Shubhra Kanti Karmaker (Santu)

Shubhra Kanti Karmaker (Santu)

Assistant Professor
3106 Shelby Center
Department of CSSE
College of Engineering
Auburn University
Phone: 334-844-6274

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

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Posts

Project TextMall: Text-Mining Made Simple for All

2 minute read

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The objective of this research is to develop a general-purpose text analytics platform, i.e., Text-Mall, which would enable real-world users to easily explore the power of Text-Mining in a simple and interactive fashion without worrying about the underlying details of Natural Language Processing.

Project Annotate: Zero-Shot Topic Inference

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Information retrieval and Knowledge mining become much easier if data is categorized and annotated precisely. With the rapid growth of Big-data, it is infeasible to perform manual annotation, as it is slow and expensive. Although the area of text annotation is not in the nascent phase, it has not been well-studied from a user-centric point of view, which is the goal of this project.

Project A2I-MOOC: Artificially Intelligent and Interactive MOOCs

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MOOCs have abysmal retention rates (5-15%) and high student failure rates (7-13%). In this project, we propose two ways to increase student engagement in MOOCs and other online courses through an artificially intelligent system that leverages machine learning and natural language processing. This system will (1) process, prioritize and organize students’ questions in real-time and provide the most relevant questions to instructors for answering during their live lectures, and (2) automate the creation of breakout rooms (which have recently become popular in Zoom classes) based on high-interest topics emerging from student questions and populated by like-minded students during live lectures.

Project Robust IR and NLP Evaluation

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We propose the new framework of IR evaluation with both upper and lower bound (UL) normalization of traditional metrics and systematically study the effect of UL-normalization on three popular evaluation metrics. We also propose three different variations of the proposed upper and lower bound (UL) normalized evaluation framework and experiment each of them with three evaluation metrics individually, creating nine new evaluation metrics in total. We show how we can compute more realistic query-specific lower-bounds for evaluation metrics by computing their expected values for each query in case of a randomized ranking of the corresponding documents. We also theoretically prove their correctness.

Project Deep-MD: Molecular Dynamics Modeling with Deep Time series Forecasting

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We explore machine-learning methodologies for predicting the outcomes of MD simulations by preserving their accurate time labels. This idea will greatly reduce the computational expenses associated with performing MD, making it broadly accessible beyond the current user-base of scientific researchers to high schools and colleges, where the computational resources are sparse.

Research Statement 2018. (Archived)

19 minute read

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Broadly, my research interest lies at the intersection of Text Mining, Natural Language Processing and Information Retrieval. More specifically, I have been studying how to mine Big Text Data across different application domains to find interesting patterns that can provide novel insights to domain experts, which is, otherwise, difficult to perceive due to the scale of the data.

portfolio

publications

talks

teaching

Lecturer at BUET

Undergraduate course, Bangladesh University of Engineering and Technology, 2012

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I have taught 3 theory course and more than 10 Lab courses at BUET CSE department at the undergraduate level.

Teaching Assistant at UIUC

Graduate course, University of Illinois Urbana Champaign, 2017

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I worked as one of the teaching assistants of CS 510 (Advanced Information Retrieval) Course in this semester under Prof. Chengxiang Zhai. The details of the course schedule can be found here

Instructor: Natural Language Processing

Graduate course, Auburn University, 2021

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I am teaching “Natural Language Processing” as a special topic CS/ Data Science course during Fall 2021 at Auburn University. I introduced this course at Auburn University. I have designed the course syllabus, assignments, exams and projects for this course can be found here.

Instructor: Information Retrieval

Graduate course, Auburn University, 2022

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I am teaching Information Retrieval as a special topic CS/ Data Science course during Fall 2022 at Auburn University. I introduced this course at Auburn University. I have designed the course syllabus, assignments, exams and projects for this course can be found here.

Instructor: Machine Learning

Undergraduate course, Auburn University, 2022

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I am teaching “Machine Learning” CS undergraduate course during Fall 2022 at Auburn University.