My research interest covers a wide range of areas, including high-performance computing, large-scale data analysis systems, data mining and machine learning. I got a PhD from the department of computer science at the Johns Hopkins University. Account. Cart 0. Search: Search

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I am a professor of computer science at the University of Ontario Institute of Technology. I also direct the visual computing lab that focuses on problems residing at the intersection of computer vision, visual sensor networks, large-scale processing and computer graphics.
Feb 25, 2014 · 1. BIG DATA Prepared By Nasrin Irshad Hussain And Pranjal Saikia M.Sc(IT) 2nd Sem Kaziranga University Assam 2. Content 1. Introduction 2. What is Big Data 3. Characteristic of Big Data 4. Storing,selecting and processing of Big Data 5. Why Big Data 6. How it is Different 7. Big Data sources 8. Tools used in Big Data 9. Application of Big Data 10.

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Ben Harris did his undergraduate studies at Colgate University, receiving a Bachelors of Arts in Biology and Computer Science with honors in both disciplines. His undergraduate biology thesis was on redefining cancer testis antigens (CTAs) using the GTEx and TCGA RNAseq data. It was a continuation of his work as an URP in Dr. Mickey Atwal’s lab. You will write production-grade code and train Brain-Computer Interface models on large amounts of EEG data. Professional Requirements. 3+ years of programming experience, 2+ years in Python; Bachelor’s degree in Computer Science, Electrical Engineering, Applied Math, Computational Neuroscience or comparable discipline, Masters preferred.
Jan 15, 2017 · ## 'data.frame': 699 obs. of 10 variables: ## $ classes : Factor w/ 2 levels "benign","malignant": 1 1 1 1 1 2 1 1 1 1 ... ## $ clump_thickness : num 5 5 3 6 4 8 1 2 2 4 ... ## $ uniformity_of_cell_size : num 1 4 1 8 1 10 1 1 1 2 ... ## $ uniformity_of_cell_shape : num 1 4 1 8 1 10 1 2 1 1 ... ## $ marginal_adhesion : num 1 5 1 1 3 8 1 1 1 1 ... ## $ single_epithelial_cell_size: num 2 7 2 3 2 ...

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Welcome to the portal for all of your Grainger Engineering course websites for this semester. Course websites can be accessed via the links below or by going to https://courses.grainger.illinois.edu/XXXYYY where "XXXYYY" is the course rubric and number (e.g., ENG100).

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Training. An important component of the ACNN Spoke is its focus on training, education and diversity. With its strong and integrated programs in Neuroscience, Computer Science, and high performance computing resources, ACNN aims to build a skill cadre of young scientists by building innovative educational resources, interactive learning activities, sharing of powerful neuroscience data ...

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News (Dec 2020) Our ScholarPhi paper is accepted to CHI 2021. (Nov 2020) Talks at KAIST AI Colloquium and GIST EECS Colloquium. (Sep 2020) Two main papers (self-supervised text planning, sociable dialogue generation) and two workshop papers (document-level definition detection, augmentation for generation) are accepted to EMNLP 2020.

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Python data science handbook: Essential tools for working with data. O'Reilly Media, 2016. Mainly about systems and frameworks for Big Data: Carpenter, Jeff, and Eben Hewitt. Cassandra: the definitive guide: distributed data at web scale. O'Reilly Media, 2020. Chambers, Bill, and Matei Zaharia. Spark: The definitive guide: Big data processing ...

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data warehouse: A data warehouse is a federated repository for all the data that an enterprise's various business systems collect. The repository may be physical or logical.

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Graduated with distinction (2nd place) in computer science; Academic Interests. Machine Learning / Artificial Intelligence Mathematical foundations beyond machine learning/deep learning; Algorithmic problems in machine learning; Natural Language Processing Machine translation; Algorithms Algorithms for handling large-scale data

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Sep 29, 2016 · Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate ...

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Home | Computer Science and Engineering | University of South ... I am the Director of Machine Learning at the Wikimedia Foundation.I have spent over a decade applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts.

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Scrapinghub's Scrapy Cloud is a battle-tested cloud platform for running web crawlers. Manage and automate your web spiders at scale. Benefit: 1 Free Forever Scrapy Cloud Unit - unlimited team members, projects or requests. Unlimited crawl time and 120 day data retention. Developer tools

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Nov 01, 2020 · The authors believe that data analytics applications in the field of flood risk management should adopt a modular view, moving from a component based to a national scale. At present, data analytics research remains in its developing phase into existing workflows and practices.

