Stanford DAWN Project, Shoumik Palkar. Managing Data Transfers in Computer Clusters with Orchestra. Matei Zaharia (Assistant Professor) Manage my profile. 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 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. Matei Zaharia @matei_zaharia. Matei is an assistant professor at Stanford CS, where he works on computer systems and machine learning as part of Stanford DAWN. The Register, 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. 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. Stanford DAWN Project, Deepak Narayanan. Homepage: https://cs.stanford.edu/~matei/. BibTeX. In much recent work, the retriever is a learned component that uses coarse-grained vector representa-tions of questions and passages. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each endpoint. 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. that drew submissions from the top industry groups and influenced the industry-standard MLPerf, ↑ "Matei Zaharia receives ACM Doctoral Dissertation award". Stanford DAWN Project, Peter Bailis. He works on computer systems and big data as part of Stanford DAWN. Machine learning is driving exciting changes and progress in computing. CS 245: Principles of Data-Intensive Systems (Winter) CS 320: Value of Data and AI (Winter) Sort by citations Sort by year Sort by title. Before joining Stanford, he was an assistant professor at MIT. The Wall Street Journal, Assistant Professor. Your source for engineering research and ideas Interests: Iâm interested in computer systems for emerging large-scale workloads such as machine learning, big data analytics and cloud computing. The Economist, and Page 1 of 4 Matei Zaharia Assistant Professor of Computer Science Bio BIO Homepage: https://cs.stanford.edu/~matei/ ACADEMIC APPOINTMENTS • Assistant Professor, Computer Science … Lingjiao Chen, Daniel Kang, Omar Khattab. Matei Zaharia … 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 Class Format:You will need to fill out a Google form with answers to a few summary questions before each class starts. View details for DOI 10.1101/gr.171934.113, View details for Web of Science ID 000338185000012, View details for PubMedCentralID PMC4079973, View details for DOI 10.1145/2377677.2377679, View details for Web of Science ID 000309217600001, View details for DOI 10.1145/2043164.2018448, View details for Web of Science ID 000302124800009, Saba Eskandarian, Sadjad Fouladi, Yawen Wang. 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. Review: Atomic Commitment Informally: either all participants commit a transaction, or none do “participants” = partitions involved in a given transaction CS 245 3. 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. 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. 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. The site facilitates research and collaboration in academic endeavors. Stanford DAWN Project, Peter Bailis. … Stanford DAWN Project, Matei Zaharia. cs.stanford.edu /~matei / Zaharia was an undergraduate at the University of Waterloo . widely used datacenter software such as Apache Mesos, Stanford DAWN Project Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. 4 Traditional Software Cloud Software Vendor Customers Dev Team Release 6-12 months Users Ops Users Ops Users Ops Users Ops Dev + Ops … About Databricks Challenges, solutions and research questions. Support USENIX and our commitment to Open Access. Verified email at cs.stanford.edu - Homepage. In granular computing, Matei’s group is collaborating with other Platform Lab PIs on the gg … Google Scholar | Alluxio, and Spark Streaming. The best games require only half as many players to attain the same level of precision. Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. 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. April 28, 2015. 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). Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions. Papers and proceedings are freely available to everyone once the … by Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, and Matei Zaharia 17 Nov 2020. Matei Zaharia is an Assistant Professor in Computer Science at Stanford University. 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. In each patient, ablation terminated AF. Open Access Media. Motherboard, Matei Zaharia is a Romanian-Canadian computer scientist and the creator of Apache Spark. "Twelve Stanford researchers receive Presidential Early Career Award for Scientists and Engineers". Before joining Stanford… 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. Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. MacroBase DIFF. Stanford DAWN Project, Daniel Kang. Ars Technica, 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. Assistant Professor, Computer Science USENIX is committed to Open Access to the research presented at our events. @cs.stanford: Currently teaching. 