https://www.linkedin.com/in/sravan-dabbiru-b99b34131 Summary: R esearch experience with BIG DATA, Software Engineering and core Web technologies. 2 years of industry experience with Web Services and NoSQL technologies.
Experience: Data Structures and Algorithms, Object-oriented Programming, Database Systems, Scripting. Educational Qualifications:
Aug ‘16 - ongoing M.S. (Comp. Sci.) SUNY Binghamton, NY. 3.87/4.0 Oct ‘10 - May ‘14 B.Tech (E.C.E) JNTU Kakinada, India. 79.76% Work Experience:
Jun’17 - ongoing SUNY Binghamton, NY. Research Assistant Jan ‘17 - ongoing SUNY Binghamton, NY. Teaching Assistant Dec ‘15 - July ‘16 TCS - Apple Relationship project Team Lead July ‘14 - July ‘16 Tata Consultancy Services Ltd., Bangalore, India. Systems Engineer Skills:
● Programming languages and tools: Java, Python, PL/SQL, HTML5, C, MySQL, Bash, MongoDB,Weka.
● CLOUD and BIG DATA Technologies: Apache Mesos, Aurora, Docker, Kubernetes and Micro-Services.
● Deep and continuous learning for runtime estimates for chemistry applications in SEAGrid Science gateway
(Python,Machine Learning): D evelopment of a deep and continuous model that predicts the runtime estimation of long running scientific jobs. Used a centralized service called 'KARNAK' that predicts the approximate running time of various experiments launched from the SEAGrid science gateway.
● Global Customers Relationship Management (Java, Bash, Splunk): Enterprise and individual customer related data of Apple was maintained in relational tables (schema developed with People-Soft technology). The stored data was for customer centric applications of Apple. CRM tool had various automation scripts to monitor these applications’ health and performed actions based on events.
● Faulty Steel Plate Classification (Python, Scikit Learn, Weka) :T he dataset from Semeion, Research Center of Sciences of Communication is used to classify the types of surface defects on stainless steel plates. The K-Nearest Neighbor and Random Forest classifiers were used to build and compare predictions for fault detection.
● Generic Checkpointing Library (Java, Design Patterns): Serialized Java objects to a XML format and de-serialized them back to in-memory objects. The library used Java Reflection, Strategy pattern, and the Dynamic Proxy pattern.
● Students Registration System (Java, PL/SQL, JDBC, JSP, Servlets): Students can maintain enrollments to classes and administrators can manage/access courses and enrollments. Used concepts like E-R modeling, normalization and triggers. Graduate Coursework: D ata Structures and Algorithms; Databases; Programming Languages; Operating Systems; Advanced Programming for the WEB; Data Mining; Cloud Computing; Computer Architecture; Design Patterns for Software Engineering.