Celebrating 30 years of the SMIA
October 23 to 28 • Ensenada, Baja California, Mexico
Wednesday October 25
|8:00 AM||Hotel Cortez||Hotel Coral & Marina|
|8:30 AM||Hotel Coral & Marina||CICESE|
|9:00 PM||UABC||Hotel Coral & Marina|
(anticipated); special issues of journals.
Keynote speakers: to be announced.
Tours to unique attractions: to be announced.
Acceptance rate (oral session, LNAI volume) target around 25% - See information about student grants
Collocated: Workshops / Call for Workshops, Tutorials / Call for Tutorials, Call for Doctoral Consortium
Iwate PU, Japan
U. Houston, USA
U. California, Irvine
INAOE Puebla, Mexico
characterized by Springer as premier conference in Artificial
Intelligence. It is a
high-level peer-reviewed international conference covering all areas of
Intelligence, traditionally held in
All previous editions of MICAI were published in Springer LNAI, and the Special Sessions of most of the past MICAI events have been published by the CPS; At past MICAI events, extended versions of a considerable number of the LNAI papers were invited to special issues of journals, including ISI JCR-indexed journals; see for example a special issue on MICAI of Expert Systems with Applications. Recent MICAI events received over 300–400 submissions from over 40 countries each, with acceptance rate around 25% for the main session.
Publication: Papers accepted for long oral presentation will be published by Springer in a volume of the series Lecture Notes in Artificial Intelligence (LNAI). Special Session papers (or main session papers of which the authors choose this) will be published post-conference by CPS and a number of prestigious journals. Best papers awards will be granted to the 1st, 2nd and 3rd places. Special issues of journals are anticipated for best papers. Extended versions of selected papers are expected to be invited to special issues of journals, including ISI JCR-indexed journals. Traditionally at MICAI, the results of the SMIA Best Thesis in Artificial Intelligence Contest are announced.
|June 16, 2017||
Expression of interest: registration of draft abstracts -- just a general idea of what your paper will be about (why not submitting it now and change it later if needed?)
|July 12, 2017||
Uploading of full text of registered papers for blind review.
|August 12, 2017||
|September 08, 2017||
Camera-ready and payment deadlines for LNAI papers only
|to be announed||
Camera-ready deadline for poster papers
Pre-conference events (registration, meetings)
|October 23 to 24||
Pre-conference events (workshops, tutorials)
|October 25 to 27||
Main conference (keynote and regular talks, cultural program)
Post-conference events (meetings, cultural program)
Tours to unique attractions:
Hamido Fujita, Iwate Prefectural University, Japan
Title: Challenges on Big data based Clouds Health-Care for Risk Predictions based on Ensemble Classifiers and Subjective Analysis
Abstract: Discovering patterns from big data attracts a lot of attention due to its importance in discovering accurate patterns and features that are used in predictions of decision making. The challenges in big data analytics are the high dimensionality and complexity in data representation analytics especially for on-line feature selection. Granular computing and feature selection on data streams are among the challenge to deal with big data analytics that is used for Decision making. We will discuss these challenges in this talk and provide new projection on ensemble learning for on-line health care risk prediction. In decision making most approaches are taking into account objective criteria, however the subjective correlation among different ensembles provided as preference utility is necessary to be presented to provide confidence preference additive among it reducing ambiguity and produce better utility preferences measurement for good quality predictions. Different type of data (time series, linguistic values, interval data, etc.) imposes some difficulties to data analytics due to preprocessing and normalization processes which are expensive and difficult when data sets are raw, or imbalanced. We will highlight these issues through project applied to health-care for elderly, by merging heterogeneous metrics from multi-sensing environment for providing health care predictions for active aging elderly at home. We have utilized ensemble learning as multi-classification techniques on multi-data streams using incremental learning for modified data. Subjectivity (i.e., service personalization) would be examined based on correlations between different contextual structures that are reflecting the framework of personal context, for example in nearest neighbor based correlation analysis fashion. Some of the attributes incompleteness also may lead to affect the approximation accuracy. Attributes with preference-ordered domain relations properties become one aspect in ordering properties in rough approximations. We outline issues on Virtual Doctor Systems, and highlights its innovation in interactions with elderly patients, also discuss these challenges in multiclass classification and decision support systems research domains. In this talk I will present the current state of art and focus it on health care risk analysis applications with examples from our experiments.
