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
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
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
CHARLES UNIVERSITY
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 — Magisterský studijní program •
University Graduate — Master’s programme of study
Hlavní studijní program/obor • Main study programme/subject:
Informatika/Diskrétní modely a algoritmy • Computer Science/Discrete Models and Algorithms
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: 2 roky • 2 years
Forma studia • Mode of study: Prezenční • Full-time
Trvání studia • Duration of study:
studoval • had been studying 18.9.2013 - 13.9.2016
absolvoval • graduated 13.9.2016
Tento výpis byl vygenerován ze studijního informačního systému Univerzity Karlovy, údaje v něm uvedené byly k datu a času 10.10.2016 19:53:35 zapsány ve studijním informačním systému univerzity.
This transcript was generated from the Study Information System of Charles University, the above mentioned data were written into the system to the date/time 10.10.2016 19:53:35.
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
E
C
T
S
Datum
Date
Známka
Exam
grade
NOPT001
Dynamické programování •
Dynamic Programming
1 1 - 12. 12. 2013 1*
NOFY016
Fyzika pro nefyziky I - Svět kolem nás •
Physics for Non-Physicists I - The World around Us
1 1 3 12. 12. 2013 1
NOPT034
Matematické programování a polyedrální kombinatorika •
Mathematical Programming and Polyhedral Combinatorics
1 1 - 12. 12. 2013 U, 1*
NOPT004
Optimalizační procesy I •
Optimization Processes I
1 1 - 12. 12. 2013 U, 1*
NMAI040
Úvod do teorie čísel •
Introduction to Number Theory
1 1 - 12. 12. 2013 1*
NMAI065
Základy teorie kategorií pro informatiky •
Fundamentals of Category Theory for Computer Scientists
1 1 - 12. 12. 2013 1*
NDMI064
Aplikovaná diskrétní matematika •
Applied Discrete Mathematics
1 1 3 16. 12. 2013 1
NTIN062
Složitost I •
Complexity I
1 1 5 23. 01. 2014 Z, 1
NOPT021
Teorie her •
Game Theory
1 1 3 29. 01. 2014 1
NTIN064
Vyčíslitelnost I •
Computability I
1 1 3 03. 02. 2014 1
NMAI072
Lineární algebra III •
Linear algebra III
1 1 3 13. 02. 2014 1
NDMI067
Toky, cesty a řezy •
Flows, Paths and Cuts
1 1 3 09. 06. 2014 1
NTIN066
Datové struktury I •
Data Structures I
1 1 3 19. 06. 2014 1
NDEK009
Přidělené kredity za povinně volitelné předměty •
Assigned credits for optional core courses
1 1 38 -
NOPT016
Celočíselné programování •
Integer Programming
1 2 - 12. 12. 2013 U, 1*
NMAI064
Matematické struktury •
Mathematical Structures
1 2 - 12. 12. 2013 U, 1*
NOPT005
Optimalizační procesy II •
Optimisation Processes II
1 2 - 12. 12. 2013 1*
NOPT015
Parametrická optimalizace •
Parametric Optimization
1 2 - 12. 12. 2013 U, 1*
NMAI066
Topologické a algebraické metody •
Topological and Algebraic Methods
1 2 - 12. 12. 2013 1*
NOPT036
Rozšířené formulace polytopů •
Extended Formulations of Polytopes
1 2 3 14. 05. 2014 1
NSZZ023
Diplomová práce I •
Diploma Thesis I
1 2 6 20. 05. 2014 Z
NSZZ024
Diplomová práce II •
Diploma Thesis II
1 2 9 20. 05. 2014 Z
NSZZ025
Diplomová práce III •
Diploma Thesis III
1 2 15 20. 05. 2014 Z
NOFY017
Fyzika pro nefyziky II - Svět kolem nás •
Physics for Non-Physicists II - The World around Us
1 2 3 22. 05. 2014 1
NJAZ093
Akademická angličtina •
Academic English
1 2 3 23. 05. 2014 Z
NOPT013
Matematická ekonomie •
Mathematical Economics
1 2 6 26. 05. 2014 1
NOPT017
Vícekriteriální optimalizace •
Multiobjective Optimisation
1 2 3 30. 09. 2014 1
NDEK011
Přidělené kredity za volitelné předměty •
Assigned credits for optional courses
1 2 4 -
Průměr za ročník • Study year average: 1,00
NOPT018
Základy nelineární optimalizace •
Fundamentals of Nonlinear Optimization
2 1 6 06. 02. 2015 Z, 1
Průměr za ročník • Study year average: 1,00
Průměr za celé studium • Complete study average: 1,00
R, Y - rok • year S - semestr • semester Celkem ECTS • Total ECTS: 122
Tento výpis byl vygenerován ze studijního informačního systému Univerzity Karlovy, údaje v něm uvedené byly k datu a času 10.10.2016 19:53:36 zapsány ve studijním informačním systému univerzity.
This transcript was generated from the Study Information System of Charles University, the above mentioned data were written into the system to the date/time 10.10.2016 19:53:36.
3. Tento výpis byl vygenerován ze studijního informačního systému Univerzity Karlovy, údaje v něm uvedené byly k datu a času 10.10.2016 19:53:36 zapsány ve studijním informačním systému univerzity.
This transcript was generated from the Study Information System of Charles University, the above mentioned data were written into the system to the date/time 10.10.2016 19:53:36.
4. VÝPIS SPLNĚNÝCH STUDIJNÍCH POVINNOSTÍ •
TRANSCRIPT OF COMPLETED STUDY REQUIREMENTS
Jméno • Full name:
Ha Karel
Diplomová práce • Diploma thesis Datum • Date: 13. 09. 2016 Známka • Exam Grade: 1
Solving Endgames in Large Imperfect-Information Games such as Poker
Státní závěrečná zkouška • Final state examination
Datum
Date
Známka
Exam
grade
Diskrétní modely a algoritmy • Discrete Models and Algorithms 08. 06. 2016 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
Studijní průměr se skládá ze zkoušek, souborných zkoušek a všech neúspěšných pokusů, bez uznaných předmětů. • The arithmetical average of study grades
shall be taken over the examinations, comprehensive examinations, inclusive all unsuccessful sits, but exclusive recognized subjects.
* 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: 10. 10. 2016
Opatřeno uznávanou elektronickou značkou Univerzity Karlovy dle zákona o elektronickém podpisu
The qualified electronic seal of Charles University attached under the Electronic Signature Act
Powered by TCPDF (www.tcpdf.org)
Tento výpis byl vygenerován ze studijního informačního systému Univerzity Karlovy, údaje v něm uvedené byly k datu a času 10.10.2016 19:53:36 zapsány ve studijním informačním systému univerzity.
This transcript was generated from the Study Information System of Charles University, the above mentioned data were written into the system to the date/time 10.10.2016 19:53:36.