- Comp-neuro - lists.cnsorg.org

Recruiting Postdocs in Theoretical / Computational Neuroscience

by Zylberberg, Joel L

Hi All, Several postdoctoral positions in theoretical/computational neuroscience are available in the Department of Physiology and Biophysics (PBio) at the University of Colorado's Anschutz Medical Campus, located in Aurora Colorado. The successful candidate(s) will work under the supervision of Dr. Joel Zylberberg to understand how the brain functions so robustly, despite the high levels of variability in the activities of its component neurons. This work will involve the development of mathematical /computational models of neural computation and/or neural communication that can be tested using experimental data. The PBio department has strengths in olfaction, audition, decision-making, motor control, cardiac pacemaking, and development, providing a rich scientific environment. Dr. Zylberberg also has on-going collaborations with retinal and hippocampal physiologists, providing additional opportunities for theory-experiment interaction. Requirements: Applicants should have (or soon be completing) a Ph.D. in either neuroscience, or in a quantitative discipline like applied mathematics, physics, computer science, electrical engineering, etc. The ideal candidate is a highly motivated problem solver with strong programming skills, previous experience in theoretical / computational neuroscience, and a deep desire to understand the principles underlying the functioning of the nervous system. Excellent communication skills are a must. Applicants should email Dr. Zylberberg (joel(dot)zylberberg(at)ucdenver.edu) with the following (please provide these in .pdf format): 1. C.V. including a list of publications, research experience, and education 2. A 1-2 page summary of previous research accomplishments 3. A 1-2 page description of future research goals and interests 4. Names and contact information for 2-3 people who can provide letters of reference Applications will be reviewed as they arrive. For more information about the PBio department, please visit our website at medschool.ucdenver.edu/physiology. __ Joel Zylberberg Assistant Professor Department of Physiology and Biophysics University of Colorado School of Medicine Aurora, CO ~the neuroscience is theoretical, but the fun is real

Sept. 8, 2015

Researcher Position, Machine Learning for User Modelling and HRI, Personal Robotics Lab, Imperial College London

by Demiris, Yiannis

Dear colleagues, a research position (either at PhD-candidate or postdoctoral levels) in machine learning for user-modelling and human-robot Interaction are available at the Personal Robotics Laboratory of the Department of Electrical and Electronic Engineering at Imperial College London. Successful applicants will work under the supervision of Dr Yiannis Demiris (www.demiris.info<http://www.demiris.info/>) in the context of the new EU H2020 project PAL (Personal Assistants for healthy Lifestyle, 2015-2019), which aims to develop personalised robotic systems and avatars to assist diabetic (T1DM) children and their caregivers. The PAL position are available from 1st of October 2015 until the end of the project at the end of Feb 2019 subject to renewal. Applicants should have an excellent background in mathematics, machine learning and software engineering, and should be committed to applying their research to real systems interacting with people in challenging environments. The positions offer an excellent working environment in one of the world's top research universities, in one of the most exciting cities in the world. The Department of Electrical and Electrical Engineering at Imperial was ranked as the top EE department in the UK in the recent REF 2014, while its Personal Robotics Laboratory offers an energetic, friendly intellectual environment with state of the art facilities http://www.imperial.ac.uk/PersonalRobotics For further information and links to application material: http://www.imperial.ac.uk/personalrobotics/join_us With best wishes, Yiannis ---- Dr Yiannis Demiris, FIET, FBCS, FRSS Reader (Associate Professor) in Personal Robotics, Department of Electrical and Electronic Engineering, Rm 1014, Imperial College London, South Kensington Campus, Exhibition Road, London, SW7 2BT, UK Tel: +44-(0)2075946300, Fax: +44-(0)2075946274 http://www.iis.ee.ic.ac.uk/yiannis Lab: http://www.imperial.ac.uk/PersonalRobotics

