
Dr. Tingting Mu (穆婷婷) Senior Lecturer (Associate Professor) Department of Computer Science Kilburn Building University of Manchester Manchester, M13 9PL United Kingdom Email: tingting.mu@manchester.ac.uk Phone: +44 (0)161 275 6169 
I received a B.Eng. degree
from the School of the
Gifted Young, University of
Science and Technology, China in 2004, and a Ph.D. from The University of Liverpool
in 2008. I am a Senior Lecturer (Associate Professor) in Machine Learning at the University of Manchester
(UoM), a Turing Fellow and an Honorary Research Fellow on Representation Learning at UoM Harwell.
Before I joined UoM in 2016, I was a Lecturer in the Department of Electrical
Engineering and Electronics at the University of Liverpool.
RESEARCH
INTERESTS
I am a machine learning
researcher and my work focuses on mathematical modelling and optimisation
techniques for: i) the development of AI solutions, and ii) the analysis of
realworld complex data. For the former, I aim at constructing effective
machine learning models to automate tasks such as matching, recognition,
prediction, ranking, inference, and characterisation. For the latter, I develop
algorithms that discover latent structure and extract information from
largescale, noisy and unstructured data. My general application interests
include image, text, and speech data analysis and their associated practical AI
solutions.
A particular focus of my
work is representation learning, e.g., how to extract refined information from
raw data and knowledge, and encode it in representation spaces for use in
pattern recognition and prediction tasks. My team and I work on algorithm
design and the theoretical understanding of a diverse range of representation
learning methodologies, such as manifold learning, matrix factorisation, neural
representation learning, etc.
Some currently active research directions I have engaged with over the
years with my team and collaborators.
Data Visualisation (since 2015):

The goal is to enable the user to look at
the data to facilitate understanding and data navigation. The most common way
of “looking” at highdimensional data is to embed it into a 1D, 2D or 3D
space, but this inevitably compromises information content. The central
research question is: what patterns and structures to preserve in such
lowdimensional spaces and how to do it in an accurate and fast way. 
See some of our work: X. Evangelopoulos, A. J. Brockmeier, T. Mu, J. Y.
Goulermas, Circular object arrangement using spherical embeddings, Pattern
Recognition, 103:107192, 2020. X. Evangelopoulos, A. J. Brockmeier, T. Mu, J. Y.
Goulermas, Continuation methods for approximate large scale object
sequencing, Machine Learning, 108(4):595626, 2019. T. Mu, J. Y. Goulermas, S. Ananiadou, Data
visualization with structural control of global cohort and local data
neighborhoods, IEEE TPAMI, 40(6):13231337, 2018. J. Y. Goulermas, A. Kostopoulos, T. Mu, A new measure
for analyzing and fusing sequences of objects, IEEE TPAMI, 38(5):833848,
2015. 
Relation Encoding (since 2012):

We are interested in a special type of data
representing (directly or indirectly) relation information and measurements,
e.g., distances, (dis)similarities, correlations, relations, links,
interactions, graph and correspondence data. Such data occupies a vast
portion of the information generated and recorded in our world today, through
networks, rating preferences, knowledge bases, geometric locations,
biological pathways, etc. We aim at developing algorithms which construct
effective representations that encode the observed relations so that missing
and new relations can be inferred. We are also interested in establishing a
theoretical understanding of this type of data and algorithms. 
See some of our work: T. Mu, J. Y. Goulermas, Provable Relation
Factorisation, under preparation. A. J. Brockmeier, T. Mu, S. Ananiadou, J. Y. Goulermas,
Quantifying the informativeness of similarity measurements, JMLR,
18(76):161, 2017. Y. Wu, T. Mu, J. Y. Goulermas, Translating on pairwise
entity space for knowledge graph embedding, Neurocomputing, 260:411419,
2017. Y. Wu, T. Mu, P. Liatsis, J. Y. Goulermas, Computation
of heterogeneous object coembeddings from relational measurements, Pattern
Recognition, 65:146163, 2017. T. Mu, J. Y. Goulermas, I. Korkontzelos, S. Ananiadou,
Descriptive document clustering via discriminant learning in a coembedded space
of multilevel similarities, JASIST, 67(1):106133, 2016. T. Mu, J. Y. Goulermas, Automatic generation of
coembeddings from relational data with adaptive shaping, IEEE TPAMI,
35(10):23402356, 2013. 
