Machine Learning, Artificial
Intelligence, and Statistical Data Mining.
Application areas: medical decision
support systems, information retrieval, web search, computational advertising
and computational finance.
Ph. D. in
Electrical & Computer Engineering,
Dissertation: Adaptive classifier design using
labeled and unlabeled data
Contributions: Developed algorithms for
supervised, semi-supervised and unsupervised statistical pattern recognition;
Active learning. The theory was applied to problems in cancer diagnosis,
detection of land-mines & under-water mines
MS in
Electrical & Computer Engineering,
Thesis: Multi-aspect target detection in
SAR imagery
B.Tech. in
Electrical Engineering, Indian Institute of Technology (IIT),
Dissertation: Wavelet Vector-Quantization based
still grayscale Image Compression
Graduate
Research Assistantship, Dept of Electrical Engineering,
Travel-grant
for attending international research conference, RECOMB, Apr '03
Recipient
of IMO Math Scholarship awarded by the Dept. of Atomic Energy, Govt. of
India,'95-'99
Ranked
27th in the Indian National Math Olympiad (INMO), 1994
Recipient
of National Science Talent Scholarship (NTSE) awarded by Govt. of India'94-'99
Current (full-time)
position
Senior
Staff Scientist, Dec ’07-present
Staff
Scientist, Nov ’04-Nov ’07
Siemens
Medical Solutions USA, Image & Knowledge Management, CAD group (
At Siemens
Medical Solutions, I have developed novel products in three broad areas. First,
I have developed several computer aided
diagnosis (CAD) products that automatically identify early stage cancer of
the Breast, Lung, and
Second, I
have worked on personalized
therapy-selection. Modern medicine has placed increasing emphasis on
choosing optimal therapy for groups of patients (eg T1 stage Cancer patients)
based on the population averages of outcomes from clinical trials. However,
this approach ignores the large heterogeneity among the patients in any such
group, and also does not explicitly account for the extent of medical information
that would be missing for any patient. The systems I developed are personalized
to predict the optimal therapy for each patient, based on the available data.
Third, I
have developed medical decision support
systems and medical data mining systems that combine all available
structured and unstructured data for a patient, including textual notes from
doctors, medical images, billing records, lab records, etc. Such systems
support retrospective analysis of a hospital’s compliance with various
quality measures. These systems can also automatically identify patients who
are eligible for participation in various clinical trials.
Internships
Research-Intern,
Microsoft Research Ltd. (
I
developed a probabilistic model for ink in user drawn figures on Tablet PCs.
Based on this model, I designed algorithms for understanding hand-drawn
sketches as a new user-interface for MS-Office.
Intern,
ITC Ltd., the Indian subsidiary of BAT plc. (
I
developed an expert-system for quality control. The implementation of this
system resulted in an 8% improvement in internal quality metrics.
1.
Balaji
Krishnapuram, Lawrence Carin, and Alexander Hartemink, “Gene expression
analysis: Joint feature selection and classifier design,” in Kernel
Methods in Computational Biology, B. Scholkopf, K. Tsuda, and J.-P. Vert
(editors), pp. 299-318, MIT press, 2004.
2.
Ya
Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram, “Multi-Task
learning for classification with Dirichlet process priors,” accepted for
publication in Inductive Transfer, D. Silver, K. Bennet, R. Caruana (editors),
Springer Verlag, 2007.
3.
Balaji
Krishnapuram, Jefferey Sichina, and Lawrence Carin, “Physics based
detection of targets in SAR imagery using support vector machines,” IEEE
Sensors Journal, Vol. 3, No. 2, pp. 147-158, April 2003.
4.
Balaji
Krishnapuram, Lawrence Carin, and Alexander Hartemink, “Joint classifier
and feature optimization for comprehensive cancer diagnosis using gene
expression data,” Journal of Computational Biology, Vol. 11, pp 227--242,
March 2004.
5.
Balaji
Krishnapuram, Alexander Hartemink, Lawrence Carin, and Mario Figueiredo,
“A Bayesian approach to joint feature selection and classifier
design,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 26,
No. 9, pp. 1105-1111, September 2004.
6.