Sept. 8, 2015

Workshop announcement: Supercomputing for Neuroscientists (Juelich, 3.11.2015)

by Abigail Morrison

Supercomputing for Neuroscientists - How High-Performance Computing can help your neuroscience projects Date: 3 November 2015 Venue: Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany Registration deadline: 25 October 2015 This workshop aims at bringing neuroscientists interested in using HPC technology for their projects together with experts from the Jülich Supercomputing Centre’s Simulation Laboratory Neuroscience<www.fz-juelich.de:ias:jsc:slns> whose main goal is to bridge the gap between Computational Neuroscience and HPC. The workshop is primarily intended for PIs whose affiliation is in Germany and who may therefore apply for computing time on the Jülich supercomputers through the national GCS and NIC calls<http://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/ComputingTime/…>, it will also touch on how European supercomputing resources, including those of Jülich, may be accessed by PIs from outside of Germany through the European PRACE access schemes<http://www.prace-ri.eu/hpc-access>. For detailed information on the workshop and registration, please visit: www.fz-juelich.de/ias/jsc/scn<www.fz-juelich.de:ias:jsc:scn> Organizer: SimLab Neuroscience (slns(a)fz-juelich.de) -- Prof. Dr. Abigail Morrison Institute of Neuroscience and Medicine (INM-6) Functional Neural Circuits Group Jülich Research Center http://www.fz-juelich.de/inm/inm-6/ Office: +49 2461 61-9805 Fax # : +49 2461 61-9460 ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Sebastian M. Schmidt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

Sept. 7, 2015

Postdoc positions (Wolpert lab, University of Cambridge)

by Daniel Wolpert

A Postdoc position is available in the Computational and Biological Learning Group, University of Cambridge to work on theoretical and/or experimental approaches to human sensorimotor control. The project is led by Professor Daniel Wolpert and involves investigating the processes involved in sensorimotor decision-making, learning, integration and control. Further details on http://www.wolpertlab.com <http://www.wolpertlab.com/> Closing date Sept 23rd

Sept. 7, 2015

CFP: MLINI 2015 - 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner (corrected version)