Highdimensional Data Embedding (since 2009):

The goal here is to understand and learn
embeddings and hidden representation spaces to encode highdimensional data.
We are investigating the invariant principles and unification of the
overarching algorithmic formulations of the different approaches and
establish theoretical understanding. We also develop neural networks as
effective feature extractors to facilitate computer vision and natural
language processing applications, addressing issues like insufficient data
labelling, fusing data modalities, and model explainability. 
See some of our work: Embedding and Dimensionality Reduction H. W. J. Reeve, T. Mu and G. Brown, Modular
Dimensionality Reduction, ECML/PKDD, 2018. E. RodriguezMartinez, T. Mu, J. Jiang, and J. Y.
Goulermas, Automated induction of heterogeneous proximity measures for
supervised spectral embedding, IEEE TNNLS, 24(10):15751587, 2013. T. Mu, J. Jiang, Y. Wang and J. Y. Goulermas, Adaptive
data embedding framework for multiclass classification, IEEE TNNLS,
23(8):12911303, 2012. T. Mu, J. Y. Goulermas, T. Tsujii and S. Ananiadou,
Proximitybased frameworks for generating embeddings from multioutput data,
IEEE TPAMI, 34(11):22162232, 2012. Computer Vision Applications X. Gao, T. Mu, J. Y. Goulermas, M. Wang,
Improving image similarity learning by adding external memory, IEEE TKDE,
early access, 2020. X. Gao, T. Mu, J. Y. Goulermas, J.
Thiyagalingam, M. Wang, An interpretable deep architecture for similarity
learning built upon hierarchical concepts, IEEE TIP, 29:39113926, 2020. X. Gao, T. Mu, J. Y. Goulermas, M. Wang,
Attention driven multimodal similarity learning, Information Sciences,
432:530542, 2018. Y. Hao, T. Mu, R. Hong, M. Wang, N. An, J.
Y. Goulermas, Stochastic multiview hashing for largescale nearduplicate
video retrieval, IEEE TM, 19(1):114, 2017. Natural Language Processing Applications Y. Hao, T. Mu, R. Hong. M. Wang, X. Liu, J. Y.
Goulermas, Crossdomain sentiment encoding through stochastic word embedding,
IEEE TKDE, 32(10):
1909  1922, 2019. D. Bollegala, T. Mu, J. Y. Goulermas, Crossdomain
sentiment classification using sentiment sensitive embeddings, IEEE TKDE,
28(2):398410, 2016. T. Mu, M. Miwa, T. Tsujii and S. Ananiadou, Discovering
robust embeddings in (dis)similarity space for highdimensional linguistic
features, Computational Intelligence, 30(2): 285315, 2014. 
Knowledgedriven
Machine Learning (since 2019):

The goal is to develop effective approaches
to merge human knowledge with datadriven machine learning. In practice,
annotated data is often limited, expensive to obtain and noisy. Datadriven
machine learning models with complex architectures work in a blackbox manner
“unfriendly” to users. We have been working on reducing reliance on labels
and improving model explainability, by fusing images and common sense and
domain knowledge in representation spaces. 
See some of our work: A. Sarullo, T. Mu. ZeroShot HumanObject Interaction
Recognition via Affordance Graphs, AAAI Workshop, 2021. M. Jayathilaka, T. Mu and U. Sattler, Ontologybased
nball Concept Embeddings Informing Fewshot Image Classification, NeSy 2021. 
Here are some
new directions I have started to explore more recently:
Adversarial
Robustness, Attack and Defence:
No matter how
successful deep neural networks are, they are vulnerable to imperceptible
perturbations calculated by adversarial attacks. We are interested in
establishing provable relationships between the existence of adversarial
attacks and the conditions imposed over data and models, and to develop
effective defending algorithms.
Bias, Variance
and Diversity:
This is a very
classical topic exploring the generalisation of machine learning models by
studying the principles of how models differ and average out under stochastic
settings. I have recently joined this interesting direction, hoping to
contribute to the underlying theory.
