“It has been a great pleasure to work with Vishakha at Roche for the past year. She is a highly-experienced data scientist; her team members will agree that Vishakha is a great listener, provides excellent guidance, and is an all-around great person! In my role as the Senior Technical Project Manager, I worked closely with Vishakha and learned much from her expertise in clinical healthcare terminologies and Natural Language Processing (NLP), and much more. Always patient and pleasant to work with, my interactions with her were super productive and enjoyable. Vishakha is very creative and has a knack for transforming complexities into digestible components, which makes her an indispensable member of the Diagnostic Information Solutions team at Roche. ”
About
Activity
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I am very pleased to share that I have joined Genentech Research & Early Development (gRED) as domain lead -Lab to Insights. It is a fascinating time…
I am very pleased to share that I have joined Genentech Research & Early Development (gRED) as domain lead -Lab to Insights. It is a fascinating time…
Liked by Vishakha Sharma, PhD
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I had a wonderful time speaking at Genentech last week on AI with heavyweights Sam De Brouwer, Hector Corrada Bravo,Vishakha Sharma, PhD, Mat Torgow…
I had a wonderful time speaking at Genentech last week on AI with heavyweights Sam De Brouwer, Hector Corrada Bravo,Vishakha Sharma, PhD, Mat Torgow…
Liked by Vishakha Sharma, PhD
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I’m incredibly proud of the great work of the team to deliver this foundational IVD clearance for the Roche digital pathology solution…
I’m incredibly proud of the great work of the team to deliver this foundational IVD clearance for the Roche digital pathology solution…
Liked by Vishakha Sharma, PhD
Experience & Education
Licenses & Certifications
Volunteer Experience
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Local Arrangements and Program Chair
New Jersey Programming Languages and Systems (NJPLS) Seminar
- Present 10 years 9 months
Science and Technology
http://www.njpls.org/
The New Jersey Programming Languages and Systems Seminar Series is an informal forum that promotes interaction among programming languages and systems researchers in the New Jersey area. The series provides an opportunity to present unfinished research-in-progress and receive feedback and constructive criticism. We hold one-day meetings every few months, for instance at Agere, AT&T Florham Park, Avaya, Bell Labs Lucent, Rutgers, Princeton University, Stevens, or U…http://www.njpls.org/
The New Jersey Programming Languages and Systems Seminar Series is an informal forum that promotes interaction among programming languages and systems researchers in the New Jersey area. The series provides an opportunity to present unfinished research-in-progress and receive feedback and constructive criticism. We hold one-day meetings every few months, for instance at Agere, AT&T Florham Park, Avaya, Bell Labs Lucent, Rutgers, Princeton University, Stevens, or U. Penn. Meeting agendas are coordinated by a chair who is selected by popular vote. -
Stevens Annual Workshop on Middle and High School Computer Science
Stevens Institute of Technology
- 3 years
Education
Provided demos for the educators and students from the middle and high school to help move CS education forward in NJ high schools.
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Poster Committee Member
Association for Computing Machinery
- Present 7 years 10 months
Science and Technology
ACM Student Research Competition (SRC) held at Grace Hopper Celebration of Women in Computing (GHC), Houston, TX, October 19 - 21, 2016.
