Chennai, Tamil Nadu, India
Big Data: Good hands-on experience with Hadoop Map Reduce, HDFS, GFS, SparkMLlib, Spark RDD, Mahout, Pyspark, Weka, R, SPSS Analytics Tool, Tableau, Cassandra (basic), MongoDB (basic), Ability to interpret and manipulate large data sets, storage, ETL, modelling and parallelization of algorithms to work on big data applications. Cloud: Sound knowledge in Cloud Architectures, Cloud Security Issues, Virtualization and its challenges. Interest in troubleshooting Linux-based issues and motivated to use open-source software. Exposure to Cloud Services provided by Google, Azure and Amazon. Capability to build private cloud using open-source software such as Eucalyptus and OpenNebula. Parallelization of Algorithms and Machine Learning: Design and build Machine Learning models for predictive systems with high accuracy and performance metric targets. Design and conduct experiments to ensure optimal performance of Machine Learning models. Parallelization of Machine Learning Algorithms for improved performance and throughput. Prediction of Air and Water Quality using ML and DL models for real time case studies. Data Science: Python Programming (NumPy and Pandas), basics of Scala, ReactJS. Perform Exploratory Data Analysis (EDA) for obtaining insights of data. Data Pre-processing, Machine Learning and Deep Learning models for Data Modelling in Data Science. Apply Statistical models on data to interpret and derive conclusion on data. Sound knowledge in SQL, Expertise in Data Warehouse and Data Mining concepts and competencies. Visualization of Data and predictive analytics using Tableau and write inferences. Research Activities: More inclined towards Cancer Research for treatment and monitoring. Highly interested to work in the research areas of Cancer treatment, Parallel and Distributed Computing. Computational Biology to identify cancer biomarkers and suggest suitable drugs for patient-centric treatment. Addressing the Cloud Security issues with Cryptographic techniques. Optimization of Cloud Storage for scalability. Experience in contributing to the research communities through patents, publishing papers in high impact factor journals and international conferences. Testing: Hands-on experience on Data Stage (ETL tool), System and System Integration Testing, Quality Center. Training: In depth knowledge of Object-Oriented Programming, basic Java, Data Structures, Database concepts, Distributed Systems, Cloud Computing, Data Warehousing and Data Mining, Information Storage Management, Foundations of Data Science, Image Processing, Machine Learning, Digital Logic
Big Data Analysis i. Good hands-on experience with Hadoop Map Reduce, HDFS, GFS, SparkMLlib, Spark RDD, Mahout, Pyspark, Weka, R, SPSS Analytics Tool, Tableau, Cassandra (basic), MongoDB (basic), ii. Ability to interpret and manipulate large data sets, storage, ETL, modelling and parallelization of algorithms to work on big data applications. Parallelization of Algorithms and Machine Learning i. Design and build Machine Learning models for predictive systems with high accuracy and performance metric targets. ii. Design and conduct experiments to ensure optimal performance of Machine Learning models. iii. Parallelization of Machine Learning Algorithms for improved performance and throughput. iv. Prediction of Air and Water Quality using ML and DL models for real time case studies. Research Activities i. More inclined towards Cancer Research for treatment and monitoring. ii. Highly interested to work in the research areas of Cancer treatment, Parallel and Distributed Computing. iii. Computational Biology to identify cancer biomarkers and suggest suitable drugs for patient-centric treatment. iv. Addressing the Cloud Security issues with Cryptographic techniques. v. Optimization of Cloud Storage for scalability. vi. Experience in contributing to the research communities through patents, publishing papers in high impact factor journals and international conferences. Carried out Research and Teaching. Worked on below projects with students. 1. Prediction of Drug Responses from Multi-faceted Omics and Drug Data for Lung Cancer (2023) 2. Prediction of Genetic Mutations based on Predictive and Prognostic Biomarkers in Lung Cancer (2023) 3. Prediction Of Cancer Staging Using Gene Expression Data and Deep Learning Models (2022) 4. Deployable and Weighted Ensemble-based Deep Learning Model for Alzheimer’s Disease Detection (2022) 5. Multi-Level Classification of Lung Diseases using Deep Learning Techniques (2021) Published a paper in Thomson Reuters Journal.
Cloud Technologies i. Sound knowledge in Cloud Architectures, Cloud Security Issues, Virtualization and its challenges. ii. Interest in troubleshooting Linux-based issues and motivated to use open-source software. iii. Exposure to Cloud Services provided by Google, Azure and Amazon. iv. Capability to build private cloud using open-source software such as Eucalyptus and OpenNebula. Training Students i. In depth knowledge of Object-Oriented Programming, basic Java, Data Structures, Database concepts, Distributed Systems, Cloud Computing, Data Warehousing and Data Mining, Information Storage Management, Foundations of Data Science, Image Processing, Machine Learning, Digital Logic. Carried out Research and Teaching. Worked on below projects with students. 1. Improving Classification Accuracy of Cancer Types using Hybrid Feature Selection on Microarray Gene Expression Data. (2018) 2. Time-Based Proxy Re-Encryption for User Revocation with Reduced UAKs in Cloud Storage. (2017) 3. Verifying Dynamic Data Integrity and Remanence in the Cloud using Public Auditing. (2017) 4. Prediction of Cancer Sub-Types with Combined Clustering and Classification on Spark. (2017) 5. Secure Sharing and Searching of Encrypted Personal Health Records in Cloud Using ABE. (2016) 6. Improving Accuracy of Parallel Classifiers in Hadoop Map Reduce using Random Forest. (2016) 7. Preserving Electronic Health Records in Cloud Using Attribute Based Encryption and Digital Signature. (2016) 8. Checkpoint Recovery and Dynamic Data Integrity using Public Auditing in Cloud Storage. (2015) Published 4 papers in Thomson Reuters journal.
Skills: Cloud Computing, Virtualization, Information Storage Management , Databases, Data Structures, Distributed Systems Tasks Accomplished: Mentored students to develop solutions in key areas like Cloud Security, Data Anonymization, Data Integrity in Cloud Storage, Machine Learning,Deep Learning. Designed the syllabus for courses like Distributed Systems, Cloud Computing, Principles of Data Science, User Interface Design, Service OrientedArchitecture. Developing the course content material, handling courses, and evaluating students. Collaborated with industrial partners for mentoring projects / products Giving guest lectures to other Institutes Published papers in international journals and conferences Research in Cloud Security and Parallelization of Machine Learning Algorithms on Spark. Additional Roles: Acted a reviewer for conferences. External examiner for the Cloud Computing Laboratory course. Subject expert in setting the question paper for the courses such as Distributed System, Management and Ethical Practices, Cloud Computing,Object Oriented Analysis and Design, Foundations of Data Science. Organize awareness and hands-on workshops and seminar in Big Data Analytics. Scheduling timetable for the practical courses.
1. Worked in ETL under Group Data warehouse for Lloyds and Verde Bank of Banking and Financial Vertical. 2. Worked on Business Process Management and Genesis modules of Verde Bank. 3. Performed manual testing which constitutes System Testing and System Integration Testing, 4. Hands-on experience with Oracle 9i, Mainframes, Data Stage, Mainframes, Informatica, Cognos and Quality Centre. Writing queries and Test cases to perform System Testing.