[UAI] Open-rank tenure/tenure-track positions

[Christian Shelton]

The Department of Computer Science & Engineering at the University of
California at Riverside is searching for two open-rank professor positions.
The first position is for machine learning, natural language processing, or
artificial intelligence.  The second is for visualization, human-computer
interaction, virtual/augmented reality, or algorithms.


Assistant Professor Description and Application (either area):
https://aprecruit.ucr.edu/apply/JPF00980

Associate/Full Professor Description and Application (either area):
https://aprecruit.ucr.edu/apply/JPF00981


Application review will begin Jan. 1, 2019.  If you have any questions,
please feel free to contact the search chair, Christian Shelton, at
h...@cs.ucr.edu.

___
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai

[UAI] Postdoctoral Fellowship in Artificial Intelligence at the Basque Center of Applied Mathematics (BCAM)

[Santiago Mazuelas]

Dear all

Applications are invited for a Fellowship position within the Machine Learning 
group at the Basque Center for Applied Mathematics. The focus of the project 
will be on Artificial Intelligence, as well as Machine Learning, Computer 
Vision and Deep Learning.

The Postdoctoral Fellow will work under the supervision of Jose Antonio Lozano, 
leader of the Machine Learning research line at BCAM.
The requirements are the following:
•   PhD completed before the end of 2018, preferably in Computer 
Science or Applied Mathematics 
•   Research experience and interest in Artificial Intelligence in 
general with particular emphasis in areas such as: machine learning, computer 
vision, reasoning under uncertainty
•   Experience in working with real Artificial Intelligence 
problems and expertise on the mathematical modelling of problems and algorithms 
in Artificial Intelligence

The application deadline is the 30th of November. You can find more information 
and submit your application in the site: 
http://www.bcamath.org/en/research/job/postdoctoral-fellowship-in-artificial-intelligence?utm_source=Recruitment&utm_campaign=a4ede6e93d-EMAIL_CAMPAIGN_2018_10_17_02_53_COPY_02&utm_medium=email&utm_term=0_d309d7bd58-a4ede6e93d-63770285

Best,
Santiago Mazuelas
___
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai

[UAI] CFP: AKBC 2019 - Conference on Automated Knowledge Base Construction

[Sameer Singh]

AKBC 2019

1st Conference on Automated Knowledge Base Construction (AKBC)

May 20-22, 2019, Monday-Wednesday, Amherst, MA

www: http://www.akbc.ws ; email: i...@akbc.ws

Key dates

   -

   November 16, 2018, Friday: Conference paper submissions due
   -

   January 11, Friday: Workshop topics announced
   -

   February 15, 2019, Friday: Conference notification
   -

   May 20-21, 2019, Monday-Tuesday: Conference, UMass Amherst
   -

   May 22, 2019, Wednesday: Workshops, UMass Amherst


Knowledge Base Construction

Knowledge gathering, representation, and reasoning are among the
fundamental challenges of artificial intelligence.  Large-scale
repositories of knowledge about entities, relations, and their abstractions
are known as “knowledge bases”.  Most major technology companies now have
substantial efforts in knowledge base construction, and related scholarly
work spans many research areas, including machine learning, natural
language processing, computer vision, information integration, databases,
search, data mining, knowledge representation, human computation,
human-computer interfaces, and fairness.  The AKBC conference serves as a
research forum for all these areas, in both academia and industry.

New Conference

Nearly ten years after the first AKBC workshop in Grenoble, France, AKBC is
becoming a conference.  Why a new stand-alone conference?

   -

   Long-standing and growing interest in the area, now with too much
   material for a one-day workshop.  We have sufficient material for a two-day
   conference plus topical workshops.
   -

   We want to grow and connect the community beyond existing individual
   conference communities, bringing together ML, NLP, DB, IR, KRR, semantics,
   reasoning, common sense, QA, human computation, dialog, HCI.
   -

   We want to set our own culture, including reviewing practices, and
   meeting format. We have fond memories of the first AKBC 2010 in Grenoble: a
   two-day meeting that included an afternoon hike in the Alps with much great
   scientific discussion.
   -

   Why now?  Growing interest across many areas.  Disconnect among multiple
   relevant communities.  Growing industry and government interest.