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Peer Reviewer for 2017 AMIA Joint Summits on Translational Science
AMIA (American Medical Informatics Association)
- 2 months
Science and Technology
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Member of ACM-W Scholarships Committee
Association for Computing Machinery
- Present 7 years 6 months
Science and Technology
Publications
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Process Simulation of Complex Biological Pathways in Physical Reactive Space and Reformulated for Massively Parallel Computing Platforms.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 2, pp. 365-379, March-April 1 2016
Biological systems encompass complexity that far surpasses many artificial systems. Modeling and simulation of large and complex biochemical pathways is a computationally intensive challenge. Traditional tools, such as ordinary differential equations, partial differential equations, stochastic master equations, and Gillespie type methods, are all limited either by their modeling fidelity or computational efficiency or both. In this work, we present a scalable computational framework based on…
Biological systems encompass complexity that far surpasses many artificial systems. Modeling and simulation of large and complex biochemical pathways is a computationally intensive challenge. Traditional tools, such as ordinary differential equations, partial differential equations, stochastic master equations, and Gillespie type methods, are all limited either by their modeling fidelity or computational efficiency or both. In this work, we present a scalable computational framework based on modeling biochemical reactions in explicit 3D space, that is suitable for studying the behavior of large and complex biological pathways. The framework is designed to exploit parallelism and scalability offered by commodity massively parallel processors such as the graphics processing units (GPUs) and other parallel computing platforms. The reaction modeling in 3D space is aimed at enhancing the realism of the model compared to traditional modeling tools and framework. We introduce the Parallel Select algorithm that is key to breaking the sequential bottleneck limiting the performance of most other tools designed to study biochemical interactions. The algorithm is designed to be computationally tractable, handle hundreds of interacting chemical species and millions of independent agents by considering all-particle interactions within the system. We also present an implementation of the framework on the popular graphics processing units and apply it to the simulation study of JAK-STAT Signal Transduction Pathway. The computational framework will offer a deeper insight into various biological processes within the cell and help us observe key events as they unfold in space and time. This will advance the current state-of-the-art in simulation study of large scale biological systems and also enable the realistic simulation study of macro-biological cultures, where inter-cellular interactions are prevalent.
Other authorsSee publication -
Computational Modeling of the Effects of Counterfeit Components
Proceedings of the Summer Computer Simulation Conference (SCSC 2014)
The problem of counterfeiting is of particular concern in the Department of Defense (DoD) supply chain, where it may lead to lack of assurance of reliability of complex systems, and it may endanger operational performance and safety. In this paper, we present a computational approach to studying complex socio-technical systems relevant to the analysis of the effects of counterfeit parts in the military supply chain. We illustrate our technique with the case study of Magellan GPS 315. Using the…
The problem of counterfeiting is of particular concern in the Department of Defense (DoD) supply chain, where it may lead to lack of assurance of reliability of complex systems, and it may endanger operational performance and safety. In this paper, we present a computational approach to studying complex socio-technical systems relevant to the analysis of the effects of counterfeit parts in the military supply chain. We illustrate our technique with the case study of Magellan GPS 315. Using the Stochastic Pi Machine (SPiM), we build a stochastic agent-based computational model to study the effects of counterfeit components in the performance of a complex multi-component system. We discuss the combinatorial complexity of the agent-based model. We implement a visualization of the system. Finally, statistical tests are performed to analyze the difference in multi-component systems' performance relative to the proportions verified and counterfeit components.
We believe that computational models like this can contribute to identifying counterfeiting and studying its effects in the military supply chain.Other authors -
A Calculus of Located Entities
Proceedings of the Ninth International Workshop on the Developments in Computational Models (DCM 2013)
We define BioScape^L, a stochastic pi-calculus in 3D-space. A novel aspect of BioScapeL is that
entities have programmable locations. The programmer can specify a particular location where to
place an entity, or a location relative to the current location of the entity. The motivation for the extension comes from the need to describe the evolution of populations of biochemical species in space, while keeping a sufficiently high level description, so that phenomena like diffusion…We define BioScape^L, a stochastic pi-calculus in 3D-space. A novel aspect of BioScapeL is that
entities have programmable locations. The programmer can specify a particular location where to
place an entity, or a location relative to the current location of the entity. The motivation for the extension comes from the need to describe the evolution of populations of biochemical species in space, while keeping a sufficiently high level description, so that phenomena like diffusion, collision, and confinement can remain part of the semantics of the calculus. Combined with the random diffusion movement inherited from BioScape, programmable locations allow us to capture the assemblies of configurations of polymers, oligomers, and complexes such as microtubules or actin filaments.