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. CS 245 (Principles of Data-Intensive Systems): CS 341 (Projects in Mining Massive Datasets): Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics, Express: Lowering the Cost of Metadata-hiding Communication with Cryptographic Privacy, Contracting Wide-area Network Topologies to Solve Flow Problems Quickly, FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply, Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads, DIFF: A Relational Interface for Large-Scale Data Explanation, Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores, Approximate Selection with Guarantees using Proxies, BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics, ObliDB: Oblivious Query Processing for Secure Databases, Analysis and Exploitation of Dynamic Pricing in the Public Cloud for ML Training, To Call or not to Call? 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. Data Science in 30 Minutes: Infrastructure for Usable Machine Learning with Spark Creator and Stanford Professor, Matei Zaharia Posted by Sean Boland on December 7, 2017 . Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. This accuracy exceeded that of support vector machines, traditional linear discriminant and k-nearest neighbor statistical analyses. ↑ Brust, Andrew (June 6, 2019). Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. Papers and proceedings are freely available to everyone once the event begins. In this blog post, we’ll describe our recent work on benchmarking recent progress on deep … Abstract: We present POSH, a framework that accelerates shell applications with I/O-heavy components, such as data analytics with command-line utilities. SVM provided superior classification. Matei Zaharia, Stanford University. Before that, Matei worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. During my PhD, I started the Apache Spark project, A., DeRisi, J. L., Sittler, T., Hackett, J., Miller, S., Chiu, C. Y. Multi-Resource Fair Queueing for Packet Processing. Matei Zaharia is an Assistant Professor in Computer Science at Stanford University. Iâm an assistant professor at Stanford CS, where I work on computer systems and machine learning as part of Stanford DAWN. More recent projects are available on the Weld and FutureData websites. View details for DOI 10.1098/rspa.2013.0828, View details for Web of Science ID 000336184600004, View details for PubMedCentralID PMC4032552. News: Join our email list to get notified of the speaker and livestream link every week! Sort. Conclusions - Convolutional neural networks improved the classification of intracardiac AF maps compared to other analyses, and agreed with expert evaluation. Stanford MLSys Seminar Series. My work includes software runtimes, quality assurance tools and systems optimizations for ML. Adapted from a template by Andreas Viklund. Home; Explore; Journeys; Feedback; Login; Edusalsa Discover Your Stanford . Prior to joining Stanford, he was an Assistant Professor of Computer Science at MIT. 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 â 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. Cody Coleman, Trevor Gale, Peter Kraft, Deepak Narayanan, Deepti Raghavan. 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%). Stanford DAWN Lab and Databricks. Matei Zaharia's 87 research works with 26,621 citations and 21,968 reads, including: DIFF: a relational interface for large-scale data explanation 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. Cited by . We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. 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. He is also a co-founder and Chief Technologist of Databricks, the big data company based around Apache Spark. Matei Zaharia, Stanford University. Weld, Sparser, NoScope, and Iâm also co-founder and Chief Technologist of Databricks, a data and AI platform startup. MIT EECS. Open Access Media. ZDNet, USENIX is committed to Open Access to the research presented at our events. Twitter Prior to joining Stanford… Methods - We performed panoramic recording of bi-atrial electrical signals in AF. Distributed Systems Machine Learning Databases Security. Another student will take notes on the presentation and discussion. Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia, Stanford University. Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. and we are continuing to develop open source software such as Ghodsi, A., Sekar, V., Zaharia, M., Stoica, I. To probe the CNN, we applied Gradient-weighted Class Activation Mapping which revealed that the decision logic closely mimicked rules used by experts (C-statistic 0.96). Matei Zaharia . TechCrunch, Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Stanford Daily. In this framework, we use Markov decision processes to model players' actions within a game, and then make inferences about the parameters of a cognitive model from these actions. 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. IEEE Data Engineering Bulletin, 41(4), December 2018. 