Call For Papers

We invite the submission of papers describing previously unpublished
research, including new methodology, datasets, evaluations, surveys,
reproduced results, negative results, and visionary positions.

Topics of interest include, but are not limited to:

   -

   Natural language processing, information extraction, extraction of
   entities, relations, and events, semantic parsing, coreference, machine
   reading, entailment, web mining, multilingual NLP.
   -

   Information integration, entity resolution, schema & ontology alignment,
   text and structure alignment, federated KBs, Semantic Web.
   -

   Machine learning, supervised, unsupervised, lightly-supervised and
   distantly-supervised learning, deep learning, symbolic learning, multimodal
   learning, embeddings of knowledge.
   -

   Search, question-answering, reasoning, knowledge base completion,
   queries on mixtures of structured and unstructured data; querying under
   uncertainty.
   -

   Multi-modal knowledge bases: structured data, text, images, video, audio.
   -

   Human-computer interaction, crowdsourcing, interactive learning.
   -

   Fairness, accountability, transparency, misinformation, multiple
   viewpoints, uncertainty.
   -

   Databases, probabilistic databases, distributed databases, database
   cleaning, scalable computation, distributed computation, dynamic data,
   online adaptation of knowledge.
   -

   Systems, languages and toolkits, demonstrations of existing knowledge
   bases.
   -

   Evaluation of AKBC, datasets, evaluation methodology.


Authors of accepted papers will have the option for their conference paper
to be archival (with full text in AKBC Proceedings, and be considered for
best paper awards) or non-archival (listed in AKBC Conference schedule,
with full text in OpenReview, and the flexibility to also submit
elsewhere).  Double-blind reviewing will be performed on the OpenReview
platform, with papers, reviews and comments publicly visible, much like
ICLR 2018.

Papers are restricted to a maximum of 15 pages excluding references in the
AKBC format  (JAIR-like single
column, equivalent of about 8 pages double column). Shorter submissions
will not be penalized. The reviewing is double-blind, and thus the
submissions should be anonymous. Submission site:
http://www.akbc.ws/2019/submission

Dual Submission Policy: Submissions that are identical (or substantially
similar) to versions that have been previously published, or accepted for
publication, are not allowed and violate our dual submission policy.
However, papers that cite previous related work by the authors and papers
that have appeared on non-peered reviewed websites (like arXiv) or that
have been presented at workshops (i.e., venu

[UAI] Call for Book Chapters on Deep Biometrics

[Richard Jiang]

Dear Colleagues,



We would like to invite you to contribute a chapter for the upcoming volume 
entitled “Deep Biometrics” to be published by Springer, the largest global 
scientific, technical, and medical ebook publisher. The volume will be 
available both in print and in ebook format by late 2018/early 2019 on 
SpringerLink, one of the leading science portals that includes more than 8 
million documents, an ebook collection with more than 160,000 titles, journal 
archives digitized back to the first issues in the 1840s, and more than 30,000 
protocols and 290 reference works.



Below is a short description of the volume:

Recent development in machine learning, particularly deep learning, has brought 
out drastic impact on Biometrics, which is a classic topic to utilize Machine 
Learning for biometric identification. Particularly, Deep Learning can benefit 
from the training with large unlabelled datasets via semi-supervised or 
unsupervised learning.



This book aims to highlight recent research advances in biometrics using 
semi-supervised and unsupervised new methods such as Deep Neural Networks, Deep 
Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary 
Networks, Ensemble Methods, and so on, and exploit these novel methods in the 
emerging new areas such as privacy and security issues, cancellable biometrics 
and soft biometrics, smart cities, big biometric data, biometric banking, 
medical biometrics, and healthcare biometrics, etc..