Further new aspects of BioScape^L include random translation and scaling. Random translation
is instrumental in describing the location of new entities relative to the old ones. For example, when
a cell secretes a hydronium ion, the ion should be placed at a given distance from the originating cell, but in a random direction. Additionally, scaling allows us to capture at a high level events such as division and growth; for example, daughter cells after mitosis have half the size of the mother cell.Other authorsSee publication -
BioScape: A Modeling and Simulation Language for Bacteria-Materials Interactions
Electronic Notes in Theoretical Computer Science, 293(0):35 - 49, 2013. Proceedings of the Third International Workshop on Interactions Between Computer Science and Biology (CS2Bio 2012)
We design BioScape, a concurrent language for the stochastic simulation of biological and bio-materials processes in a reactive environment in 3D space. BioScape is based on the Stochastic Pi-Calculus, and it is motivated by the need for individual-based, continuous motion, and continuous space simulation in modeling complex bacteria-materials interactions. Our driving example is a bio-triggered drug delivery system for infection-resistant medical implants. Our models in BioScape will help in…
We design BioScape, a concurrent language for the stochastic simulation of biological and bio-materials processes in a reactive environment in 3D space. BioScape is based on the Stochastic Pi-Calculus, and it is motivated by the need for individual-based, continuous motion, and continuous space simulation in modeling complex bacteria-materials interactions. Our driving example is a bio-triggered drug delivery system for infection-resistant medical implants. Our models in BioScape will help in identifying biological targets and materials strategies to treat biomaterials associated bacterial infections.
The novel aspects of BioScape include syntactic primitives to declare the scope in space where species can move, diffusion rate, shape, and reaction distance, and an operational semantics that deals with the specifics of 3D locations, verifying reaction distance, and featuring random movement. We define a translation from BioScape to 3π and prove its soundness with respect to the operational semantics.Other authorsSee publication -
Computational and Mathematical Models of the JAK-STAT Signal Transduction Pathway
Proceedings of the Summer Computer Simulation Conference (SCSC 2013)
The JAK (Janus kinase)-STAT (Signal transducer and activator of transcription) signal transduction pathway is a cascade of downstream cellular events initiated from outside of the cell through the cell surface to the DNA in the nucleus, causing transcription. The conventional modeling approach for signal transduction pathways involves solving ordinary differential equations (ODEs). We study here a computational alternative. We build two models of 46 reactions in the JAK-STAT pathway and compare…
The JAK (Janus kinase)-STAT (Signal transducer and activator of transcription) signal transduction pathway is a cascade of downstream cellular events initiated from outside of the cell through the cell surface to the DNA in the nucleus, causing transcription. The conventional modeling approach for signal transduction pathways involves solving ordinary differential equations (ODEs). We study here a computational alternative. We build two models of 46 reactions in the JAK-STAT pathway and compare the results. We implement a deterministic mathematical model using the ODEs solver COPASI, and we build a stochastic computational model using the Stochastic Pi Machine (SPiM).
Since dysregulation in the functionality of JAK-STAT pathway results in immune deficiency syndrome and cancers, like lymphomas, leukemia and breast cancer, we believe that models like this have the potential to contribute to cancer research.Other authorsSee publication -
Simulating Anti-adhesive and Antibacterial Bifunctional Polymers for Surface Coating using BioScape
Proceedings of the ACM Conference of on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB 2013)
Traditionally biomaterials development consists of designing a surface and testing its properties experimentally. This trial-and-error approach is limited because of the resources and time needed to sample a representative number of configurations in a combinatorially complex scenario. Therefore, computational modeling is of significant importance in identifying best antibacterial materials to prevent and treat implant related biofilm infections.
In this paper we focus on bifunctional…Traditionally biomaterials development consists of designing a surface and testing its properties experimentally. This trial-and-error approach is limited because of the resources and time needed to sample a representative number of configurations in a combinatorially complex scenario. Therefore, computational modeling is of significant importance in identifying best antibacterial materials to prevent and treat implant related biofilm infections.
In this paper we focus on bifunctional surface with polymer brushes and Pluronic-Lysozyme conjugates developed by Henk Busscher's group in Groningen, The Netherlands. The bifunctional brushes act as anti-adhesive due to the unmodified polymer brushes and antibacterial, because of the Pluronic-Lysozyme conjugates. They developed and studied three different surfaces with varying proportions of antibacterial and anti-adhesive properties. In order to aid the development of optimal bifunctional surfaces, we build a three dimensional computational model using BioScape, an agent-based modeling and simulation language developed by Compagnoni's group at Stevens. We model two different experimental phases: adhesion and growth. We use the results of experiments on two surfaces as training data, and we validate our model by reproducing the experimental results from the third surface. The resulting model is able to simulate varying configurations of surface coatings both at adhesion and growth phases at a fraction of the time necessary to perform in-vitro experiments.