Yijun
Yu, Balaji Krishnapuram, and Lawrence Carin, “Inverse scattering with
sparse Bayesian vector regression,” Inverse Problems, Special Issue on
Electromagnetic Characterization of Buried Obstacles, Vol. 20, No. 6, pp.
S217-S231, December 2004.
7.
Balaji
Krishnapuram, Lawrence Carin, Mario Figueiredo, and Alexander Hartemink,
“Sparse multinomial logistic regression: fast algorithms, and
generalization bounds,” IEEE Trans. Pattern Analysis and Machine
Intelligence, Vol. 27, No. 6, pp. 957-968, June 2005.
8.
Shihao
Ji, Balaji Krishnapuram, and Lawrence Carin, “Variational Bayes for
continuous hidden Markov models and its application to active learning,”
IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28, No 4, pp. 522-532, April 2006.
9.
Ya
Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram, “Multi-Task
learning for classification with Dirichlet process priors,” Journal of
Machine Learning Research, Vol 8, pp 33-63, Jan 2007.
10.
David
Williams, Ya Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram,
“On incomplete data classification,” IEEE Trans. Pattern Analysis
and Machine Intelligence, Vol. 29, No. 3, pp. 427-436, Mar 2007.
11.
R. Seigneuric, M.H.W. Starmans, G. Fung, Balaji Krishnapuram, D.S.A.
Nuyten, A. van Erk, M.G. Magagnin, K.M. Rouschop, S. Krishnan, R. Bharat Rao, C.T.A.
Evelo, A.C. Begg, B.G.
Wouters, P. Lambin, “Impact of a supervised gene signature of early
hypoxia on patient survival”, accepted to Radiotherapy & Oncology,
2007.
12.
Vikas
Raykar, Ramani Duraiswami, Balaji Krishnapuram, “Efficient algorithms for
learning preference relations,” accepted to IEEE Trans. Pattern Analysis
and Machine Intelligence, 2007.
13.
Glenn
Fung, Murat Dundar, Balaji Krishnapuram, and R. Bharat Rao, “Multiple
instance learning via alternate optimization,” accepted to IEEE Trans.
Biomedical Engineering, 2007.
14.
Eric
Jones, Jiangqi He, Balaji Krishnapuram, John Pormann, John A. Board, and
Lawrence Carin, “An electromagnetic simulation and SAR processing
environment,” 2001 SPIE AeroSense Conference, Proceedings of SPIE, Vol.
4367, Orlando, FA, April 2001.
15.
Balaji
Krishnapuram and Lawrence Carin, “Support vector machines for improved
multi-aspect target recognition using the fisher kernel scores of hidden markov
models,” 2002 IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), Vol. 3, pp 2989-2992, IEEE Press, Orlando, FA, May
2002.
16.
Balaji
Krishnapuram, Lawrence Carin and Alexander J. Hartemink, “Applying
logistic regression and RVM to achieve accurate probabilistic cancer diagnosis
from gene expression profiles,” 2002 IEEE Workshop on Genomic Signal
Processing and Statistics (GENSIPS) , IEEE Press, Raleigh, NC, October 2002.
17.
Balaji
Krishnapuram, Lawrence Carin and Alexander J. Hartemink, “Joint
classifier and feature optimization for cancer diagnosis using gene expression
data,” The Seventh Annual International Conference on Research in Computational
Molecular Biology (RECOMB) 2003, ACM press,
18.
Qiuhua
Liu, Balaji Krishnapuram, Pallavi Pratapa, Xuejun Liao, Alexander Hartemink and
Lawrence Carin, “Identification of differentially expressed proteins
using MALDI-TOF mass spectra,” 2003 Asilomar Conference on Signals,
Systems and Computers, Pacific Grove, CA, November 2003.
19.
Xuejun
Liao, Hui Li and Balaji Krishnapuram, “An M-ary KMP classifier for
multi-aspect target classification,” IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP),
20.
Balaji
Krishnapuram, Christopher Bishop and Martin Szummer, “Generative Bayesian
models for shape recognition,” The Ninth International Workshop on
Frontiers in Handwriting Recognition (IWFHR-9),
21.
Balaji
Krishnapuram, David Williams, Ya Xue, Lawrence Carin, Alexander Hartemink, and
Mario Figueiredo, “On semi-supervised classification,” Neural
Information Processing Systems (NIPS), Vancouver, Canada, December 2004.