The goal of this volume is to summarize the recent advances in using Deep 
Learning in the area of biometric security and privacy. Topics of interest 
include: (but not limited to)

• Deep Learned Biometric Features

• Convolutional Neural networks

• Deep Stacked Autoencoder

• Deep Face Detection

• Deep Gait Recognition

• Biometrics in Cybersecurity

• Biometrics in Cognitive Robot

• Healthcare Biometrics

• Medical Biometrics

• Biometrics in Social Computing

• Biometric Block Chain

• Privacy and Security Issues

• Iris, Fingerprints, DNA, Palmprints

• Gait, EEG, Heart rates

• Multimodal Fusion

• Soft Biometrics

• Cancellable Biometrics

• Big data issues in Biometrics

• Biometrics for Internet of things

Each contributed chapter is expected to present a novel research study, a 
comparative study, or a survey of the literature. Note that there will be no 
publication fees for accepted chapters.



Important Dates:

  Submission of abstracts: as soon as possible

  Notification of initial editorial decisions: 2-3 days after abstract 
submission

  Submission of full-length chapters Dec 15, 2018

  Notification of final editorial decisions Jan 15, 2019

  Submission of revised chapters Feb 15, 2019



All submissions should be done via EasyChair:

  https://easychair.org/conferences/?conf=deepbio2019

Original artwork and a signed copyright release form will be required for all 
accepted chapters. For author instructions, please visit:

  http://www.springer.com/authors/book+authors?SGWID=0-154102-12-417900-0

Please feel free to contact us via email 
(perceptualscie...@outlook.com, or any 
editors below) regarding your chapter ideas.

Editorial Board

• Dr Richard Jiang

   Computer and Information Sciences,

   Northumbria University, United Kingdom

   Email: richard.ji...@unn.ac.uk

 • Dr Weizhi Meng

   Applied Mathematics & Computer Science

   Technical University of Denmark, Denmark

   Email: w...@dtu.dk

• Professor Chang-Tsun Li

   School of Computing and Mathematics,

   Charles Sturt University, Australia

   Email: c...@csu.edu.au

 • Professor Christophe Rosenberger

   Computer Security

   ENSICAEN – GREYC, France

   Email: 
christophe.rosenber...@ensicaen.fr



Contact:

All questions about submissions can be emailed to 
perceptualscie...@outlook.com or any 
editor in the board.



Many thanks!



Kind Regards,

Editors of the Book



This message is intended solely for the addressee and may contain confidential 
and/or legally privileged information. Any use, disclosure or reproduction 
without the sender’s explicit consent is unauthorised and may be unlawful. If 
you have received this message in error, please notify Northumbria University 
immediately and permanently delete it. Any views or opinions expressed in this 
message are solely those of the author and do not necessarily represent those 
of the University. Northumbria University email is provided by Microsoft 
Office365 and is hosted within the EEA, although some information may be 
replicated globally for backup purposes. The University cannot guarantee that 
this message or any attachment is virus free or has not been intercepted and/or 
amended.

This message is intended solely for the addressee and may contain confidential 
and/or legally privileged inform

[UAI] Faculty Position KU Leuven (Geel Campus): DATA SCIENCE FOR RARE AND EXTREME EVENTS

[Luc De Raedt]

There is a full-time academic vacancy (part of the senior academic staff) in 
the Faculty of Engineering Technology, Department of Computer Science, at KU 
Leuven’s Geel Campus. The research on Machine Learning and Statistics within 
the ADVISE research group at Geel Campus is embedded in the Computer Science 
Technology Cluster. This Technology Cluster focuses primarily on demand-driven 
research, often in collaboration with industry and other organizations. 
Together with the Computer Science part of EAVISE at De Nayer 
(Sint-Katelijne-Waver) Campus, the Computer Science part of ADVISE at Geel 
Campus is affiliated with the internationally renowned research group DTAI 
(Declarative Languages and Artificial Intelligence) of the Department of 
Computer Science. The teaching activities are situated in the Faculty of 
Engineering Technology, which has developed a unique multi-campus model spread 
across seven campuses in Flanders. For this vacancy, both education and 
research activities are mainly situated at Geel Campus.
Unit website: http://wms.cs.kuleuven.be/tc_cs 