by Irina Rish

Call for Papers MLINI 2015 - 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner https://sites.google.com/site/mliniworkshop2015/ December 11-12, 2015, Montreal, Quebec, Canada Submission deadline: Monday, October 12th, 2015 Workshop Overview: ----------------------- MLINI workshop focuses on machine learning approaches in neuroscience, neuroimaging, with an extension to behavioral experiments and psychology. This year, our focus is specifically on "Going Beyond the Scanner", which includes making inferences about mental states of a subject from ''cheaper'' (as opposed to fMRI) behavioral data such as subject's speech and/or text, audio, video, EEG and signals collected from various wearable devices. We believe that machine learning has a prominent role in shaping how questions in neuroscience are framed, and that the machine-learning mind set is now entering modern psychology and behavioral studies. It is also equally important that practical applications in these fields motivate a rapidly evolving line or research in the machine learning community. In parallel, there is an intense interest in learning more about brain function in the context of rich naturalistic environments and scenes. Efforts to go beyond highly specific paradigms that pinpoint a single function, towards schemes for measuring the interaction with natural and more varied scene are made. In this context, many controversies and open questions exist. The goal of the workshop is to pinpoint the most pressing issues and common challenges across the fields, and to sketch future directions and open questions in the light of novel methodology. The proposed workshop is aimed at offering a forum that joins machine learning, neuroscience, and psychology community, and should facilitate formulating and discussing the issues at their interface. Motivated by the previous workshops in this series, MLINI ‘11, MLINI’12, and MLINI’13, we will center this workshop around invited talks and a panel discussion. Triggered by the discussions, this year we plan to adapt the workshop topics to a less traditional scope neuroimaging scope and investigate the role of behavioral models and psychology, including topics such as psycholinguistics. Open questions and possible topics for contribution include, but are not limited to, the following: - How can we move towards more naturalistic stimuli, tasks and paradigms in neuroimaging and neuro-signal analysis? - What kind of mental states can be inferred from cheaper and easier to collect data sources (as an alternative to fMRI), such as text, speech, audio, video, EEG, and wearable devices? - How can we leave the lab when acquiring neuroimaging data, towards exploiting mobile acquisition (EEG and NIRS)? - What type of features should be extracted from naturalistic stimuli such as text, voice, etc., to detect specific mental states and/or mental disorders? - How can we combine traditional neuroimaging with naturalistic data collected from a subject or group of subjects? - In general, can we characterize situations when multivariate predictive analysis (MVPA) and inference methods are better suited for brain imaging analysis than more traditional techniques? - Given recent advances of deep learning in image analysis and other applications, a natural question to ask is whether neuroimaging analysis can benefit from such approaches? - How well can functional networks and dynamical models capture the brain activity, and when using network and dynamics information is superior to standard task-based brain activations? Workshop Format: -------------------------- In this two-day workshop we will explore perspectives and novel methodology at the interface of Machine Learning, Inference, Neuroimaging and Neuroscience. We aim to bring researchers from machine learning and neuroscience community together, in order to discuss open questions, identify the core points for a number of the controversial issues, and eventually propose approaches to solving those issues. Each session will be opened by several invited talks, and an in depth discussion. This will be followed by original contributions. Original contributions will also be presented and discussed during a poster session. The workshop will end with a panel discussion, during which we will address specific questions, and invited speakers will open each segment with a brief presentation of their opinion. Paper Submission: -------------------------- We seek for submission of original (previously unpublished) research papers. The length of the submitted papers should not exceed 8 pages in Springer format, excluding the references (LaTeX2e style files are available on the workshop page). Submission of previously published work is possible as well, but the authors are required to mention this explicitly. Previously published work can be presented at the workshop, but will not be included into the workshop proceedings (which are considered peer-reviewed publications of novel contributions). Moreover, the authors are welcome to present their novel work but choose to opt out of the workshop proceedings in case they have alternative publication plans. Important dates: -------------------------- - October 12, 2014 - paper submission - October 26, 2014 - notification of acceptance/rejection - December 11-12, 2014 - Workshop at Montreal, Quebec, Canada , following the NIPS conference Organizing Committee: -------------------------- Guillermo Cecchi (IBM T.J. Watson Research Center) Kai-min Kevin Chang (Language Technologies Institute, Carnegie Mellon University) Moritz Grosse-Wentrup (Max Plank Institute for Intelligent Systems) Georg Langs (Medical University of Vienna, CSAIL, MIT) Brian Murphy (Machine Learning Department, Carngie Mellon University) *Irina Rish (IBM T.J. Watson Research Center) - main contact Leila Wehbe (University of California, Berkeley)

Sept. 6, 2015

Call for Abstracts: NIPS Workshop on Statistical Methods for Understanding Neural Systems