22.
Balaji
Krishnapuram, David Williams, Ya Xue, Lawrence Carin, Mario Figueiredo, and
Alexander Hartemink, “Active learning of features and labels,”
Workshop on learning with multiple views at the 22nd International
Conference on Machine Learning (ICML), Bonn, Germany, August 2005
23.
Glenn
Fung, Romer Rosales, and Balaji Krishnapuram, “Learning rankings via
convex hull separation,” Neural Information Processing Systems (NIPS),
24.
Ya
Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram, “Learning
multiple classifiers with Dirichlet process mixture priors,” Workshop on
Nonparametric Bayesian methods, Neural Information Processing Systems (NIPS),
25.
Glenn
Fung, Balaji Krishnapuram, Nicolas Merlet, Eli Ratner, Phlippe Bamberger,
Jonathan Stoeckel and R. Bharat Rao, “Addressing image variability while
learning classifiers for detecting clusters of micro-calcifications,“
International Workshop on Digital Mammography (IWDM), Manchester, UK, June 2006
26.
Volkan
Vural, Glenn Fung, Balaji Krishnapuram, and Jennifer Dy, “Batch
Classification with applications to Computer Aided Diagnosis,” European
Conference on Machine Learning (ECML),
27.
Vivian
van den Boogaart, Annemarie Dingemans, Victor Thijssen, Robert-Jan van Suylen,
Balaji Krishnapuram, Arjan Griffioen, “Angiogenesis gene expression
profiling as prognostic marker in non-small cell lung cancer”, October
2006.
28.
Glenn
Fung, Murat Dundar, Balaji Krishnapuram, and R. Bharat Rao, “Multiple
Instance Algorithms for Computer Aided Diagnosis,” Neural Information
Processing Systems (NIPS),
29.
Murat
Dundar, Balaji Krishnapuram, Jinbo Bi, and R. Bharat Rao, “Learning
classifiers when the training data is not IID,” International Joint Conference on
Artificial Intelligence (IJCAI) January 2007
30.
Vikas
Raykar, Ramani Duraiswami, and Balaji Krishnapuram, “A fast algorithm for
learning large scale preference relations,” International Conference on
Artificial Intelligence and Statistics (AISTATS),
31.
Balaji
Krishnapuram, C. Dehing, H. Steck,
H. van der Weide, D. De
Ruysscher, B. Nijsten, S. Wanders, L. Boersma, R.B. Rao, and Ph. Lambin, “A
knowledge-model for predicting radiation-induced Esophagitis,“ 49th
Annual meeting of the American Society for Therapeutic Radiology and Oncology
(ASTRO), Nov 2007.
32.
33.
Vikas
Raykar, Harald Steck, Balaji Krishnpuram, Cary Dehing-Oberije, and Philippe
Lambin, “On ranking in survival analysis: bounds on the concordance
index,” Neural Information Processing Systems (NIPS),
34.
Balaji
Krishnapuram, Jonathan Stoeckel, Vikas Raykar, R. Bharat Rao, Philippe
Bamberger, Eli Ratner, Nicolas Merlet, Inna stainvas, Menahem Abramov, and
Alexandra Manevitch, “Multiple instance learning improves CAD detection
of masses in digital mammography,“ International Workshop on Digital
Mammography (IWDM),
35.
Isaac
Leichter, Richard Lederman, Eli Ratner, Nicolas Merlet, Glenn Fung, Balaji
Krishnapuram, and Philippe Bamberger, “Does a mammography CAD algorithm
with varying filtering levels of detection marks, used to reduce the false mark
rate, adversely affect the detection of small masses?,” International
Workshop on Digital Mammography (IWDM),
36.
Vikas
Raykar, Balaji Krishnapuram, Murat Dundar, Jinbo Bi, and R. Bharat Rao
“Bayesian multiple instance learning: automatic feature selection and
inductive transfer,” 25th
International Conference on Machine Learning (ICML),
37.
Volkan
Vural, Glenn Fung, Balaji Krishnapuram, and Jennifer Dy, “Batch-wise
classification with applications to Computer Aided Diagnosis,“ submitted
for review to IEEE Trans. Pattern Analysis and Machine Intelligence.
38.
