table of contents - ncbr€¦ · prof. krzysztof krawiec deep feature engineering for segmentation,...

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Table of Contents Agenda of the Poland-Taiwan Seminar on Artificial Intelligence ............................................................. 3

Piotr Sankowski ....................................................................................................................................... 7

Youn-Long Lin ......................................................................................................................................... 8

Abstract ................................................................................................................................................ 9

Tsung-Yi Ho ........................................................................................................................................... 10

Dominik Ślęzak ...................................................................................................................................... 11

Abstract .............................................................................................................................................. 12

Chang-Shing Lee ................................................................................................................................... 13

Abstract .............................................................................................................................................. 14

Krzysztof Krawiec .................................................................................................................................. 15

Abstract .............................................................................................................................................. 15

Hsun-Ping Hsieh .................................................................................................................................... 16

Abstract .............................................................................................................................................. 16

Jakub Nalepa ......................................................................................................................................... 17

Abstract .............................................................................................................................................. 17

Jason S. Chang ..................................................................................................................................... 19

Abstract .............................................................................................................................................. 19

Grzegorz J. Nalepa ................................................................................................................................ 21

Abstract .............................................................................................................................................. 21

Ming-Feng Tsai ...................................................................................................................................... 23

Abstract .............................................................................................................................................. 23

Radosław Gwarek ................................................................................................................................. 24

Abstract .............................................................................................................................................. 25

Piotr Babieno ......................................................................................................................................... 26

Abstract .............................................................................................................................................. 26

Sheng-Jyh Wang ................................................................................................................................... 28

Abstract .............................................................................................................................................. 28

Cezary Dołęga ....................................................................................................................................... 29

Abstract .............................................................................................................................................. 30

Winston Hsu .......................................................................................................................................... 31

Abstract .............................................................................................................................................. 31

Bartosz Wilczyński ................................................................................................................................. 32

Abstract .............................................................................................................................................. 32

Notes ..................................................................................................................................................... 33

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Ladies and gentlemen,

It is my great privilege to present to you the Booklet of Poland – Taiwan Seminar on Artificial Intelligence.

The National Centre for Research and Development (NCBR) was established in 2007. This year the Centre celebrates the 10th anniversary of its presence in the national ecosystem. With our programmes we have managed to respond to the needs of entrepreneurs seeking assistance in their efforts to become more innovative. We have created a strong support system for science institutions, entrepreneurs and consortia that are willing to join forces in transforming Polish economy.

We are proud to observe more and more beneficiaries of NCBR programmes who make their mark on the national or global market with newly developed, advanced technologies, implementing innovations and challenging global competitors.

International activity is one of the crucial components of our programme portfolio, in line with the Foreign Expansion pillar of Poland’s economic development. NCBR is involved in a variety of programmes executed with sister agencies abroad. Initiated in 2012 by National Centre for Research and Development and National Science Council, the bilateral programme with Taiwan has proved to be a very successful story in offering financial support to Polish and Taiwanese research teams. To this date NCBR and MOST selected 28 projects for co-financing. The overall budget of these projects amounts to 2,9 M USD. The annual calls for proposals include the technologies which correspond to priority areas of the two countries: neuroscience, energy efficiency, materials science, agricultural biotechnology and biocatalysis, ICT and artificial intelligence.

In order to spread the idea of bilateral calls for research projects among potential stakeholders in both countries, the two funding parties decided to launch seminars on an annual basis. This year’s edition will be the 6th meeting of distinguished Professors and innovation-oriented entrepreneurs where they present the most promising ideas for future collaboration under the umbrella of joint calls. The Artificial Intelligence Seminar takes place in Warsaw while every second year the seminar is organised in Taiwan. It engages representatives of institutions and companies dealing with science and innovations related to the topic of the seminar.

I am very much pleased to convey to all participants of the Poland – Taiwan Seminar on Artificial Intelligence the wishes of interesting presentations which shall inspire to create and implement ambitious projects under Polish – Taiwanese collaboration. NCBR will continue to support the funding scheme in the future and endeavour to promote the calls thoroughly. Our goal in all bilateral programmes is to help Polish teams become more competitive on the international research stage. We also want to attract Polish researchers who developed their scientific careers abroad as well as foreign investigators to use world-class research infrastructure available at Polish universities and technology centres.

Let this seminar be a big step forward in bilateral cooperation in artificial intelligence!

Prof. Maciej Chorowski

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Agenda of the Poland-Taiwan Seminar on Artificial Intelligence

October 18, 2017, Venue: Baltic Ballroom I, Warsaw Marriott Hotel,

Al. Jerozolimskie 65/79, 00-697 Warsaw

8.30 – 9.00

Registration and coffee

9.00 – 9.15

Welcome speech

Prof. Aleksander Nawrat

Deputy Director of the National Centre for Research and Development

Ambassador Weber V. B. Shih

Representative and Head of the Taipei Economic and Cultural Office in Poland

9.15 – 9.20

Introduction – coordinator

Prof. Piotr Sankowski

Faculty of Mathematics, Informatics and Mechanics, University of Warsaw

9.20 – 9.40

Prof. Youn-Long Lin

Introduction to AI Innovation Center in Taiwan

Department of Computer Science, National Tsing Hua University

9.40 – 10.00

Prof. Dominik Ślęzak

Toward Approximate Intelligence – Lessons Learnt from Development of KDD Methods

and Approximate Query Engines


10.00 – 10.20

Prof. Chang-Shing Lee

Intelligent Agent for Human and Machine Co-Learningon Game of Go

Department of Computer Science and Information Engineering, National University of Tainan

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10.20 – 10.40

Prof. Krzysztof Krawiec

Deep Feature Engineering for Segmentation, Anomaly Detection

and Diagnosing in Medical Imaging

Faculty of Computing, Poznan University of Technology

10.40 – 11.05

Coffee break

11.05 – 11.25

Prof. Hsun-Ping Hsieh

Mining Geo-Social Media and Urban Informatics

for Smart Environmental and Business Applications

Department of Electrical Engineering, National Cheng Kung University

11.25 – 11.45

Prof. Jakub Nalepa

Towards Automatic Design of Deep Neural Networks Through Evolutionary Computation

