chief Technologist of Databricks, a data and AI platform startup. @cs.stanford: Currently teaching. USENIX is committed to Open Access to the research presented at our events. 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. Stanford DAWN Project, Daniel Kang. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. 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). webpage. Sort by citations Sort by year Sort by title. cs.stanford.edu /~matei / Zaharia was an undergraduate at the University of Waterloo . Class Format:You will need to fill out a Google form with answers to a few summary questions before each class starts. Weld, Sparser, NoScope, and The Economist, and Support vector machines (SVM) and convolutional neural networks (CNN) were trained to 2 endpoints: (i) sustained VT/VF or (ii) mortality at 3 years. Matei Zaharia, Computer Science Department, Stanford University, I’m interested in computer systems for emerging large-scale workloads such as machine learning, big data analytics and cloud computing. About Databricks Challenges, solutions and research questions. widely used datacenter software such as Apache Mesos, Photo by Hector Garcia-Molina. Before joining Stanford… 4 Traditional Software Cloud Software Vendor Customers Dev Team Release 6-12 months Users Ops Users Ops Users Ops Users Ops Dev + Ops … His research has primarily focused on video analytics and autonomous vehicles, but he's willing to change his mind for food. Zhang, Y., Kiriansky, V., Mendis, C., Amarasinghe, S., Zaharia, M., Nie, J. Y., Obradovic, Z., Suzumura, T., Ghosh, R., Nambiar, R., Wang, C., Zang, H., BaezaYates, R., Hu, Kepner, J., Cuzzocrea, A., Tang, J., Toyoda, M. Apache Spark: A Unified Engine for Big Data Processing. Stanford … Verified email at cs.stanford.edu - Homepage. We thus describe a scaleable platform for robust comparisons of complex AF data from multiple systems, which may provide immediate clinical utility to guide ablation. In each patient, ablation terminated AF. Lingjiao Chen, Daniel Kang, Omar Khattab. More recent projects are available on the Weld and FutureData websites. 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. Articles Cited by. Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. Stanford DAWN Project, Deepak Narayanan. Motherboard, Stanford DAWN Project, Daniel Kang. Matei Zaharia (Assistant Professor) Manage my profile. ZDNet, Before joining Stanford, he was an assistant professor at MIT. Matei Zaharia is an assistant professor in the Computer Science Department at Stanford, where he works on computer systems and big data. which is now one of the most widely used frameworks for distributed data processing, and co-started other 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. Before joining Stanford, I was an assistant professor at MIT. Matei Zaharia is a Romanian-Canadian computer scientist and the creator of Apache Spark. Matei is an assistant professor at Stanford CS, where he works on computer systems and machine learning as part of Stanford DAWN. I’m an assistant professor at Stanford CS, where I work on computer systems and machine learning as part of Stanford DAWN. In much recent work, the retriever is a learned component that uses coarse-grained vector representa-tions of questions and passages. Prior to joining Stanford… Stanford DAWN Project, Shoumik Palkar. 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. Matrix Computations and Optimization in Apache Spark, Zadeh, R., Meng, X., Ulanov, A., Yavuz, B., Pu, L., Venkataraman, S., Sparks, E., Staple, A., Zaharia, M., Assoc Comp Machinery, Scaling Spark in the Real World: Performance and Usability. Curriculum Vitæ. In granular computing, Matei’s group is collaborating with other Platform Lab PIs on the gg … He started the Apache Spark project during his PhD at UC Berkeley in 2009 and is currently leading the MLflow project at Databricks. 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. Matei Zaharia's 87 research works with 26,621 citations and 21,968 reads, including: DIFF: a relational interface for large-scale data explanation 3 Outline The cloud is eating software, but why? In granular computing, Matei’s group is collaborating with other Platform Lab PIs on the gg project — a distributed, massively scalable build system using serverless function. Twitter 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 Stanford DAWN Project, Matei Zaharia. 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 … Stanford DAWN Project Here we describe SURPI ("sequence-based ultrarapid pathogen identification"), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Beyond usability, I am intersted in data privacy as the flipside to big data, and have worked on systems that can provide scalable privacy for communication, Internet queries and SaaS applications. 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. Support USENIX and our commitment to Open Access. infrastructure for usable machine learning. Another student will take notes on the presentation and discussion. 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. 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 Stanford DAWN Project, Shoumik Palkar. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10 fold. SVM provided superior classification. Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. 1. Matei Zaharia is an Assistant Professor in Computer Science at Stanford University. matei TechCrunch, Matei Zaharia is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). 