Hybrid Signal
Processing and Machine Learning:
I have just
started to explore the interesting direction of developing hybrid modelling
strategies to merge the strength of machine learning and signal processing
techniques, where image denoising is primarily used as our test platform.
·
2021now: Xray image denoising by machine
learning and signal processing (with UoM Harwell and STFC, PDRA: Dr. S.
Pinilla)
·
2021now: Developing text retrieval and
analysis tools to support tender scan (with Thornton and Lowe Ltd, PDRA: I.
Tsantilas)
·
2020now: Developing vulnerability detection
tools in speech communication (with VoiceIQ Ltd, PDRA: Dr. X. Cui)
·
2021now: Crossmodality generative models
for largescale scientific data (funded by STFC, PhD student: S. Jackson)
·
2020now: Lipschitz constant estimation and
regularisation for adversarial robustness (funded by EPSRC DTP, PhD student: Y.
Sulehman)
·
2019now: Differential geometry in machine
learning (funded by CSC, PhD student: Y. Deng)
·
2018now: Surrogate machine learning model
for advanced gascooled reactor graphite core safety analysis (funded by EPSRC
iCASE and EDF Energy, PhD student: H. Jones)
·
2018now: Image understanding informed by
ontological background knowledge (funded by CS Departmental studentship, PhD
student: M. Jayathilaka)
·
2017now: Cohort data visualisation informed
by ordinal information (funded by CS Departmental studentship, PhD student: R.
Qin)
·
2021, Dr. A. Sarullo on zeroshot learning of
humanobject interactions through commonsense knowledge (primary supervisor).
·
2021, Dr. M. Li on graphical and deep
learning models for natural language labelling tasks with multiple annotators
(cosupervisor).
·
2018, Dr. J. Wu on sentence matching for
question answering with neural networks (primary supervisor).
·
2017, Dr. Y. Wu on embedding approach for
relational data (primary supervisor).
·
2017, Dr. Y. Hao on nearduplicate video
retrieval and crossdomain sentiment classification based on embedding learning
(primary UK supervisor, visiting PhD).
·
2017, Dr. X. Gao on Multimodal similarity
learning (primary UK supervisor, visiting PhD).
·
2014, Dr. L. Gong on nonnegative matrix
analysis for data clustering and compression (primary supervisor).
·
2014, Dr. C. Wei on advanced modelling and
feature extraction for fault diagnosis of power apparatus (cosupervisor).
AcaDEMIC
SerVICE
·
Associate Editor, IEEE Transactions
on Neural Networks and Learning Systems, 2020present.
·
Associate Editor, Neurocomputing,
2014present.
·
Program Committee Member: AAAI, IJCAI, ECML,
NAACLHLT, ACL, EMNLP, and more.
·
Journal Reviewer for IEEE TPAMI, TNNLS, TIP,
TKDE, Pattern Recognition, and more.
Also, in Google Scholar & DBLP
Journal publications:
1.
X. Gao, Z. Zhang, T. Mu, X. Zhang, C. Cui and
M. Wang, Selfattention driven adversarial similarity learning network, Pattern
Recognition, 105:107331, 2020. [Link]
2.
X. Evangelopoulos, A. J. Brockmeier, T. Mu
and J. Y. Goulermas, Circular object arrangement using spherical embeddings,
Pattern Recognition, 103:107192, 2020. [Link]
3.
J. Wu, T. Mu, J. Thiyagalingam, and J. Y.
Goulermas, Building interactive sentenceaware representation based on
generative language model for community question answering, Neurocomputing,
389:93107, 2020. [Link]
4.
X. Gao, T. Mu, J. Y. Goulermas, J.
Thiyagalingam and M. Wang, An interpretable deep architecture for similarity
learning built upon hierarchical concepts, IEEE Trans. on Image Processing,
29:39113926, 2020. [Link]
5.
H. Ma, Z. Qian, T. Mu and S. Shi, Fast and
accurate 3D measurement based on lightfield camera and deep learning, Sensors,
19(20):4399, 2019. [Link]
6.
