BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Talklet -- DTG and AI group - Diana A. Vasile\, Naruemon Pratanwan
 ich (Ploy)
DTSTART:20160218T130000Z
DTEND:20160218T140000Z
UID:TALK64034@talks.cam.ac.uk
CONTACT:Helen Yannakoudakis
DESCRIPTION:*Speaker*: Diana A. Vasile\n\n*Title*: End-to-end encryption f
 or software-as-a-service\n\n*Abstract*: Cloud and software-as-a-service ap
 plications such as Google Docs\, Evernote\, iCloud and Dropbox are very co
 nvenient for users\, but problematic from a security point ofview. As thes
 e services process data in unencrypted form on their servers\, users must 
 blindly trust the cloud provider to prevent unauthorised access and to mai
 ntain integrity of the data. A security breach of the cloud provider could
  have disastrous consequences.\n\nIn this project\, we are exploring techn
 iques for Trust-Reducing Verifiable Exchange of data (TRVE Data\, pronounc
 ed "true data"). Our goal is to create the foundations for applications th
 at are as usable and convenient as today's cloud services\, while reducing
  the amount of trust that is placed in third parties.\n\nIn particular\, w
 e are exploring cryptographic techniques for improving confidentiality\, p
 reventing unauthorised access to sensitive data\; integrity\, ensuring dat
 a items cannot be tampered with\; and availability\, ensuring continued ac
 cess to data in the face of malice.\n\nThis project touches on many areas 
 of computer security\, distributed systems and human-computer interaction.
  We are applying end-to-end encryption and integrity proof techniques to t
 he domain of databases and real-time collaborative applications. We are de
 signing user interfaces to encourage safe user behaviour. We are building 
 upon a long history of distributed systems research to create data synchro
 nisation mechanisms that are robust to both malicious and accidental netwo
 rk interference.\n\n\n\n------------\n\n*Speaker*: Naruemon Pratanwanich (
 Ploy)\n\n*Title*: Predicting disease-therapeutic gene targets from multipl
 e evidences\n\n*Abstract*: Accurate prediction of disease-gene association
 s requires the integration of different evidences\, such as genome-wide as
 sociation studies\, literate databases and gene expression experiments. Fr
 equently\, these data are complementary and capture different aspects of t
 he landscape of gene-disease associations. A certain degree of consensus a
 mong these evidences makes us more confident on true associations.\n\nHere
 \, our objective is to impute the missing associations between drug target
 s and diseases as well as discover the agreement over multiple datasets (v
 iews). Considering multiple datasets simultaneously allows those diseases/
 genes that are not studied at all in one view can be imputed by borrowing 
 the information from other views.\n\nIn order to address this issue\, we d
 evelop a joint matrix factorisation approach that shares the same latent f
 actors across different views\, where each latent factor is considered a s
 oft-cluster for both diseases and targets. Still\, we allow each view to b
 e mapped from the latent space to their own data space with different tran
 sformation functions. In particular\, our model automatically chooses the 
 transformation parameters e.g. scaling and location shifting for each view
  such that transformed data are well-modelled by the joint Gaussian-likeli
 hood matrix factorisation. We believe that including the transformation as
  an integral part of the matrix factorisation model rather than as an ad-h
 oc step not only make the multi-view analysis possible\, but also yields t
 he better performance on prediction.\n\nWe apply the approach to disease-g
 ene matrices generated by the Centre for Therapeutic Target Validation and
  present results on how this model-based approach can help to impute missi
 ng relationships and unravel core consensus across different views.\n
LOCATION:Computer Laboratory\, William Gates Building\, Room FW26
END:VEVENT
END:VCALENDAR