drprabhakar

Computational Biologist/Data Scientist/Cloud Architect

Design, Deploy and Administer cloud solutions(AWS) for Android Mobile apps. Design, Deploy and Administer cloud solutions(AWS) for Computational Biology Research. Develop Algorithm, Methodology and Source Code for performing Biological Data Analysis using R. Run Machine Learning Model predictions using R code to analyze Biological Data. Administer and Manage Data warehouse in the Cloud.

Data Scientist (Remote)

Work profile included IT, analytics, machine learning, product development and/or software consulting services and other responsibilities related to Data Scientist Consultant.

Database Lead (Remote)

Architecting the back-end of the healthcare database application. Data mining and automating procedures for creating, populating and updated databases. Creating the front-end of the database application.

Postdoctoral Researcher

o Develop and optimize programs and tools related to Bioinformatics o C Source code to compute pairwise similarity between rows of huge data matrices (Comparing Bioassay results of NCBI PubChem compounds for evaluating their activity)

Project Assistant

My project was to develop web based knowledge portals using the data mined from Protein Data Bank. For backend I wrote Perl and PHP codes for datamining and populating the MySQL databases. I used HTML, JavaScript and CSS for the UI (Frontend). Additionally I designed a streamlined automated updating modules for updating the databases regularly.

Research Assistant

My project dealt with predicting the structures of viral proteins using Homology Modeling methodology (Modeller, Accelry's InsightII), refining the models using molecular dynamics simulation (Amber9). I performed insilico immunological sudies based on structural bioinformatics approaches(Conformational Epitope Prediction), the predicted results were used in the wet lab for experimental design & evaluation.

Computer Programmer

The objective of the Project is to develop software tools for simulating / engineering the Metabolic Pathways by using available full genome data, 3D- structural data of the proteins, metabolites, enzymes etc. As a member of Metabolic Pathway Engineering project I developed:

PhD

M.Sc. Biophysics

Post Graduate Diploma in Computer Applications

B.Sc. Physics

MBA

Diploma in Computer Applications (2000)

Basics Computer Skills MS Office Applications Managing images and data Internet Basics Working with Web design programs Desktop publishing Graphic design Spreadsheets and presentations Computer languages and programming

Diploma in Unix & C (2004)

Unix: Operating System, File Management, vi Editor, Shell Programming, System Administration - System Calls and Program Development. C: Data Types, Conditional Compilation, Arrays & Pointers, Structures & Unioins, Linked List, File Handling, Memory Management. C++: Function Prototypes, Encapsulation and abstraction of Data, Linear and Multiple - Polymorphism, File Handling Using Stream Classes, Stream and String Classes.

Integrated Analysis in Systems Biology (2015)

This course focused on developing integrative skills in systems biology through a set of directed reading and analysis of the current primary literature

Big Data Technology Fundamentals (2016)

AWS Training Module 1: Big Data Overview Module 2: Database Architecture Module 3: Hadoop and MapReduce Module 4: Hive and Pig Module 5: AWS and Big Data

Amazon Web Services - Learning and Implementing AWS Solutions (2017)

Introduction to AWS Elastic Cloud Compute - AWS EC2 Databases - AWS DynamoDB or RDB Amazon S3 - Simple Storage Service

AWS Certified Solution Architect (2017)

AWS - 10,000 Feet Overview Identity Access Management (IAM) AWS Object Storage and CDN - S3, Glacier and CloudFront EC2 - The Backbone of AWS Route53 Databases on AWS VPC Application Services Kinesis The Real World - Creating a fault tolerant Word Press Site The Well Architected Framework

AWS Certified Developer Associate (2017)

AWS Simple Storage Service (S3) Dynamo DB Simple Notification Service (SNS) Simple Queue Service (SQS) Simple Workflow Service (SWS) Elastic Beanstalk Route53 & DNS

Cloud Computing With Amazon Web Services (2017)

Create Virtual Servers in Cloud Configure Storage in Cloud Manage Users and Groups Monitoring and Notification Services Content Delivery Network Configure Highly Available and Scalable Infrastructure Bulk Email Solutions Database Solutions Highly Available DNS System and Virtual Private Cloud Continuous Integration and Deployment using Jenkins

AWS Essentials (2017)

Account Basics Identity & Access Management Virtual Private Cloud (VPC) Simple Storage Service (S3) Elastic Compute Cloud (EC2) Databases Simple Notification Service (SNS) CloudWatch Elastic Load Balancer (ELB) Auto Scaling Route 53 Lambda

AWS Concepts (2017)

Introduction to Core AWS Services AWS Global Infrastructure

R Basics - R Programming Language Introduction (2016)

Introduction Getting Started with Coding.

R Level 1 - Data Analytics with R (2017)

Theoretical background Creating objects Functions in R Loops Apply family Graphs in R Advanced topic: working with strings Using the R Commander GUI

Amazon Web Services - Learning and Implementing AWS (2017)

Elastic Cloud Compute - AWS EC2 Databases - AWS DynamoDB or RDB Amazon S3 - Simple Storage Service

Introduction to Deep Learning (2018)

This introductory course provides an overview of Deep Learning (DL) concepts. In the course, we discuss the AWS services available for DL and walk through a case study of an AWS customer who is innovating with DL.

Introduction to Machine Learning (2018)

This course introduces you to machine learning concepts and the role data plays. We explore use cases, discuss an innovative way to build smart applications, and review the AWS frameworks and services you can use for machine learning.

Machine Learning Terminology and Process (2018)

This course introduces you to basic machine learning concepts and the machine process the data goes through. We explore each step in the machine learning process in detail and explain some of the common terms and techniques that occur during a phase of a ML project.

Implicit vs. Explicit Solvent Models for Calculating X-ray Solution Scattering Curves

To investigate the effect of the implicit and explicit solvent models on the calculated scattering curves, we developed a new program, X-ray Solution Scattering (XSoS) based on implicit (XSoS-implicit) and explicit (XSoS-explicit) solvent models.

High-throughput instant quantification of protein expression and purity (PYP turn off/on label)

We demonstrated a new method based on the Photoactive yellow protein turn Off/On Label (POOL) system that can instantly quantify the concentration and purity of a target protein.