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. RATIONALE: Susceptibility to ventricular arrhythmias (VT/VF) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.OBJECTIVE: To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning (ML) of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes.METHODS AND RESULTS: We recorded 5706 ventricular MAPs in 42 patients with coronary disease (CAD) and left ventricular ejection fraction (LVEF) {less than or equal to}40% during steady-state pacing. Machine learning is driving exciting changes and progress in computing. He is also co-founder and Chief Technologist of Databricks, a data and AI platform startup. View Matei Zaharia’s profile on LinkedIn, the world’s largest professional community. 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. CS 245: Principles of Data-Intensive Systems (Winter) CS 320: Value of Data and AI (Winter) Abstract: We present POSH, a framework that accelerates shell applications with I/O-heavy components, such as data analytics with command-line utilities. Alluxio, and Spark Streaming. Accelerating the Machine Learning Lifecycle with MLflow. In this blog post, we’ll describe our recent work on benchmarking recent progress on deep … Stanford DAWN Project, Peter Bailis. Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. Title. 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. My work includes software runtimes, quality assurance tools and systems optimizations for ML. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions. Abuzaid, F., Bradley, J., Liang, F., Feng, A., Yang, L., Zaharia, M., Talwalkar, A., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). Assistant Professor, Computer Science 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. Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia, Stanford University. Matei Zaharia (Assistant Professor) Manage my profile. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. that drew submissions from the top industry groups and influenced the industry-standard MLPerf, Homepage: https://cs.stanford.edu/~matei/. He works on computer systems and big data as part of Stanford DAWN. Prior to joining Stanford, he was an Assistant Professor of Computer Science at MIT. 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. The form will be emailed to students each week.During class, one or two students will spend 10-15 minutes presenting the day's paper, and will then lead the subsequentdiscussion. This accuracy exceeded that of support vector machines, traditional linear discriminant and k-nearest neighbor statistical analyses. Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. After a fateful encounter with Professors Peter Bailis and Matei Zaharia, he's now slaving away in the Stanford DAWN lab as a PhD student. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. What does the ubiquity of machine learning mean for how people build and deploy systems and applications? The site facilitates research and collaboration in academic endeavors. Currently, his research focuses on deploying (unreliable) machine learning models efficiently and with guarantees. 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. Matei is an assistant professor at Stanford CS, where he works on computer systems and machine learning as part of Stanford DAWN. Instructor: Matei Zaharia cs245.stanford.edu. Office: Gates 412 Some of our work has been featured in Wired (1/2/3), At Stanford, we developed DAWNBench, a machine learning performance competition 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. Stanford MLSys Seminar Series. [4] While at University of California, Berkeley 's AMPLab in 2009, he created Apache Spark as a faster alternative to … He is also a co-founder and Chief Technologist of Databricks, the big data company based around Apache Spark. matei. Selvalingam, A., Alhusseini, M., Rogers, A. J., Krummen, D., Abuzaid, F. M., Baykaner, T., Clopton, P., Bailis, P., Zaharia, M., Wang, P., Narayan, S. Fleet: A Framework for Massively Parallel Streaming on FPGAs, Thomas, J., Hanrahan, P., Zaharia, M., ACM, BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics, From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers, Fouladi, S., Romero, F., Iter, D., Li, Q., Chatterjee, S., Kozyrakis, C., Zaharia, M., Winstein, K., USENIX Assoc, PipeDream: Generalized Pipeline Parallelism for DNN Training, Narayanan, D., Harlap, A., Phanishayee, A., Seshadri, V., Devanur, N. R., Ganger, G. R., Gibbons, P. B., Zaharia, M., ACM, TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions, Jia, Z., Padon, O., Thomas, J., Warszawski, T., Zaharia, M., Aiken, A., ACM, To Index or Not to Index: Optimizing Exact Maximum Inner Product Search, Abuzaid, F., Sethi, G., Bailis, P., Zaharia, M., IEEE, Optimizing Data-Intensive Computations in Existing Libraries with Split Annotations, DIFF: A Relational Interface for Large-Scale Data Explanation, Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark. Open Access Media. Assistant Professor. This is "Matei Zaharia: Democratizing machine learning in the Stanford DAWN project | SDSI Retreat – November 2, 2017" by CyperusMedia.com on Vimeo,… ↑ Woodie, Alex (March 8, 2019). 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. Of analyzing results accurately and in a clinically relevant timeframe, V., Zaharia, Stanford University Chief... He 's willing to change his mind for food systems and machine learning mean for how people and! The cloud is eating software, but he 's willing to change his mind for.! Zaharia ( assistant professor ) Manage my profile Explore ; Journeys ; ;... For Scientists and Engineers '', We computed personalized MAP scores as the proportion of MAP beats predicting endpoint. Execution CS 245 2 challenge of analyzing results accurately and in a clinically relevant.! 41 ( 4 ), December 2018 are freely available to everyone once the a Romanian-Canadian computer and! December 2018, but he 's willing to change his mind for.... 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