Department of Automatic Control, Electronics and Computer Science,

Silesian University of Technology

11.45 – 12.05

Prof. Jason S. Chang

Inducing Lexical Grammar from Monolingual and Bilingual Corpora

for Computer Assisted Language Learning and Translation


12.05 – 12.25

Prof. Grzegorz J. Nalepa

Context, Autonomy, Affect. Methodological and Ethical Challenges of Today's AI

Department of Applied Computer Science, AGH University of Science and Technology

12.25 – 13.25

Lunch break

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13.25 – 13.45

Prof. Ming-Feng Tsai

Textual Data Analytics in Finance

Department of Computer Science, National Chengchi University

13.45 – 14.05

Radosław Gwarek

Meta AI – The artificial mind

11bit studios SA

14.05 – 14.25

Piotr Babieno

Hidden Horror and Catharsis 2.0

CEO Bloober Team SA

14.25 – 14.45

Coffee break

14.45 – 15.05

Prof. Sheng-Jyh Wang

An Efficient Sequential Partitioning Method for Clustering and Manifold Learning

Department of Electronic Engineering, National Chiao Tung University

15.05 – 15.25

Cezary Dołęga

NeuroCar – Applications of artificial intelligence in Intelligent Transport Systems:

traffic safety, intelligent infrastructure and autonomous vehicles

Neurosoft Sp. z o.o.

15.25 – 15.45

Prof. Winston Hsu

Learning from Multimodal Data Streams

Department of Computer Science and Information Engineering, National Taiwan University

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15.45 – 16.05

Prof. Bartosz Wilczyński

Machine learning at work in modern functional genomics

Faculty of Mathemathics, Informatics and Mechanics, University of Warsaw

16.05 – 16.30

Conclusions and expectations for the future – moderator and all (general discussion)

Closing remarks – moderator and coordinators

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Piotr Sankowski


e-mail: [email protected]

phone number: +48 22 55 44 427

Piotr Sankowski is Professor of Computer Science at Warsaw University. He received

a habilitation in computer science in 2009 and a PhD in computer science in 2005, both from

the University of Warsaw, where he was working on combinatorial optimization problems, with

special emphasis on dynamic computations and stochastic properties of data. He also received

a PhD in physics in 2009 from the Polish Academy of Sciences, where he was working on solid

state theory. In 2010, he received an ERC Starting Independent Researcher Grant, and in

2015 an ERC Proof of Concept grant. He is a co-founder of MIM Solutions – a spin-off company

that aims to commercialize algorithmic market modelling.

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Youn-Long Lin


Program Office Director, Taiwan AI Innovation Research


phone number: +886-975-123-895

Youn-Long Lin is a Tsing Hua Chair Professor of Computer Science of National Tsing Hua

University. He was born in Yun-Lin, Taiwan. He received his B.S. degree in electronics

engineering from National Taiwan University of Science and Technology (formerly, National

Taiwan Institute of Technology), Taipei, Taiwan, in 1982, and his Ph.D. in computer science

from the University of Illinois, Urbana-Champaign, IL, U.S.A. in 1987. Upon his graduation,

he joined National Tsing Hua University, Hsin-Chu, Taiwan, where he established the THEDA

Group (Tsing Hua EDA), served as Director of the University Computer and Communication

Center, Chairman of the Department of Computer Science, Secretariat General of the

University, Chief Librarian of the University Library, and the Dean of Research & Development.

He is also an adjunct professor of Peking University, Beijing and a guest professor of Waseda

University, Japan.

Between 1977-1979, Dr. Lin was in military service with Chung Cheng Armed Forces

Preparatory School as a Second Lieutenant. In 1979-1980, he was with Taiwan Cement Corp.

as a field instrument engineer. In 1981-1983, he was with the Atomic Power Division of Taiwan

Power Corp. as a mini-computer system administrator and database AP developer.

In 1984-1987, he was a research assistant with the Department of Computer Science,

University of Illinois, Urbana-Champaign, IL, USA. In 1998-1999, on sabbatical leave from

Tsing Hua, he co-founded Global UniChip Corp., a SOC Design Foundry, serving as its Chief

Technical Advisor. Between 2001 and 2003, he took a leave-of-absence from Tsing Hua

working for UniChip as its Chief Technical Officer and Executive Vice President.

Professor Lin's primary research interest is in computer-aided design (CAD) of very large-scale

integrated circuits (VLSI) with emphasis on physical design automation, high-level synthesis,

and VLSI design for video coding. He co-authored the book “High Level Synthesis –

Introduction to Chip and System Design.” He edited the book “Essential Issues in SOC

Design.” He also spend great effort in promoting the VLSI design and CAD education in

Taiwan. His current research focus is on design technology for System-on-a-Chips (SOC)

targeted towards multimedia applications.

He has served on program committee, organizing committee, steering committee, and

executive committee for several conferences and workshops on various aspects of CAD

including the Design Automation Conference(DAC), the International Conference on

CAD(ICCAD), the Asia South-Pacific Conference on Design Automation(ASP-DAC), the

Design, Automation, & Test of Europe (DATE) Conference, the Asia Pacific Conference on

Hardware Description Languages (APCHDL), the workshop on Synthesis And System

Integration of MIxed technology(SASIMI), the International Symposium on System

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Synthesis(ISSS), the International Symposium on Physical Design (ISPD), the ACM

Embedded Software Conference (EMSOFT), the International Symposium on VLSI Design,

Automation and Test (VLSI-DAT), the International SOC Conference (ISOCC), and the

VLSI/CAD Symposium of Taiwan. Professor Lin has also served on the editorial boards of the

ACM Transactions on Design Automation of Electronic Systems (TODAES) , the ACM

Transactions on Embedded Computing Systems (TECS) , Journal of the Chinese Engineering

Society, and the Journal of Information Science and Engineering (JISE) .

Dr. Lin has been a consultant to the Institute for Information Industry (III), the Industrial

Technology Research Institute (ITRI), the Electronic Research and Service Organization

(ERSO), and the Computer and Communication research Laboratory (CCL). He is an advisor

of the Science and Technology Advisory Office, Ministry of Education, R.O.C., in charge of

VLSI design education program, and a co-PI of the Chip and System Implementation Center

(CIC) program of the National Science Council (NSC), R.O.C. He also served on Intel's Asia-

Pacific Advisory Board and advisory boards of several EDA and consumer electronics startups.

He is an independent board member of Etron Technology Inc.

Professor Lin received a national fellowship for studying abroad from the Ministry of Education,

R.O.C. in 1983, co-received the Outstanding Young Author Award from the IEEE Circuit and

System Society in 1990, the Outstanding Design Award from the ASP-DAC University LSI

Design Contest in 2000, and received the Highest-Honored Research Award from the NSC

three consecutive times in 1992, 1994, and 1996. He has been an NSC research fellow since

1998. In 2007, he received the first Industrial Contribution Award from the Ministry of Economic

Affairs, R.O.C., for his impact on Taiwan IC design industry.