The output of the model not only plots populations over time, but it also produces 3D-rendered videos of bacteria-surface interactions enhancing the visualization of the system's behavior.Other authorsSee publication -
Parallel BioScape: A Stochastic and Parallel Language for Mobile and Spatial Interactions
Electronic Proceedings in Theoretical Computer Science, 100(0):101 - 106, 2012. Proceedings of the Sixth Workshop on Membrane Computing and Biologically Inspired Process Calculi (MeCBIC 2012).
BioScape is a concurrent language motivated by the biological landscapes found at the interface of biology and biomaterials. It has been motivated by the need to model antibacterial surfaces, biofilm formation, and the effect of DNAse in treating and preventing biofilm infections. As its predecessor, SPiM, BioScape has a sequential semantics based on Gillespie's algorithm, and its implementation does not scale beyond 1000 agents. However, in order to model larger and more realistic systems, a…
BioScape is a concurrent language motivated by the biological landscapes found at the interface of biology and biomaterials. It has been motivated by the need to model antibacterial surfaces, biofilm formation, and the effect of DNAse in treating and preventing biofilm infections. As its predecessor, SPiM, BioScape has a sequential semantics based on Gillespie's algorithm, and its implementation does not scale beyond 1000 agents. However, in order to model larger and more realistic systems, a semantics that may take advantage of the new multi-core and GPU architectures is needed. This motivates the introduction of parallel semantics, which is the contribution of this paper: Parallel BioScape, an extension with fully parallel semantics.
Other authorsSee publication -
Simulation and Study of Large-Scale Bacteria-Materials Interactions via BioScape Enabled by GPUs.
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB 2012)
Biological systems encompass complexity that far surpasses many artificial systems. Modeling and simulation of large and complex biological systems is a computationally intensive challenge. We present a GPU based simulation framework in a reactive environment in 3D space, along with the modeling language, BioScape, in order to describe various biological processes. We also present an efficient computational framework to study the interactions enabled by the massively parallel processing…
Biological systems encompass complexity that far surpasses many artificial systems. Modeling and simulation of large and complex biological systems is a computationally intensive challenge. We present a GPU based simulation framework in a reactive environment in 3D space, along with the modeling language, BioScape, in order to describe various biological processes. We also present an efficient computational framework to study the interactions enabled by the massively parallel processing capability of the GPUs. Our driving example is a bio-triggered drug delivery system for infection-resistant medical implants. The modeling and simulation framework presented here will help in identifying biological targets and materials to treat biomaterials associated bacterial infections. The computational framework will offer a deeper insight into various biological processes compared to traditional modeling via implicit differential equations, and help us observe the key events as they unfold.
Other authorsSee publication
Courses
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Advanced Algorithm Design and Implementation
CS 600
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Advanced Computational Modeling in Biology
CS 694
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Computer Architecture
CS 514
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Cost Estimation & Metrics
SSW 533
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Database Management Systems I
CS 561
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Database Management Systems II
CS 562
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Engineering Enterprise Software Systems
CS 548
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Fundamentals of Cybersecurity
CS 573
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Fundamentals of Quantitative Software Engineering (QSE) I
CS 540
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Introduction to Operating Systems
CS 520
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Introduction to Project Management
MGT 609
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Introduction to Systems Biology
CS 691
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Knowledge Discovery & Data Mining
CS 513
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Molecular Biology Lab Techniques
CH 684
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Molecular Genetics
CH 687
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Software Architecture & Design
CS 565
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Software Testing, Quality Assurance & Maintenance
SSW 567
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Web Programming
CS 546
Projects
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DNN Speech Recognizer
Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: Speech Recognition, Deep Neural Network Architecture, Connectionist Temporal Classification, Feature Extraction, Recurrent Neural Networks, Convolutional Neural Networks, Keras, GPU execution
Description: Built a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline. -
Face Generation - DCGAN
Part of Deep Learning Foundation Nanodegree Program (Udacity)
Skills: Generative Adversarial Networks
Description: Used a DCGAN on the CelebA dataset to generate images of novel and realistic human faces.