Y. Hao, T. Mu, R. Hong. M. Wang, X. Liu, J.
Y. Goulermas, Crossdomain sentiment encoding through stochastic word
embedding, IEEE Trans. on Knowledge and Data Engineering, 32(10): 19091922, 2020. [Link]
7.
X. Evangelopoulos, A. J. Brockmeier, T. Mu,
J. Y. Goulermas, Continuation methods for approximate large scale object
sequencing, Machine Learning, 108(4)595626, 2019. [Link]
8.
A. J. Brokmeier, T. Mu, S. Ananiadou and J.
Y. Goulermas, Selftuned descriptive document clustering using a predictive
network, IEEE Trans. on Knowledge and Data Engineering, 30(10):19291942 2018.
[Link]
9.
W. Fu, M. Wang, S. Hao and T. Mu, FLAG:
Faster learning on anchor graph with label predictor optimization, IEEE Trans.
on Big Data, early access, 2017. [Link]
10.
X. Gao, T. Mu, J. Y. Goulermas and M. Wang,
Attention driven multimodal similarity learning, Information Sciences,
432:530542, 2018. [Link]
11.
T. Mu, J. Y. Goulermas and S. Ananiadou, Data
visualization with structural control of global cohort and local data
neighbourhoods, IEEE Trans on Pattern Analysis and Machine Intelligence, 40(6):
13231337, 2018. [Link]
12.
X. Gao, T. Mu, J. Y. Goulermas and M. Wang,
Topic driven multimodal similarity learning with multiview voted convolutional
features, Pattern Recognition, 75:223234, 2018. [Link]
13.
L. Gong, T. Mu, M. Wang, H. Liu and J. Y.
Goulermas, Evolutionary nonnegative matrix factorization with adaptive control
of cluster quality, Neurocomputing, 272:237249, 2018. [Link]
14.
A. J. Brokmeier, T. Mu, S. Ananiadou and J.
Y. Goulermas, Quantifying the informativeness of similarity measurements,
Journal of Machine Learning Research, 18(76):1−61, 2017. [Link]
15.
Y. Hao, T. Mu, J. Y. Goulermas, R. Hong, N.
An and M. Wang, Unsupervised tdistributed video hashing and its deep
extension, IEEE Trans. on Image Processing, 26(11):55315544, 2017. [Link]
16.
G. Kontonatsios, A. J. Brockmeier, P.
Przybyła, J. McNaught, T. Mu, J. Y. Goulermas and S. Ananiadou, A
semisupervised approach using label propagation to support citation screening,
Journal of Biomedical Informatics, 72:6776, 2017. [Link]
17.
Y. Wu, T. Mu and J. Y. Goulermas, Translating
on pairwise entity space for knowledge graph embedding, Neurocomputing,
260:411419, 2017. [Link]
18.
Y. Wu, T. Mu, P Liatsis and J. Y. Goulermas,
Computation of heterogeneous object coembeddings from relational measurements,
Pattern Recognition, 65:146163, 2017. [Link]
19.
Y. Hao, T. Mu, R. Hong, M. Wang, N. An and J.
Y. Goulermas, Stochastic multiview hashing for largescale nearduplicate
video retrieval, IEEE Trans. on Multimedia, 19(1):114, 2017. [Link]
20.
D. Bollegala, T. Mu, J. Y. Goulermas,
Crossdomain sentiment classification using sentiment sensitive embeddings,
IEEE Trans. on Knowledge and Data Engineering, 28(2):398410, 2016. [Link]
21.
X. Gao, T. Mu and M. Wang, Local voting based
multiview embedding, Neurocomputing, 171:901909, 2016. [Link]
22.
T. Mu, J. Y. Goulermas, I. Korkontzelos and
S. Ananiadou, Descriptive document clustering via discriminant learning in a coembedded
space of multilevel similarities, Journal of the Association for Information
Science and Technology, 67(1):106133, 2016. [Link]
23.
J. Y. Goulermas, A. Kostopoulos and T. Mu, A
new measure for analyzing and fusing sequences of objects, IEEE Trans. on
Pattern Analysis and Machine Intelligence, 38(5): 833848, 2016. [Link]
24.