Dr. Lin is a senior member of the IEEE, the IEEE Computer Society, the IEEE Circuit and

System Society, the IEEE Solid State Circuit Society, and the Association for Computing

Machinery. He is a board member of the Taiwan IC Design Society. He is elected the president

of Taiwan Integrated Circuit Design Society (TICD) for 2005-06. He is a nation-certified

electrical engineer.

Abstract

AI Innovation Research Program of Taiwan

Starting from 2017, Taiwan’s Ministry of Science and Technology (MOST) initiates a 5-year

USD500M program for AI innovation research. Its goals include: incubating high-quality

talents, developing cutting-edge technology, and empowering industry with AI technology.

Taiwan has been known for strong at electronics hardware manufacturing but weak at

software. The on-going AI revolution is changing the existing paradigm. We seek international

collaborations that complement each other. In this talk, I will briefly introduce the program

contents and ask for your comment/advice.

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Tsung-Yi Ho



phone number: +886-922-271-858

Tsung-Yi Ho received his Ph.D. in Electrical Engineering from National Taiwan University in

2005. He is a Professor with the Department of Computer Science of National Tsing Hua

University, Hsinchu, Taiwan. His research interests include design automation and test for

microfluidic biochips and nanometer integrated circuits. He has presented 10 tutorials and

contributed 10 special sessions in ACM/IEEE conferences, all in design automation for

microfluidic biochips. He has been the recipient of the Invitational Fellowship of the Japan

Society for the Promotion of Science (JSPS), the Humboldt Research Fellowship by the

Alexander von Humboldt Foundation, and the Hans Fischer Fellow by the Institute of Advanced

Study of the Technical University of Munich. He was a recipient of the Best Paper Awards at

the VLSI Test Symposium (VTS) in 2013 and IEEE Transactions on Computer-Aided Design

of Integrated Circuits and Systems in 2015. He served as a Distinguished Visitor of the IEEE

Computer Society for 2013-2015, the Chair of the IEEE Computer Society Tainan Chapter for

2013-2015, and the Chair of the ACM SIGDA Taiwan Chapter for 2014-2015. Currently he

serves as an ACM Distinguished Speaker, a Distinguished Lecturer of the IEEE Circuits and

Systems Society, and Associate Editor of the ACM Journal on Emerging Technologies in

Computing Systems, ACM Transactions on Design Automation of Electronic Systems, ACM

Transactions on Embedded Computing Systems, IEEE Transactions on Computer-Aided

Design of Integrated Circuits and Systems, and IEEE Transactions on Very Large Scale

Integration Systems, Guest Editor of IEEE Design & Test of Computers, and the Technical

Program Committees of major conferences, including DAC, ICCAD, DATE, ASP-DAC, ISPD,

ICCD, etc.

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Dominik Ślęzak



phone number: +48 660 495 076

Dominik Ślęzak received his Ph.D. in Computer Science in 2002 from University of Warsaw,

Poland, and D.Sc. in 2011 from Institute of Computer Science of Polish Academy of Sciences.

After finishing his Ph.D. he started working as Assistant Professor in Polish-Japanese

Academy of Information Technology, Poland, and then at University of Regina, Canada.

In 2005 he suspended his academic career and co-founded Infobright, where he worked as

Chief Scientist until 2017. In the meantime, in 2009 he re-joined University of Warsaw, where

he currently holds full-time position of Associate Professor in Institute of Informatics. He also

cooperated as Adjunct Professor with McMaster and York University in Canada.

Dominik is continually involved in both academic and industry R&D projects, in the areas of

Artificial Intelligence, Machine Learning and Knowledge Discovery. His research has strong

roots in Rough Set Theory co-developed by his scientific supervisor and mentor, Professor

Andrzej Skowron. His interests have been always related to Data Exploration and Decision

Support tools and methods that would be easy to use by domain experts and practitioners.

In Infobright he co-designed Analytical Database Engine aimed at fast low-resource

processing of massive volumes of machine-generated data, based on the paradigms of

Columnar Data Stores and Rough Set Approximations. In 2015 he co-established the team

working on highly scalable Approximate Analytics Engine based on the elements of

Approximate Computing and Information Granulation, whose development is now continued

in cooperation with Security on Demand. In parallel, since 2012 he has been active as

co-organizer of open data mining challenges at Knowledge Pit. He also played leading roles

in several government-funded projects including SYNAT and DISESOR.

Dominik co-edited over 20 books and volumes of conference proceedings. He co-authored

over 150 papers for books, journals and conferences. He is co-inventor in five US patents.

He delivered invited plenary talks at over 20 international scientific events. He also serves

actively for the community. He is Associate Editor / Editorial Board Member for several journals

including Information Sciences and Intelligent Information Systems (both since 2007). He was

one of Founding Editors of Communications in Computer and Information Science.

He co-organized over 20 international conferences including a series of FedCSIS/AAIA

Symposia on AI Applications (2013-2017). In 2012-2014 he served as President of

International Rough Set Society (IRSS). In 2015 he was appointed as IRSS Fellow. In 2014

he received Outstanding Service Award from Web Intelligence Consortium. Since 2014 he has

served in IEEE Technical Committee on Intelligent Informatics.

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Abstract

Toward Approximate Intelligence – Lessons Learnt from Development of KDD Methods

and Approximate Query Engines

Artificial Intelligence methods are currently regaining a lot of attention in the areas of data

analytics and decision support. Given the increasing amount of information and computational

resources available, it is now possible for intelligent algorithms to learn from the data and assist

humans more efficiently. Still, there is a question about the goals of learning and a form of the

resulting data-driven knowledge. It is evident that humans do not operate with fully exact /

precise information in decision-making and, therefore, it might be unnecessary (or even

inadvisable) to provide them with complete outcomes of analytical processes. Consequently,

the next question arises whether incomplete (yet sufficiently rich) results of computations on

large data could be delivered faster than their standard counterparts, in order to improve

interactions between humans and intelligent processes. Such questions – referring to human-

computer interactions – are analogous to questions about precision of calculations conducted

internally by machine learning and knowledge discovery methods, whereby various heuristic

algorithms could be accelerated by letting them rely on approximate computations. This leads

us toward discussion on the importance of approximations in the areas of machine intelligence

and business intelligence and, more broadly, the meaning of approximate representations and

computations for various aspects of Artificial Intelligence. In this talk, we will refer to this

discussion using two cases studies – an example of approximate database software developed

using the paradigms of rough-granular computing and an example of heuristic feature selection

method that was successfully applied in several KDD projects.

