Recent technological advances in DNA/RNA sequencing allow tackling some most important questions in many biological fields, including evolutionary genetics. Monitoring genomic signatures of natural selection are key to gain insights into such diverse phenomena as the evolution of susceptibility to common human diseases, as well as resistance to antibiotics and pesticides.
With access to large-scale population genomic data, we now have the opportunity to understand how evolution has shaped individual genomes. In particular, we can look into one of the most elusive questions in evolutionary biology: the extent to which natural selection has driven beneficial alleles to spread in time and space, within and among populations.
For this, the use of deep neural networks is a natural and effective solution, as it integrates the predictive power of machine learning with scalability to large datasets.
As a particular test case, my research will focus on novel deep learning methods to study the spread of insecticide-resistance in Anopheles gambiae, the malaria vector mosquitoes. Specifically, I will strive to:
a) incorporate temporal dimension for time-series predictions using sequence models (such as recurrent neural networks or dilated convolutional networks);
b) seek the optimal representation of population genomic data for machine learning;
c) and experiment with various ways to estimate probabilities of mutations migrating between populations.
presentation on "Dynamic Routing Between Capsules" (https://arxiv.org/abs/1710.09829) for "Pattern Recognition and Computer Vision Reading Group, Czech Technical University" (http://cmp.felk.cvut.cz/~toliageo/rg/index.html), 27th March 2018
source code: https://github.com/mathemage/CapsNet-presentation
AI Supremacy in Games: Deep Blue, Watson, Cepheus, AlphaGo, DeepStack and Ten...Karel Ha
my presentation on AI Supremacy in Games: Deep Blue, Watson, Cepheus, AlphaGo, DeepStack and TensorCFR for STRETNUTIE DOKTORANDOV on 13. 6. 2018 (https://zona.fmph.uniba.sk/detail-novinky/back_to_page/fmfi-uk-zona/article/stretnutie-doktorandov-1362018/calendar_date/2018/june/)
LaTeX source code is available at https://github.com/mathemage/AISupremacyInGames-presentation
my presentation on AlphaZero (https://arxiv.org/abs/1712.01815) for AI seminar (http://ktiml.mff.cuni.cz/~bartak/ui_seminar/)
LaTeX source code is avalailable at https://github.com/mathemage/AlphaZero-presentation
Solving Endgames in Large Imperfect-Information Games such as PokerKarel Ha
My master thesis on solving endgames in imperfect-information games.
keywords: algorithmic game theory, imperfect-information games, Nash equilibrium, subgame, endgame, counterfactual regret minimization, Poker
AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree SearchKarel Ha
the presentation of the article "Mastering the game of Go with deep neural networks and tree search" given at the Optimization Seminar 2015/2016
Notes:
- All URLs are clickable.
- All citations are clickable (when hovered over the "year" part of "[author year]").
- To download without a SlideShare account, use https://www.dropbox.com/s/p4rnlhoewbedkjg/AlphaGo.pdf?dl=0
- The corresponding leaflet is available at http://www.slideshare.net/KarelHa1/leaflet-for-the-talk-on-alphago
- The source code is available at https://github.com/mathemage/AlphaGo-presentation
Mastering the game of Go with deep neural networks and tree search: PresentationKarel Ha
the presentation of the article "Mastering the game of Go with deep neural networks and tree search" given at the Spring School of Combinatorics 2016
Notes:
- All URLs are clickable.
- All citations are clickable (when hovered over the "year" part of "[author year]").
- To download without a SlideShare account, use https://www.dropbox.com/s/4njuiaaou1po0y4/AlphaGo.pdf?dl=0
- The corresponding handout is available at http://www.slideshare.net/KarelHa1/mastering-the-game-of-go-with-deep-neural-networks-and-tree-search-handout
- The video is available at https://youtu.be/Lso2kE58JrI
- The source code is available at https://github.com/mathemage/AlphaGo-presentation
This is the final report for my project as a Technical Student at CERN.
