Name: PyData Edinburgh: Time Series Analysis with Markov State Models
Start: 2018-09-06T18:15:00+01:00
End: 2018-09-06T21:00:00+01:00
Location: SolarWinds

Welcome to the September meetup for the PyData Edinburgh group!
We look forward to welcoming you all to another great venue — SolarWinds, who have kindly invited us to use their great facilities.

We'll have our regular update on the PyData community, lightning talks and we're delighted to have Antonia Mey join us from the University of Edinburgh to give our main talk.

We look forward to seeing you there - doors open at 1815, make sure you've clicked attend to reserve your place!

TALK DETAILS
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Time series analysis with Markov State Models
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Presenter: Antonia Mey
I am a research software engineer in computational chemistry at the University of Edinburgh on problems related to computer aided drug design. My professional training consists of a PhD in statistical physics from the University of Nottingham & postdoctoral experience at the Freie Universität Berlin, where I have been working on problems such as dimensionality reduction of large datasets from biomolecular simulations and developing software to help with simulation and analysis of complex biological processes such as ligand and protein binding. In my spare time I work as a freelancer for the mathematics communication project IMAGINARY (www.imaginary.org) as a project manager, organising exhibitions and workshop on various mathematics topics around the world with a primary focus on the African continent.

Description:
Understanding the dynamics of complex bimolecules, such as proteins, is of invaluable interest to bimolecular and pharmaceutical sciences. In the last 20-30 years simulation techniques to study protein molecules have developed quickly and interest in the analysis of the simulation data has gained momentum. From the simulations large time series datasets of protein motion with atomistic detail are generated. It is easy to generate pretty molecular movies from these datasets, but it is much more difficult to extract quantitative observables that can be compared to experiments and guide experimental design. In my talk I will give an introduction to TICA, Time resolved Independent Component Analysis and Markov state modelling. These are techniques used for dimensionality reduction and extracting relevant dynamic information from the time series data. Furthermore I will introduce the software package pyEMMA which is an easily usable python tool that implements TICA and Markov State Modelling algorithms.

LIGHTNING TALKS
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Share your stories! Share your successes & failures! Share your hints & tips! Whether you are a beginner or an expert, we want to hear from you.
Lightning talks have been really popular at other PyData meetups, so let's get the ideas flowing in Edinburgh!
No need for slides (bring them on a memory stick if you do have some though) & no need to be an expert speaker!
Just sign up on the evening: anything goes (within reason!) and no more than 5 minutes long — we'll be bringing the alarm clock!

LOGISTICS
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Please note all attendees MUST sign up to the Meetup to be admitted.

1815: Doors open, drinks & networking
1845: Talks start - welcome & community announcements followed by our main speaker, then lightning talks.
We'll end the evening with pizza and more refreshments, wrapping up by 2100.

SPONSORS & REFRESHMENTS
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As always, we couldn't do this without our sponsors help to provide a venue and drink & pizzas — Cathcart Associates (https://www.cathcartassociates.com), Solarwinds (https://www.solarwinds.com) and Canon Medical (https://research.eu.medical.canon)

CODE OF CONDUCT
====================
The PyData Code of Conduct governs this meetup. ( http://pydata.org/code-of-conduct.html ) To discuss any issues or concerns relating to the code of conduct or the behavior of anyone at a PyData meetup, please contact NumFOCUS Executive Director Leah Silen (+1 512-222-5449; leah@numfocus.org) or the group organizer.

Martina Pugliese

Thomas Kober

Sam Rhynas

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PyData Edinburgh: Time Series Analysis with Markov State Models

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