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This challenge focuses on two topics, namely large-scale multi-modal (text and image) classification and cross-modal retrieval. The goal of the multi-modal classification task is to predict each product’s 'type code' as defined in the catalog of Rakuten France.

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Galaxy is a web platform for data-intensive biology using geographically-distributed supercomputers. LabKey Server is an extensible platform for integrating, analyzing and sharing all types of biomedical research data. It provides secure, web-based access to research data and includes a customizable data processing pipeline. For the 2020-2021 academic year I am on sabbatical. My current research is in computational inverse methods mainly applied to problems in the Geosciences, but also to more general imaging, signal processing and machine learning problems.

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Graph data management has seen a resurgence in recent years, because of an increasing realization that querying and reasoning about the structure of the interconnections between entities can lead to interesting and deep insights into a variety of phenomena.

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[Aug. 2020] Media Coverage: A large-scale person re-identification dataset named SYSU-30k is released, link. [Nov. 2020] I have completed my PhD thesis defense.NEW! [Oct. 2020] I have served as a reviewer of Signal Processing: Image Communication. [Aug. 2020] I will serve as a reviewer of AAAI-2021.

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May 29, 2019 · Unlike conventional approaches that arbitrarily scale network dimensions, such as width, depth and resolution, our method uniformly scales each dimension with a fixed set of scaling coefficients. Powered by this novel scaling method and recent progress on AutoML , we have developed a family of models, called EfficientNets, which superpass state ...

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M.S. students in the Computer Science general program can only take up to ONE (3-4 unit) course from approved EE, DSCI (formerly INF), MATH, DSO, or ISE courses. This policy still applies even if the course is cross-listed to the CSCI prefix from another academic unit. The list of approved non-CSCI courses can be found below.

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    B.S., Computer Science and Engineering, Tsinghua University, China (1982) Ph.D., Computer Science, University of Illinois at Urbana-Champaign (1992) Grants and Awards. NSF CSSI: Extensible Geospatial Data Framework towards FAIR (Findable, Accessible, Interoperable, Reusable) Science (PI, $4,571,811, 10/2018 – 9/2023) Designing Code Analyses for Large-scale Software Systems, 04/2018 – 08/2018, Paderborn University. (Master course) Seminar Secure Systems Engineering, 10/2017 – 02/2018, Paderborn University. (Master course) Student Tutor. Introduction to Computability, Complexity and Formal Languages, 10/2016 – 02/2017, Paderborn University. (Bachelor ... CSE 511 Data Processing at Scale. Instructor, Graduate level, Computer Science, Arizona State University Demonstrating GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark. Road network traffic data has been widely studied by researchers and practitioners in different areas such as urban planning, traffic ...Dec 28, 2020 · eBook Details: Paperback: 748 pages Publisher: WOW! eBook (December 22, 2020) Language: English ISBN-10: 1839210648 ISBN-13: 978-1839210648 eBook Description: MongoDB Fundamentals: Learn how to deploy and monitor databases in the cloud, manipulate documents, visualize data, and build applications running on MongoDB using Node.js MongoDB is one of the most popular database technologies for ... He is also interested in statistical data analysis, compressive sensing, low-rank matrix recovery, and large-scale optimization, which provides theoretical foundation for his research. He also received the best paper award at the Symposium on Geometry Processing 2013, the best dataset award at the Symposium on Geometry Processing 2018, and the ... Nov 16, 2020 · The DNN training data included some additional features such as average basket size and count of returns. The DNN model outputs only the overall customer monetary value. If you're interested in predicting frequency or churn, you must perform a few additional tasks: Prepare the data differently to change the target and possibly the threshold date. Aug 14, 2020 · CSE 5544: Introduction to Data Visualization AU 2020 (online) Instructor: Prof. Han-Wei Shen Class: WF 11:10 AM - 12:30 PM via ZOOM Office Hour: WF 1:00-2:00 PM via ZOOM Hi there, I am Edward! I'm a data scientist at OOCL, where I build and ship machine learning systems at scale. Natural Language Processing (NLP) and Data Science Platform Architecture are my focus field. I initialize a nlpaug (text augmentaion library) for boostup NLP model performance.