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Chang-Shing Lee

Department of Computer Science and Information Engineering, National University of Tainan


phone number: +886-6-2602573

Chang-Shing Lee (SM’09) received the Ph.D. degree in Computer Science and Information

Engineering from the National Cheng Kung University, Tainan, Taiwan, in 1998. He is currently

a Professor with the Department of Computer Science and Information Engineering, National

University of Tainan and he was the Dean of Research and Development Office from January

2011 to July 2015. His current research interests include adaptive assessment, intelligent

agent, ontology applications, Capability Maturity Model Integration (CMMI), fuzzy theory and

applications, and machine learning. He also holds several patents on Fuzzy Markup Language

(FML), ontology engineering, document classification, image filtering, and healthcare. He was

awarded Certificate of Appreciation for outstanding contributions to the development of IEEE

Standard 1855TM-2016 (IEEE Standard for Fuzzy Markup Language). He was the Emergent

Technologies Technical Committee (ETTC) Chair of the IEEE Computational Intelligence

Society (CIS) from 2009 to 2010 and the ETTC Vice-Chair of the IEEE CIS in 2008. He is also

an Associate Editor or Editor Board Member of International Journals, such as IEEE

Transactions on Computational Intelligence and AI in Games (IEEE TCIAIG), Applied

Intelligence, Soft Computing, Journal of Ambient Intelligence & Humanized Computing (AIHC),

International Journal of Fuzzy Systems (IJFS), Journal of Information Science and Engineering

(JISE), and Journal of Advanced Computational Intelligence and Intelligent Informatics

(JACIII). He also guest edited IEEE TCIAIG, Applied Intelligence, Journal of Internet

Technology (JIT), and IJFS. Prof. Lee was awarded the outstanding achievement in

Information and Computer Education & Taiwan Academic Network (TANet) by Ministry of

Education of Taiwan in 2009 and the excellent or good researcher by National University of

Tainan from 2010 to 2016. Additionally, he also served the general co-chair of 2015 IEEE

Conference on Computational Intelligence and Games (IEEE CIG 2015), the general chair of

the 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2015),

the program chair of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE

2011), and the competition chair of the FUZZ-IEEE 2013, the competition co-chair of the FUZZ-

IEEE 2015, FUZZ-IEEE 2017, and 2016 IEEE World Congress on Computational Intelligence

(IEEE WCCI 2016) and IEEE WCCI 2018. He is also a member of the Program Committees

of more than 50 conferences. He is a senior member of the IEEE CIS, a member of the

Taiwanese Association for Artificial Intelligence (TAAI), and the Software Engineering

Association Taiwan. He was a member of the standing committee of TAAI from 2011 to 2016

and one of the standing supervisors of Academia-Industry Consortium for Southern Taiwan

Science Park from 2012 to 2013.

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Abstract

Intelligent Agent for Human and Machine Co-Learning on Game of Go

In this talk, we will demonstrate the application of Fuzzy Markup Language (FML) to construct

an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human–

Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises

an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal

development agent, and an intelligent agent. The intelligent game bot is based on the open-

source code of Facebook’s Darkforest, and it features a representational state transfer

application programming interface mechanism. The proximal development agent contains

a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy

knowledge base and rule base. The intelligent agent contains a GoSocket engine and

a summarization agent that is based on the estimated win rate, real-time simulation number,

and matching degree of predicted moves. Additionally, the FML for player performance

evaluation and linguistic descriptions for game results commentary are presented.

We experimentally verify and validate the performance of the FDAA and variants of the FHMCS

by testing five games in 2016 and 60 games of Google’s Master Go, a new version of the

AlphaGo program, in January 2017. The experimental results demonstrate that the proposed

FDAA can work effectively for Go applications.

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Krzysztof Krawiec

Faculty of Computing, Poznan University of Technology


phone number: +48 61 66 53 061

Krzysztof Krawiec. Associate professor in the Institute of Computing Science, Poznan

University of Technology. Past work includes program synthesis, in particular using genetic

programming; deep learning for image analysis and game playing; evolutionary computation

for machine learning, games and pattern recognition; coevolutionary algorithms and test-based

problems. K. Krawiec is an author of over 100 papers on the above topics, an associate editor

of the Genetic Programming and Evolvable Machines journal, and formerly a visiting

researcher at University of California and Massachusetts Institute of Technology. More at

http://www.cs.put.poznan.pl/kkrawiec

Abstract

Deep Feature Engineering for Segmentation, Anomaly Detection

and Diagnosing in Medical Imaging

Deep neural networks facilitate composing effective models from a rich repertoire of

components: feature maps, embeddings, convolutions, etc., allowing so to address a range of

tasks of practical interest. In my talk, I will illustrate this characteristic of contemporary neural

models using three image analysis problems we approached in my group: segmentation of the

blood vessel network in ophthalmology (for 2D fundus imaging and 3D optical coherence

tomography), anomaly detection in computer tomography, and diagnostic decision support for

fluorescein angiography. Problems, methods, neural architectures, and main results will be

presented, followed by a brief discussion of future outlooks and related ongoing projects.

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Hsun-Ping Hsieh

Department of Electrical Engineering, National Cheng Kung University


phone number: +886-6-2757575 ext. 62322

Hsun-Ping Hsieh is now an Assistant Professor and leads Urban Science and Computing Lab

(UCLAB) at Department of Electrical Engineering, National Cheng Kung University, Taiwan.

He received his Ph.D. degree in Graduate Institute of Networking and Multimedia at National

Taiwan University. Hsun-Ping’s international recognition includes ACM KDD Cup 2010 First

Prize, Garmin Fellowship 2014 and 2013, and Best Intern Award at Microsoft Research Asia.

His research interests include big data mining, urban computing, and AI-based smart city

services. In the past years, he published a series of papers in location-based services and

urban computing, including ACM TIST, KAIS, ACM WWW, ACM SIGKDD, ACM SIGIR, ACM

MM, IEEE ICDM, PKDD, AAAI ICWSM and ACM CIKM. He had been the main tutorial

presenter on the topic of social media analytics at PAKDD 2016, WWW 2015, and on route

planning at ICWSM 2014 and ASONAM 2014. Hsun-Ping’s research is interdisciplinary from

the perspective of Data Science. In 2016, he won EIGHT best dissertation awards from several

leading academia associations and industrial companies in Taiwan.