by Jascha Sohl-Dickstein

NIPS WORKSHOP 2015 CALL FOR ABSTRACTS Statistical Methods for Understanding Neural Systems Friday, December 11th, 2015 Montreal, Canada -------- Organizers: Allie Fletcher Jakob Macke Ryan Adams Jascha Sohl-Dickstein -------- Overview: Recent advances in neural recording technologies, including calcium imaging and high-density electrode arrays, have made it possible to simultaneously record neural activity from large populations of neurons for extended periods of time. These developments promise unprecedented insights into the collective dynamics of neural populations and thereby the underpinnings of brain-like computation. However, this new large-scale regime for neural data brings significant methodological challenges. This workshop seeks to explore the statistical methods and theoretical tools that will be necessary to study these data, build new models of neural dynamics, and increase our understanding of the underlying computation. We have invited researchers across a range of disciplines in statistics, applied physics, machine learning, and both theoretical and experimental neuroscience, with the goal of fostering interdisciplinary insights. We hope that active discussions among these groups can set in motion new collaborations and facilitate future breakthroughs on fundamental research problems. Call for Papers We invite high quality submissions of extended abstracts on topics including, but not limited to, the following fundamental questions: How can we deal with incomplete data in a principled manner? In most experimental settings, even advanced neural recording methods can only sample a small fraction of all neurons that might be involved in a task, and the observations are often indirect and noisy. As a result, many recordings are from neurons that receive inputs from neurons that are not themselves directly observed, at least not over the same time period. How can we deal with this `missing data' problem in a principled manner? How does this sparsity of recordings influence what we can and cannot infer about neural dynamics and mechanisms? How can we incorporate existing models of neural dynamics into neural data analysis? Theoretical neuroscientists have intensely studied neural population dynamics for decades, resulting in a plethora of models of neural population dynamics. However, most analysis methods for neural data do not directly incorporate any models of neural dynamics, but rather build on generic methods for dimensionality reduction or time-series modelling. How can we incorporate existing models of neural dynamics? Conversely, how can we design neural data analysis methods such that they explicitly constrain models of neural dynamics? What synergies are there between analyzing biological and artificial neural systems? The rise of ‘deep learning’ methods has shown that hard computational problems can be solved by machine learning algorithms that are built by cascading many nonlinear units. Although artificial neural systems are fully observable, it has proven challenging to provide a theoretical understanding of how they solve computational problems and which features of a neural network are critical for its performance. While such ‘deep networks’ differ from biological neural networks in many ways, they provide an interesting testing ground for evaluating strategies for understanding neural processing systems. Are there synergies between analysis methods for analyzing biological and artificial neural systems? Has the resurgence of deep learning resulted in new hypotheses or strategies for trying to understand biological neural networks? Confirmed Speakers: Matthias Bethge Mitya Chklovskii John Cunningham Surya Ganguli Neil Lawrence Guillermo Sapiro Tatyana Sharpee Richard Zemel Workshop Website: h <http://users.soe.ucsc.edu/%7Eafletcher/hdnips2013.html> ttps://users.soe.ucsc.edu/~afletcher/neuralsysnips.html <https://users.soe.ucsc.edu/%7Eafletcher/neuralsysnips.html> Email : smnips2015(a)rctn.org Submission details: Submissions should be in the NIPS_2015 format ( http://nips.cc/Conferences/2015/PaperInformation/StyleFiles) with a maximum of four pages, not including references. Submissions will be considered both for poster and oral presentation. Submit at https://cmt.research.microsoft.com/SMN2015/Protected/Author/ or via the link on the workshop website. Important dates: Submission deadline: 10 October, 2015 11:59 PM PDT (UTC -7 hours) Acceptance notification: 24 October, 2015

Sept. 4, 2015

Call for Participation: Bernstein Conference Satellite Workshop "In Vitro Neuronal Networks from 2D to 3D, " Sept. 14, 2015, Heidelberg, Germany

