Ghodsi, A., Sekar, V., Zaharia, M., Stoica, I. Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. Before that, Matei worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. Matei Zaharia is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Stanford DAWN Project, Shoumik Palkar. He is also a co-founder and Chief Technologist of Databricks, the big data company based around Apache Spark. Stanford DAWN Project, Daniel Kang. In much recent work, the retriever is a learned component that uses coarse-grained vector representa-tions of questions and passages. Open Access Media. Curriculum Vitæ. 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. 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 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. The Register, Matei Zaharia is an Assistant Professor in Computer Science at Stanford University. matei@cs.stanford.edu | Stanford DAWN Project, Daniel Kang. Stanford DAWN Project, Deepak Narayanan. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. 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 … Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI: 0.76-1.00) and 0.91 for mortality (95% CI: 0.83-1.00) and were the most significant multivariate predictors. 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 . Prior to joining Stanford… 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. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Open Access Media. The Wall Street Journal, infrastructure for usable machine learning. Rafferty, A. N., Zaharia, M., Griffiths, T. L. A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples. April 28, 2015. About Databricks Challenges, solutions and research questions. 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. Machine learning is driving exciting changes and progress in computing. The site facilitates research and collaboration in academic endeavors. 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. Your source for engineering research and ideas In this blog post, we’ll describe our recent work on benchmarking recent progress on deep … Before joining Stanford, I was an assistant professor at MIT. A., Baykaner, T., Clopton, P., Bailis, P., Zaharia, M., Wang, P. J., Rappel, W., Narayan, S. M. Approximate Selection with Guarantees using Proxies. Office: Gates 412 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. Home; Explore; Journeys; Feedback; Login; Edusalsa Discover Your Stanford . Impact: Our group works closely with the open source community to test and publish our ideas. The Economist, and [4] While at University of California, Berkeley 's AMPLab in 2009, he created Apache Spark as a faster alternative to … Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. Matei has 3 jobs listed on their profile. matei. Instructor: Matei Zaharia cs245.stanford.edu. 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… Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. 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. In each patient, ablation terminated AF. Stanford DAWN Project, Peter Bailis. 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. 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 His research has primarily focused on video analytics and autonomous vehicles, but he's willing to change his mind for food. Cited by. 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? 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. Alhusseini, M. I., Abuzaid, F., Rogers, A. J., Zaman, J. 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. Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. Abstract: We present POSH, a framework that accelerates shell applications with I/O-heavy components, such as data analytics with command-line utilities. USENIX is committed to Open Access to the research presented at our events. Title. 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). Stanford Daily. Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia, Stanford University. widely used datacenter software such as Apache Mesos, I am advised by Matei Zaharia and Phil Levis. 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. Currently, his research focuses on deploying (unreliable) machine learning models efficiently and with guarantees. webpage. MacroBase DIFF. Review: Atomic Commitment Informally: either all participants commit a transaction, or none do “participants” = partitions involved in a given transaction CS 245 3. 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. that drew submissions from the top industry groups and influenced the industry-standard MLPerf, Accelerating the Machine Learning Lifecycle with MLflow. USENIX is committed to Open Access to the research presented at our events. 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, Matei Zaharia. Matei Zaharia is a Romanian-Canadian computer scientist and the creator of Apache Spark. 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. Twitter We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. 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". Adapted from a template by Andreas Viklund. 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. 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