The Intel Xeon/Phi platform is a powerful x86 multi-core engine with a very high-speed memory interface. In its next version it will be able to operate as a stand-alone system with a very high-speed interconnect. This makes it a very interesting candidate for (near) real-time applications such as event-building, event-sorting and event preparation for subsequent processing by high level trigger software algorithms.
- introductory presentation to series of lectures on Algorithmic Game Theory
- talk given for Spring School of Combinatorics 2014, Charles University in Prague
- video available at https://youtu.be/E1T0kdE1OvI
Recent technological advances in DNA/RNA sequencing allow tackling some most important questions in many biological fields, including evolutionary genetics. Monitoring genomic signatures of natural selection are key to gain insights into such diverse phenomena as the evolution of susceptibility to common human diseases, as well as resistance to antibiotics and pesticides.
With access to large-scale population genomic data, we now have the opportunity to understand how evolution has shaped individual genomes. In particular, we can look into one of the most elusive questions in evolutionary biology: the extent to which natural selection has driven beneficial alleles to spread in time and space, within and among populations.
For this, the use of deep neural networks is a natural and effective solution, as it integrates the predictive power of machine learning with scalability to large datasets.
As a particular test case, my research will focus on novel deep learning methods to study the spread of insecticide-resistance in Anopheles gambiae, the malaria vector mosquitoes. Specifically, I will strive to:
a) incorporate temporal dimension for time-series predictions using sequence models (such as recurrent neural networks or dilated convolutional networks);
b) seek the optimal representation of population genomic data for machine learning;
c) and experiment with various ways to estimate probabilities of mutations migrating between populations.
presentation on "Dynamic Routing Between Capsules" (https://arxiv.org/abs/1710.09829) for "Pattern Recognition and Computer Vision Reading Group, Czech Technical University" (http://cmp.felk.cvut.cz/~toliageo/rg/index.html), 27th March 2018
source code: https://github.com/mathemage/CapsNet-presentation
AI Supremacy in Games: Deep Blue, Watson, Cepheus, AlphaGo, DeepStack and Ten...Karel Ha
my presentation on AI Supremacy in Games: Deep Blue, Watson, Cepheus, AlphaGo, DeepStack and TensorCFR for STRETNUTIE DOKTORANDOV on 13. 6. 2018 (https://zona.fmph.uniba.sk/detail-novinky/back_to_page/fmfi-uk-zona/article/stretnutie-doktorandov-1362018/calendar_date/2018/june/)
LaTeX source code is available at https://github.com/mathemage/AISupremacyInGames-presentation
my presentation on AlphaZero (https://arxiv.org/abs/1712.01815) for AI seminar (http://ktiml.mff.cuni.cz/~bartak/ui_seminar/)
LaTeX source code is avalailable at https://github.com/mathemage/AlphaZero-presentation
Solving Endgames in Large Imperfect-Information Games such as PokerKarel Ha
My master thesis on solving endgames in imperfect-information games.
keywords: algorithmic game theory, imperfect-information games, Nash equilibrium, subgame, endgame, counterfactual regret minimization, Poker
AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree SearchKarel Ha
the presentation of the article "Mastering the game of Go with deep neural networks and tree search" given at the Optimization Seminar 2015/2016
Notes:
- All URLs are clickable.
- All citations are clickable (when hovered over the "year" part of "[author year]").
- To download without a SlideShare account, use https://www.dropbox.com/s/p4rnlhoewbedkjg/AlphaGo.pdf?dl=0
- The corresponding leaflet is available at http://www.slideshare.net/KarelHa1/leaflet-for-the-talk-on-alphago
- The source code is available at https://github.com/mathemage/AlphaGo-presentation
Mastering the game of Go with deep neural networks and tree search: PresentationKarel Ha
the presentation of the article "Mastering the game of Go with deep neural networks and tree search" given at the Spring School of Combinatorics 2016
Notes:
- All URLs are clickable.
- All citations are clickable (when hovered over the "year" part of "[author year]").