E. RodriguezMartinez, T. Mu and J. Y.
Goulermas, Sequential projection pursuit with kernel matrix update and symbolic
model selection, IEEE Trans. on Cybernetics, 44(12):2458  2469, 2014. [Link]
25.
K. Nikolaidis, T. Mu and J. Y. Goulermas,
Prototype reduction based on direct weighted pruning, Pattern Recognition
Letters, 36:2228, 2014. [Link]
26.
T. Mu, M. Miwa, T. Tsujii and S. Ananiadou,
Discovering robust embeddings in (dis)similarity space for highdimensional
linguistic features, Computational Intelligence, 30(2): 285315, 2014. [Link]
27.
Y. Wang, J. Jiang and T. Mu, Contextaware
and energydriven route optimization for fully electric vehicles, IEEE Trans.
on Intelligent Transportation Systems, 14(3):13311345, 2013. [Link]
28.
E. RodriguezMartinez, T. Mu, J. Jiang, and J.
Y. Goulermas, Automated induction of heterogeneous proximity measures for
supervised spectral embedding, IEEE Trans. on Neural Networks and Learning
Systems, 24(10):15751587, 2013. [Link]
29.
T. Mu and J. Y. Goulermas, Automatic
generation of coembeddings from relational data with adaptive shaping, IEEE
Trans. on Pattern Analysis and Machine Intelligence, 35(10):23402356, 2013. [Link]
30.
T. Mu, J. Jiang and Y. Wang, Heterogeneous
delay embeddings for travel time and energy cost prediction via regression
analysis, IEEE Trans. on Intelligent Transportation Systems, 14(1):214224,
2013. [Link]
31.
T. Mu, J. Jiang, Y. Wang and J. Y. Goulermas,
Adaptive data embedding framework for multiclass classification, IEEE Trans.
on Neural Networks and Learning Systems, 23(8):12911303, 2012. [Link]
32.
T. Mu, J. Y. Goulermas, T. Tsujii and S.
Ananiadou, Proximitybased frameworks for generating embeddings from
multioutput data, IEEE Trans. on Pattern Analysis and Machine Intelligence, 34(11):22162232,
2012. [Link]
33.
Y. Korkontzelos, T. Mu and S. Ananiadou,
ASCOT: A text miningbased webservice for efficient search and assisted
creation of clinical trials, BMC Medical Informatics and Decision Making, 12
(Suppl 1):S3, 2012. [Link]
34.
E. Romero, T. Mu and P. J. G. Lisboa,
Cohortbased kernel visualisation with scatter matrices, Pattern Recognition,
45:14361454, 2012. [Link]
35.
T. C. Pataky, T. Mu, K. Bosch, D. Rosenbaum
and J. Y. Goulermas, Gait recognition: highly unique dynamic plantar pressure
patterns amongst 104 individuals, Journal of the Royal Society Interface,
9(69):790800, 2012. [Link]
36.
E. RodriguezMartinez, K. Nikolaidis, T. Mu,
J. Y. Goulermas and J. F. Ralph, Towards collaborative feature extraction for
face recognition, Natural Computing, 11(3):395404, 2012. [Link]
37.
T. C. Pataky, K. Bosch, T. Mu, N. L. W.
Keijsers, B. Segers, D. Rosenbaum and J. Y. Goulermas, An anatomically unbiased
foot template for intersubject plantar pressure evaluation, Gait &
Posture, 33:418422, 2011. [Link]
38.
E. RodriguezMartinez, J. Y. Goulermas, T. Mu
and J. F. Ralph, Automatic induction of projection pursuit indices, IEEE Trans.
on Neural Networks, 21(8):12811295, 2010. [Link]
39.
S. Ananiadou, P. Thompson, J. Thomas, T. Mu,
S. Oliver, M. Rickinson, Y. Sasaki, D. Weissenbacher and J. McNaught,
Supporting the education evidence portal via text mining, Philosophical
Transactions of the Royal Society A: Mathematical, Physical and Engineering
Science, 368(1925):38293844, 2010. [Link]
40.