by Tanskanen Jarno

Bernstein Conference Satellite Workshop no. 8 ** In Vitro Neuronal Networks from 2D to 3D ** September 14, 2015, 9:00 - 18:30 Ruprecht-Karls-Universität Heidelberg, Neue Universität Universitätsplatz, Grabengasse 3-5, 69117 Heidelberg, Germany More Information and Registration http://www.bernstein-conference.de/ The program and workshop abstract as a pdf: http://goo.gl/D1hwou Welcome! Jari Hyttinen and Jarno Tanskanen Computational Biophysics and Imaging Group Department of Electronics and Communications Engineering Tampere University of Technology, BioMediTech Finland --- P R O G R A M Welcome & Introduction to 3D in Vitro Technologies - Case: 3DNeuroN Jari Hyttinen, Tampere University of Technology * Session 1: 3D and Controlled Culturing of Neuronal Networks 3D Neural Networks in Engineered Hydrogel Matrices Nicolas Broguiere, Swiss Federal Institute of Technology Zurich Forming Neuron Networks with Controlled Topology in 2D and 3D Janos Vörös, Swiss Federal Institute of Technology Zurich * Session 2: Neuronal-astrocytic Networks and their Modeling Role of Astrocytes in Formation of Functional Neuronal Networks in Vitro Susanna Narkilahti, University of Tampere Computational Modeling of Neuronal-astrocytic Networks Kerstin Lenk, Tampere University of Technology * Session 3: Technology Signal Acquisition and Measurement System for Neural Networks in Vitro Peter Husar, Ilmenau University of Technology Technology and Material Developments for 3D Multielectrode Arrays Heike Bartsch, Ilmenau University of Technology Microbioreactor Designs with Integrated 3D MEA Devices for Cultivation of Neuronal Cells Andreas Schober, Ilmenau University of Technology * Session 4: Stimulation, Analysis, and 3D Network Modeling Investigation of Cultured Neuronal Networks and their Dynamics Induced by Novel Stimulation Paradigms Peter Husar, Fraunhofer IDMT Signal Analysis Methods for Neuronal Microelectrode Array Data Jarno Tanskanen, Tampere University of Technology Computational Modeling from 2D to 3D Networks Kerstin Lenk, Tampere University of Technology * Panel Discussion: From 2D to 3D * Closing of the Workshop Jari Hyttinen, Tampere University of Technology ****

Sept. 4, 2015

CFP: MLINI 2015 - 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner

by Irina Rish

Call for Papers MLINI 2015 - 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner https://sites.google.com/site/mliniworkshop2015/ December 11-12, 2015, Montreal, Quebec, Canada Submission deadline: Monday, October 12th, 2015 Workshop Overview: ----------------------- MLINI workshop focuses on machine learning approaches in neuroscience, neuroimaging, with an extension to behavioral experiments and psychology. This year, our focus is specifically on "Going Beyond the Scanner", which includes making inferences about mental states of a subject from ''cheaper'' (as opposed to fMRI) behavioral data such as subject's speech and/or text, audio, video, EEG and signals collected from various wearable devices. We believe that machine learning has a prominent role in shaping how questions in neuroscience are framed, and that the machine-learning mind set is now entering modern psychology and behavioral studies. It is also equally important that practical applications in these fields motivate a rapidly evolving line or research in the machine learning community. In parallel, there is an intense interest in learning more about brain function in the context of rich naturalistic environments and scenes. Efforts to go beyond highly specific paradigms that pinpoint a single function, towards schemes for measuring the interaction with natural and more varied scene are made. In this context, many controversies and open questions exist. The goal of the workshop is to pinpoint the most pressing issues and common challenges across the fields, and to sketch future directions and open questions in the light of novel methodology. The proposed workshop is aimed at offering a forum that joins machine learning, neuroscience, and psychology