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        <h1 class="mb-5">5th International Winter School on Big Data</h1>
        <h3 style="font-weight: 100">BigDat 2019</h3>
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        <h3 style="font-weight: 100">Cambridge, United Kingdom - January 7-11, 2019</h3>
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Latest information

Urgent News

► The first lecture by Andrey Ustyuzhanin will be on Friday at 09:30, room LT1 (due to travel delay).


► Course by Zhongfei Zhang cancelled because of health issues.

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                <h3>Important dates</h3>
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          <h3>Big Data</h3>

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SCOPE

BigDat 2019 will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of big data, which covers a large spectrum of current exciting research and industrial innovation with an extraordinary potential for a huge impact on scientific discoveries, medicine, engineering, business models, and society itself. Renowned academics and industry pioneers will lecture and share their views with the audience.

Most big data subareas will be displayed, namely foundations, infrastructure, management, search and mining, security and privacy, and applications (to biological and health sciences, to business, finance and transportation, to online social networks, etc.). Major challenges of analytics, management and storage of big data will be identified through 2 keynote lectures, 24 four-hour courses, and 1 round table, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Interaction will be a main component of the event.

An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles.

ADDRESSED TO

Master's students, PhD students, postdocs, and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, BigDat 2019 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen and discuss with major researchers, industry leaders and innovators.

VENUE:

BigDat 2019 will take place in Cambridge, a city home of a world-renowned university. The venue will be:


  • University of Cambridge
  • Department of Engineering
  • Trumpington Street
  • Cambridge CB2 1PZ
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STRUCTURE

3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.

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        <h3>Keynotes and Courses (24)</h3>
        <B>Keynote </B><BR><BR>
         <li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Takeda.jpg">Kenji Takeda (Director, Health and AI Partnerships, Microsoft Research) <BR><strong><a href="/coursedescription/#Takeda">Big Data and AI - What's It Really Good for ?</a></strong></li>            <B>Courses </B>
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        <li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Back.jpg"> Thomas Bäck & Hao Wang (Leiden University) [introductory/intermediate]<BR><strong><a href="/coursedescription/#Bäck">Data Driven Modeling and Optimization for Industrial Applications</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Bonneau.jpg"> Richard Bonneau (New York University) [introductory]<BR><strong><a href="/coursedescription/#Bonneau">Large Scale Machine Learning Methods for Integrating Protein Sequence and Structure to Predict Gene Function</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Cakir.jpg"> Altan Cakir (Istanbul Technical University) [introductory/intermediate]<BR><strong><a href="/coursedescription/#Cakir">Processing Big Data with Apache Spark: From Science to Industrial Applications</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Cao.jpg"> Jiannong Cao (Hong Kong Polytechnic University) [introductory/intermediate]<BR><strong><a href="/coursedescription/#Cao">Cross-Domain Multi-Source Big Data Fusion and Analytics</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Chawla.jpg"> Nitesh Chawla (University of Notre Dame) [intermediate/advanced]<BR><strong><a href="/coursedescription/#Chawla">Network Science: Representation Learning and Higher Order Networks</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Cristianini.jpg"> Nello Cristianini  (University of Bristol) [introductory]<BR><strong><a href="/coursedescription/#Cristianini">The Interface between Big Data and Society</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Fox.jpg"> Geoffrey C. Fox (Indiana University, Bloomington) [intermediate]<BR><strong><a href="/coursedescription/#Fox">High Performance Big Data Computing</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Gerbing.jpg"> David Gerbing (Portland State University) [introductory]<BR><strong><a href="/coursedescription/#Gerbing">Data Visualization with R</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Knoblock.jpg"> Craig Knoblock (University of Southern California) [intermediate/advanced]<BR><strong><a href="/coursedescription/#Knoblock">Building Knowledge Graphs</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/McLachlan.jpg"> Geoff McLachlan (University of Queensland) [intermediate/advanced]<BR><strong><a href="/coursedescription/#McLachlan">Applying Finite Mixture Models to Big Data</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Meyer.jpg"> Folker Meyer (Argonne National Laboratory) [intermediate]<BR><strong><a href="/coursedescription/#Meyer">Skyport2: A Multi Cloud Framework for Executing Scientific Workflows</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Minor.jpg"> Wladek Minor (University of Virginia) [introductory/advanced]<BR><strong><a href="/coursedescription/#Minor">Big Data in Biomedical Sciences</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Mohanty.jpg"> Soumya Mohanty (University of Texas Rio Grande Valley) [introductory/intermediate]<BR><strong><a href="/coursedescription/#Mohanty">Swarm Intelligence Methods for Statistical Regression</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Pal.jpg"> Sankar K. Pal (Indian Statistical Institute) [introductory/advanced]<BR><strong><a href="/coursedescription/#Pal">Machine Intelligence and Soft Granular Mining: Features, Applications and Challenges</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Rokach.jpg"> Lior Rokach (Ben-Gurion University of the Negev) [introductory/advanced]<BR><strong><a href="/coursedescription/#Rokach">Ensemble Learning</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Rosenblum.jpg"> Michael Rosenblum (University of Potsdam) [introductory/intermediate]<BR><strong><a href="/coursedescription/#Rosenblum">Synchronization Approach to Time Series Analysis</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Samet.jpg"> Hanan Samet (University of Maryland) [introductory/intermediate]<BR><strong><a href="/coursedescription/#Samet">Sorting in Space: Multidimensional, Spatial, and Metric Data Structures for Applications in Spatial and Spatio-textual Databases, Geographic Information Systems (GIS), and Location-based Services</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Smith.jpg"> Rory Smith (Monash University) [intermediate/advanced]<BR><strong><a href="/coursedescription/#Smith">Bayesian Inference: 18th century insight into 21st century data science</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Srivastava.jpg"> Jaideep Srivastava (University of Minnesota) [intermediate]<BR><strong><a href="/coursedescription/#Srivastava">Social Computing - Concepts and Applications</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Suarez.jpg"> Mayte Suárez-Fariñas (Icahn School of Medicine at Mount Sinai) [intermediate]<BR><strong><a href="/coursedescription/#Suarez-Farinas">A Practical Guide to the Analysis of Longitudinal Data Using R</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Ullman.jpg"> Jeffrey Ullman (Stanford University) [introductory]<BR><strong><a href="/coursedescription/#Ullman">Big-data Algorithms That Aren't Machine Learning</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Ustyuzhanin.jpg"> Andrey Ustyuzhanin (National Research University Higher School of Economics) [intermediate/advanced]<BR><strong><a href="/coursedescription/#Ustyuzhanin">Challenge-driven Data Science: Cracking Domain Problems by Crowd Intelligence</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Wil.jpg"> Wil van der Aalst (RWTH Aachen University) [introductory/intermediate]<BR><strong><a href="/coursedescription/#Wil van der Aalst">Process Mining: Data Science in Action</a></strong><HR></li><li style="margin-bottom:10px"><img style="padding:5px;" width=40px src="/content/coursedescription/images/Zhang.jpg"> Zhongfei Zhang (Binghamton University) [introductory/advanced]<BR><strong><a href="/coursedescription/#Zhang">Relational and Multimedia Data Learning</a></strong><HR></li></ul>
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SPECIAL SESSIONS