Duties: Research
Advanced sensor technologies make it possible to quickly and reliably obtain 
insight into processes in a data-driven manner. Data collection may be 
difficult, however, for events that are rare or expensive to measure.  For many 
applications, it is important to have reliable models and predictions in those 
cases too. Consider, for instance, the analysis of high-qualitative industrial 
processes, where little data or other information about defects is available.  
Not only do the monitoring & modelling aspects play an important role here, but 
also the development of highly-informative sampling procedures. 
In the context described above, you will develop a research programme on data 
science for rare and extreme events at KU Leuven, Geel Campus. You will focus 
on applied research within the areas of data science and statistical quality 
control. For instance, you may develop statistical methods for predicting rare 
events more accurately. Theories of extreme values and acceptance sampling in 
quality control will play an important role. The applied research is 
complementary to the research vision and ongoing `Machine learning and applied 
statistics’ research at Geel Campus. 
You develop strategies for valorisation of the research results. You develop a 
network, both within the academic and non-academic world, to strengthen the 
collaboration between both, among others, for the valorisation of scientific 
results. You are able to acquire competitive funding, which includes 
project-based government funding as well as industrial funding.
You collaborate with other research groups at KU Leuven and other universities. 
In the broader context of Data Science, there are extensive collaboration 
opportunities both with the Department of Mathematics, Division of Statistics 
and within the Department of Computer Science, research group DTAI (research on 
anomaly detection, predictive maintenance, and industrial and medical 
applications) and research group EAVISE at De Nayer (research on Applications) 
Campus.  Additionally, cooperation with the Bio-Engineering Technology Cluster 
at Geel Campus provides opportunities for applying the statistical techniques 
in biosciences in general and in the food industry in particular.
You provide additional expertise, thereby strengthening the fundamental 
research, and you identify topics within existing research programmes that are 
relevant for your own research, providing a fundamental basis for your own 
research.
Duties: Teaching
You provide high-quality education in computer science, mathematics, and 
statistics, in the Faculty of Engineering Technology, Geel Campus. You teach 
bachelor subjects in Mathematics: Algebra and Analysis, in addition to 
Statistics, Bio-statistics and master subjects in Machine Learning/Big Data 
Analysis. 
You commit yourself to the quality of the programme as a whole.
You contribute to the faculty’s and the university’s pedagogical project by 
guiding student projects (for example bachelor’s and master’s theses) and 
supervising PhD students.
You develop your teaching in accordance with KU Leuven’s views on activating 
and researched-based education, and make use of the possibilities for 
educational professionalisation offered by the faculty and the university.
Your teaching duties are limited in the first years of your appointment. In 
your further career, the faculty will also pay a great deal of attention to the 
balance between research and teaching time.
Duties: Service
You are prepared to provide scientific, societal and internal services.
You play an active part in profiling the Faculty of Engineering Technology 
towards new students and the wider professional field by participating in open 
days, networking events and fairs,...
Profile
We are looking for internationally orientated candidates with both educationa

[UAI] Call for Papers for Intelligent Learning Technologies Track at FLAIRS-32

[Alan Carlin]

Hi - this email address came up in looking for the UAI mailing list.  Would it 
be possible to send the Call for Papers below to the UAI mailing list?  Thanks!