T. Mu, T. C. Pataky, A. H. Findlow, M. S. H.
Aung and J. Y. Goulermas, Automated nonlinear feature generation and
classification of foot pressure lesions, IEEE Trans. on Information Technology
in Biomedicine, 14(2):418424, 2010. [Link]
41.
T. Mu and A. K. Nandi, Multiclass
classification based on extended support vector data description, IEEE Trans.
on Systems, Man and Cybernetics  Part B, 39(5):12061216, 2009. [Link]
42.
T. Mu and A. K. Nandi, Automatic tuning of
L2SVM parameters employing the extended Kalman filter, Expert Systems,
26(2):160175, 2009. [Link]
43.
T. Mu, A. K. Nandi and R. M. Rangayyan,
Screening of kneejoint vibroarthrographic signals using the strict 2surface
proximal classifier and genetic algorithm, Computers in Biology and Medicine,
38(10):11031111, 2008. [Link]
44.
T. Mu, A. K. Nandi and R. M. Rangayyan,
Classification of breast masses using selected shape, edgesharpness and
texture features with linear and kernelbased classifiers, Journal of Digital
Imaging, 21(2):153169, 2008. [Link]
45.
T. Mu, A. K. Nandi and R. M. Rangayyan,
Analysis of breast tumours in mammograms using the pairwise Rayleigh quotient
classifier, Journal of Electronic Imaging, 16(4):043004:111, 2007. [Link]
46.
T. Mu, A. K. Nandi and R. M. Rangayyan,
Classification of breast masses via nonlinear transformation of features based
on a kernel matrix, Medical and Biological Engineering and Computing,
45(8):769780, 2007. [Link]
47.
T. Mu and A. K. Nandi, Breast cancer
detection from FNA using SVM with different parameter tuning systems and
SOMRBF classifier, Journal of the Franklin Institute  Engineering and Applied
Mathematics, 344(34):285311, 2006. [Link]
Conference publications:
1.
X. Cui, A. Gamage, T.
Hanley and T. Mu, Identifying indicators of vulnerability from short speech
segments using acoustic and textual features. Proc. Interspeech, 2021. [Link]
2.
M. Jayathilaka, T. Mu and U.
Sattler, Ontologybased 𝑛ball concept embeddings informing fewshot image classification,
NeSy’2021. [Link]
3.
A. Sarullo and T. Mu.
Zeroshot humanobject interaction recognition via affordance graphs, AAAI
Workshop on Common Sense Knowledge Graphs, 2021. [Link]
4.
A. Sarullo and T. Mu,
On class imbalance and background filtering in visual relationship detection,
International Joint Conference on Neural Networks, Budapest, Hungary, IJCNN,
2019. [Link]
5.
M. Li, A. F. Myrman, T.
Mu and S. Ananiadou, Modelling instancelevel annotator reliability for natural
language labelling tasks, Annual Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language Technologies,
NAACLHLT, Minneapolis, USA, 2019. [Link]
6.
H. Reeve, T. Mu and G.
Brown, Modular Dimensionality Reduction, European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery in Databases,
ECMLPKDD, Dublin, Ireland, 2018. [Link]
7.
X. Evangelopoulos, A.
J. Brockmeier, T. Mu and J. Y. Goulermas, A graduated nonconvexity relaxation
for large scale seriation, SIAM International Conference on Data Mining,
Houston, Texas, USA, 2017. [Link]
8.
M. Sato, A. J.
Brockmeier, G. Kontonatsios, T. Mu, J. Y. Goulermas, J. Tsujii, S. Ananiadou,
Distributed document and phrase coembeddings for descriptive clustering,
European Chapter of the Association for Computational Linguistics, EACL,
Valencia, Spain, 2017. [Link]
9.
L. Gong, T. Mu, and J.
Y. Goulermas, Evolutionary nonnegative matrix factorization for data
compression, International Conference on Intelligent Computing, ICIC, 2015. [Link]
10.
M. Tufail, F. Coenen
and T. Mu, Mining movement patterns from video data to inform multi agent based
simulation, 10th International Workshop on Agents and Data Mining Interaction,
ADMI, 2014. [Link]
11.
