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SUMMARY:Women@CL Talklet Event - Sandra Servia\, Catalina Cangea\, Angelik
 i Koutsoukou-Argyraki
DTSTART:20180525T120000Z
DTEND:20180525T130000Z
UID:TALK106360@talks.cam.ac.uk
CONTACT:Ayat Fekry
DESCRIPTION:*Title:* Mobile sensing at the service of mental wellbeing\n\n
 *Abstract*\n\nThe pervasiveness of smartphones and their rich-set of built
 -in sensors\, including accelerometer\, GPS and microphone\, have allowed 
 the emergence of many platforms to passively monitor health and behaviour 
 through experience sampling and sensing\, at low cost and large scale. How
 ever\, studies at the confluence of mental health and mobile sensing have 
 been longitudinally limited\, controlled\, and confined to a small number 
 of participants. In this talk\, I will report on what we believe is the la
 rgest longitudinal\, in-the-wild study of mood through smartphones\, which
  includes data from ~18\,000 participants for a period of three years. Usi
 ng data collected with an Android app\, which includes self-reported moods
 \, system triggered experience sampling data and passive sensing data\, we
  are able to identify routines and their relation with demographics\, perc
 eived health and psychological traits\, as well as exploring the predictab
 ility of users’ mood from passive sensing data. Although this large scal
 e data collection is very suitable for population studies\, the collection
  and use of sensitive data comes with privacy issues and chances of data m
 isuses. In this line\, I will comment on the trade-offs between utility\, 
 battery consumption and latency of private-by-design mobile health apps th
 at rely on on-device processing with limited cloud offloading.\n\n--------
 ----------------------------------------------\n\n\n*Title:* Cross-modal t
 echniques for data integration\n\n*Abstract*\n\nMultimodal learning is a n
 atural and necessary progression from the traditional methods that typical
 ly learn to represent a single modality. \nApplications exist in a variety
  of scenarios\, a few notable examples being medicine\, environmental risk
  and robotics. In this talk\, I will present two classification tasks from
  the audiovisual and chemical domains\, along with the deep learning archi
 tectures that I have designed to improve on existing methods.\n\n\n-------
 ----------------------------------------------------\n\n\n*Title:* Proof M
 ining Mathematics\, Formalizing Mathematics-the ALEXANDRIA project\n\n*Abs
 tract*\n\nProof mining is a research program in applied proof theory invol
 ving the extraction of quantitative\, computable information from (even\nn
 onconstructive) mathematical proofs of statements of a certain logical for
 m\, via a pen-and-paper i.e. \\textit{not} automated logical analysis. \nT
 he program originated as   ``unwinding of proofs'' in the ideas of \nGeorg
  Kreisel from the fifties\, and has been developed by Ulrich Kohlenbach an
 d his collaborators during the past two decades.\nA great deal of applicat
 ions for proofs in different research directions in Mathematics  has been 
 achieved.\nALEXANDRIA  is a new ERC project at the University of Cambridge
  under the  leadership of Lawrence Paulson aiming at the creation of a pro
 of development environment for working mathematicians through a collaborat
 ion of mathematicians and computer scientists. This will be achieved by fo
 rmalizing mathematical proofs with the proof assistant \\textit{Isabelle}.
 \nThe focus of the project is the management and use of large-scale mathem
 atical knowledge\, both as theorems and as algorithms.\nIn addition to the
  obvious importance of proof verification for Mathematics and the usefulne
 ss of libraries of formalized proofs for (the future generations of) mathe
 maticians\, the formalization of mathematical proofs could possibly shed l
 ight on interesting proof theoretic questions. Moreover\, enriching the li
 braries with formalized proof-mined proofs could open the way for the exci
 ting prospect of automating proof mining itself.\n
LOCATION:Computer Laboratory\, William Gates Building\, Room SS03
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