wesley_de_neve_multimedia_biotech r1

Structure “Research” on Academic Website
● 8 research areas (same as key expertise on industrial site, see below)
● x research topics per research area
o these correspond to a ZAP team, part of a ZAP team, or the union of
(parts of) ZAP teams
o (Part of) a ZAP (team) can be in more than one research topic
Template per research topic
● Research area (make a choice)
o Distributed intelligence in IoT
o Machine Learning & Data Mining
o Semantic Intelligence
o Multimedia Coding and Delivery
o Communication Sub-Systems
o Wireless Networking
o Fixed Networking
o Cloud & Big Data Infrastructures
o OTHER
● Title
o shorter = better (cf. links and breadcrumbs)
● Text describing the research topic
o 2 – 4 paragraphs is okay
o example below is quite long and will be shortened…
● Staff
o list of names
● Researchers
o list of names
● Projects (optional)
o list of projects, possibly with a link to external project page
o please provide funding channel, acronym, link, and title
● Key publications
o ca. 3-7 key publications: please provide a bullet list of references
and the biblio ID per publication
● Pictures
o 1-3 relevant and representative (appealing) pictures (possibly
with a caption)
o also send pictures as attachments to email (to avoid problems
extracting pictures from Word).
RESEARCH TRACK 1:
● Research area (make a choice)
Semantic Intelligence
● Title
o shorter = better (cf. links and breadcrumbs)
Multimedia Content Analysis
● Text describing the research topic
o 2 – 4 paragraphs is okay
o example below is quite long and will be shortened…
The past few years have witnessed a significant increase in both the
consumption and availability of online multimedia content. This can be
attributed to the introduction of easy-to-use devices and online services, the
availability of cheap storage and bandwidth, and more and more people going
online. To give a few statistics, as of January 2015, people watch six billion hours
of video content each month on YouTube, and upload 300 hours of video content
each minute. Similarly, 300 to 500 million microposts are created on Twitter on
a daily basis. These statistics are indicative of the problem of multimedia content
overload (infobesity): our ability to manage and consume multimedia content is
not able to keep up with our ability to create multimedia content.
Our main research objective is to narrow the gap between multimedia content
creation on the one hand, and multimedia content management and
consumption on the other hand. To that end, we focus on the development of
novel techniques for machine-based understanding of both textual and visual
content, paying particular attention to the use of deep learning. The term ‘deep
learning’ was coined in 2006, and refers to machine learning algorithms that
make use of multiple non-linear layers to construct feature hierarchies, typically
through the use of artificial neural networks.
Specifically, our research efforts focus on the following topics:
● Machine learning for modeling and understanding of natural language
● Natural language processing of noisy and short form text, such as status
updates on social networks
● Machine learning for modeling and understanding of visual data, such as
news broadcast video clips and short-form noisy video clips
● Staff
o list of names
Wesley De Neve, Azarakhsh Jalalvand, Joni Dambre.
● Researchers
o list of names
Fréderic Godin, Baptist Vandersmissen.
● Projects (optional)
o list of projects, possibly with a link to external project page
o please provide funding channel, acronym, link, and title
− MiX-ICON STEAMER (https://www.iminds.be/en/projects/steamer):
Smart Text Enrichment Algorithms for MEdia Retrieval applications
− MiX-ICON Audience Measurement
(https://www.iminds.be/en/projects/METEN-KRIJTLIJNEN):
Research into new measurement protocols and multi-platform media
consumption
− ICON iRead+ (https://www.iminds.be/en/projects/iread): The
Intelligent Reading Companion
● Key publications
o ca. 3-7 key publications: please provide a bullet list of references
and the biblio ID per publication
IDs:
− https://biblio.ugent.be/publication/8048654
− https://biblio.ugent.be/publication/5939109
− https://biblio.ugent.be/publication/7033443
− https://biblio.ugent.be/publication/5939119
− https://biblio.ugent.be/publication/5817852
− https://biblio.ugent.be/publication/5732010
− https://biblio.ugent.be/publication/5817855
− Baptist Vandersmissen, Lucas Sterckx, Thomas Demeester, Azarakhsh
Jalalvand, Wesley De Neve and Rik Van de Walle (2016). An
automated end-to-end pipeline for fine-grained video annotation
using deep neural networks. International Conference on Multimedia
Retrieval. p. 409-412
− Fréderic Godin, Wesley De Neve and Rik Van de Walle (2015). Part-ofspeech tagging of Twitter microposts only using distributed word
representations and a neural network. Computational linguistics in
the Netherlands (CLIN 2015). p. 45
− Fréderic Godin, Baptist Vandersmissen, Wesley De Neve and Rik Van
de Walle (2015). Multimedia Lab @ ACL W-NUT NER shared task:
named entity recognition for Twitter microposts using distributed
word representations. ACL 2015 Workshop on Noisy User-generated
Text, p. 146-153
− Hans Paulussen, Francisco Capdevila, Pedro Debevere, Maribel M.
