Machine Learning,
Artificial Intelligence, and Statistical Data Mining.
Application areas: medical decision
support systems, information retrieval, web search, computational advertising
and quantitative 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
Best Paper
Award, International Workshop on Digital Mammography, IWDM, 2008
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.
Editing Journals, ’06-present
Section
Editor, Journal of Pattern Recognition Research, Aug 08-present
Member
of Editorial Board, Open Electrical and Electronic Engineering Journal,
’06-present
Workshop Organization in International Research Conferences, 04-present
Co-organizer, The
NIPS 2008 Workshop on Cost Sensitive Learning,
Co-organizer, The
Annual International Data Mining Competition, KDD Cup-2008,
Co-organizer, The ACM
SIGKDD Workshop on Mining Medical Data, Las Vegas, NV, Aug 08
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 selected conferences 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).
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”, Radiotherapy &
Oncology, Vol. 83 , No. 3 , pp. 374-382, 2007.
12.
Glenn
Fung, Murat Dundar, Balaji
Krishnapuram, and R. Bharat Rao, “Multiple
instance learning via alternate optimization,” IEEE Trans. Biomedical
Engineering, Vol. 55, No. 3, pp. 1015-1021, Mar 2008.
13.
Vikas
Raykar, Ramani Duraiswami, and Balaji Krishnapuram, “Efficient
algorithms for learning preference relations,” IEEE Trans. Pattern
Analysis and Machine Intelligence, Vol. 30, No. 7, pp. 1158-1170, Jul 2008.
14.
Volkan
Vural, Glenn Fung, Balaji Krishnapuram, and Jennifer
Dy, “Using local dependencies within batches to improve large margin
classifiers,“ accepted for publication in the
Journal of Machine Learning Research, 2008.
15.
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”, accepted for publication in the British Journal of Cancer,
2008.
16.
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.
17.
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.
18.
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.
19.
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,
20.
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.
21.
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),
22.
Balaji
Krishnapuram, Christopher Bishop and Martin Szummer,
“Generative Bayesian models for shape recognition,” The Ninth
International Workshop on Frontiers in Handwriting Recognition (IWFHR-9),
23.
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.
24.
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
25.
Glenn
Fung, Romer Rosales, and Balaji Krishnapuram,
“Learning rankings via convex hull separation,” Neural Information
Processing Systems (NIPS),
26.
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),
27.
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
28.
Volkan
Vural, Glenn Fung, Balaji Krishnapuram, and Jennifer
Dy, “Batch Classification with applications to Computer Aided
Diagnosis,” European Conference on Machine Learning (ECML),
29.
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.
30.
Glenn
Fung, Murat Dundar, Balaji
Krishnapuram, and R. Bharat Rao, “Multiple
Instance Algorithms for Computer Aided Diagnosis,” Neural Information
Processing Systems (NIPS),
31.
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.
32.
Vikas
Raykar, Ramani Duraiswami, and Balaji Krishnapuram, “A fast algorithm for
learning large scale preference relations,” International Conference on
Artificial Intelligence and Statistics (AISTATS), Puerto Rico, March 2007.
33.
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.
34.
35.
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),
36.
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),
37.
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),
38.
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),
39.
Murat Dundar, Balaji Krishnapuram, Jinbo Bi, and R. Bharat
Rao, “Learning classifiers from non IID data,” submitted for review
to Pattern Recognition.
40.
Shipeng
Yu, Balaji Krishnapuram, Romer Rosales, Harald Steck, and R. Bharat Rao, “Bayesian Multi-view Learning,” submitted for review to IEEE Trans.
Pattern Analysis and Machine Intelligence.
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),
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