Abstract

Mining Geo-Social Media and Urban Informatics

for Smart Environmental and Business Applications

With the maturity of location-acquisition techniques and mobile devises, a novel type of online

services, geo-social media (e.g. Foursquare, Facebook, and Flickr), have caught not only

users’ attention but also researchers' efforts on solving real-world problems. People are

allowed to keep track of what they see, where they go, who they know, what they experience,

and when they do anything in the forms of images, locations, social network, short texts, and

time stamps respectively. On the other hand, we deploy sensors such as weather stations,

invigilators or vehicle detector to collect urban informatics in the physical environment of the

city. In this talk, we seek to point out and discuss a new research direction that connects

geo-social media, urban sensor data to enable novel environmental and business applications.

We develop a general-purpose spatial-temporal inference framework to not only model

user-environment interactions, but also is preliminarily validated to be capable of accurately

infer air quality, noise degree, and commercial foot traffic. Such promising results are believed

to encourage a brave new direction for making a city smart fusing geo-social media and urban

informatics.

17

Jakub Nalepa

Department of Automatic Control, Electronics and Computer Science, Silesian University of

Technology

Future Processing, Gliwice, Poland

e-mail: [email protected], [email protected]

phone number: +48 32 237 2117

Jakub Nalepa received a M.Sc. degree (2011) and a Ph.D. degree (2016), both with distinction

in computer science from the Silesian University of Technology, Gliwice, Poland. Dr. Nalepa

is currently a researcher at the Department of Automatic Control, Electronics and Computer

Science, Silesian University of Technology. Since 2010, he has been a research scientist at

Future Processing, Gliwice, Poland, where he is currently the lead research scientist in

machine learning and medical imaging projects. Dr. Nalepa's research interests encompass

machine learning, evolutionary algorithms, especially (adaptive and self-adaptive) genetic and

memetic algorithms, pattern recognition, optimization, medical imaging, parallel computing,

deep learning and interdisciplinary applications of these methods. He has been involved in

several projects related to the above-mentioned domains – in both academia and industry.

So far, Dr. Nalepa published more than 60 papers in these fields. Dr. Nalepa received the best-

paper awards at the 3PGCIC 2016 and IBERAMIA 2016 conferences, and was nominated to

the best-paper award at the EvoCOP 2017 conference. Dr. Nalepa, together with Michael

P. Hayball (CEO of Cambridge Computed Imaging Ltd), gave an invited talk on applying deep

networks in medical imaging at the University of Cambridge (at the Centre for Mathematical

Imaging in Healthcare seminar, 2017). He is a reviewer for several international journals and

conferences. Dr. Nalepa is an IEEE and ACM member.

Abstract

Towards Automatic Design of Deep Neural Networks Through Evolutionary Computation

Introducing deep neural networks (DNNs) was undoubtedly a breakthrough in artificial

intelligence. DNNs have achieved remarkable success in a plethora of tasks. However, the

performance of such deep systems strongly depends on their hyper-parameters and

architectures which often must be selected by an experienced practitioner. Selection of

hyper-parameters remains a substantial obstacle in designing DNNs in practice and it is a very

time-consuming process. In this talk, we show how these hyper-parameters can be evolved

using a particle swarm optimization (PSO) technique [1]. We demonstrate that PSO efficiently

explores the space of hyper-parameter values, allowing DNNs of a minimal topology to obtain

competitive classification performance over the multi-class MNIST dataset. We showed that

very small DNNs optimized by PSO retrieve promising classification accuracy for a challenging

CIFAR-10 dataset. Importantly, PSO can be successfully used to improve the performance of

existent deep topologies. Extensive experimental study, backed-up with the statistical tests,

mailto:[email protected]

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revealed that PSO is an effective technique for automating hyper-parameter selection and it

efficiently exploits computational resources.

References

1. Lorenzo, P.R., Nalepa, J., Kawulok, M., Ramos, L.S., Pastor, J.R.: Particle swarm

optimization for hyper-parameter selection in deep neural networks. In: Proceedings of

the Genetic and Evolutionary Computation Conference. pp. 481-488. GECCO'17,

ACM, New York, NY, USA (2017), http://doi.acm.org/10.1145/3071178.3071208

19

Jason S. Chang



phone number: +886-3-5731069

Prof. Jason S. Chang has served as Professor of Computational Linguistics at National Tsing

Hua University, Taiwan. He has working on better ways of using technology to enhance second

language learning. Under the CANDLE projects, significant progress was made on developing

novel online tools, including a fast trillion-word linguistic search engine Linggle, and an

interactive writing environment WriteAhead based on induced grammars.

Prof. Chang was a founding member and President of Association for CLCLP and member of

Standing Committee on Language Policy, Ministry of Education (MOE). He has served as

Program Committee Area Chair, the Annual Conference of the Association for Computational

Linguistics (ACL), and on PC of numerous international conferences, as well as Advisory Board

of the Journal of CLCLP.

Prof. Chang earned a B.S. in Mechanical Engineering and an M.S. in Computer Science from

NTHU. He then went to New York University and obtained Ph.D. (1986) in Computer Science.

His publications include over a hundred referred papers in international conferences, technical

journals, including the prestigious Computational Linguistics published by MIT Press. When

not busy researching and writing code, he writes monthly columns in Scientific American,

Taiwanese Editions.

Abstract

Inducing Lexical Grammar from Monolingual and Bilingual Corpora

for Computer Assisted Language Learning and Translation

The past decade has witnessed the emergence of corpus-based statistical methods for

automatic error correction and essay rating with ever increasing performance. However, the

goal of fully automatic, high-quality Grammar Error Correction (GEC) is still elusive. In this talk,

I will describe our ongoing work on induction of lexical Pattern Grammar (PG) as well as

Synchronous Pattern grammar (SPG) for tasks such as Interactive Writing Environment,

Grammatical Error Correction, Computational Lexicography, and Machine Translation.

As example applications of this line of research, I will demonstrate a linguistic search engine

Linggle and an interactive writing system, WriteAhead, aimed at helping language learners by

providing writing help as solicited or unsolicited search results. Linggle accepts queries with

keywords, wildcard, wild part of speech (PoS), synonymous words, and additional regular

expression (RE) operators, and returns bundles with frequency counts. WriteAhead provides

L2 learners with writing and editing prompts to help them write fluently and accurately. For that,

we automatically analyze a given corpus, and extract grammar patterns and common error-

and-edit patterns. At run-time, as learners type (or mouse over) a word, the system

automatically retrieves and displays grammar patterns and examples, most relevant to the

word. The user can opt for patterns from a general corpus, academic corpus, learner corpus,

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or common error corpus. WriteAhead proactively engages the user by providing steady, timely,

and relevant grammar patterns (or synchronous grammar patterns) for effective assisted

writing and translation.

