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publications

talks

Complex-Valued AI in Agriculture

Published:

This section contains a series of SPAI Group's expositions (or resources) that focus on Complex-valued AI in Agriculture.

Complex-Valued AI in Chemistry

Published:

This section contains a series of SPAI Group's expositions (or resources) that focus on Complex-valued AI in Chemistry, under the joint guidance of Profs. Anjali Devi J S and Mahesh Mohan M R.

Complex-Valued AI in Finance

Published:

This section contains a series of SPAI Group's expositions (or resources) that focus on Complex-valued AI in Finance.

Understanding Complex-Valued AI

Published:

This section contains a series of SPAI Group's expositions (or resources) that focus on Understanding Complex-valued AI.

Complex-Valued AI in LLMs

Published:

This section contains a series of SPAI Group's expositions (or resources) that focus on Complex-valued AI in LLMs.

Complex-Valued AI in Quantum Computing

Published:

This section contains a series of SPAI Group's expositions (or resources) that focus on Complex-valued AI in Quantum Computing.

Complex-Valued AI in Structural Analysis

Published:

This section contains a series of SPAI Group's expositions (or resources) that focus on Complex-valued AI for Structural Analysis, under the joint guidance of Drs. Sumith Sreedhar and Mahesh Mohan M R.

teaching

Lectured on various topics in Deep Learning

Lab Talk, IIT Madras, EE Dept., 2015

This Lecture Series started at a time when Deep Learning was taking off, and it aims to explain the (then) emerging field of Deep Learning for the (then) members of Image Processing and Computer Vision Lab and Computational Imaging Lab, headed by Prof. A. N. Rajagopalan and Prof. Kaushik Mitra, respectively.
                Here, the first lecture introduces Deep Learning (L1), later, followed by a doubt-clearing session (L1.1). Lecture 2 covers Convolutional Neural Network (L2), and Lecture 3 provides a mathematical interpretation of Deep Learning; O Lost! This series is concluded with Lecture 4, which discusses several Deep Learning approaches for Image Processing. [Slides-L1] [Slides-L1.1] [Slides-L2] [Slides-L3] [Slides-L4]

Mentored two undergrad students towards the best Deep Learning course-project

Graduate Course (Deep Learning), IITM, EE Dept., 2017

Here, I proposed a problem statement Efficient Sketch Classification for Resource-constrained platforms, and designed a research plan for the same. Undergrads Akshit Kumar and Sachin Agrawal joined with me and finally got recognition for the best Deep Learning course-project. Presently, both the students are continuing research in Deep Learning; the former is a Master's student at the University of Michigan, US, and the latter is a Data Scientist at Daimler, Germany. [Slides]

Mentored (with Prof. A. N. Rajagopalan) a project associate towards a funded project.

Funded Project, IITM, EE Dept., 2018

Lots of accidents happen while exiting a vehicle from its side doors, for it is difficult to ensure whether another vehicle(s) are coming from behind and how fast it is. This project attempts to solve this issue; project associate Madumithra Krishnamoorthy was incharge. Together, we employ a side view camera and propose a deployable algorithm using image processing techniques to address this. Later, she joined as a graduate research student in image processing. [Demo]

Deep Learning Foundations and Applications AI61002, 2024 Spring, IIT Kharagpur

Teaching, IIT Kharagpur, Centre of Excellence in AI, 2024

Course Overview: Moodle Snapshot

Teaching Distribution: Taught after Mid-semester, Convolutional Neural Network (preceeded by Prof. Somdyuti Paul, followed up with Prof. Jiaul Hoque Paik).

Logistics: In-person Mode, Weekly 3 hrs lecture + 1 hr Tutorial; Credits: 4

Students: 137 (Senior UG/PG students from all departments, selected based on CGPAs)

Student Feedback: IIT-KGP ERP Snapshot

Machine Learning Foundations and Applications AI42001, 2024 Spring, IIT Kharagpur

Teaching, IIT Kharagpur, Centre of Excellence in AI, 2024

Course Overview: Moodle Snapshot

Teaching Distribution: Taught till Mid-semester, i.e., till Support Vector Machine (followed up with Prof. Somdyuti Paul and Prof. Sudheshna Sarkar).

Logistics: In-person Mode, Weekly 3 hrs lecture + 3 hrs Lab; Credits: 5

Students: 108 (UG/PG students from all departments, selected based on CGPAs)

Student Feedback: IIT-KGP ERP Snapshot