Open session

An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing title, authors, and summary of the research to david@irdta.eu by December 30, 2018.

Schedule available here.

Industrial session

A session will be devoted to 10-minute demonstrations of practical applications of big data in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. At least one of the people participating in the demonstration must register for the event. Expressions of interest have to be submitted to bigdata2019@mrao.cam.ac.uk by December 30, 2018.

Employer session

Firms searching for personnel well skilled in big data will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for, to be circulated among the participants prior to the event. At least one of the people in charge of the search must register for the event. Expressions of interest have to be submitted to david@irdta.eu by December 30, 2018.

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Registration

It has to be done at:

http://bigdat2019.irdta.eu/registration/

The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an approximation of the respective demand for each course. During the event, participants will be free to attend the courses they wish.

Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration facility disabled when the capacity of the venue is exhausted. It is highly recommended to register prior to the event.

Fees

Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline.

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Certificate

A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.

Question and further information

David Silva: david@irdta.eu

Acknowledgments

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              <strong>Active links:</strong><BR><span style='font-size:0.75em'><a href='http://tpnc2019.irdta.eu/'><strong>TPNC 2019</strong> - 8th International Conference on the Theory and Practice of Natural Computing</a></span><BR><span style='font-size:0.75em'><a href='http://slsp2019.irdta.eu/'><strong>SLSP 2019</strong> - 7th International Conference on Statistical Language and Speech Processing</a></span><BR><span style='font-size:0.75em'><a href='http://deeplearn2019.irdta.eu/'><strong>DeepLearn 2019</strong> - 3rd International Summer School on Deep Learning</a></span><BR><span style='font-size:0.75em'><a href='http://alcob2019.irdta.eu/'><strong>AlCoB 2019</strong> - 6th International Conference on Algorithms for Computational Biology</a></span><BR><span style='font-size:0.75em'><a href='http://lata2019.irdta.eu/'><strong>LATA 2019</strong> - 13th International Conference on Language and Automata Theory and Applications</a></span><BR>
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                <strong>Past links:</strong><BR>
                <span style='font-size:0.75em'><a href='http://grammars.grlmc.com/BigDat2018/'>BigDat 2018</a></span><BR><span style='font-size:0.75em'><a href='http://grammars.grlmc.com/BigDat2017/'>BigDat 2017</a></span><BR><span style='font-size:0.75em'><a href='http://grammars.grlmc.com/BigDat2016/'>BigDat 2016</a></span><BR><span style='font-size:0.75em'><a href='http://grammars.grlmc.com/BigDat2015/'>BigDat 2015</a></span><BR>
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