Bio: Hamido Fujita is professor at Iwate Prefectural University (IPU), Iwate, Japan, as a director of Intelligent Software Systems. He is the Editor-in-Chief of Knowledge-Based Systems, Elsevier of impact factor (3.325) for 2015. He received Doctor Honoris Causa from Óbuda University in 2013, and a title of Honorary Professor from Óbuda University, Budapest, Hungary in 2011. He received honorary scholar award from University of Technology Sydney, Australia on 2012. He is Adjunct professor to Stockholm University, Sweden, University of Technology Sydney, National Taiwan Ocean University and others. He has supervised PhD students jointly with University of Laval, Quebec, Canada; University of Technology, Sydney, Australia; Oregon State University (Corvallis), University of Paris 1 Pantheon-Sorbonne, France and University of Genoa, Italy. He has four international Patents in Software System and Several research projects with Japanese industry and partners. He is vice president of International Society of Applied Intelligence, and Co-Editor in Chief of Applied Intelligence Journal, Springer. He has given many keynotes in many prestigious international conferences on intelligent system and subjective intelligence. He headed a number of projects including Intelligent HCI, a project related to Mental Cloning as an intelligent user interface between human user and computers and SCOPE project on Virtual Doctor Systems for medical applications.
University of Houston, USA
Title: Named Entity Recognition in Challenging Contexts
Abstract: Named entities (NE), usually proper names, are the who, the what, the where and the when of events. NE recognition refers to the task of identifying the sequences of words that correspond to NE mentions, and this task is the first step in information extraction systems. NEs are also part of the pipeline of many natural language processing systems, including machine translation, summarization, and question answering. In the late 1990's and early 2000's there were several NE recognition shared tasks and because of the high performance of these systems (reported f-measures of 88% even in cross-lingual settings), this task was considered solved. This all changed with the widespread use of social media and micro-blogging sites. The relaxed grammar, the non-standard spellings and the frequent typos, combined with the great diversity in topics covered in social media data, create new challenges for text processing tools, including NE recognition systems. In this talk I will present our ongoing efforts to solve the NE recognition task in text from social media and micro-blogging platforms, including Reddit, YouTube, Twitter and StackExchange. I will discuss the need to model text at different levels to achieve robustness to noise, and I will motivate our need to combine a traditional machine learning approach to NE recognition with the muscle of deep learning architectures. This system is the top performing system at the recent shared task on Novel and Emerging Entity Recognition hosted by the Empirical Methods in Natural Language Processing (EMNLP) Workshop on Noisy User Generated Text 2017. After discussing our most recent results, I will conclude the talk with a discussion on the outstanding challenges in this area.
Bio: Thamar Solorio is an Associate Professor in the Department of Computer Science at the University of Houston (UH). She is the founder and director of the Research in Text Understanding and Analysis of Language (RiTUAL) group at UH. Her main research interests include stylistic modeling of text, syntactic analysis of mixed language data, language assessment, and information extraction from social media data. She has M.S. and PhD degrees in Computer Science from INAOE, Puebla, Mexico. The Department of Defense and the National Science Foundation currently fund her research program. She is the recipient of a CAREER award for her work in authorship analysis, and the 2014 Denice Denton Emerging Leaders ABIE Award. She serves as editorial board member for the Journal on Artificial Intelligence Research (JAIR) and the Computer Speech and Language Journal.
|Pierre Baldi, University of California , Irvine
Title: Deep Learning Theory, Algorithms, and Applications in the Natural Sciences
Abstract: The process of learning is essential for building natural or artificial intelligent systems. Thus, not surprisingly, machine learning is at the center of artificial intelligence today. And deep learning--essentially learning in complex systems comprised of multiple processing stages--is at the forefront of machine learning. In the last few years, deep learning has led to major performance advances in a variety of engineering disciplines from computer vision, to speech recognition, to natural language processing, and to robotics. In this talk we will first address some fundamental theoretical issues about deep learning through the theory of local learning and deep learning channels. We will then describe inner and outer algorithms for designing deep recursive neural architectures to process structured, variable-size, data such as biological or natural language sequences, phylogenetic or parse trees, and small or large molecules in biochemistry. Finally we will present various applications of deep learning to problems in the natural sciences, such as the detection of exotic particles in high-energy physics, the prediction of molecular properties and reactions in chemistry, and the prediction of protein structures in biology.
