Shankru Guggari is a Researcher of Computer Science and Engineering at B.M.S. College of Engineering, Bengaluru. His research interests span all aspects of building practical and robust machine learning systems, and he has worked on projects that improve the performance of classical machine learning algorithms. More recently, his research has focussed on:

1. Classification Techniques (Decision Trees, Neural Network, Support Vector Machines etc.)

2. Computer Vision (Biometric Systems)

3. Cognitive Science (Linguistics)

1. Shankru Guggari, Vijayakumar Kadappa and Umadevi V, ‘Music Rhythm tree based Partitioning Technique’, Neural Computing and Applications (Under Review)[Scopus Indexed].

2. Non-sequential partitioning approaches to decision tree classifier, S Guggari, V Kadappa, V Umadevi Future Computing and Informatics Journal 3 (2), 275-285, 2018 (Elsevier Publication)

3. Frequent-itemsets based partitioning approach to decision tree classifiers,S Guggari, V Kadappa, V Umadevi, Lecture notes in artificial intelligence, NIT, Goa, 2019 (Springer Publication).

4. Theme-Based Partitioning Approach to Decision Tree: An Extended Experimental Analysis, S Guggari, V Kadappa, V Umadevi, Emerging Research in Electronics, Computer Science and Technology, 117-127, 2019 (Springer Publication).

5. A Map-Reduce Model of Decision Tree Classifier using Attribute Partitioning, N Shivaraju, V Kadappa, S Guggari, International Conference on Current Trends in Computer, Electrical 2017.

I have conducted several workshops for faculties and UG students