Doctoral Candidate @SUNY Binghamton, USA. Advisor: Dr. Hiroki Sayama A Researcher, Coder, Learner and Dreamer........
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"Sriniwas has a wonderful rapport with people of all ages, especially students. He is extremely organized and reliable. He can work independently and is able to follow through to ensure that the job gets done. He is a dependable, mature, and great supporter." -Dr Sraban Kr Mohanty (Former Supervisor and Professor)
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I have experience in Software development, Research and Academia.
Courses: Computational Tools and Enterprise Systems Engineering Duties include: • Delivering lectures, • Evaluation and grading • Maintaining student records • Assisting students in their academic endeavors
Courses: Operating Systems, Database management Duties include: • Preparing and delivering lectures • Conducting exams and seminars • Project Supervision • In-charge of Computer Science department laboratories
Developed a Wikipedia mining toolkit and worked on a project on wikidata analysis • Worked as Teaching Assistant for courses Data Mining & Introduction to programming
• Application of High-performance computing tools like (openMP, CUDA, MPI) in data mining. • Applied openMP constructs in Wikipedia mining.
• Java application development using J2EE techniques like Java-Spring and Hibernate. • Application development for android platform for an insurance organization
Objective: Quantify Eccentricity for each post based on the neighbors of the post-author and find the relation of eccentricity and the attention the post receives
Technology: Doc2Vec, Principal Component Analysis, Clustering
Outcome: There is a trend of getting more likes with increasing eccentricity.
Objective: Tracking users' eccentricity over time and classifying users based on the pattern of change in the eccentricity with time.
Outcome: Most of the users who remain consistent in terms of the eccentricity in their neighborhood, actually become more eccentric with time in terms of their own opinions.
Objective: Analyze how eccentricity of our opinion is related with our neighbors and the topic of discussion. Experiments were conducted with the university students to collect data for two different tasks.
Outcome: Low-collaborative tasks generate more eccentric ideas as compared to high-collaborative tasks. Another finding was that the groups with disperse background generate less eccentric ideas.
Objective: Form clusters of demographic areas according to the mortality rate and various diseases.
Technology: Principal Component Analysis and Clustering
Outcome: Clusters of counties based on mortality rate and disease that can be used to form policies
Objective: Find interesting patterns in Indian languages Wikipedia edition edit histories.
Technology: Hadoop - MapReduce with Java.
Outcome: Identified some interesting editor behavior patterns for Hindi, Marathi, Telugu Wikipedia editions
Objective: Adapt the existing algorithms for large data without changing the core functionality and methodology of the algorithms.
Technology: External Memory model, C++, STXXL
Outcome: Designed a clustering algorithm for large datasets.
Objective: Design a new effective clustering algorithm with least restrictions on the shape, size, density and type of data.
Technology: Python, Random walk model
Outcome: Developed a new algorithm that combines concept of random walk with shared near neighbor technique.
[Presentation] Sriniwas Pandey and Hiroki Sayama, Dynamics of user eccentricity on GAB social media, presented as a talk at the 2021 Conference on Complex Systems (CCS 2021), October 25-29, 2021, Lyon, France / online.
[Presentation] Sriniwas Pandey and Hiroki Sayama, Analyzing eccentric behavior of GAB social media users, presented as a poster (interactive presentation) at NERCCS 2021: Fourth Northeast Regional Conference on Complex Systems, March 31-April 2, 2021, conference held online
Pandey Sriniwas, Pankaj Kumar Y., Samal M., Sraban Kumar M. (2019) Nearest Neighbor-Based Clustering Algorithm for Large Data Sets. Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 760. Springer, Singapore
Pandey Sriniwas, Samal M., Mohanty S.K. (2020) An SNN-DBSCAN Based Clustering Algorithm for Big Data. In: Pati B., Panigrahi C., Buyya R., Li KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1082. Springer, Singapore.