--Alan

https://sites.google.com/view/flairs-ilt2018/home

Call for Papers: Intelligent Learning Technologies
Held at FLAIRS-32. May 19-22 2019, Sarasota, Florida
What are Intelligent Learning Technologies?
Intelligent learning technologies (ILT) include a diverse array of 
computer-based systems and tools designed to foster meaningful student 
learning. These technologies are intelligent to the extent they implement 
artificial intelligence principles and techniques to create teachable structure 
from content, analyze and model inputs from the learner, and generate 
personalized and adaptive feedback and guidance. Intelligent tutoring systems 
(ITSs) represent a classic example. ITSs, broadly defined, possess an "outer 
loop" that intelligently selects the next relevant task, or content object, for 
learners to complete based on prior performance, and an "inner loop" that 
provides iterative and intelligent feedback as learners work toward completing 
their tasks. However, intelligent learning technologies encompass more than 
just intelligent tutors. Increasingly, educational games, automated writing 
evaluation, virtual pedagogical agents, simulations, virtual worlds, open-ended 
problem solving, generative concept maps, AI-assisted authoring systems, 
learning content aggregation programs, and e-textbooks rely on some form of 
artificial intelligence to enrich the learning experience.
What is the GOAL of this track?
The purpose of this track is to bring together an international group of 
scientists to present innovative empirical research, technical innovations, and 
well-grounded theory related to artificial intelligence in learning 
technologies. This track will inform attendees about recent developments in the 
design, implementation, and evaluation of such systems.
This track is a continuation of the 2016-2018 FLAIRS ILT track, which naturally 
emerged from the longstanding Intelligent Tutoring Systems special track. The 
aim of the Intelligent Learning Technologies track is to be more representative 
and inclusive of the diverse work being conducted with intelligent systems that 
support student learning.
What kind of studies will be of interest?
The Intelligent Learning Technologies special track welcomes original, 
well-written reports on empirical evaluations of learning technologies, 
innovative designs and implementations, and theoretical principles that advance 
the field. The preference for all submissions is to include both substantive 
references to the existing literature and empirical data. We seek submissions 
that address a variety of intelligent learning technology issues including, but 
not limited to:
1. Adaptive scaffolding in open-ended learning environments
2. Assistive technologies for learners with special needs
3. Automated writing evaluation
4. Educational data mining and learning analytics
5. Interaction-based learner modeling from novice or expert populations
6. Effective design principles for intelligent learning technologies
7. Game-based, narrative-based, and virtual learning environments
8. Intelligent tutoring systems
9. Natural language processing to support intelligent interaction and feedback
10. Novel designs, interfaces, and scaffolds
11. Overcoming challenges within the field (e.g., gaming the system, 
ill-defined domains)
12. Teachable agents, learning companions, and other pedagogical agents
13. Tests of existing intelligent learning technologies
14. Efficient sampling methods for experiments in learning environments
15. Analysis and analytics of Massive Open Online Courses (MOOC)
Important Dates
November 19, 2018 - Paper submission deadline
January 21, 2019 - Paper acceptance notification
February 25, 2019 - Camera ready version due
May 19-22, 2019 - Conference
Track Co-Chairs:
Alan Carlin, Aptima, Inc. acar...@aptima.com
Benjamin Nye, University of Southern California Institute for Creative 
Technologies. n...@ict.usc.edu
Stephen E. Fancsali, Carnegie Learning, Inc. 
sfancs...@carnegielearning.com
Conference Proceedings
Papers will be refereed and all accepted papers will appear in the conference 
proceedings, which will be published by AAAI Press.
In cooperation with: Association for the Advancement of Artificial 
Intelligence
Submission Guidelines
Interested authors should format their papers according to AAAI formatting 
guidelines.
 The papers should be original work. Papers should not exceed 6 pages (4 pages 
for a poster) and are due by November 19, 2018. F

[UAI] Postdoctoral Research Associate Position at Stanford

[Stefano Ermon]

Professor Stefano Ermon is seeking an outstanding researcher for a
postdoctoral position at the Stanford SUSTAIN group (
http://sustain.stanford.edu/). The postdoc will carry out Machine Learning
research on a broad range of  topics, including learning with limited
supervision, spatio-temporal modeling, learning from multimodal data, and
computer vision applications. The postdoc will have the opportunity to
collaborate with other researchers across disciplines at Stanford,
particularly those working on areas of sustainable development as part of a
Data for Development Initiative. We welcome applications from candidates
with diverse educational backgrounds.

*Required qualifications:*
A Ph.D. (completed by start of employment) in Computer Science, or an
relevant area
Training in Machine Learning and Artificial Intelligence
Programming experience (e.g., Python)

*Desired qualifications:*
Experience in interdisciplinary research, working in collaborative teams,
and managing research assistants
Experience with deep learning tools (e.g., TensorFlow, PyTorch)
Experience with remote sensing data is a plus

*Duration:* This is a one-year position with the expectation of renewal for
additional years conditional on performance.

