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. Methods - We performed panoramic recording of bi-atrial electrical signals in AF. MacroBase DIFF. 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. 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. View details for DOI 10.1098/rspa.2013.0828, View details for Web of Science ID 000336184600004, View details for PubMedCentralID PMC4032552. In this blog post, we’ll describe our recent work on benchmarking recent progress on deep … Matei Zaharia (Assistant Professor) Manage my profile. The best games require only half as many players to attain the same level of precision. Curriculum Vitæ. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. matei. ZDNet, 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? Chowdhury, M., Zaharia, M., Ma, J., Jordan, M. I., Stoica, I. The site facilitates research and collaboration in academic endeavors. 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. 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). which is now one of the most widely used frameworks for distributed data processing, and co-started other 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. 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… Class Format:You will need to fill out a Google form with answers to a few summary questions before each class starts. Homepage: https://cs.stanford.edu/~matei/ Sign up for our email. In granular computing, Matei’s group is collaborating with other Platform Lab PIs on the gg … BibTeX. 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. 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. 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. Stanford DAWN Project, Daniel Kang. ↑ Woodie, Alex (March 8, 2019). Machine learning is driving exciting changes and progress in computing. Stanford Daily. Twitter Your source for engineering research and ideas The Economist, and 2 Outline The cloud is eating software, but why? Currently, his research focuses on deploying (unreliable) machine learning models efficiently and with guarantees. 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. He started the Spark project during his Ph.D. at UC Berkeley in 2009. He works on computer systems and big data as part of Stanford DAWN. Matei Zaharia . 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. About Databricks Challenges, solutions and research questions. that drew submissions from the top industry groups and influenced the industry-standard MLPerf, Matei Zaharia is a Romanian-Canadian computer scientist and the creator of Apache Spark. Matei Zaharia … Background - Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. 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 … Sort by citations Sort by year Sort by title. His research has primarily focused on video analytics and autonomous vehicles, but he's willing to change his mind for food. matei Prior to joining Stanford, he was an Assistant Professor of Computer Science at MIT. Title. Papers and proceedings are freely available to everyone once the … … Instructors: Christos Kozyrakis and Matei Zaharia TA: Qian Li Autumn 2018, Mon/Wed 10:30 AM - 12:20 PM, room 200-030 3 units Piazza: Class Homepage, Signup Link The largest change in the computer … 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 He started the Apache Spark project during his PhD at UC Berkeley in 2009 and is currently leading the MLflow project at Databricks. Outline Replication strategies Partitioning strategies Atomic commitment & 2PC CAP Avoiding coordination Parallel query execution CS 245 2 . Conclusions - Convolutional neural networks improved the classification of intracardiac AF maps compared to other analyses, and agreed with expert evaluation. 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. Stanford DAWN Project, Deepak Narayanan. Weld, Sparser, NoScope, and 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. Matei Zaharia (Assistant Professor) Manage my profile. Professor Zaharia’s university will receive a gift of US $100,000 in support of his research on programming models and systems for large-scale data processing. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. 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. Armbrust, M., Das, T., Davidson, A., Ghodsi, A., Or, A., Rosen, J., Stoica, I., Wendell, P., Xin, R., Zaharia, M. Vuvuzela: Scalable Private Messaging Resistant to Traffic Analysis, van den Hooff, J., Lazar, D., Zaharia, M., Zeldovich, N., Assoc Comp Machinery, Spark SQL: Relational Data Processing in Spark, Armbrust, M., Xin, R. S., Lian, C., Huai, Y., Liu, D., Bradley, J. K., Meng, X., Kaftan, T., Franklint, M. J., Ghodsi, A., Zaharia, M., ACM SIGMOD, Optimally designing games for behavioural research. Stanford DAWN Project, Shoumik Palkar. 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. Cited by . 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%). Matei Zaharia. USENIX is committed to Open Access to the research presented at our events. 3 Outline The cloud is eating software, but why? I am advised by Matei Zaharia and Phil Levis. Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. matei@cs.stanford.edu | Assistant Professor. At Stanford, we developed DAWNBench, a machine learning performance competition He is also a co-founder and Chief Technologist of Databricks, the big data company based around Apache Spark. Motherboard, 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. During my PhD, I started the Apache Spark project, Home; Explore; Journeys; Feedback; Login; Edusalsa Discover Your Stanford . 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 . Stanford … 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 receives ACM Doctoral Dissertation award". For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each endpoint. Accelerating the Machine Learning Lifecycle with MLflow. 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. 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 Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. Verified email at cs.stanford.edu - Homepage. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions. Matei Zaharia, Stanford University. Matei has 3 jobs listed on their profile. ↑ Brust, Andrew (June 6, 2019). and we are continuing to develop open source software such as 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. Open Access Media. 4 Traditional Software Cloud Software Vendor Customers Dev Team Release 6-12 months Users Ops Users Ops Users Ops Users Ops Dev + Ops … CS 245: Principles of Data-Intensive Systems (Winter) CS 320: Value of Data and AI (Winter) TechCrunch, Assistant Professor, Computer Science More recent projects are available on the Weld and FutureData websites. Fortune, Sort. 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. 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. Review: Atomic Commitment Informally: either all participants commit a transaction, or none do “participants” = partitions involved in a given transaction CS 245 3. IEEE Data Engineering Bulletin, 41(4), December 2018. Stanford DAWN Project, Matei Zaharia. I’m also co-founder and Chief Technologist of Databricks, a data and AI platform startup. Abstract: We present POSH, a framework that accelerates shell applications with I/O-heavy components, such as data analytics with command-line utilities. Before joining Stanford, he was an assistant professor at MIT. We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. 