21

Grzegorz J. Nalepa

Department of Applied Computer Science, AGH University of Science and Technology;

Jagiellonian University


phone number: +48 12 61 73 856

Grzegorz J. Nalepa (http://gjn.re) is an engineer with degrees in computer science – artificial

intelligence, and philosophy. He has been working in the area of intelligent systems and

knowledge engineering for over 15 years. He formulated the eXtended Tabular Trees rule

representation method, as well as the Semantic Knowledge Engineering approach.

He authored a book "Modeling with Rules using Semantic Knowledge Engineering" (Springer

2017). He co-edited a book "Synergies Between Knowledge Engineering and Software

Engineering" (Springer 2017). He co-authored over 150 research papers in international

journals and conferences. He coordinates GEIST - Group for Engineering of Intelligent

Systems and Technologies (http://geist.re) cooperating with AGH University and Jagiellonian

University in Krakow, Poland. For almost 10 years he has been co-chairing the Knowledge and

Software Engineering Workshop (KESE) at KI, the German AI conference, Spanish CAEPIA,

as well ECAI. He is the President of the Polish Artificial Intelligence Society (PSSI), member

of EurAI. He is also a member of IEEE, Italian Artificial Intelligence Society (AI*IA), KES, Polish

Cognitive Science Society (PTK). His recent interests include context-aware systems and

affective computing.

Abstract

Context, Autonomy, Affect. Methodological and Ethical Challenges of Today's AI

Research in the area of pervasive computing and ambient intelligence aims to make use of

context information to allow devices or applications behave in a context-aware, thus

“intelligent” way. By a classic definition context is any information that can be used to

characterize the situation of an entity. Cognitive assistants, or more recently smart advisors,

are computer systems that augment human capabilities by providing personalized context-

driven decision support. Today, such systems use wearable devices and ambient intelligence

technologies to be constantly available to the user. Affective Computing is a field of study that

puts interest in the design and description of systems that are able to collect, interpret, and

process emotional states. Assuming that emotions are physical and cognitive and as such they

can be studied interdisciplinary by computer science, bio-medical engineering, cognitive

science, and psychology. Research challenges in this area include detection and classification

of affective states, as well as inducement of emotions in laboratory studies. We argue, that

human-centric cognitive computer systems should be able to detect and interpret changes of

emotional state of the users. To this goal we propose to combine to computing approaches:

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affective computing and context-aware systems. Context-awareness boosts opportunities for

cognitive assistants to be autonomous, whereas emotion-awareness can make them more

human-like in the interaction with the users. The practical development of such systems poses

number of methodological challenges, including the management of big data, bridging of

symbolic knowledge-based approaches with machine learning methods, as well as providing

explanation capabilities. Finally, the design of these technologies raises important ethical

concerns and societal issues. Such AI systems should be understandable, transparent, and

safe, to have positive economical and societal impact.

23

Ming-Feng Tsai

Department of Computer Science, National Chengchi University


phone number: +886-2-2939-3091 ext. 62558

Ming-Feng Tsai is currently an Associate Professor in the Department of Computer Science at

National Chengchi University. In 2006, he was at Microsoft Research Asia as an intern with

the Web Search & Mining Group, and was awarded by the research institution the Best Intern

of the Year, and invited to visit the headquarters of Microsoft and Bill Gates’ house in

Redmond. He received his Ph.D. degree from National Taiwan University in 2009. After

receiving his Ph.D. degree, he worked at National University of Singapore as a Research

Fellow, participating in a research project related to machine translation. In 2010, sponsored

by Ministry of Scienceand Technology, he joined University of Illinois at Urbana-Champaign

as a visiting scientist, working on the projects associated with advanced Web search and

mining. His research interests span the areas of information retrieval, machine learning,

recommender systems, natural language processing, computational social science.

Abstract

Textual Data Analytics in Finance

The growing amount of public financial data makes it more and more important to learn how

to discover valuable information for financial decision-making. This talk presents our recent

studies on exploring and mining soft information in finance, which usually refers to text,

including opinions, ideas, and market commentary. This talk will cover several machine

learning techniques, such as learning to rank and word embedding, on financial reports for the

study of financial risk among companies, and the demonstrations on our newly developed

web-based information system, Fin10K, to showcase its ability for textual analytics in finance.

24

Radosław Gwarek

11 bit studios SA

e-mail:[email protected]

phone number: +48 504 721 801

Working in the games industry since 2011. Designer of AI systems for a number of successful

games, including This War of Mine and Frostpunk.Lecturer at the Warsaw Film School,

teaching new generations of video game developers creating games using Unity Engine,

basics of game programming and balancing combat systems for games.

Deadly Monsters (2017)

A modification for PC version of Minecraft that adds new critters, featuring new and unique AI

behaviours of enemies, which require a new approach to fighting them. The entire modification

was created using Java programming language.

Pixwing (2015)

Mobile game in which the player pilots an airplane by moving their phone or tablet. The game

tracks the movements using accelerometers, gravity sensors, gyroscopes and rotational vector

sensors. Virtual Reality helmets utilise the same motion tracking technology, which allowed

me to use sensor fusion algorithms from VR helmets.

Frostpunk (2015-2017)

City building game in post-apocalyptic setting, including elements of society simulation. Design

of complex AI system for multiple agents, design of necessary tools for AI development (editor,

debugger). Creating behaviours according to the requirements of the story designers.

Implementing my own ideas for additional behaviours. Maintaining test cases in order to keep

the game error-free and stable.

This War of Mine, This War of Mine: The Little Ones (2013-2015)

Survival game about the fate of civilians in a modern conflict. More traditional approach to

gameplay AI with a smaller number of agents. Design of AI editor and debugging tools.

Flix (2013)

A logic game for Android systems. Players use puzzles to create a path for their character.

The main goal is to reach the finish line, while avoiding obstacles and enemies. Programming,

graphics, overall design.

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Anomaly 2 (2012)

Reversed tower defense game, in which player controls the attackers. Technical side of the

design, creating scripts supporting additional events during the game such as tutorial or

interactive cutscenes. Diagnostics and/or fixing of more important problems reported by

Quality Assurance Department.

Funky Smugglers (2011)

Casual arcade game for mobile platforms and PCs. General design of the game, particularly

the balancing of the economy values and the difficulty levels.