*To apply:* Applicants should email their CV and research statement to
stanfordcspostdocapplicat...@gmail.com. Please include the contact
information of at least 3 references in your CV. Review of applications
will begin immediately and will continue until the position is filled.
___
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai

[UAI] Fwd: CFP IEEE TNNLS Special Issue on Deep Representation and Transfer Learning for Smart and Connected Health

[Ariel Ruiz-Garcia]

Call for Papers: *IEEE Transactions on Neural Networks and Learning Systems*

*Special Issue on **Deep Representation and Transfer Learning for Smart and
Connected Health*


*Important Dates*

31 March 2019 – Deadline for manuscript submission

30 June 2019 – Reviewer’s comments to authors

31 August 2019 – Submission of revised papers

31 October 2019 – Final decision of acceptance

30 November 2019 – Camera-ready papers

December 2019-February 2020 – Tentative publication date



*Aims and Scope**:*

Deep neural networks have proven to be efficient learning systems for
supervised and unsupervised tasks in a wide range of challenging
applications. However, learning complex data representations using deep
neural networks can be difficult due to problems such as lack of data,
exploding or vanishing gradients, high computational cost, or incorrect
parameter initialization, among others. Transfer Learning (TL) can
facilitate the learning of data representations by taking advantage of
transferable features learned by a model in a source domain, and adapting
the model to a new domain. This approach has demonstrated to produce better
generalization performance than random weight initialization, and has
produced state-of-the-art results in signal and visual processing tasks.
Accordingly, emerging and challenging domains, such as smart and connected
health (SCH), can benefit from new theoretical advancements in
representation and transfer learning (RTL) methods.



One of the main advantages of TL is its potential to be applied in a wide
range of domains and for different learning tasks. For instance, in facial
affect recognition, the representations learned by a deep model trained to
recognize faces in an unsupervised fashion can be employed and improved by
a second model to perform emotion recognition in supervised manner.
Nonetheless, learning data representations that provide a good degree of
generalization performance remains a challenge. This is due to issues such
as the inherent trade-off between retaining too much information from the
input and learning universal features. Similarly, despite the obvious
advantages of TL, effective use of parameters learned by a given model in a
different domain is a challenge, particularly when there is limited data in
the target domain. This challenge increases when the joint distribution of
the input features and output labels is different in the target domain. In
addition, determining how to reject unrelated information or remove dataset
bias during TL is yet to be solved. Other limitations are caused by lack of
existing theoretical approaches in RTL capable of explaining or
interpreting the learning process of deep models, or determining how to
best learn a set of data representations that are ideal for a given task,
whether in a regression or classification problem.  Therefore, new n
theoretical mechanisms and algorithms are required to improve the
performance and learning process of deep neural networks.



Despite these constraints, RTL will play an essential role in building the
next generation of intelligent systems designed to assists humans with
their daily needs. Consequently, domains of great interest to human
society, such as SCH, will benefit from new advancements in RTL. For
instance, one of the main challenges in designing effective SCH
applications is overcoming the lack of labelled data. RTL can overcome this
limitation by training a model to learn universal data representations on
larger corpora in a different domain, and then adapting the model for use
in a SCH context. Similarly, RTL can be used in conjunction with generative
adversarial networks to overcome class imbalance problems by generating new
healthcare-related data, which can also be used to improve the
generalization performance of deep models in SCH applications. Furthermore,
RTL can be used to initialize and improve the learning of deep
reinforcement learning models designed for continuous learning in
patient-centered cognitive support systems, among others. Nonetheless, the
use of RTL in designing SCH applications requires overcoming problems such
as dataset bias or neural network co-adaptation.



This special issue on Deep Representation and Transfer Learning for Smart
and Connected Health invites researchers and practitioners to present
novel contributions
addressing theoretical aspects of representation and transfer learning. The
special issue will provide a collection of high quality research articles
addressing theoretical work aimed to improve the generalization performance
of deep models, as well as new theory attempting to explain and interpret
both concepts. State-of-the-art works on applying representation and
transfer learning for developing smart and connected health intelligent
systems are also very welcomed. Topics of interest for this special issue
include but are not limited to:


*Theoretical Methods:*

·  Distributed representation learning;

·  Transfer learnin

[UAI] Extended paper deadline 11/30; BICOB 2019: Int'l Conf Bioinformatics & Comp Biology

[Al-Mubaid, Hisham]

BICOB-2019:  11th International Conference on Bioinformatics and Computational 
Biology
March 18-20, 2019   -   Honolulu, HI, USA
Waikiki Beach Marriott Resort and Spa