Y. Korkontzelos, T. Mu,
A. Restificar and S. Ananiadou, Text mining for efficient search and assisted
creation of clinical trials, ACM 5th International Workshop on Data and Text
Mining in Biomedical Informatics, DTMBIO’11, Glasgow, Scotland, UK, 2011. [Link]
12.
T. Mu, J. Tsujii and S.
Ananiadou, Proximitybased graph embeddings for multilabelclassification,
Int’l Conference on Knowledge Discovery and Information Retrieval, KDIR,
Valencia, Spain, 2010. [Link]
13.
T. Mu, X. Wang, J.
Tsujii and S. Ananiadou, Imbalanced classification using dictionarybased
prototypes and hierarchical decision rules for entity sense disambiguation, the
23rd Int’l Conference on Computational Linguistics, Coling, Beijing, China,
2010. [Link]
14.
E. Romero, A. S.
Fernandes, T. Mu and P. J.G. Lisboa, Cohortbased kernel visualisation with
scatter matrices, Int’l Joint Conference on Neural Networks, IJCNN, Barcelona,
Spain, 2010. [Link]
15.
T. Mu and A. K. Nandi,
Breast cancer diagnosis and prognosis using different kernelbased classifiers,
Int'l Conference on Bioinspired Systems and Signal Processing, BIOSIGNALS, pp.
342348, Funchal, Madeira, Portugal, 2008. [Link]
16.
T. Mu and A. K. Nandi
and R. M. Rangayyan, Breast cancer diagnosis from fineneedle aspiration using
supervised compact hyperspheres and establishment of confidence of malignancy,
the 16th European Signal Processing Conference, EUSIPCO, Lausanne, Switzerland,
2008. [Link]
17.
T. Mu, A. K. Nandi and
R. M. Rangayyan, Strict 2surface proximal classification of kneejoint
vibroarthrographic signals, the 29th Annual Int’l Conference of the IEEE
Engineering in Medicine and Biology Society, EMBC, pp. 49114914, Lyon, France,
2007. [Link]
18.
T. Mu and A. K. Nandi,
A proximal classification method based on two smallest and supervised
hyperspheres, the IEEE Int'l Workshop on Machine Learning for Signal
Processing, MLSP, pp. 6368, Thessaloniki, Greece, 2007. [Link]
19.
T. Mu, A. K. Nandi and
R. M. Rangayyan, Strict 2surface proximal classifier with application to
breast cancer detection in mammograms, the 32nd Int'l Conference on Acoustics,
Speech and Signal Processing, ICASSP, 2:477480, Hawaii, USA, 2007. [Link]
20.
T. Mu, A. K. Nandi and
R. M. Rangayyan, Pairwise Rayleigh quotient classifier with application to the
analysis of breast tumors, the 4th IASTED Int'l Conference on Signal
Processing, Pattern Recognition and Applications, SPPRA, pp. 356361,
Innsbruck, Austria, 2007. [Link]
21.
T. Mu, A. K. Nandi and
R. M. Rangayyan, Classification of breast masses via transformation of features
using kernel principal component analysis, the 5th IASTED Int'l Conference on
Biomedical Engineering, BioMED, pp. 396401, Innsbruck, Austria, 2007. [Link]
22.
T. Mu and A. K. Nandi,
EKF based multiple parameter tuning system for a L2SVM classifier, the IEEE
Int'l Workshop on Machine Learning for Signal Processing, MLSP, 229233,
Maynooth, Ireland, 2006. [Link]
23. T. Mu and A. K. Nandi, Detection of breast cancer using vSVM and RBF networks with self organized selection of centers, In Proc. of the 3rd IEE Int'l Seminar on Medical Applications of Signal Processing, MASP, pp.4752, London, UK, 2005. [Link]
I welcome PhD applications all around the year from people with strong
technical (i.e., mathematical & programming) skills and even stronger
commitment. PhD applicants can apply to work in these two areas (but other
interestingonly areas are welcome!):
·
Representation Learning
and its Applications (here)
·
Machine Learning for
Vision and Language Understanding (here)
Competitionbased
PhD funding opportunities are also available (here).
Postdoctoral
research posts as well as externally funded PhD positions will be advertised
when a vacancy becomes available.