Abstract

Meta AI – The artificial mind

Complex AI systems used in video games can exhibit unexpected behaviours. This is usually

treated as an error, but in some circumstances the results are surprisingly good, leading to AIs

behaving in their own way, performing lifelike actions that have never been designed.

Examples will be based on the game “This War of Mine”, where we will look deeper into the

system and see how characters make their decisions. Quickly switching between multiple parts

of the behaviour tree modules may produce unique behaviour that exists only in a specific time

frame. During this state, the agent can perform additional actions that, although resulting from

error, can be perceived as humanlike behaviour. The whole process can be compared to

evolution, with the developer playing the role of natural selection, eliminating critical errors and

keeping the “good” ones – quite similar to how harmful mutations are weeded out of the gene

pool and beneficial ones propagate. The final effect is a new, artificially created set of

behaviours which are a result of random errors.

26

Piotr Babieno

CEO Bloober Team SA

[email protected]

+48 662 216 616

Mr. Babieno has overall over a decade of professional leadership experience in the field of

gaming industry, business development and sales. He studied at Technical University of

Krakow, Warsaw School of Film and Polish Open University in Kraków. Since 2006, he has

served as managing developer in companies creating games on next generation consoles.

Mr. Babieno is a lecturer, and the co-creator of the program and the syllabus of the

postgraduate course “European Academy of Games” organized by Jagiellonian University in

collaboration with the Academy of Mining and Metallurgy. He is also a member of the Program

Board of EAG. At the invitation of the Government of Canada he was a lecturer at Jalloo

Festival of Animation and Gaming. In 2009 thanks to extensive experience and knowledge of

new technologies, Mr. Babieno was appointed an expert for “Technology Perspectives Krakow

– Malopolska 2020” – Foresight.

Abstract

Hidden Horror and Catharsis 2.0

Most horrors tell a story, hidden horror tackles certain subjects, most often philosophical

or psychological in their nature to provide an experience. Each part of the game, from the

plot to level design, works to strengthen and envision the subject.

Horror by definition needs to provide a feeling of relief at its end; HH goes one step further

and provides a cathartic experience that goes beyond just an emotional relief – the

dilemmas and decisions presented in the game should make you ponder on what kind of

person you are. We always have a point of view on each and every aspect of our lives

– this tells us a lot about ourselves.

The dilemmas we design for our games are not abstract, they live in our imagination as

something we know exists but happens to the friends of our friends, or we read about them

in newspapers. Living through those experiences in a game may prepare us to face such

situations in real life – think of them as preventive coaching or even therapy.

Catharsis (1.0) is the process of releasing, and thereby providing relief from, strong or

repressed emotions. Most horror games revolve around this concept – giving plenty of fear

to people who are willing to live it through and feel stronger because of it. We went deeper

than that – we still provide tons of scares for people, but what we also do is we add a lot of

moral dilemmas during that frightening journey. The catharsis 2.0 is not only about being

relieved from repressed feelings – it’s a journey to understand not only emotions but also

our way of reasoning – how we see the world and what makes us tick. A kind of introvert

relief – we have a lot of bottled layers of our psyche that we do not explore, they may lay

27

dormant even our entire lives – with Catharsis 2.0 we want to provide those experiences

to people.

Horror has always been most popular during times of biggest social tensions. It’s role was

always cathartic, so it already had a deeper cultural layer to it in comparison to other

genres. Horror is also meant for older audiences, which obviously is part of our main target,

because of the level of their understanding of the world. And the baggage of experiences

that burdens their shoulders.

Hidden Horror is considered as a therapeutic tool by a friendly scientific unit, and one of

the players is writing a doctoral dissertation on overcoming his depression because of his

experience with Layers of Fear.

28

Sheng-Jyh Wang

Department of Electronic Engineering, National Chiao Tung University


phone number: +886-3-5712121 ext. 54177

Sheng-Jyh Wang (M’95) received the B.S. degree in electronics engineering from National

Chiao Tung University, Taiwan, in 1984, and the M.S. and Ph.D. degrees in electrical

engineering from Stanford University, USA, in 1989 and 1994, respectively.

He is currently a professor with the Institute of Electronics, NCTU. His research interests are

image processing, video processing, image analysis, and machine learning.

Abstract

An Efficient Sequential Partitioning Method for Clustering and Manifold Learning

How to automatically extract useful information from data has long been a crucial issue in

plentiful scientific studies, especially in the era of big data. In this presentation, we will mention

how we modify over the Bayesian Sequential Partitioning (BSP) algorithm to develop a new

algorithm that can be used for data clustering and manifold learning. The BSP algorithm was

originally designed for the estimation of probability density function in the feature space,

especially in a high-dimensional case. By properly modifying the score function in the BSP

algorithm, we turn the original BSP algorithm into an effective method for data grouping and

for the exploration of data manifolds. We also develop a hierarchical framework to analyze the

correlations among partitioned data groups at different scales. By applying the multi-scale data

analysis to image segmentation, we develop a hierarchical image segmentation algorithm that

outperforms the state-of-the-arts image segmentation algorithms.

29

Cezary Dołęga

Neurosoft Sp. z o.o.


phone number: +48 601 774 737

Co-founder and co-owner of Neurosoft Sp. z o.o. Master of Electronics Engineering, graduated

from Wroclaw University of Technology in 1990. Researcher at the Institute of Technical

Cybernetics from 1990 to 1999. Since the beginning of Neurosoft – Vice-President, Chief

Technology Officer. Served as Scientific Project Manager of numerous research projects,

including:

ISKIP – Intelligent Comprehensive System Vehicle Identification. PL: Operational Program

Innovative Economy, UDA-POIG.01.03.01-00-147/11 (2011-2013);

ISM3P – Intelligent monitoring and enforcement system for overloaded vehicles.

PL: National Centre for Research and Development, Applied Research Programme,

no. PBS1/B3/4/2012 (2012-2014);

MOBIS – Methods of pedestrian safety assessment using video analysis. PL National

Centre for Research and Development, Applied Research Programme,

no. PBS1/A2/8/2012 (2013-2015);

OptiCITIES – Optimise Citizen Mobility and Freight Management in Urban Environments.

EU: 7th Framework Program, FP7-SST-2013-RTD-1 (2013-2016);

AUDIOSCOPE – System for automatic keyword spotting in spontaneous speech.

PL: National Centre for Research and Development, Applied Research Programme,

Applied Research Programme, no. PBS3/A9/36/2015 (2015-2017);

NEUROPARK – Intelligent Multi-Sensor Parking Lot Management System.