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Convolutional Neural Networks (CNN) - Dog Breed Classifier
Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: Deep Convolutional Neural Networks, Transfer Learning
Description: Built an algorithm to identify an estimate of canine breed of a given an image of a dog. If given image of a human, algorithm identifies resembling dog breed. -
Machine Translation
Part of Deep Learning Foundation Nanodegree Program (Udacity)
Skills: Recurrent Neural Networks (RNNs)
Description: Train a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.
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Recurrent Neural Networks (RNN) - Time Series Prediction and Text Generation
Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: Recurrent Neural Networks
Description: Built RNNs that can generate sequences based on input data - with a focus on two applications: used real market data in order to predict future Apple stock prices using an RNN model. The second one will be trained on Sir Arthur Conan Doyle's classic novel Sherlock Holmes and generates wacky sentences based on it that may - or may not - become the next great Sherlock Holmes novel. -
Using Keras to analyze IMDB Movie Data
Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: Sentiment Analysis, Keras, Neural Networks
Description: Analyzed a dataset of 25,000 IMDB reviews and use it to predict the sentiment analysis of a review. Built a neural network using Keras, trained it, and evaluated using methods such as dropout/regularization, and categorical_crossentropy loss, and rmsprop optimizers. Obtained an accuracy of > 85%
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Generate TV Scripts - Text Generation
Part of Deep Learning Foundation Nanodegree Program (Udacity)
Skills: Recurrent Neural Networks (RNNs)
Description: Train a recurrent neural network on scripts from The Simpson's (copyright Fox) dataset of scripts from 27 seasons to generate a new TV script for a scene at Moe's Tavern. -
Image Classification
Part of Deep Learning Foundation Nanodegree Program (Udacity)
Skills: Convolutional Neural Networks (CNN), TensorFlow
Description: Build a convolutional neural network with TensorFlow to classify CIFAR-10 images. -
Build a Neural Network
Part of Deep Learning Foundation Nanodegree Program (Udacity)
Skills:Neural Networks, Numpy
Description: Built Neural Network in Numpy to predict bike sharing rides.
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Build a Sign Language Recognizer
Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: Probabilistic Graphical Models, Model Selection
Description: Built a system that can recognize words communicated using the American Sign Language (ASL). Trained a set of Hidden Markov Models (HMMs) using part of a preprocessed dataset of tracked hand and nose positions extracted from video to try and identify individual words from test sequences. Experimented with model selection techniques including BIC, DIC, and…Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: Probabilistic Graphical Models, Model Selection
Description: Built a system that can recognize words communicated using the American Sign Language (ASL). Trained a set of Hidden Markov Models (HMMs) using part of a preprocessed dataset of tracked hand and nose positions extracted from video to try and identify individual words from test sequences. Experimented with model selection techniques including BIC, DIC, and K-fold Cross Validation. -
Implement a Planning Search
Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: PDDL (Planning Domain Definition Language), Heuristics, Search, Logistics
Description: Defined a group of problems in classical PDDL (Planning Domain Definition Language) for the air cargo domain. Set up the problems for search, experiment with various automatically generated heuristics, including planning graph heuristics, to solve the problems, and then provided an analysis of the results. Wrote a short research…Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: PDDL (Planning Domain Definition Language), Heuristics, Search, Logistics
Description: Defined a group of problems in classical PDDL (Planning Domain Definition Language) for the air cargo domain. Set up the problems for search, experiment with various automatically generated heuristics, including planning graph heuristics, to solve the problems, and then provided an analysis of the results. Wrote a short research review paper on the historical development of planning techniques and their use in artificial intelligence. -
Build a Game-Playing Agent
Part of the Artificial Intelligence Nanodegree Program (Udacity)
Skills: Iterative Deepening, Alpha-Beta Pruning
Description: Built a Game-Playing agent that defeats opponents in isolation. Along the way, learned about advanced Game-Playing techniques such as Iterative Deepening, Alpha-Beta Pruning.
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Multi-Level Socio-Technical Modeling (Funding: DoD)
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Objective: To identify innovative approaches to support application of SE in complex enterprise contexts for architecting, engineering and evolving enterprises, and for designing and engineering systems which effectively support user needs as part of a larger enterprise. This characterization and analysis should include human behavior and performance, individually, in teams, or in groups. Social networks that enable communication/propagation of information and ideas are also of interest.…
Objective: To identify innovative approaches to support application of SE in complex enterprise contexts for architecting, engineering and evolving enterprises, and for designing and engineering systems which effectively support user needs as part of a larger enterprise. This characterization and analysis should include human behavior and performance, individually, in teams, or in groups. Social networks that enable communication/propagation of information and ideas are also of interest. Economic decision making relating to strategic and tactical incentives should be included, as well as social and cultural norms that influence all the above.