Important Dates
Paper Submission Deadline: November 30, 2018 (extended)
 Notification of Acceptance: December 30, 2018
 Pre-registration and Camera-ready Manuscripts: January 20, 2019

Paper submission: https://easychair.org/conferences/?conf=bicob2019

 Conference website: http://sce.uhcl.edu/bicob19/


The 2019 International Conference on Bioinformatics and Computational Biology 
BICOB-2019 is one of the front platforms for disseminating latest research 
results and techniques in bioinformatics, computational biology, genomics, and 
medical informatics. BICOB-2019 is the 11th year of the conference and will be 
held between 18-20 March 2019 in Honolulu, HI, USA. BICOB-2019 will provide a 
vibrant atmosphere and venue for bioinformatics and computational biology 
scientists to present and publish their research results, investigations, and 
studies. In recent years, most areas of bioinformatics, computational biology, 
and medical informatics have experienced significant advances driven by 
computational techniques and big data. Moreover, bioinformatics and 
computational biology continue to be a vibrant research area with broadening 
applications and new emerging challenges. BICOB-2019 seeks original and 
high-quality contributions in the fields of bioinformatics, computational 
biology, systems biology, medical informatics and the related areas. The 
conference includes a Best Paper Award to be presented during the conference 
banquet. We also encourage work in progress and research results in the 
emerging and evolutionary computational areas. Work in the computational 
methods related to, or with application in, bioinformatics is also encouraged 
including computational intelligence and its application in bioinformatics, 
bio-data mining and text mining, evolutionary algorithms, nature-inspired 
computation, machine learning and bio-NLP, biomedical ontology, biomathematics, 
modeling and simulation, pattern recognition, data visualization, 
biostatistics. The topics of interest include (and are not limited to):


* Genome analysis: Genome assembly, Next-Gen genomics and metagenomics, genome 
and chromosome annotation, gene finding, alternative splicing, EST analysis and 
comparative genomics.

* Sequence analysis: Multiple sequence alignment, sequence search and 
clustering, next-generation sequencing NGS, function prediction, motif 
discovery, functional site recognition in protein, RNA and DNA sequences.

* Phylogenetics: Phylogeny estimation, models of evolution, comparative 
biological methods, and population genetics.

* Systems biology: Systems approaches to molecularbiology, 
multiscale modeling, pathways, gene networks, transcriptomics - microarray data 
analysis, proteomics, and epigenomics.

* Healthcare Informatics: Healthcare data acquisition, analysis and mining. 
Clinical decision support systems, and healthcare information systems.

* Structural Bioinformatics: Structure matching, prediction, analysis and 
comparison; methods and tools for docking; protein design

* Analysis of high-throughput biological data: Microarrays (nucleic acid, 
protein, array CGH, genome tiling, and other arrays), EST, SAGE, MPSS, 
proteomics, mass spectrometry, query languages, interoperability, bio-ontology 
and bio-data mining.

* Genetics and population analysis: Linkage analysis, association analysis, 
population simulation, haplotyping, marker discovery, and genotype calling.


Moreover, BICOB welcomes submissions in all areas of computing with impact on 
life sciences including algorithms, databases, languages, systems, and 
high-performance computing. For example: Parallel and high-performance 
techniques, computational biology on emerging architectures and hardware 
accelerators.



Journal Publication:

Authors of selected high quality papers in BICOB-2019 will be invited to submit 
extended version of their papers for possible publication in bioinformatics 
journals including the Journal of Bioinformatics and Computational Biology 
(JBCB) (selected papers in previous BICOB conferences were published in JBCB).



Important Dates:
Paper Submission Deadline: October 31 >> extended: November 30, 2018
Notification of Acceptance: December 30, 2018

For more information: please contact:



Program co-Chairs:   Hisham Al-Mubaid - Email: 
his...@uhcl.edu  Tel: +1-281-283-3802

Oliver Eulenstein - Email: oeule...@iastate.edu  
Tel: +1-515-294-2407



  Conference Chair: Qin Ding - Email:  
di...@ecu.edu  Tel: +1-252-328-9686



Publicity Chair: Nurit Haspel - Email: 
nurit.has...@umb.edu  Tel: +1-6