Working in the field of Cloud Computing and Web Services from last three years. I specially worked on Amazon Web Services (AWS). I designed architecture of hosted solution to deploy desktop and web based MTBC-EHR to client by using the concept of Cloud Computing for one of fastest growing company of USA ([login to view URL]). I Successfully done research, implementation and deployed different Software by working in in the field of cloud computing.
1. Successfully deployed Desktop Applications in Cloud Based hosted Environment for our Remote Clients in USA.
2. Successfully deployed Web Based application in the Cloud by using the Amazon Web Services.
3. Designed Multi Server More Secure and optimized Architecture for deployment of our Applications to different hospital (Practices) of USA.
4. Implemented fully automated snapshot, AMI, Auto Scaling, Cloud Watch Alarms creation and Virtual Cloud Network.
5. Designed and implemented fail-over backup setup for Cloud Server to other Region of Amazon AWS which is fully synchronized with the original Servers.
5. Worked on Auto scaling to minimize the costs and maximize the cloud resource utilization.
6. Provide expert technical architectural support and guidance for Cloud Based(Hosted) MTBC-EHR opportunities, including integration into existing management and monitoring systems for MTBC ([login to view URL]).
7. Primary Responsible for MTBC Cloud Servers maintenance, deployment of software to new clients.
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Itens de Portfólio
American Medical Logistics
Cloud Based Hosted EHR by using AWS EC2, S3, VPC, SNS, HIPPA
Cloud Video Hosting Solution for Videowise.tv
Setup of 50 Core Multiple AWS EC2 instances via VPC to run 3
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Hosted Cloud EHR
jan. 2011 - Atual
Plan and implement solutions to strategic problems of varying complexities of Deploying Hosted EHR by using Amazon Web Services and Elastic Compute Cloud.
Design architecture and hosted solution to deploy desktop MTBC-EHR to client by using the concept of Cloud Computing.
Provide expert technical architectural support and guidance for Cloud (Hosted) MTBC-EHR opportunities, including integration into existing management and monitoring systems.
Work collaboratively with Product Management and Engineering d
ago. 2009 - jan. 2011 (1 ano, 5 meses)
1. Designing, developing, de-bugging and maintaining company's software using C# and Ms. SQL tools.
2. Work with the team to write test plans for components and overall system.
3. Work closely with development staff towards the resolution of errors.
4. Configure, administer and troubleshoot software test environmental.
5. Testing and installation of the Company's softwares into the client machines after taking their remote controls.
6. Handle the issues occurs in using the software applicat
Vehicle Number Plate Recognition System
abr. 2010 - mai. 2010 (1 mês, 1 dia)
Worked as an Internee in Generation Next (GENE) Software House Islamabad where I work on Vehicle Number Plate Recognition System which won Pakistan Software House Association (P@SHA) ICT Award 2010.
MS Computer Science (Cloud Computing)
King Fahd University of Petroleum & Minerals, 2011 - 2012
BS Computer Science
National University of Computer and Emerging Sciences, 2006 - 2010
1st position Certificate in Software Competition
Got 1st position in Software Competition while overall in Hardware & Software Competition got 2nd position in NUTEC-2010 (All Pakistan Technology Competition) held FAST-NUCES Peshawar (Pakistan).
1st position Certificate in ACM Software Project Competition
ACM and FAST NU
Got 1st position in ACM Software Project Competition by presenting my Final Year Project "Brain Inspector" held in FAST-NUCES (Islamabad Campus)
1st position Certificate in Software & Hardware Competition
NED University Karachi
Got 1st position in Software & Hardware Competition in mega event ITEC-2010 held at NED University Karachi.
Scalable Fast Parallel SVM on Cloud Clusters for Large Datasets Classification
Scientific Conference, MOE, KSA
A support vector machine (SVM) is supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. For testing and training of a multidimensional large datasets requires a lot of computing resources in terms of memory and computational power. We purposed a scalable and cost effective technique for running support vector machine in parallel on distributed cloud cluster nodes which reduced memory requirements and computational power. We divide the datasets
Brain MRI Segmentation and 3D Reconstruction
Conference on Computer Science & Computational Mathematics
The field of medical visualization of organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also requires accurate 3D visualization of the brain. Detection and 3D visualization of the brain and possibly tumors from MRI is a computationally time consuming and error-prone task. The proposed system detects and presents a 3D reconstruction model of the brain and tumors inside it which greatly helps the radiologist to effectively diagnose and analyze bra
Classification and Segmentation of Brain Tumor using Texture Analysis
International Conference on Artificial Intelligence, Knowledge Engineering and Databases (AIKED 2010), University of Cambridge UK
Brain tumor diagnosis is a very crucial task. This system provides an efficient and fast way for diagnosis of the brain tumor. Proposed system consists of multiple phases. First phase consists of texture feature extraction from brain MR images. Second phase classify brain images on the bases of these texture feature using ensemble base classifier. After classification tumor region is extracted from those images which are classified as malignant using two-stage segmentation process. Segmentation consists of
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