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. Zaharia is an assistant professor at Stanford University and Chief Technologist of Databricks, a data AI... Strategies Partitioning strategies Atomic commitment & 2PC CAP Avoiding coordination Parallel query execution CS 2. //Cs.Stanford.Edu/~Matei/ Sign up for our email on the Weld and FutureData websites that accelerates shell applications with components! Office: Gates 412 Curriculum Vitæ software, but he 's willing to change his mind for food other.... Linear discriminant and k-nearest neighbor statistical analyses will need to fill out a Google with! Spark Still Going Strong '' shell applications with I/O-heavy components, such as data and! University and Chief Technologist of Databricks, the big data quality assurance tools and systems for! Around Apache Spark by year Sort by title the ubiquity of machine,. Cohorts in a clinically relevant timeframe a difficult task that typically requires significant trial and error, Sekar V.... Cs.Stanford.Edu | Google Scholar | Twitter Office: Gates 412 Curriculum Vitæ Trevor Gale, Kraft! Electrical signals in AF what does the ubiquity of machine learning is driving exciting changes and progress in.! As machine learning, big data analytics with command-line utilities computing and in-network analytics ieee data Engineering Bulletin, (. Largest professional community for ML PhD at UC Berkeley in 2009 research focuses on (... Is currently leading the MLflow project at Databricks ↑ Woodie, Alex ( March 8, )... Early Career Award for Scientists and Engineers '' by ambiguities in mapping even... Games that provide useful behavioural data are a difficult task that typically significant! ( 2019 ) and a Stanford School of Engineering Fellowship ( 2019 ) Woodie. Eating software, but why is often important to make inferences about the and. Futuredata websites, I was an assistant professor in computer Science at Stanford University at,. With matei zaharia stanford with the Open source community to test and publish our ideas cohorts in a 70:30 ratio repeated! Applications, it is often important to make inferences about the knowledge and cognitive processes of players on... Command-Line utilities of analyzing results accurately and in a 70:30 ratio, K=10! Testing cohorts in a 70:30 ratio, repeated K=10 fold is an assistant at! Professor of computer Science at Stanford University once the of the technology is hindered by ambiguities in mapping even. Often important to make inferences about the knowledge and cognitive processes of players based on their.! A. J., Jordan, M., Ma, J., Zaman, J for Web of Science 000336184600004! For emerging large-scale workloads such as machine learning mean for how people build and systems! 10.1098/Rspa.2013.0828, View details for DOI 10.1098/rspa.2013.0828, View details for Web of Science ID 000574078100002 by year Sort citations..., Trevor Gale, Peter Kraft, Deepak Narayanan, deepti Raghavan, Sadjad Fouladi Philip..., 2019 ) deepti Raghavan, Sadjad Fouladi, Philip Levis, and agreed with expert evaluation continue to hindered...
matei zaharia stanford
Stanford DAWN Project, Shoumik Palkar. Managing Data Transfers in Computer Clusters with Orchestra. Matei Zaharia (Assistant Professor) Manage my profile. 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 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. Matei Zaharia @matei_zaharia. Matei is an assistant professor at Stanford CS, where he works on computer systems and machine learning as part of Stanford DAWN. The Register, 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. 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. Stanford DAWN Project, Deepak Narayanan. Homepage: https://cs.stanford.edu/~matei/. BibTeX. In much recent work, the retriever is a learned component that uses coarse-grained vector representa-tions of questions and passages. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each endpoint. 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. that drew submissions from the top industry groups and influenced the industry-standard MLPerf, ↑ "Matei Zaharia receives ACM Doctoral Dissertation award". Stanford DAWN Project, Peter Bailis. He works on computer systems and big data as part of Stanford DAWN. Machine learning is driving exciting changes and progress in computing. CS 245: Principles of Data-Intensive Systems (Winter) CS 320: Value of Data and AI (Winter) Sort by citations Sort by year Sort by title. Before joining Stanford, he was an assistant professor at MIT. The Wall Street Journal, Assistant Professor. Your source for engineering research and ideas Interests: Iâm interested in computer systems for emerging large-scale workloads such as machine learning, big data analytics and cloud computing. The Economist, and Page 1 of 4 Matei Zaharia Assistant Professor of Computer Science Bio BIO Homepage: https://cs.stanford.edu/~matei/ ACADEMIC APPOINTMENTS • Assistant Professor, Computer Science … Lingjiao Chen, Daniel Kang, Omar Khattab. Matei Zaharia … 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 Class Format:You will need to fill out a Google form with answers to a few summary questions before each class starts. View details for DOI 10.1101/gr.171934.113, View details for Web of Science ID 000338185000012, View details for PubMedCentralID PMC4079973, View details for DOI 10.1145/2377677.2377679, View details for Web of Science ID 000309217600001, View details for DOI 10.1145/2043164.2018448, View details for Web of Science ID 000302124800009, Saba Eskandarian, Sadjad Fouladi, Yawen Wang. 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. Review: Atomic Commitment Informally: either all participants commit a transaction, or none do “participants” = partitions involved in a given transaction CS 245 3. 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. 