PL: National Centre for Research and Development, no. POIR.01.01.01-00-0472/15

(2015-2017);

MOVIESTA – Mobile Self-Adjusting Video-based Detections System for Traffic

Applications. EU: Eurostars Programme, Project id: 10196 (2016-2018);

NEUROSPACE – Intelligent Modeling and Monitoring System for Infrastructure Spatial

Systems, PL: National Centre for Research and Development, no. POIR.01.01.01-00-

1176/15-00 (2016-2018);

NEUROWIM – Intelligent Vehicle Weigh-in-Motion System with Enhanced Accuracy.

PL: National Centre for Research and Development, no. POIR.01.01.01-00-0612/16

(2017-2018).

30

Abstract

NeuroCar – Applications of artificial intelligence in Intelligent Transport Systems:

traffic safety, intelligent infrastructure and autonomous vehicles

The future of transport systems will certainly be closely linked to the development of "smart"

technologies that will be used in various areas. First, it is necessary to make more effective

use of existing infrastructure (road network, parking, urban space) by optimizing the behavior

of its users. Secondly, a constant trend is to increase transport comfort, e.g. by raising safety

levels or automating processes (like driver-less vehicles). Thirdly, transport activity begins to

function in new, previously unused spaces such as urban airspace (drones) or outer space.

In each of these cases, artificial intelligence methods are more and more widespread, and in

particular artificial neural networks.

Neurosoft has been developing the NeuroCar product line for over 10 years, in which artificial

intelligence algorithms have been practically applied. In the beginning the NeuroCar VI

(Vehicle Identification) system will be presented. The system is used to automatically detect

and identify vehicles (vehicle detection, vehicle class identification, license plate recognition,

country of origin recognition, make and model recognition, determination of vehicle movement

parameters such direction and speed, detection of dangerous goods). The applications of this

type of system for traffic measurement and road safety improvement will be presented.

The second will be the NeuroCar RL (Red Light Enforcement) system, which is used to detect

red light traffic violations. The third will be the NeuroCar PARK (Parking Lot Management)

system that is used to determine the occupancy of parking spaces, detection of free parking

spots and the parking lots occupancy optimization. The NeuroCar Flow for traffic

measurement, which uses semantic image segmentation and object tracking algorithms (deep

neural networks), will be the latest to be presented.

The last part of the presentation will contain brief overview of new research projects at

Neurosoft: NeuroSpace – an autonomous multi-rotor UAV for road infrastructure monitoring;

NeuroWIM – high accuracy vehicle Weigh-in-Motion system and MoVieSta – an intelligent road

infrastructure to support self-steering vehicles.

31

Winston Hsu

Department of Computer Science and Information Engineering, National Taiwan University


phone number: +886-2-3366-4888 ext. 512

Prof. Winston Hsu is an active researcher dedicated to large-scale image/video

retrieval/mining, visual recognition, and machine intelligence. He is keen to realizing advanced

researches towards business deliverables via academia-industry collaborations. He is

a Professor in the Department of Computer Science and Information Engineering, National

Taiwan University. He founded MiRA (Multimedia indexing, Retrieval, and Analysis) research

group and co-leads Communication and Multimedia Lab (CMLab). Working closely with the

industry, he was a Visiting Scientist at Microsoft Research Redmond (2014) and had his

sabbatical leave (2016-2017) at IBM TJ Watson Research Center, New York, to enhance

Watson’s visual cognition, where he contributed the first AI produced movie trailer. He is the

founding Director for NVIDIA AI Lab (NTU), the 1st in Asia. He received Ph.D. (2007) from

Columbia University, New York. Before that, he was a founding engineer and research

manager in CyberLink Corp, now a public image/video software company. He serves as the

Associate Editors for IEEE Multimedia Magazine and IEEE Transactions on Multimedia, two

premier journals.

Abstract

Learning from Multimodal Data Streams

Images, videos, audios, and user-contributed logs, etc., are major data types nowadays

essential for disruptive opportunities in social media, entertainments, education, healthcare,

IoT, etc. However, the developed techniques are far behind the dire needs. For leveraging

multimodal data streams with effective deep neural networks, we will present advanced and

novel methods for jointly considering spatial and sequential neural networks and their

variations, which well approximate the multimodal data streams. We will show the importance

and the problems for cross-domain learning – computing data of different types in the same

semantic space – and present several solutions. We will demonstrate how to utilize different

modalities for improving challenging learning tasks in the end-to-end neural networks. We will

showcase some recent works published in the top venues e.g., CVPR, ICCV, etc.

32

Bartosz Wilczyński



phone number: +48 22 55 44 577

Bartosz Wilczyński is an assistant professor in the Institute of Informatics since 2011.

He obtained his PhD in Mathematics from Polish Academy of Science and has worked also in

Lawrence Livermore National Laboratory in USA and European Molecular Biology Laboratory

in Heidelberg.

His research is focused on applying computational methods to the problems arising in modern

genomics. He worked on mathemetical models of regulatory networks, Bayesian Networks and

applications of machine learning methods to functional genomic element identification. He is

the principal investigator in a number of projects funded by grants including EMBO Installation

Grant, ERA-NET European grant, and multiple national projects by Polish National Science

Centre and Foundation for Polish Science.

Abstract

Machine learning at work in modern functional genomics

As the abundance and complexity of data in modern genomics is growing exponentially and

consistently exceeds the gains in computational power (as described by the Moore’s law),

there is a growing need for using smart computational strategies for data analysis. Our team

of interdisciplinary scientists is aiming to provide the scientists working on functional genomics

with computational methodology adequate for the challenges of the next-generation

sequencing.

In particular, we have worked on the problem of identification of functional regulatory

sequences in the genomes of different animals, from fruit flies, through laboratory mice to

humans. This problem requires effective methods for scanning through terabytes of

sequencing data to fish for regions enriched in specific sequence features, specific biochemical

contacts or both. It is usually done for the data, where the training data is limited and the

datasets are noisy, so any attempts of using out-of-box machine learning libraries is prone to

erroneous results. However, due to the fact that the data availability is growing very quickly,

there is certainly room for use of customised machine learning methods to arrive at reliable

conclusions based on cross-comparisons between datasets.

In my talk, I will describe some of our methodology, including our work on Dynamic Bayesian

Networks for the task of classification of regulatory regions. I will also discuss our experiences

in using other, more standard classification techniques like Random Forests or Support Vector

Machines and specific scenarios where it led to biologically relevant conclusions.

33

Notes

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Notes

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