Perez, Martin Vanbrabant, Wesley De Neve and Stefan De
Wannemacker (2014). Building an NLP pipeline within a digital
publishing workflow. Computational Linguistics in the Netherlands
Journal. p. 71-84
− Fréderic Godin, Jasper Zuallaert, Baptist Vandersmissen, Wesley De
Neve and Rik Van de Walle (2014). Beating the bookmakers:
leveraging statistics and Twitter microposts for predicting soccer
results. Workshop on Large-Scale Sports Analytics
− Baptist Vandersmissen, Fréderic Godin, Abhineshwar Tomar, Wesley
De Neve and Rik Van de Walle (2014). The rise of mobile and social
short-form video: an in-depth measurement study of Vine. CEUR
workshop proceedings. 1198. p. 1-10
− Fréderic Godin, Baptist Vandersmissen, Azarakhsh Jalalvand, Wesley
De Neve and Rik Van de Walle (2014). Alleviating manual feature
engineering for part-of-speech tagging of Twitter microposts using
distributed word representations. NIPS Workshop on Modern
Machine Learning and Natural Language Processing
● Pictures
o 1-3 relevant and representative (appealing) pictures (possibly
with a caption)
o also send pictures as attachments to email (to avoid problems
extracting pictures from Word).
Visualization of a word vector space of the 1000 most frequent words on
Twitter. During training, the model learned which words are similar to each
other. For example, the word “the” is similar to the slang word “da” on Twitter.
Visualization of the automatic detection of products in a YouTube video clip and
linkage to a retailer website. Machine learning models learn to recognize and link
products in video clips to facilitate e-commerce.
RESEARCH TRACK 2:
●
Research area (make a choice)
Machine Learning & Data Mining
●
Title
○
shorter = better (cf. links and breadcrumbs)
Biotech Data Processing and Analysis
● Text describing the research topic
○ 2 – 4 paragraphs is okay
○ example below is quite long and will be shortened…
Recent technological advances have led to the generation of vast collections of
biotech data. As an example, the European Bioinformatics Institute (EBI)
currently stores 20 petabytes of data and back-ups about genes, proteins, and
small molecules. This data avalanche has created a strong need for novel
mathematical and computational techniques to address challenges related to
disease and our environment.
In our research, we explore how multimedia technology can be used to process
and analyze biotech data. In that regard, we have two major research objectives.
A first objective is to leverage state-of-the-art video compression technology for
the compression of genomics data, so to mitigate issues in terms of storage,
transportation, and analysis. A second objective is to leverage deep machine
learning in the context of several biotech use cases, ranging from genome
annotation (e.g., splice site detection in DNA sequences) to tumor detection in
medical images. Of particular interest is the application of techniques for natural
language understanding to the analysis of genomics data.
● Staff
○ list of names
Wesley De Neve, Joni Dambre
● Researchers
○ list of names
Tom Paridaens, Jasper Zuallaert, Mijung Kim, Lionel Pigou
● Projects (optional)
○ list of projects, possibly with a link to external project page
○ please provide funding channel, acronym, link, and title
N/A.
● Key publications
○ ca. 3-7 key publications: please provide a bullet list of references
and the biblio ID per publication
IDs:
− https://biblio.ugent.be/publication/7198983
− https://biblio.ugent.be/publication/5821901
− https://biblio.ugent.be/publication/4378035
− Tom Paridaens, Jens Panneel, Wesley De Neve, Peter Lambert and Rik
Van de Walle (2016). Leveraging CABAC for no-reference compression
of genomic data with random access support. Data Compression
Conference (DCC).
− Tom Paridaens, Yves Van Stappen, Wesley De Neve, Peter Lambert
and Rik Van de Walle (2014). Towards block-based compression of
genomic data with random access functionality. Workshop on
Genomic Signal Processing and Statistics. p. 1528-1531
− Tom Paridaens, Wesley De Neve, Peter Lambert and Rik Van de Walle
(2014). Genome sequences as media files. 8th International joint
conference on biomedical engineering systems and technologies.
· Pictures
o 1-3 relevant and representative (appealing) pictures (possibly with a caption)
o also send pictures as attachments to email (to avoid problems extracting
pictures from Word).
The proposed solution for DNA compression allows for a flexible trade-off
between efficiency, effectiveness, and functionality, setting our solution apart
from the state-of-the-art.
A deep neural network has been trained to recognize whether a given image
contains a malignant lesion. When this is the case, the deep neural network
subsequently localizes and extracts the malignant lesion.
Automatic genome annotation, such as predicting the location of splice sites,
translation initiation sites, or secondary structures. By detecting indicative
patterns in an exemplary set of DNA sequences, machine learning models can
make predictions for new DNA sequences.