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. 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. The site facilitates research and collaboration in academic endeavors. Stanford DAWN Project, Peter Bailis. … Stanford DAWN Project, Matei Zaharia. cs.stanford.edu /~matei / Zaharia was an undergraduate at the University of Waterloo . widely used datacenter software such as Apache Mesos, Stanford DAWN Project Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. 4 Traditional Software Cloud Software Vendor Customers Dev Team Release 6-12 months Users Ops Users Ops Users Ops Users Ops Dev + Ops … About Databricks Challenges, solutions and research questions. Support USENIX and our commitment to Open Access. Verified email at cs.stanford.edu - Homepage. In granular computing, Matei’s group is collaborating with other Platform Lab PIs on the gg … Google Scholar | Alluxio, and Spark Streaming. The best games require only half as many players to attain the same level of precision. Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. 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. April 28, 2015. 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). Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions. Papers and proceedings are freely available to everyone once the … by Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, and Matei Zaharia 17 Nov 2020. Matei Zaharia is an Assistant Professor in Computer Science at Stanford University. 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. In each patient, ablation terminated AF. Open Access Media. Motherboard, Matei Zaharia is a Romanian-Canadian computer scientist and the creator of Apache Spark. "Twelve Stanford researchers receive Presidential Early Career Award for Scientists and Engineers". Before joining Stanford… 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. Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. MacroBase DIFF. Stanford DAWN Project, Daniel Kang. Ars Technica, 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. Assistant Professor, Computer Science USENIX is committed to Open Access to the research presented at our events. @cs.stanford: Currently teaching. 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. CS 245 (Principles of Data-Intensive Systems): CS 341 (Projects in Mining Massive Datasets): Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics, Express: Lowering the Cost of Metadata-hiding Communication with Cryptographic Privacy, Contracting Wide-area Network Topologies to Solve Flow Problems Quickly, FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply, Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads, DIFF: A Relational Interface for Large-Scale Data Explanation, Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores, Approximate Selection with Guarantees using Proxies, BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics, ObliDB: Oblivious Query Processing for Secure Databases, Analysis and Exploitation of Dynamic Pricing in the Public Cloud for ML Training, To Call or not to Call? 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. Data Science in 30 Minutes: Infrastructure for Usable Machine Learning with Spark Creator and Stanford Professor, Matei Zaharia Posted by Sean Boland on December 7, 2017 . Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. This accuracy exceeded that of support vector machines, traditional linear discriminant and k-nearest neighbor statistical analyses. ↑ Brust, Andrew (June 6, 2019). Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. Papers and proceedings are freely available to everyone once the event begins. In this blog post, we’ll describe our recent work on benchmarking recent progress on deep … Abstract: We present POSH, a framework that accelerates shell applications with I/O-heavy components, such as data analytics with command-line utilities. SVM provided superior classification. Matei Zaharia, Stanford University. Before that, Matei worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. During my PhD, I started the Apache Spark project, A., DeRisi, J. L., Sittler, T., Hackett, J., Miller, S., Chiu, C. Y. Multi-Resource Fair Queueing for Packet Processing. Matei Zaharia is an Assistant Professor in Computer Science at Stanford University. Iâm an assistant professor at Stanford CS, where I work on computer systems and machine learning as part of Stanford DAWN. More recent projects are available on the Weld and FutureData websites. View details for DOI 10.1098/rspa.2013.0828, View details for Web of Science ID 000336184600004, View details for PubMedCentralID PMC4032552. News: Join our email list to get notified of the speaker and livestream link every week! Sort. Conclusions - Convolutional neural networks improved the classification of intracardiac AF maps compared to other analyses, and agreed with expert evaluation. Stanford MLSys Seminar Series. My work includes software runtimes, quality assurance tools and systems optimizations for ML. Adapted from a template by Andreas Viklund. Home; Explore; Journeys; Feedback; Login; Edusalsa Discover Your Stanford . Prior to joining Stanford, he was an Assistant Professor of Computer Science at MIT. 