Bio: Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Chancellor's Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines. He has made several contributions to the theory of deep learning, and developed and applied deep learning methods for problems in the natural sciences such as the detection of exotic particles in physics, the prediction of reactions in chemistry, and the prediction of protein secondary and tertiary structure in biology. He has written four books and over 300 peer-reviewed articles. He is the recipient of the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, and a 2014 Google Faculty Research Award. He is and Elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.
|Eduardo Morales, INAOE, Puebla, Mexico Title: The Child Robot Abstract: In his 1950 article, Alan Turing proposed, in addition to his famous test, that rather than programming a machine to simulate the adult mind, it would make more sense to program a Child Machine that could be educated to obtain an adult mind. Recent advances in Machine Learning and Robotics are starting to get closer to this vision. In this talk we will revisit the original ideas proposed by Turing, talk about some of our research work on Machine Learning techniques applied to Robotics, in particular, for learning models of objects and for learning how to perform tasks, and our perspectives over possible future developments. Bio: Eduardo Morales holds a Ph.D. from the Turing Institute - University of Strathclyde, in Scotland and an MSc in Artificial Intelligence from the University of Edinburgh. He is a member of the National Researcher System (Level 3) and member of the Mexican Academy of Science. He has been responsible for more than 25 research projects and has more than 150 peer-review papers. He was an Invited Researcher at the Electric Power Research Institute, in Palo Alto, CA (1986), a Technical Consultant (1989-1990) at the Turing Institute, a Researcher at the "Instituto de Investigaciones Electricas" (1986-1988 and 1992-1994) and at ITESM - Campus Cuernavaca (1994-2005). He is currently a senior researcher at the "Instituto Nacional de Astrofísica, Óptica y Electrónica" (INAOE) in Mexico where he conducts research in Machine Learning and Robotics.|
|Jeff Dean, Google, USA Title: Building Intelligent Computer Systems with Large Scale Deep Learning Abstract: For the past five years, the Google Brain team (g.co/brain) has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has open-sourced the TensorFlow system (tensorflow.org), a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the design decisions we made in building TensorFlow, discuss research results produced within our group in areas such as computer vision, language understanding, translation, healthcare, and robotics, and describe ways in which these ideas have been applied to a variety of problems in Google's products, usually in close collaboration with other teams. I will also touch on some exciting areas of research that we are currently pursuing within our group. This talk describes joint work with many people at Google. Bio: Jeff Dean (research.google.com/people/jeff) joined Google in 1999 and is currently a Google Senior Fellow in Google's Research Group, where he co-founded and leads the Google Brain team, Google's deep learning and artificial intelligence research team. He and his collaborators are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools. Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing.|
To be announced
Topics of interest are all areas of Artificial Intelligence, including but not limited to:
(This information is tentative and may slightly change in the future; please visit this page later.) The registration fee amount for authors is USD 590 (the same as in all previous years) or MXN 11,000. This includes one paper only. If one author has more than one accepted paper, in order for the papers to be published the fee is to be paid for each of them independently, with a discount of USD 100 or MXN 2000, i.e., for the second etc. paper presented by the same author, an additional fee of USD 490 or MXN 9,000 is to be paid. If you pay via bank transfer or PayPal, you should add the bank fees, so the amount that you will pay will be higher. In summary:
|Bank from Mexico||Bank from abroad||PayPal|
|First paper||USD 590 = MXN 11,000||USD 590 plus bank fees||USD 620|
|Second etc. paper presented by the same author||USD 490 = MXN 9,000||USD 490 plus bank fees||USD 520|
This fee includes publication of one paper (up to the page limit) in any of the proceedings volumes or journal issues derived from the conference, access to a digital copy of the pre-print versions of all accepted papers (unless copyright holders prevent us from distributing such copies), conference material, and attending the keynote lectures, regular technical presentations, and the poster session.
Additional fee applies for participating in some of the cultural activities, and for accompanying persons to participate in some of the cultural activities.
Registration fee for non-author attendees (main conference, tutorials, and workshops) and workshop authors will be lower. It will be announced later.
A very limited number of student grants for full-time students from developing countries (Mexico included) will be given to applicants in thoroughly justified cases and only to the applicants that will attend the conference in person. Strong preference will be given to the authors of the papers that received excellent evaluation scores and of which the applicant is the main author.
We solicit original research papers written in English. The submissions must not have been previously published or be under review for another conference or journal. Only complete and finished papers will be reviewed, not abstracts. After your paper is accepted you will have a chance to improve it according the comments of the reviewers, but the reviewers will assume that the text that they are reading is the text that is to be published, with the only changes they explicitly request (as opposed to reviewing a draft or abstract). In particular, the papers must be submitted in the required format. We reserve the right to reject without review the submissions that do not follow the format guidelines.
Submission procedure. Submissions are received electronically. The submission and reviewing procedure is handled the the EasyChair system. To submit a paper:
If you have not a user of the EasyChair system, you will need to register using the "I have no EasyChair account" button. Please do not send us your submissions by email. Please contact us in case of problems.