Other creators
Honors & Awards
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IEEE Senior Member
Institute of Electrical and Electronics Engineers (IEEE)
The world's largest technical professional organization for the advancement of technology.
IEEE Senior Membership is an honor bestowed only to those who have made significant contributions to the profession. -
Department of Computer Science Graduation Award
Stevens Institute of Technology, Hoboken, NJ
Awarded by the Computer Science Faculty of Stevens Institute of Technology to the graduating graduate student who most contributed to the department’s activities.
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Grace Hopper Celebration 2014 Scholarship
Anita Borg Institute
GHC Scholarship sponsored by Neustar to attend Grace Hopper Celebration of Women in Computing (GHC), Phoenix, AZ, October 8 - 10, 2014.
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ACM Richard Tapia Conference 2014 Scholarship
ACM Richard Tapia Celebration of Diversity in Computing (Doctoral Consortium)
ACM Richard Tapia Conference (Doctoral Consortium) Scholarship to attend ACM Richard Tapia Celebration of Diversity in Computing, Seattle, WA, February 5 - 8, 2014.
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Winter Simulation Conference 2013 Scholarship
INFORMS - Winter Simulation Conference (PhD Colloquium)
Winter Simulation Conference (PhD Colloquium) Scholarship to attend Winter Simulation Conference (WSC), Washington D. C., December 8 - 11, 2013.
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Grace Hopper Celebration 2013 Scholarship
Anita Borg Institute
GHC Scholarship sponsored by SAP to attend Grace Hopper Celebration of Women in Computing (GHC), Minneapolis, MN, October 2 - 5, 2013.
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ACM BCB 2013 Travel Award
NSF (National Science Foundation)
NSF support to participate in ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB), Bethesda, MD, September 22 - 25, 2013.
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NSF Summer Institute Fellowship
NSF (National Science Foundation)
NSF Summer Institute Fellowship to attend the Materials Genome Short Course: The Materials
Genome: Current Practice and Future Promise, Evanston, IL, June 10 - June 12, 2013. -
ACM Richard Tapia Conference 2013 Scholarship
ACM Richard Tapia Celebration of Diversity in Computing Conference
ACM Richard Tapia Conference Scholarship to attend ACM Richard Tapia Celebration of Diversity in Computing, Washington D. C., February 7 - 10, 2013.
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Department of Defense (DoD) Funding
Center for Complex Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ
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ACM BCB 2012 Travel Award
NSF (National Science Foundation)
NSF support to participate in ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB), Orlando, FL, October 7 - 10, 2012.
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ACM-W Scholarship
Anita Borg Institute
ACM-W Scholarship to attend Grace Hopper Celebration of Women in Computing (GHC), Baltimore, MD, October 3 - 6, 2012.
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BioInnovation Initiative Funding
Center for Healthcare Innovation, Stevens Institute of Technology, Hoboken, NJ
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Department of Defense (DoD)/Ravenshield Funding
Stevens Institute of Technology, Hoboken, NJ
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Department of Defense (DoD)/Ravenshield Funding
Stevens Institute of Technology, Hoboken, NJ
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PhD Graduate Scholarship
Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ
Languages
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English
Full professional proficiency
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Hindi
Full professional proficiency
Organizations
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Additional Organizations
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ACM, IEEE, SIGPLAN, SIGBio, SIGSIM, ISCB
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I will be speaking at #DIA2024 as part of the panel discussing “Exploiting Real World Data from Social Media in Patient-Focused Drug Development”. I…
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I want to thank Bryn Roberts and Moritz Hartmann for their support and mentorship during my 6.5 year journey at Roche. I wish the Roche team the…
I want to thank Bryn Roberts and Moritz Hartmann for their support and mentorship during my 6.5 year journey at Roche. I wish the Roche team the…
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To celebrate 2024 National Nurses Week where the theme is "Nurses Make the Difference"--I celebrate these nurses. Nurses who have come together to…
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