Siddharth Sharma Email & Phone Number

Sunnyvale, Ca, Us

Mountain View, Ca, Us

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Santa Clara, California, Us

I work on Natural Language Understanding.

Baltimore, Md, Us

Simulated High-Performance Computing (HPC) systems with natural and artificially injected faults using The Structural Simulation Toolkit (SST).Created a framework using python to perform node-based and task-based reliability analysis on logs generated by simulated HPC systems. This analysis is independent of Network Structure.Built a Support Vector Machine based classifier to identify artificial fault injection. Weibull and Log-Normal lifetime models were used to parameterize the reliability curves.

Sunnyvale, Ca, Us

Simulated human annotators using Bayesian modeling to create synthetic annotated data for Speaker Identification (SID) system.Used Unsupervised Label Refinement (ULR) methods (like Dawid Skene) and showed that these methods work better than Majority Voting for SID annotation.Evaluated human annotator's False Acceptance Rate (FAR) and False Recognition Rate (FRR) for speaker identification by created ground truth data of varying difficulty.Showed that the current annotation process was unacceptable even if we use ULR on labels from multiple annotators to reduce errors.Showed that metadata of test utterance and enrolled utterance did not have enough signal to judge annotation difficulty.Created training and testing data to evaluate domain classifier.

Created training data for localization and detection of Zika virus-carrying mosquito species for a custom built trap.Used Faster Region-based Convolutional Neural Networks (R-CNN) to localize the mosquito species and Residual Neural Network (ResNet) to detect mosquito species with 81% accuracy.The results of this work helped in getting funding for this project.

Le Chesnay Cedex, France, Fr

Integrated clinical and -omics data of Acute Myeloid Leukemia (AML) from The Cancer Genome Atlas (TCGA) Multimodal Representation Learning.Used Regularized Generalized Canonical Correlation Analysis (RGCCA) and Sparse Generalized Canonical Correlation Analysis (SGCCA) for creating multimodal representation and selecting important genes.Suggested design matrices for RGCCA by using graphical methods from MixOmics package and by considering biological results already provided by The Cancer Genome Atlas (TCGA) consortium.Reproduced results of Lasso-based models of data integration and a paper on Survival Analysis.

L-1160 Luxembourg, Lu

Windows Server 2012Project Mentor: Alexandr Chsherbov• Installed server 2012 and maintained 50 clients.• Activated services like authentication, mailing, active directory service, internet access, remote client management and group policy management.

Master'S Degree, Computer And Science

Bachelor Of Technology (B.Tech.), Computer Science Engineering