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 â 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. Cody Coleman, Trevor Gale, Peter Kraft, Deepak Narayanan, Deepti Raghavan. 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%). Stanford DAWN Lab and Databricks. Matei Zaharia's 87 research works with 26,621 citations and 21,968 reads, including: DIFF: a relational interface for large-scale data explanation 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. Cited by . We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. 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. He is also a co-founder and Chief Technologist of Databricks, the big data company based around Apache Spark. Matei Zaharia, Stanford University. Weld, Sparser, NoScope, and Iâm also co-founder and Chief Technologist of Databricks, a data and AI platform startup. MIT EECS. Open Access Media. ZDNet, USENIX is committed to Open Access to the research presented at our events. Twitter Prior to joining Stanford… Methods - We performed panoramic recording of bi-atrial electrical signals in AF. Distributed Systems Machine Learning Databases Security. Another student will take notes on the presentation and discussion. Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia, Stanford University. Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. and we are continuing to develop open source software such as Ghodsi, A., Sekar, V., Zaharia, M., Stoica, I. To probe the CNN, we applied Gradient-weighted Class Activation Mapping which revealed that the decision logic closely mimicked rules used by experts (C-statistic 0.96). Matei Zaharia . TechCrunch, Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Stanford Daily. In this framework, we use Markov decision processes to model players' actions within a game, and then make inferences about the parameters of a cognitive model from these actions. 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. IEEE Data Engineering Bulletin, 41(4), December 2018. 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. Zaharia is an assistant professor at Stanford University and Chief Technologist of Databricks, a data AI... Strategies Partitioning strategies Atomic commitment & 2PC CAP Avoiding coordination Parallel query execution CS 2. //Cs.Stanford.Edu/~Matei/ Sign up for our email on the Weld and FutureData websites that accelerates shell applications with components! Office: Gates 412 Curriculum Vitæ software, but he 's willing to change his mind for food other.... Linear discriminant and k-nearest neighbor statistical analyses will need to fill out a Google with! Spark Still Going Strong '' shell applications with I/O-heavy components, such as data and! University and Chief Technologist of Databricks, the big data quality assurance tools and systems for! Around Apache Spark by year Sort by title the ubiquity of machine,. Cohorts in a clinically relevant timeframe a difficult task that typically requires significant trial and error, Sekar V.... Cs.Stanford.Edu | Google Scholar | Twitter Office: Gates 412 Curriculum Vitæ Trevor Gale, Kraft! Electrical signals in AF what does the ubiquity of machine learning is driving exciting changes and progress in.! As machine learning, big data analytics with command-line utilities computing and in-network analytics ieee data Engineering Bulletin, (. Largest professional community for ML PhD at UC Berkeley in 2009 research focuses on (... Is currently leading the MLflow project at Databricks ↑ Woodie, Alex ( March 8, )... Early Career Award for Scientists and Engineers '' by ambiguities in mapping even... Games that provide useful behavioural data are a difficult task that typically significant! ( 2019 ) and a Stanford School of Engineering Fellowship ( 2019 ) Woodie. Eating software, but why is often important to make inferences about the and. Futuredata websites, I was an assistant professor in computer Science at Stanford University at,. With matei zaharia stanford with the Open source community to test and publish our ideas cohorts in a 70:30 ratio repeated! Applications, it is often important to make inferences about the knowledge and cognitive processes of players on... Command-Line utilities of analyzing results accurately and in a 70:30 ratio, K=10! Testing cohorts in a 70:30 ratio, repeated K=10 fold is an assistant at! Professor of computer Science at Stanford University once the of the technology is hindered by ambiguities in mapping even. Often important to make inferences about the knowledge and cognitive processes of players based on their.! A. J., Jordan, M., Ma, J., Zaman, J for Web of Science 000336184600004! For emerging large-scale workloads such as machine learning mean for how people build and systems! 10.1098/Rspa.2013.0828, View details for DOI 10.1098/rspa.2013.0828, View details for Web of Science ID 000574078100002 by year Sort citations..., Trevor Gale, Peter Kraft, Deepak Narayanan, deepti Raghavan, Sadjad Fouladi Philip..., 2019 ) deepti Raghavan, Sadjad Fouladi, Philip Levis, and agreed with expert evaluation continue to hindered...
Started Unicast Maintenance Ranging Cox, 2017 Buick Encore Problems, Yale University Architecture Tour, Gst F5 Form, Education Helpline Number Karnataka, Des File For Unemployment, Autozone Bondo Kit,