Personal Information
Entreprise/Lieu de travail
London, United Kingdom United Kingdom
Profession
Principal Applied Scientist at Microsoft
Secteur d’activité
Technology / Software / Internet
Site Web
research.microsoft.com/people/bmitra
À propos
I am a Principal Applied Scientist at Bing in Microsoft Research Cambridge. My research is focused on Web search, information retrieval and machine learning.
I started at Bing in 2007 (then called Live Search) at the Search Technology Center in India. Since then I have worked on a number of problems related to document ranking, query formulation and recommendation, query re-writing, entity ranking and evaluation.
My current research interests center on representation learning and in particular their application to modern day information retrieval.
Mots-clés
information retrieval
deep learning
neural networks
web search
neural information retrieval
deep neural networks
search
machine learning
word embeddings
search engine
learning to rank
trec
document ranking
evaluation
trec deep learning
benchmarking
recommender systems
expected exposure
stochastic ranking
ms marco
bert
document retrieval
embeddings
web ranking
demographics
search exposure
exposure
fairness
conformer-kernel
conformer
leaderboard
msmarco
efficiency
adversarial learning
reinforcement learning
representation learning
microsoft
duet
sigir 2016
neu-ir 2016
sigir2016
text embeddings
word2vec
query formulation
query auto-completion
ethics
data challegne
shared task
full retrieval
qti
query term independence
transformer
datasets
domain transfer
transfer learning
query evaluation
semantic search
natural language processing
bing
ucl
university college london
sigir 2017
www2017
adhoc retrieval
acl2016berlin
acl2016
pseudo-relevance feedback
artificial intelligence
recurrent neural networks
rnn
ai
metrics
web qna
question-answering
neu-ir2016
auto suggest
london
cntk
search solutions 2015
computational network toolkit
auto completion
dssm
pointwise mutual information
search solutions
contextual search
Tout plus
Présentations
(38)J’aime
(1)Overview of the TREC 2019 Deep Learning Track
Nick Craswell
•
il y a 4 ans
Personal Information
Entreprise/Lieu de travail
London, United Kingdom United Kingdom
Profession
Principal Applied Scientist at Microsoft
Secteur d’activité
Technology / Software / Internet
Site Web
research.microsoft.com/people/bmitra
À propos
I am a Principal Applied Scientist at Bing in Microsoft Research Cambridge. My research is focused on Web search, information retrieval and machine learning.
I started at Bing in 2007 (then called Live Search) at the Search Technology Center in India. Since then I have worked on a number of problems related to document ranking, query formulation and recommendation, query re-writing, entity ranking and evaluation.
My current research interests center on representation learning and in particular their application to modern day information retrieval.
Mots-clés
information retrieval
deep learning
neural networks
web search
neural information retrieval
deep neural networks
search
machine learning
word embeddings
search engine
learning to rank
trec
document ranking
evaluation
trec deep learning
benchmarking
recommender systems
expected exposure
stochastic ranking
ms marco
bert
document retrieval
embeddings
web ranking
demographics
search exposure
exposure
fairness
conformer-kernel
conformer
leaderboard
msmarco
efficiency
adversarial learning
reinforcement learning
representation learning
microsoft
duet
sigir 2016
neu-ir 2016
sigir2016
text embeddings
word2vec
query formulation
query auto-completion
ethics
data challegne
shared task
full retrieval
qti
query term independence
transformer
datasets
domain transfer
transfer learning
query evaluation
semantic search
natural language processing
bing
ucl
university college london
sigir 2017
www2017
adhoc retrieval
acl2016berlin
acl2016
pseudo-relevance feedback
artificial intelligence
recurrent neural networks
rnn
ai
metrics
web qna
question-answering
neu-ir2016
auto suggest
london
cntk
search solutions 2015
computational network toolkit
auto completion
dssm
pointwise mutual information
search solutions
contextual search
Tout plus