- To download without a SlideShare account, use https://www.dropbox.com/s/4njuiaaou1po0y4/AlphaGo.pdf?dl=0
- The corresponding handout is available at http://www.slideshare.net/KarelHa1/mastering-the-game-of-go-with-deep-neural-networks-and-tree-search-handout
- The video is available at https://youtu.be/Lso2kE58JrI
- The source code is available at https://github.com/mathemage/AlphaGo-presentation
This is the final report for my project as a Technical Student at CERN.
The Intel Xeon/Phi platform is a powerful x86 multi-core engine with a very high-speed memory interface. In its next version it will be able to operate as a stand-alone system with a very high-speed interconnect. This makes it a very interesting candidate for (near) real-time applications such as event-building, event-sorting and event preparation for subsequent processing by high level trigger software algorithms.
- introductory presentation to series of lectures on Algorithmic Game Theory
- talk given for Spring School of Combinatorics 2014, Charles University in Prague
- video available at https://youtu.be/E1T0kdE1OvI
1. UNIVERZITA KARLOVA V PRAZE
CHARLES UNIVERSITY IN PRAGUE
Matematicko-fyzikální fakulta • Faculty of Mathematics and Physics
VÝPIS SPLNĚNÝCH STUDIJNÍCH POVINNOSTÍ •
TRANSCRIPT OF COMPLETED STUDY REQUIREMENTS
Příjmení • Family name(s): Ha
Jméno • Other/given name(s): Karel
Datum narození • Date of Birth: 1. 2. 1991
Místo narození • Place of Birth: Ha Noi, Vietnam • Viet Nam
Identifikační číslo studenta • Student identification number: 81255304
Druh studia • Type of study:
Vysokoškolské vzdělání — Bakalářský studijní program •
University Graduate — Bachelor’s programme of study
Hlavní studijní program/obor • Main study programme/subject:
Informatika/Obecná informatika • Computer Science/General Computer Science
Studijní jazyk nebo jazyky, ve kterém je realizována výuka a zkoušky • Language or languages of instruction
and examination: Čeština • Czech
Standardní délka studia • Official length of study: 3 roky • 3 years
Forma studia • Mode of study: Prezenční • Full-time
Trvání studia • Duration of study:
studoval • had been studying 15.9.2010 - 27.6.2013
absolvoval • graduated 27.6.2013
Tento výpis byl vygenerován ze studijního informačního systému Univerzity Karlovy v Praze, údaje v něm uvedené byly k datu a času 30.1.2016 00:44:40 zapsány ve studijním informačním systému univerzity.
2. VÝPIS SPLNĚNÝCH STUDIJNÍCH POVINNOSTÍ •
TRANSCRIPT OF COMPLETED STUDY REQUIREMENTS
Jméno • Full name:
Ha Karel
Výpis splněných STUDIJNÍCH povinností • Transcript of completed STUDY requirements:
Kód předmětu
Subject Code
Název předmětu • Subject
R
Y
S ECTS
Datum
Date
Známka
Exam
grade
NSWI090
Počítačové sítě I •
Computer Networks I
1 1 3
22. 12.
2010
1
NTVY014
Tělesná výchova •
Physical Education
1 1 1
05. 01.
2011
Z
NMAI069
Matematické dovednosti •
Mathematical skills
1 1 2
06. 01.
2011
Z
NDMI050
Úvod do řešení problémů kombinatorických, mat. i jiných (IPS) I •
Introduction to Problem Solving in Combinatorics, Mathematics and Other Fields (IPS) I
1 1 3
12. 01.
2011
Z
NDMI002
Diskrétní matematika •
Discrete Mathematics
1 1 5
13. 01.
2011
1
NDBI025
Databázové systémy •
Database Systems
1 1 6
19. 01.
2011
2
NMAI054
Matematická analýza I •
Mathematical Analysis I
1 1 5
26. 01.
2011
1
NMAI057
Lineární algebra I •
Linear Algebra I
1 1 5
03. 02.
2011
1
NSWI120
Principy počítačů a operačních systémů •
Principles of Computers and Operating Systems
1 1 5
09. 02.
2011
2
NPRG030
Programování I •
Programming I
1 1 6
02. 05.