community, and should facilitate formulating and discussing the issues at their interface. Motivated by the previous workshops in this series, MLINI ‘11, MLINI’12, and MLINI’13, we will center this workshop around invited talks and a panel discussion. Triggered by the discussions, this year we plan to adapt the workshop topics to a less traditional scope neuroimaging scope and investigate the role of behavioral models and psychology, including topics such as psycholinguistics. Open questions and possible topics for contribution include, but are not limited to, the following: - How can we move towards more naturalistic stimuli, tasks and paradigms in neuroimaging and neuro-signal analysis? - What kind of mental states can be inferred from cheaper and easier to collect data sources (as an alternative to fMRI), such as text, speech, audio, video, EEG, and wearable devices? - How can we leave the lab when acquiring neuroimaging data, towards exploiting mobile acquisition (EEG and NIRS)? - What type of features should be extracted from naturalistic stimuli such as text, voice, etc., to detect specific mental states and/or mental disorders? - How can we combine traditional neuroimaging with naturalistic data collected from a subject or group of subjects? - In general, can we characterize situations when multivariate predictive analysis (MVPA) and inference methods are better suited for brain imaging analysis than more traditional techniques? - Given recent advances of deep learning in image analysis and other applications, a natural question to ask is whether neuroimaging analysis can benefit from such approaches? - How well can functional networks and dynamical models capture the brain activity, and when using network and dynamics information is superior to standard task-based brain activations? Workshop Format: -------------------------- In this two-day workshop we will explore perspectives and novel methodology at the interface of Machine Learning, Inference, Neuroimaging and Neuroscience. We aim to bring researchers from machine learning and neuroscience community together, in order to discuss open questions, identify the core points for a number of the controversial issues, and eventually propose approaches to solving those issues. Each session will be opened by several invited talks, and an in depth discussion. This will be followed by original contributions. Original contributions will also be presented and discussed during a poster session. The workshop will end with a panel discussion, during which we will address specific questions, and invited speakers will open each segment with a brief presentation of their opinion. Paper Submission: -------------------------- We seek for submission of original (previously unpublished) research papers. The length of the submitted papers should not exceed 8 pages in Springer format, excluding the references (LaTeX2e style files are available on the workshop page). Submission of previously published work is possible as well, but the authors are required to mention this explicitly. Previously published work can be presented at the workshop, but will not be included into the workshop proceedings (which are considered peer-reviewed publications of novel contributions). Moreover, the authors are welcome to present their novel work but choose to opt out of the workshop proceedings in case they have alternative publication plans. Important dates: -------------------------- - October 12, 2014 - paper submission - October 26, 2014 - notification of acceptance/rejection - December 11-12, 2014 - Workshop at Montreal, Quebec, Canada , following the NIPS conference Organizing Committee: -------------------------- Guillermo Cecchi (IBM T.J. Watson Research Center) Kai-min Kevin Chang (Language Technologies Institute, Carnegie Mellon University) Georg Langs (Medical University of Vienna, CSAIL, MIT) Brian Murphy (Machine Learning Department, Carngie Mellon University) *Irina Rish (IBM T.J. Watson Research Center) - main contact Leila Wehbe (University of California, Berkeley)

