Abstract

An algorithm is a step-by-step instruction that can apply to a given input instance of the problem to obtain the corresponding output. Unfortunately, there are many critical problems for which we are still struggling to come up with efficient algorithmic solutions. Of course, we will not find a solution if that problem is unsolvable (or not efficiently solvable). But how do we mathematically prove that it is unsolvable? The complexity theory is the study of such issues. This talk will cover some interesting topics in this area, including the famous "P vs. NP" problem.

About the Speaker

Prof. Sharma Thankachan is an Assistant Professor in the department of computer science at the University of Central Florida, Orlando, since 2017. Before joining UCF, he has worked as a Research Scientist/PostDoc in the School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, and in the Cheriton School of Computer Science, University of Waterloo, Canada. He has focused his research in Algorithms on Strings/Graphs with Applications to Computational Biology, Compressed/Succinct Data Structures, and High-Performance Computing. He received his Ph.D. in Computer Science from Louisiana State University (Spring 2014). His Bachelor's degree is in EEE from the National Institute of Technology Calicut, India.

To know more about his publication details : Click Here

Abstract

Batch codes were introduced in 2004 for recovering data objects stored over different locations in a distributive manner (Google drive, Dropbox ,etc) as well as for private information retrieval. A variation called Asynchronous batch codes were introduced in 2018 as a modified version of regular batch codes. In this talk, I will introduce traditional batch codes, asynchronous batch codes, their properties, and coding bounds. In addition, I will briefly describe two related families of codes called locally repairable codes (LRC) and codes for private information retrieval (PIR) which are extensively studied over the past few years. At the end of the talk, I expect the audience to get an overview of the above mentioned codes, their connections, and some potential applications in cloud storage.

About the Speaker

Dr. Eldho K Thomas is an Assistant Professor, Department of Mathematics Newman College, Thodupuzha. Before joining Newman College, he has worked as a Research Fellow within the coding and transmission group in the Institute of Computer Science, University of Tartu, Estonia. Formerly, he was a research fellow in the ECE department, the National University of Singapore. He completed his Ph.D. in Mathematics from Nanyang Technological University, Singapore. His current research area is Coding and Information Theory. His Master's degree is in Mathematics from the University of Liverpool, United Kingdom, and his Bachelor's degree in Mathematics from Mahatma Gandhi University.

To know more about his publication details : Click Here

Abstract

Artificial intelligence encompasses several distinct areas of research each with its own specific interests, research techniques, and terminology. These sub-areas include search technologies, knowledge representation, vision, natural language processing, robotics, machine learning. The primary purpose of Artificial intelligence is to create an acceptable model for human understanding. And these models can be prepared with the ideas and strategies from various branches of Mathematics. Mathematics helps AI scientists to solve challenging deep abstract problems using traditional methods and techniques known for hundreds of years. The concepts of Linear Algebra, Calculus, game theory, Probability, statistics, advanced logistic regressions, and Gradient Descent are all major data science underpinnings. Mathematics can enhance analytical thinking skills which are vital in Artificial intelligence. People usually think of AI is that, it is all magic, but it isn’t magic, it’s the mathematics that creates magic behind all the inventions.

About the Speaker

Dr. Tessy Mathew is currently working as Head of the Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Thiruvananthapuram. She completed her Ph.D. from Vellore Institute of Technology. She has 16 years of teaching experience and a KTU guideship. Her area of interest is Machine Learning, Cellular Automata, Predictive Analytics, and Deep Learning.

To know more about her publication details: Click Here

Abstract

The presentation discusses basic ideas of Discrete Mathematics and its relation with other areas in Mathematics, mainly Linear algebra and Abstract Algebra. We also discuss some of its applications in computer science and signal processing ( coding theory). We conclude the presentation with a discussion of a typical Mathematics researcher's interests and challenges in this field.

About the Speaker

Sajith P. is an assistant professor in the Department of Mathematics at BITS-Pilani Hyderabad Campus since 2020. He has completed his Ph.D. from the Indian Institute of Technology Bombay in 2017 and later worked as a postdoctoral fellow in the department of ECE at the Indian Institute of Science, Bangalore. He works in areas related to Graph coloring and Graph theory-related algorithms.

To know more about his publication details, visit: Click Here

Abstract

Large data sets are increasingly used in many areas like image compression, computer vision, machine learning, finance, climate change etc. The large dimension of these data sets and the interdependence of various features in the data make it difficult to interpret and extracting useful information. The idea of Principal Component Analysis is to reduce the dimensionality of a dataset while preserving as much variability as possible. This means that a new set of uncorrelated variables are found that are linear combinations of those in the original data set that successively maximize the variance. In Principal Component Analysis, many ideas in Linear Algebra like orthogonal diagonalization, linear transformation, orthogonal projection, etc. are needed.

About the Speaker

He has 39 years of teaching experience. His area of interest are Linear Algebra, Wavelet Theory, and Fourier Analysis, and other Applied Mathematical areas.