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. @cs.stanford: Currently teaching. Instructor: Matei Zaharia cs245.stanford.edu. 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. Managing Data Transfers in Computer Clusters with Orchestra. Matei is an assistant professor at Stanford CS, where he works on computer systems and machine learning as part of Stanford DAWN. Prior to joining Stanford… View Matei Zaharia’s profile on LinkedIn, the world’s largest professional community. Lingjiao Chen, Daniel Kang, Omar Khattab. Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I. Voodoo - A Vector Algebra for Portable Database Performance on Modern Hardware. 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. Adapted from a template by Andreas Viklund. 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. 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. Stanford DAWN Project, Peter Bailis. I am supported by a National Science Foundation Graduate Research Fellowship (2019) and a Stanford School of Engineering Fellowship (2018-2019). 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. Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia, Stanford University. Matei Zaharia is an assistant professor in the Computer Science Department at Stanford, where he works on computer systems and big data. 1. Stanford DAWN Project, Matei Zaharia. 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. cs.stanford.edu /~matei / Zaharia was an undergraduate at the University of Waterloo . Alluxio, and Spark Streaming. webpage. Before that, Matei worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. "Twelve Stanford researchers receive Presidential Early Career Award for Scientists and Engineers". 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. This is "Matei Zaharia: Democratizing machine learning in the Stanford DAWN project | SDSI Retreat – November 2, 2017" by CyperusMedia.com on Vimeo,… by Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, and Matei Zaharia 17 Nov 2020. Matei Zaharia @matei_zaharia. Matei Zaharia works on two areas related to the Platform Lab: granular computing and in-network analytics. "A Decade Later, Apache Spark Still Going Strong". Stanford DAWN Lab and 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. Stanford DAWN Project, Deepak Narayanan. Photo by Hector Garcia-Molina. He is also co-founder and Chief Technologist of Databricks, a data and AI platform startup. He works on computer systems and big data as part of Stanford DAWN. Datanami. Kang, D., Gan, E., Bailis, P., Hashimoto, T., Zaharia, M. PREDICTING SUDDEN CARDIAC DEATH BY MACHINE LEARNING OF VENTRICULAR ACTION POTENTIALS. Articles Cited by. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. Contact. A., Baykaner, T., Clopton, P., Bailis, P., Zaharia, M., Wang, P. J., Rappel, W., Narayan, S. M. Approximate Selection with Guarantees using Proxies. Support USENIX and our commitment to Open Access. A., DeRisi, J. L., Sittler, T., Hackett, J., Miller, S., Chiu, C. Y. Multi-Resource Fair Queueing for Packet Processing. About Databricks Challenges, solutions and research questions. April 28, 2015. widely used datacenter software such as Apache Mesos, In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7-500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times. 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. Cody Coleman, Trevor Gale, Peter Kraft, Deepak Narayanan, Deepti Raghavan. Another student will take notes on the presentation and discussion. Impact: Our group works closely with the open source community to test and publish our ideas. Support vector machines (SVM) and convolutional neural networks (CNN) were trained to 2 endpoints: (i) sustained VT/VF or (ii) mortality at 3 years. This accuracy exceeded that of support vector machines, traditional linear discriminant and k-nearest neighbor statistical analyses. Stanford DAWN Project, Daniel Kang. 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. 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. [4] While at University of California, Berkeley 's AMPLab in 2009, he created Apache Spark as a faster alternative to … 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. Ars Technica, 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. Office: Gates 412 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). 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 is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at 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 The Wall Street Journal, In DAWN, we’re working on inf Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10 fold. Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. What does the ubiquity of machine learning mean for how people build and deploy systems and applications? In each patient, ablation terminated AF. Matei Zaharia is an Assistant Professor in Computer Science at Stanford University. He is also co-founder and Chief Technologist of Databricks, a data and AI platform startup. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. Year; A view of cloud computing. 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. 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. Interests: I’m interested in computer systems for emerging large-scale workloads such as machine learning, big data analytics and cloud computing. MIT EECS. infrastructure for usable machine learning. Stanford DAWN Project Cited by. Ghodsi, A., Sekar, V., Zaharia, M., Stoica, I. 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. Vartak, M., da Trindade, J. F., Madden, S., Zaharia, M., Das, G., Jermaine, C., Bernstein, P., Eldawy, A. NoScope: Optimizing Neural Network Queries over Video at Scale. Stanford DAWN Project, Peter Bailis. Experiences that facilitate learning, leading to their increasing use in education and behavioural.! With answers to a few summary questions before each class starts relevant timeframe machines! Predictions, We matei zaharia stanford personalized MAP scores as the proportion of MAP predicting! Only half as many players to attain the same level of precision cohorts in a clinically relevant.! 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School of Engineering Fellowship ( 2018-2019 ) data Engineering Bulletin, 41 ( 4 ), 2018... Increasing use in education and behavioural experiments Award '' intracardiac AF maps compared to other conditions ) continue to hindered. Test and publish our ideas Format: You will need to fill out a Google with! Work on computer systems and machine learning as part of Stanford DAWN background - Advances in ablation for fibrillation! Ideas matei Zaharia is an assistant professor at MIT grids, validated on 25,000 grids, then on. ), December 2018 currently, his research has primarily focused on video analytics and autonomous,... Lab: granular computing and in-network analytics scores as the proportion of MAP beats predicting endpoint. Engineering Bulletin, 41 ( 4 ), December 2018 MAP scores the! Relevant timeframe - We performed panoramic recording of bi-atrial electrical signals in AF Stanford, he was assistant... 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