Sept. 4, 2015

The ReScience journal

by Nicolas P. Rougier

Following our "Long journey into reproducible computational neuroscience" (http://journal.frontiersin.org/article/10.3389/fncom.2015.00030/full) it's our great pleasure to announce the creation of "ReScience" which is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research is reproducible. To achieve such a goal, the whole editing chain is radically different from any other traditional scientific journal. ReScience lives on GitHub where each new implementation is made available together with comments, explanations and tests. Each submission takes the form of a pull request that is publicly reviewed and tested in order to guarantee that any researcher can re-use it. Students are strongly encourage to submit to ReScience. Even if the publishing model is a bit different from other academic journals, this will give them a first experience at peer-reviewed publishing where they have to use a rigorous and scientific approach. • More on the journal website: https://github.com/ReScience/ReScience/wiki • Current issue: https://github.com/ReScience/ReScience/wiki/Current-Issue • FAQ: https://github.com/ReScience/ReScience/wiki/Frequently-Asked-Questions • Follow us on twitter (@ReScienceEds): https://twitter.com/rescienceeds And if you're familiar with Git and GitHub, you can also become a reviewer: just contact us. Konrad Hinsen & Nicolas Rougier

Sept. 4, 2015

CFP: 1st International Workshop on Big Data Analytics for Healthcare and Well-being

by Youssef Iraqi

Apologies if you receive multiple copies of this message. ________________________________________________________________ CALL FOR PAPERS 1st International Workshop on Big Data Analytics for Healthcare and Well-being 2016 January 18, 2016, Hong Kong, China http://kecidev.kaist.ac.kr/bigcomp2016/workshop/ In conjunction with the 3rd International Conference on Big Data and Smart Computing (BigComp) 2016 http://conf2016.bigcomputing.org/main/ Purpose and Scope Healthcare and well-being is an important area of research in many countries as one of the emerging application domains for both big data and smart computing. This workshop aims to create opportunities to share on-going works and enhance collaboration among healthcare researchers related to big data or data science, and to foster new innovations of big data and smart computing in the areas of mobile healthcare, smart homes, digital cities, innovations in healthcare and sensing devices, and other related areas. Prospective authors are cordially invited to submit their original contributions covering completed or ongoing work related to the area of big data analytics for healthcare and well-being. The topics include but are not limited to: - Smart health and big data - Data mining, graph mining and data science for healthcare - Big data analytics and social media for healthcare - Smart devices and hardware for healthcare - Smart location-based services for healthcare - Models and tools for smart computing in healthcare - Innovations in healthcare and sensing devices - Security and privacy for big data in healthcare - Cloud and grid computing for healthcare - Standards for big data in healthcare PAPER SUBMISSION Perspective authors are invited to submit their papers, up to 8 pages each, in the IEEE paper template. Papers must be formatted in PDF according to the IEEE two-column format for conference proceedings and submitted through EasyChair, http://www.easychair.org/. The direct link for paper submission is https://easychair.org/conferences/?conf=bigdata4healthcare IMPORTANT DATES Paper Submission Due: September 30, 2015 (23:59, HKT) Notification of Acceptance: November 15, 2015 Camera ready Papers: December 13, 2015 Workshop: January 18, 2016 ORGANIZATIONAL COMMITTEE Workshop Chairs: Youssef Iraqi, Dept. of Electrical & Computer Engineering, Khalifa University, UAE Ho-Jin Choi, School of Computing, KAIST, Korea Min Song, Dept. of Library & Information, Yonsei University, Korea TPC Members: Ae Ran Kim, College of Nursing, Seoul National University, Korea Amjad Gawanmeh, Dept. of Electrical & Computer Engineering, Khalifa University, UAE Byeong-Soo Jeong, Dept. of Computer Engineering, Kyung Hee University, Korea Changseok Bae, SW.Content Research Laboratory, ETRI, Korea Ahsan Khandoker, Department of Biomedical Engineering, Khalifa University, UAE Hani Saleh, Dept. of Electrical & Computer Engineering, Khalifa University, UAE Gwan-Su Yi, Dept. of Bio & Brain Engineering, KAIST, Korea Harish Bhaskar, Dept. of Electrical & Computer Engineering, Khalifa University, UAE Hyeoun-Ae Park, College of Nursing, Seoul National University, Korea Kyuchang Kang, SW.Content Research Laboratory, ETRI, Korea Tae-Min Song, Korea Institute for Health and Social Affairs, Korea Yong-Ik Yoon, Dept. of Multimedia Science, SookMyung Women’s University, Korea Yu-Chae Jung, Dept. of Multimedia Science, SookMyung Women’s University, Korea Nan Xiang, Dept. of Computer Science, Chongqing University of Technology, China Lingyun Zhu, Dept. of Internet of Things, Chongqing University of Technology, China Yong Wang, Dept. of Network Engineering, Chongqing University of Technology, China Dr. Youssef Iraqi Associate Professor Electrical and Computer Engineering Department [cid:logo.gif] PO Box 127788, Abu Dhabi, UAE T +971 (0)2 501 8323 F +971 (0)2 447 2441 Youssef.Iraqi(a)kustar.ac.ae [cid:twitter.jpg]<https://twitter.com/KhalifaUni> [cid:fb.jpg] <https://www.facebook.com/KhalifaUniversity> [cid:youtube.jpg] <http://www.youtube.com/user/KhalifaUniversity> [cid:instagram.jpg] <http://instagram.com/khalifauni> [cid:linkedin.jpg] <https://www.linkedin.com/company/khalifa-university-?trk=biz-companies-cym> [cid:slogan.png]

Sept. 3, 2015