2011
Z
NPFL012
Úvod do počítačové lingvistiky •
Introduction to Computer Linguistics
1 1 3
02. 05.
2011
1
NTVY015
Tělesná výchova •
Physical Education
1 2 1
11. 05.
2011
Z
NJAZ091
Anglický jazyk •
English Language
1 2 1
24. 05.
2011
1
NSWI021
Počítačové sítě II •
Computer Networks II
1 2 3
26. 05.
2011
1
NMAI058
Lineární algebra II •
Linear Algebra II
1 2 5
06. 06.
2011
1
NPRG031
Programování II •
Programming II
1 2 5
13. 06.
2011
1
NMAI055
Matematická analýza II •
Mathematical Analysis II
1 2 5
22. 06.
2011
1
NTIN060
Algoritmy a datové struktury I •
Algorithms and Data Structures I
1 2 5
29. 06.
2011
1
NDMI011
Kombinatorika a grafy I •
Combinatorics and Graphs I
1 2 5
12. 07.
2011 1
NAIL063
Teorie množin •
Set Theory
1 2 3
23. 09.
2011
1
NMAI062
Algebra I •
Algebra I
2 1 6
06. 01.
2012
1
NTVY016
Tělesná výchova •
Physical Education
2 1 1
12. 01.
2012
Z
NSWI133
Firemní semináře •
Commercial Workshops
2 1 2
17. 01.
2012
Z
NMAI056
Matematická analýza III •
Mathematical Analysis III
2 1 6
20. 01.
2012
1
NAIL062
Výroková a predikátová logika •
Propositional and Predicate Logic
2 1 6
09. 02.
2012
1
NTIN061
Algoritmy a datové struktury II •
Algorithms and Data Structures II
2 1 6
21. 02.
2012
1
NMAI040
Úvod do teorie čísel •
Introduction to Number Theory
2 1 3
11. 07.
2012
1
NPRM024
Úvod do hlubin TeXu •
Introduction to the Depths of TeX
2 1 3
18. 09.
2012
Z
NTVY017
Tělesná výchova •
Physical Education
2 2 1
15. 04.
2012
Z
NSWI045
Rodina protokolů TCP/IP •
TCP/IP Protocol Suite
2 2 3
10. 05.
2012
1
NMAI063
Algebra II •
Algebra II
2 2 3
18. 05.
2012
1
NALG042
Cvičení z algebry •
Exercises in Algebra
2 2 3
29. 05.
2012
Z
Tento výpis byl vygenerován ze studijního informačního systému Univerzity Karlovy v Praze, údaje v něm uvedené byly k datu a času 30.1.2016 00:44:40 zapsány ve studijním informačním systému univerzity.
3. VÝPIS SPLNĚNÝCH STUDIJNÍCH POVINNOSTÍ •
TRANSCRIPT OF COMPLETED STUDY REQUIREMENTS
Jméno • Full name:
Ha Karel
NTIN071
Automaty a gramatiky •
Automata and Grammars
2 2 6
30. 05.
2012
1
NOPT046
Základy spojité optimalizace •
Fundamentals of Continuous Optimization
2 2 6
06. 06.
2012
1
NMAI064
Matematické struktury •
Mathematical Structures
2 2 6
13. 06.
2012
1
NMAI068
Proseminář z matematické analýzy •
Proseminar on Mathematical Analysis
2 2 3
14. 06.
2012
Z
NSWI095
Úvod do UNIXu •
Introduction to UNIX
2 2 5
27. 06.
2012
1
NPRG005
Neprocedurální programování •
Non-procedural Programming
2 2 6
18. 09.
2012
1
NPRG045
Ročníkový projekt •
Individual Software Project
2 2 4
25. 09.
2012
Z
NMAI065
Základy teorie kategorií pro informatiky •
Fundamentals of Category Theory for Computer Scientists
3 1 3
08. 01.
2013
1
NOPT004
Optimalizační procesy I •
Optimization Processes I
3 1 6
15. 01.
2013
1
NSZZ029
Bakalářská práce - rešerše •
Bachelor Thesis - literature search
3 1 2
16. 01.
