Sr. Associate Projects
- AMQ technologies. Development of Asynchronous Event Listener in memory based database GFXD, developing consumers for GFXD database with submission of retrieved events/records to AMQ.
- Developing Java Web and SOA Applications in JBOSS, Tomcat, GemFire XD, MySQL, MS SQL environment.
- C# applications, Visual Studio 2012 IDE, Framework 4.0, 4.5, XML documents, UI and database access level coding.
- TSQL with Microsoft SQL Server 2008, 2012. WCF Web Services with C# code.
- Python programming, VB scripting, SSIS packages
- UI Path AI technologies training and certification - “RPA Developer Foundation Training” diploma.
- Machine Learning training and certification.
Computer Programmer/Research Associate
- Java Web Applications on Tomcat 7; Eclipse IDE
- Data-mining programs and applications
- Different statistic and machine learning technics
- Java, Python, Perl, Linux Bash shell scripting programming languages. Created packages for Data Mining, Data Analysis, Data Classification with using classifiers as ICSIBoost, Weka’s - J48, SMO, AdaBoost M1.
- MySQL database management and programming with Java/Python/Perl application programing
- Python Programming to automate all databases, applications, and file repository updates in Linux based network. Scheduling Python programs with Linux Crontab, using full object oriented capabilities of Python programming.
- Parallel programming of Java applications to run high throughput calculations based on Linux computing clusters.
- Work, collaboration, and programming experience with Open source Data providers as: SBKB, PDB, BindingDB, BioCyc, icsiBoost developers, Pathway Commons, NCI, PharmGKB, ChEBI, ChEMBL, InterPro, Pfam, UniProt, PubChem.
- Developed several algorithms that were implemented inside working applications. One of them was for ranking protein structures using multiplication of protein attribute matrixes.
- Developed tool that is being used for drug target definition of protein structural chain, associated with diseases, by their ranking using protein functional, structural, chemical, phenotypic attributes, and the most important - by their known drug likelihood from worldwide databases.