I did my PhD at Johns Hopkins (2011). Starting Oct 2011, I will be working in Google based in Mountain View California. My resume is available here.
Major Software Undertakings

Hidden Markov Model Toolkit ( link). The entire zip file (with examples) is available here.

Vector Autoregressive Model Toolkit ( link).
Apart from this, I also have written end to end codes for  Multiple Layer Neural Network training,
 Language to Language transliteration (uses the Google FST for transducing substrings). Our work was evaluated in the top 10 in the ACL NEWS workshop for several language pairs,
 Maximum Entropy training toolkit for Language Modeling
Publications 2011  PhD Dissertation: Learning and Inference Algorithms for Dynamical System Models of Dextrous Motion (pdf).
2010
 Balakrishnan Varadarajan, Sanjeev Khudanpur and Trac. D.
Tran, "Stepwise Optimal Subspace Pursuit for Improving Sparse
Recovery", Signal Processing Letters, 2010 (pdf). Extended report (pdf)
 Balakrishnan Varadarajan, Sivaram Garimella and Sanjeev Khudanpur, Dirichlet Mixture Models to model Neural Network Posteriors in the HMM framework, Accepted in ICASSP2011
 Balakrishnan Varadarajan and Sanjeev Khudanpur, Learning and Inference Algorithms for Partially observed Structured Switching Vector Autoregressive Models, Accepted in ICASSP2011 (pdf)
 Balakrishnan Varadarajan and Delip Rao ɛextension Hidden Markov Models and Weighted Transducers for Machine Transliteration , ACL  Workshop2009 (pdf)
 Balakrishnan Varadarajan, Dong Yu, Li Deng, Alex Acero, "Using
Collective Information in SemiSupervised Learning for Speech
Recognition", ICASSP 2009, Taipei, Taiwan.
 Balakrishnan Varadarajan, Dong Yu,
Li Deng, Alex Acero, "Maximizing Global Entropy Reduction for Active
Learning In Speech Recognition", ICASSP 2009, Taipei, Taiwan.
 Dong Yu, Balakrishnan Varadarajan; Li Deng, Alex Acero, "Active Learning and Semisupervised Learning for Speech Recognition: A Unified Framework using the Global Entropy Reduction Maximization Criterion", CSL0892
 Balakrishnan Varadarajan, Carol Reiley, Henry Lin, Sanjeev Khudanpur, Gregory Hager, 'DataDerived Models for Segmentation with Application to Surgical Assessment and Training', MICCAI 2009, 426434 (pdf)
2008

Balakrishnan Varadarajan, Sanjeev Khudanpur and Emmanuel Dupoux, ' Unsupervised Learning of Acoustic Subword Units', ACL2008:HLT(In proceedings). (pdf)

Balakrishnan Varadarajan and Sanjeev Khudanpur, 'Automatically Learnin
Speakerindependent Acoustic Subword Units', Interspeech 2008

Balakrishnan Varadarajan ,Daniel Povey & Stephen M Chu, 'Quick
FMLLR for Speaker Adaptation in Speech Recognition', ICASSP 2008

Daniel Povey, Stephen M Chu & Balakrishnan Varadarajan, 'Universal Background Model Based Speech Recognition', ICASSP 2008

C.E. Reiley, H.C. Lin, B. Varadarajan, S. Khudanpur, D. D. Yuh, and G.
D. Hager, ' Automatic Recognition of Surgical Motions Using Statistical
Modeling for Capturing Variability', MMVR 2008.
Submitted Papers
 Data driven Statistical Models for Computer Integrated Surgery (PAMI) (pdf).
PDF versions of selected publications can be found here. Select Talks Learning and Inference Algorithms for Structured Switching Vector AR Models. Slides are available here
 Hidden Markov Model Toolkit for coarsely segmented motions. Slides are here
Research and technical interests Machine Learning: I am interested in exploring areas of Machine Learning that require advanced mathematical thinking and algorithmic tools. I am more inclined towards principled techniques rather than adhoc ones. The primary areas I have worked in include building computer models for Speech recognition and robot motions. In the last few years, I have devoloped algorithms to learn structure in smooth timevarying signals. I have worked on Linear dynamical system models for trajectory data.
 Compressed Sensing: I have developed algorithms that recover sparse signals more efficiently compared to preexisting greedy techniques. Compressed sensing has vast applications in image compression. An application of my interest is to apply sparse learning for linear dynamical systems and vector autoregressive models to predict the current sample using a compact representation of the previous samples.
 Algorithms : Apart from research and publishing, I have also got passion for algorithms and programming. I have interest in Mathematics, Mathematical puzzles and puzzles that require smart programming. I regularly participate in timed programming contests, notable topcoder, codeforces. Apart from timed ones, I also have interest in untimed ones and enjoy solving very hard problems. SPOJ and codechef are some of my favorites. My profile in SPOJ and codechef are available here (link) and here (link) respectively. I have won several contests. I was ranked 163 in Google Codejam Round 2, 2011 (link). I am almost always ranked in the top 20 fastest solvers in recent problems in projecteuler (link).