2013
Z
NOPT001
Dynamické programování •
Dynamic Programming
3 1 3
17. 01.
2013
1
NOPT015
Parametrická optimalizace •
Parametric Optimization
3 1 6
17. 01.
2013
1
NJAZ017
Španělský jazyk pro začátečníky I •
Spanish for Beginners I
3 1 3
21. 01.
2013
Z
NOPT034
Matematické programování a polyedrální kombinatorika •
Mathematical Programming and Polyhedral Combinatorics
3 1 5
22. 01.
2013
1
NDMI012
Kombinatorika a grafy II •
Combinatorics and Graph Theory II
3 1 6
07. 02.
2013
1
NPRG041
Programování v C++ •
Programming in C++
3 1 6
30. 04.
2013
1
NPOZ004
Přirozené a umělé myšlení I •
Natural and Artificial Thought I
3 1 3
04. 06.
2013
1
NOPT005
Optimalizační procesy II •
Optimisation Processes II
3 2 3
09. 05.
2013
1
NSZZ030
Bakalářská práce •
Bachelor Thesis
3 2 4
21. 05.
2013
Z
NMAI066
Topologické a algebraické metody •
Topological and Algebraic Methods
3 2 3
21. 05.
2013
1
NJAZ080
Španělský jazyk pro začátečníky II •
Spanish for Beginners II
3 2 3
22. 05.
2013
Z
NOPT016
Celočíselné programování •
Integer programming
3 2 6
03. 06.
2013
1
R, Y - rok • year S - semestr • semester Celkem ECTS • Total ECTS: 218
Klasifikační stupnice a vysvětlení jejího významu • Grading scheme and if available, grade distribution guidance:
1 — Výborně • Excellent; 2 — Velmi dobře • Very good; 3 — Dobře • Good; 4 — Neprospěl • Fail; Z — Zápočet • Credit; P, S — Prospěl • Pass
* Uznaný studijní výsledek z jiného studia, předchozího studia nebo studia na jiné instituci • Acknowledged record from an independent simultaneous or
previous study or a student exchange programme.
Tento výpis byl vygenerován ze studijního informačního systému Univerzity Karlovy v Praze, údaje v něm uvedené byly k datu a času 30.1.2016 00:44:40 zapsány ve studijním informačním systému univerzity.
4. VÝPIS SPLNĚNÝCH STUDIJNÍCH POVINNOSTÍ •
TRANSCRIPT OF COMPLETED STUDY REQUIREMENTS
Jméno • Full name:
Ha Karel
Bakalářská práce • Bachelor’s thesis Datum • Date: 20. 06. 2013 Známka • Exam Grade: 1
Separation axioms
Státní závěrečná zkouška • Final state examination
Datum
Date
Známka
Exam
grade
Obecná informatika • General Computer Science 27. 06. 2013 1
Celková klasifikace státní závěrečné zkoušky • Overall classification of the final state examination 1
Klasifikační stupnice a vysvětlení jejího významu • Grading scheme and if available, grade distribution guidance:
1 — Výborně • Excellent; 2 — Velmi dobře • Very good; 3 — Dobře • Good; 4 — Neprospěl • Fail; Z — Zápočet • Credit; P, S — Prospěl • Pass
* Uznaný studijní výsledek z jiného studia, předchozího studia nebo studia na jiné instituci • Acknowledged record from an independent simultaneous or
previous study or a student exchange programme.
Celková klasifikace kvalifikace • Overall classification of the qualification: prospěl s vyznamenáním • passed with honours
Další informační zdroje • Further information sources: www.cuni.cz, www.msmt.cz, www.naric.cz
Datum • Date: 30. 1. 2016
Opatřeno uznávanou elektronickou značkou Univerzity Karlovy v Praze dle zákona o elektronickém podpisu
The qualified electronic seal of Charles University in Prague attached under the Electronic Signature Act
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Tento výpis byl vygenerován ze studijního informačního systému Univerzity Karlovy v Praze, údaje v něm uvedené byly k datu a času 30.1.2016 00:44:40 zapsány ve studijním informačním systému univerzity.