    Data-Triggered Multithreading for Near Data Processing. In 1st Workshop on Near-Data Processing (WoNDP). Dec 2013. Hung-Wei Tseng and Dean M. Tullsen, Software data-triggered threads. In ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA 2012). I'm teaching COMP 150: Natural Language Processing at Tufts University Computer Science department. Research Interests Natural Language Processing (NLP), Automatic Knowledge Base Construction (AKBC), Information Extraction, Causal Reasoning, Question Answering, Machine Learning with applications to AKBC&NLP.

    Computer Science Gene Golub Fellowship, UIUC (2019) Graduate with Honor, ZJU (2016) Meritorious Winner, Mathematical Contest In Modeling, MCM (2015) National Scholarship for Undergraduate, ZJU (2014) The First Class Scholarship for Outstanding Students, ZJU (2013-2014) The First Prize in China Undergraduates Mathematical Contest (2013) Also, perhaps: CSCE 578: Text Processing Recommended Application Area courses: One of the following two sequences of classes. STAT 530 Applied Multivariate Statistics (prereq STAT 509), STAT 511 Probability (prereq MATH 241), STAT 535 Bayesian Data Analysis (prereq CSCE 582 or, STAT 511 and STAT 515) Extending relational query processing with ML inference, Karanasos, CIDR’10. This paper provides a little more detail on the concrete work that Microsoft is doing to embed machine learning inference inside an RDBMS, as part of their vision for Enterprise Grade Machine Learning. Prior to joining ASU, I worked at Amazon Web Services as a Machine Learning Scientist. I graduated with a Ph.D. in Computer Science from the University of Texas at Austin, where I was advised by Prof. Inderjit S. Dhillon and part of the Center for Big Data Analytics. I have also closely worked with Prof. Andrew B. Whinston. Before then, I ... To address this threat, a number of encryption techniques have been proposed which allow both secure storage of data, as well as query processing directly on the ciphertexts. Some basic operations such as exact match, range queries, or evaluation of inner-products are currently supported by such cryptographic primitives, in either the symmetric ...

    Barbara Solenthaler. Department of Computer Science Computer Graphics Laboratory ETH Zurich Universitätsstrasse 6 CNB G 106.1 I am a senior research scientist at the Computer Graphics Laboratory at ETH Zurich, Switzerland, where I am heading the group on Simulation and Animation.

    Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SCIBERT, a pre-trained language model based on BERT (De-vlin et al.,2019) to address the lack of high-quality, large-scale labeled scientific data. SCIBERT leverages unsupervised pretraining on a large multi-domain corpus of scien- Understand and interact with everyone in any language The Gavagai Suite is built to replace all the tedious manual work of understanding text data. Gain instant insights from your open ended feedback, or interact through next-gen conversational AI. Request Demo Understand and interact with everyone, in any language The Gavagai Suite is built to replace … To Appear, Conference on Neural Information Processing Systems (NeurIPS), 2020. ArXiv manuscript, abs/2005.05951, May 2020. Leverage Score Sampling for Faster Accelerated Regression and ERM, [*] Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford. In Conference on Algorithmic Learning Theory (ALT), 2020. Contact GitHub support about this user’s behavior. ... CSE511-Data-Processing-At-Scale. CSE 511: Data Processing at Scale Project Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and ... Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up. Computer Science at UBC. UBC Computer Science Master’s Student and Professor Awarded for Bringing Clarity to Eye-tracking Data Technology enthusiast, have explored many different fields and areas of computer science such as Machine Learning, Web and Mobile App Development, Big Data, and Data Infrastructure; Secured 65th rank globally (25th in India) in Google Kickstart Round H 2018

    Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and ... Participated in WiSSAP 2014 on Deep Learning for Multi-Lingual Speech Processing at IIIT Hyd. Selected and participated in the IMPECS School on Advanced Algorithms-2013 at IIITDM, Jabalpur. Selected and participated in IBM I-Care Winter School-2012 on Big Data Analytics at IISc Bangalore.

    He graduated with a Ph.D. in Electrical Engineering and Computer Science from MIT in 2004. Lin's research aims to build tools that help users make sense of large amounts of data. His work lies at the intersection of information retrieval and natural language processing, with a focus on large-scale distributed algorithms and infrastructure for ...

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