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resumeData.json
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resumeData.json
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{
"main": {
"name":"Sebastjan Cizel",
"occupation":"research student in mathematical physics",
"description":"",
"image":"profilepic.jpg",
"bio":"Research scientist with expertise in machine learning, probability, and programming, currently working in computer vision, researching neural network based image and video compression algorithms.",
"contactmessage":"Here is where you should write your message to readers to have them get in contact with you.",
"address":{
"street":"Magdalen College",
"city":"Oxford",
"state":"United Kingdom"
},
"website": "http://sebastjancizel.github.io",
"resumedownload":"https://www.dropbox.com/s/humefrhq1zvujay/Resume_CizelSebastjan.pdf?dl=0",
"social":[
{
"name":"mail",
"url":"mailto:sebastjancizel@gmail.com",
"className":"fa fa-envelope"
},
{
"name":"linkedin",
"url":"https://www.linkedin.com/in/sebastjan-cizel",
"className":"fa fa-linkedin"
},
{
"name":"github",
"url":"http://github.com/sebastjancizel",
"className":"fa fa-github"
}
]
},
"resume":{
"skillmessage":"Here you can create a short write-up of your skills to show off to employers",
"education":[
{
"school":"University of Oxford",
"degree":"DPhil in Mathematics",
"image": "oxford-logo.jpg",
"graduated":"October 2017 - September 2021",
"description":""
},
{
"school":"Imperial College London",
"degree":"MSc in Pure Mathematics",
"image": "icl-logo.jpg",
"graduated":"October 2016 - September 2017",
"description":""
},
{
"school":"University of Ljubljana",
"degree":"BSc in Mathematics",
"image": "unilj-logo.jpg",
"graduated":"October 2013 - September 2016",
"description":""
}
],
"work":[
{
"company":"Deep Render",
"title":"Research Scientist",
"years":"September 2021 - Present",
"description":"Deep Render is a start-up focusing on developing a perceptually optimized image and video compression algorithms based on AI. My day to day work consists of developing and testing new approaches and improvements to the video compression algorithm with the aim of optimizing both the compression and runtime performance on mobile platforms. I also maintain the codebase for a core video compression algorithm. The tech stack consists of Pytorch with Weighs and Biases for experiment tracking.",
"image": "dr.jpeg"
},
{
"company":"Sledilnik COVID19 Tracker",
"title":"Contributor (data visualizations)",
"years":"September 2020 - May 2021",
"description":"<a href='https://covid-19.sledilnik.org/en/team'>Sledilnik</a> is an open source community engaged in a comprehensive effort of track COVID19 cases in Slovenia. I contributed to the visualizations of the data gathered by the community. The work involved parsing and cleaning the data using the backend written in F# and creating visualizations using the Highcharts API. ",
"image": "sledilnik.jpg"
},
{
"company":"University of Oxford",
"title":"Tutorial Teacher",
"years":"January 2018 - Present",
"description":"From my first year as a DPhil student I have been involved in teaching tutorials for various different subjects at the University of Oxford. For a more detailed breakdown of the subjects and the colleges I have taught at, please visit my <a href='https://www.linkedin.com/in/sebastjan-cizel/'>LinkedIn page.</a>",
"image": "oxford-logo.jpg"
}
],
"skills":[
{
"title":"Probability and Statistics",
"list": "",
"description": "Highly proficient in mathematics, probability theory, and statistics, with a particular interest in generative density models."
},
{
"title":"Programming",
"list": "Python (advanced); C++, Java, F# (working knowledge)",
"description": "Very proficient in Python with 8 years of experience using the language and the standard machine learning and deep learning libraries. I have a working knowledge in several other languages including C++ (used for developing custom Torch extensions), F# (used with in my work for Sledilnik), and Java (used in <a href='https://github.com/sebastjancizel/jlox'>a personal project</a>)."
},
{
"title":"Machine Learning",
"list": "Pytorch, Numpy, TensorFlow, Pandas, scikit-learn, Weights and Biases",
"description": "In-depth understanding of the latest deep learning and optimization literature with a particular focus in computer vision. Highly proficient in designing and executing experiments using the Python deep learning stack (Numpy, Pytorch, TensorFlow, scikit- learn, Weights and Biases)."
}
]
},
"research":{
"description":"Before venturing over to deep learning I did a PhD in Mathematics, focusing on the intersection of geometry and string theory. Here as a quick description of the things I was researching: <br> String theory has, over the past few decades, been a curious venue for a profound interaction between mathematics and physics. The efforts to formulate a fundamental quantum theory of gravity have led several branches of pure mathematics to make an appearance in physics. Most notably, geometry in its various forms has started to take on a prominent role. This was not its first appearance. Since the theory of general relativity, it has become clear that there is an inherent geometry underlying the physics of gravity. However, string theorists went beyond the 4 dimensions of space and time and started thinking about increasingly intricate higher dimensional objects.<br> This is what my research was about; the geometric objects — higher-dimensional shapes — that appear in string theory and how their properties manifest in the physical theories they are associated with. Usually with opaque names like G<sub>2</sub> manifolds or GK geometries, these shapes are often of independent mathematical interest. What makes them especially exciting is the fruitful interplay of mathematics and physics that is present. Mathematics provides the foundation for physical theories, whereas physical arguments and intuition guide the mathematical explorations.",
"papers":[
{
"title":"I/c-extremization in M/F-duality",
"journal":"<a href='https://scipost.org/SciPostPhys.9.3.029'>SciPostPhys.9.3.029</a>",
"collaborators":"Marieke van Beest, Sakura Schafer-Nameki, James Sparks",
"abstract": ""
},
{
"title":"Higgs Bundles for M-theory on G<sub>2</sub>-manifolds",
"journal":"<a href='https://link.springer.com/article/10.1007/JHEP03(2019)199'>JHEP 2019, 199 (2019)</a>",
"collaborators":"Andreas Braun, Max Hubner, Sakura Schafer-Nameki",
"abstract": ""
}
]
},
"portfolio":{
"projects": [
{
"title":"NYC Taxi Data Dashboard",
"category":"Dashboard I made visualizing 120 million taxi rides.",
"image":"nyc-taxi.jpg",
"url":"https://nyc-taxi-dashboard.herokuapp.com/"
}
]
}
}