Murat
Dundar, Balaji Krishnapuram, Jinbo Bi, and R. Bharat Rao, “Learning
classifiers from non IID data,” submitted for review to Pattern
Recognition.
39.
M.H.W.
Starmans, B. Krisnapuram, H. Steck, D.S.A. Nuyten, R. Seigneuric, F.M. Buffa,
A.L. Harris, B.G. Wouters, P. Lambin, “Clinical relevance of a
knowledge-based proliferation signature: the prognostic value in published
patient microarray studies”, submitted for review to the New England
Journal of Medicine.
40.
41.
Vikas
Raykar, Balaji Krishnapuram, Murat Dundar, and R. Bharat Rao,
“Probabilistic models for Multiple Instance Learning, and their
application in active data acquisition”, to be submitted to IEEE Trans.
Pattern Analysis and Machine Intelligence.
42.
R.
Bharat Rao, Romer Rosales,
43.
Murat
Dundar, Balaji Krishnapuram, Glenn Fung, R. Bharat Rao, “Early Stage
Cancer Diagnosis,” Neural Information Processing Systems (NIPS),
Code
developed as part of my PhD thesis is used by researchers in over 50 research
laboratories in 10 countries around the world. This work has been used by
researchers in diverse fields such as: computer vision, speech recognition,
financial data analysis, landmine-detection, cancer diagnosis, gene expression
analysis in functional genomics, and proteomics. Papers describing the code
have been cited over 100 times.
1.
“Modern Statistical Machine Learning tools for Applied Clinical
Research”, Maastricht Radiation Oncology (MAASTRO) clinic, GROW Research
Institute,
2.
“Applications of Modern Machine Learning Technologies in Clinical
Decision Support Systems”, Department of Machine Learning & Data
Mining, Maastricht University, Maastricht, Netherlands, Sept ‘06
3.
“Classifying Non-IID data,” DARPA & ARO Workshop on Adaptive
Sensing and Waveform Scheduling, Duke University, Durham, NC, Jul ‘06
4.
“Bayesian Semi-parametric models for multi-task learning,” DARPA
& ARO Workshop on Adaptive Multi-Modal sensing and Waveform Scheduling,
Georgia Institute of Technology, Atlanta, GA, Aug ‘05
5. “Learning classifiers under a
limited budget for acquiring training data,”
2005 Joint Annual Meeting of the Interface and Classification Society of North
America (CSNA), Washington University in St. Louis, MO, Jun ‘05
6. “Autonomous
learning of multi-sensor classifiers from labeled and unlabeled data,” 2004
Meeting of the International Federation of Classification Societies (IFCS),
7.
“Active and semi-supervised learning for classifier design,”
Statistical and Applied Mathematical Sciences Institute (SAMSI), Theory &
Methods group, Research Triangle Park, NC, Nov’03
Member of Journal Editorial Board, ’06-present
Open
Electrical and Electronic Engineering Journal
Session Chair in Research Conference, Jul ‘04
Session chair in the 2004
meeting of the International Federation of Classification Societies, IFCS-2004, Chicago, IL, Jul
’04
Reviewer for International Research
Journals,'02-present
Journal
of Machine Learning Research, Machine Learning Journal, IEEE Trans. Pattern
Analysis and Machine Intelligence, IEEE Trans. Knowledge and Data Engineering,
Neurocomputing, IEEE Trans. Neural networks, IEEE Signal Processing Letters,
IEEE Trans. Signal Processing, EURASIP Journal on Applied Signal Processing,
IEEE Trans. Circuits and Systems (B), Bioinformatics, IEEE/ACM Trans.
Computational Biology and Bioinformatics, Technometrics, Computer Methods &
Programs in Medicine, Journal of Computer Science & Technology (Chinese
Academy of Sciences)
Reviewer for International Research Conferences (only highly respected confs. listed), '04-present
Pacific
Symposium on Biocomputing (PSB), International Conference on Machine Learning
(ICML), International Conference on Knowledge Discovery and Data Mining
(SIGKDD), Neural Information Processing Systems (NIPS).
Teacher
in Hindu Sunday School, Chinmaya Mission Balavihar program, '03-'04
Graduate
student representative in the South Asian student organization at
Library
secretary of dormitory with administrative & financial
responsibilities,'98-'99