Submission is done in two phases. First, by the expression of interest deadline, we only need the tentative title and a draft abstract of your paper (you can change this later); they will be used only to reserve the appropriate reviewers for your paper - why not doing it right now? Later, by the full paper submission deadline, we will need the full text of your paper, as a PDF file. Both the draft abstract and full paper are uploaded via the EasyChair system. If you read this late, or need more time, you can upload your paper while the system is open, or contact us for late submissions.
Submission of the paper assumes that at least one author will register at the conference and present an accepted paper or poster. Full registration fee should be paid for each accepted paper.
Size. Registration fee for authors includes publication of a paper of up to 12 pages, though more pages can be used for a small additional fee. We strongly encourage authors to use as many pages as really necessary for an excellent paper: longer papers have better impact and receive more citations. Do not sacrifice the quality of your paper to squeeze it into the page limit.
For each page that exceeds this limit small extra fee of USD 10 will be charged. If your page exceeds 20 pages, please contact the organizers first. The additional fee is charged for the pages exceeding the page limit in either the version submitted for review or in the camera-ready version, whichever is greater. In particular, you must not shorten the camera-ready version in comparison with the version submitted for review unless the reviewers required this (contact us if you feel you should do shorten it; in any case this would not reduce the fee). However, we encourage you to use as many pages as you really need for an excellent paper, even if you will pay a very small fee for it -- that it, we recommend you not to sacrifice clarity and completeness of your paper for the page limit.
Double blind review policy: the review procedure is double blind. Thus the papers submitted for review must not contain the authors' names, affiliations, or any information that may disclose the authors' identity (this information is to be restored in the camera-ready version upon acceptance). In particular, in the version submitted for review please avoid explicit auto-references, such as "in  we show" -- consider "in  it is shown". I.e., you may cite your own previous works provided that it is not deducible from the text that the cited work belongs to the authors. When citing your previous work, please keep the names:
Incorrect: In  we have shown ...
1. <Hidden for review>, Syntactic Structures, The Hague, Mouton, 1957.
In  it was shown ...
1. Chomsky, N., Syntactic Structures, The Hague, Mouton, 1957.
MICAI 2017 will be held in Ensenada, Baja California, Mexico. Later we will post here the information about the venue and the official hotel. All local transportation, such as tour buses, will be organized to/from the official hotel. We advise you do not book a room separately; we will send to all authors a link to the official hotel with a discounted price.
The program and the list of accepted papers will be announced here in due time.
The general schedule of the conference will be as follows:
Conference chair: Miguel González Mendoza (ITESM)
Program chairs: Félix Castro (UAEH), Sabino Miranda Jiménez (INFOTEC), Miguel González Mendoza (ITESM)
Local chairs: Dora Luz Flores (UABC), Everardo Gutiérrez López (UABC), Dr. Carlos Alberto Brizuela (CICESE)
Program Committee (all tentative)
|A. Pastor López-Monroy
Alejandro Antonio Torres García
Carlos Coello Coello
Carlos Alberto Reyes
Carlos Mario Zapata Jaramillo
Eric S. Tellez
Eugene C. Ezin
Felix Castro Espinoza
Francisco Viveros Jiménez
Francisco J. Hernandez-Lopez
Francisco Javier Cantú Ortiz
Hector Rodriguez Rangel
Hiram Ponce Espinosa
Hugo Jair Escalante
Isaac Martín de Diego
Ivan Vladimir Meza Ruiz
Jaime Cerda Jacobo
Jesus Ariel Carrasco-Ochoa
Jorge A. Ruiz-Vanoye
José Martínez Carranza
José A. Reyes-Ortiz
José Fco. Martínez-Trinidad
José Luis Oliveira
Juan Humberto Sossa Azuela
Juan Jose Flores
Juan M. Ramírez-Cortés
Juan Manuel Rendon-Mancha
Juana Julieta Noguez Monroy
León Dozal Garcia
Lorena Díaz González
María de Lourdes Martínez-Villaseñor
Maria Lucia Barrón-Estrada
Maricela Claudia Bravo Contreras
Miguel Angel Guevara Lopez
Noé Alejandro Castro-Sánchez
Noel Enrique Rodriguez Maya
Nohemi Alvarez Jarquin
Rafael Guzman Cabrera
Rodrigo Lopez Farias
Saúl Zapotecas Martínez
Sergio Daniel Cano Ortiz
Sofia N. Galicia-Haro
Yulia Nikolaevna Ledeneva
Download the famous
Not to be confused with MICCAI.