SRMAP Departmental Events

The concept of vector spaces and linear maps between vector spaces are used in all branches of mathematics. The Department of Mathematics is organising the sixth distinguished lecture Vector Bundles in affiliation with the Indian Institute of Science Education and Research, Tirupati, India. Renowned Indian Mathematician Prof D S Nagaraj is the keynote speaker of the lecture.

Date: September 21, 2022

Time: 3.30 PM

Venue: Tiered classroom, Level 5

The talk will explain the concept of vector bundles and mapping between vector bundles, a natural generalisation of vector spaces, and linear mapping between vector spaces. The concept of vector bundles and mapping between them is helpful in several advanced branches of mathematics.

About the speaker

Prof D S Nagaraj did his PhD from the Tata Institute of Fundamental Research (TIFR), Mumbai. He was a professor at the Institute of Mathematical Science (IMSc), Chennai, one of the premier research institutes in India. After his retirement in 2018 from IMSc, he joined as a professor at IISER, Tirupati, and currently is the head of the Mathematics Department there. A conference on the occasion of his sixtieth birthday was held at IMSc in 2018 to celebrate his works. His areas of interest include Algebraic Geometry, Commutative Algebra, and Number Theory. His profound scholarship on the subject influenced many young scholars who later became well-established mathematicians. He is exceptionally generous and approachable to young students who would try to learn algebraic geometry for the first time. He has significantly impacted a generation of mathematicians as a researcher and a great teacher. He was elected as a fellow of the Indian academy of sciences in 2010 and 2017.

Join the lecture through the zoom link!

The Department of Electronics and Communication Engineering is hosting a seminar on September 16, 2022, at 4.00 pm as part of the Guest Lecture Series. Dr Anurag Singh, Associate Professor and Dean of Research and Consultancy, NIT Delhi, will deliver a talk on the topic “Federated Learning”.

Abstract of the talk

The most crucial resource for any business, individual, or person in the world is data. Everyone, whether an individual or an institution, wants to prevent a data breach. High-quality data must be subjected to machine learning algorithms. The model is trained using traditional machine learning techniques, which save data to one server. There is a chance that this method will expose personal information. A machine learning technique called federated learning (FL) enables machine learning models to train on various datasets located on various sites without sharing data. Without putting training data in a centralised location, it enables the development of a common global model. Additionally, it permits personal information to stay in local places, lowering the risk.

A new area of machine learning called federated learning already offers greater advantages than conventional machine learning techniques.

Data Security: Training data is kept locally on the devices, negating the need for a data pool.

Data variety: Heterogeneous data since it incorporates information from various users.

Real-time continuous learning: Client data is used to enhance models continuously.

Federated Learning is applied in the field of IoT, Healthcare, smartphones, Advertising, Autonomous Vehicles etc.

Speaker’s Profile

Dr Anurag Singh is currently working as the Associate Professor and Dean of Research and Consultancy at NIT Delhi. He received his PhD from the Indian Institute of Technology Kanpur. His research areas are Network Theory, Dynamics on/of Networks, Opinion Dynamics, Epidemic Modeling, Intelligent transportation system etc. Dr Anurag Singh has teaching experience at both undergraduate and graduate levels. He has taught courses ranging from the introductory level to specialized courses in Computer Science and Engineering, and Mathematics. Currently, he is mentoring students at graduate and undergraduate levels at the National Institute of Technology Delhi, India. In addition, he has also been supervising PhD students. He has around 70 publications featured across various leading journals and three DST-funded projects to his credit.

Join here.

The Department of Electronics and Communication Engineering is hosting a seminar on September 16, 2022, at 4 pm as part of the Departmental Distinguished Lecture Series. Dr Anurag Singh, Dean of Research and Consultancy, NIT Delhi, will deliver a talk on the topic “Federated Learning”.

Abstract of the talk

The most crucial resource for any business, individual, or person in the world is data. Everyone, whether an individual or an institution, wants to prevent a data breach. High-quality data must be subjected to machine learning algorithms. The model is trained using traditional machine learning techniques, which save data to one server. There is a chance that this method will expose personal information. A machine learning technique called Federated Learning (FL) enables machine learning models to train on various datasets located on various sites without sharing data. Without putting training data in a centralised location, it enables the development of a common global model. Additionally, it permits personal information to stay in local places, lowering the risk.

A new area of machine learning called federated learning already offers greater advantages than conventional machine learning techniques.

Data Security: Training data is kept locally on the devices, negating the need for a data pool.

Data variety: Heterogeneous data since it incorporates information from various users.

Real-time continuous learning: Client data is used to enhance models continuously.

Federated Learning is applied in the field of IoT, Healthcare, smartphones, Advertising, Autonomous Vehicles etc.

Speaker’s Profile

Dr Anurag Singh is currently working as the Associate Professor and Dean of Research and Consultancy at NIT Delhi. He received his PhD from the Indian Institute of Technology Kanpur. His research areas are Network Theory, Dynamics on/of Networks, Opinion Dynamics, Epidemic Modeling, Intelligent transportation system etc. Dr Anurag Singh has teaching experience at both undergraduate and graduate levels. He has taught courses ranging from the introductory level to specialized courses in Computer Science and Engineering, and Mathematics. Currently, he is mentoring students at graduate and undergraduate levels at the National Institute of Technology Delhi, India. In addition, he has also been supervising PhD students. He has around 70 publications featured across various leading journals and three DST-funded projects to his credit.

“We cannot always build the future for our youth, but we can build our youth for the future”

                                                                                                                   – Franklin D Roosevelt

India is one of the countries in the world currently experiencing an explosion in the youth population. And undoubtedly, skilled youth is the supreme asset of any land. However, the dismaying fact is that the surpassing potential of our youth is yet to be explored to its fullest. Higher institutes of learning and universities play a pivotal role in equipping their students to face the ever-evolving world fraught with unseen challenges. Reckoning with this alarming fact, the Department of Computer Science and Engineering at SRM University-AP is organising a one-month-long programme on “Youth Empowerment and Skill Development” under the ISR activities of the university from Aug 29, 2022, to Sept 28, 2022.

The programme is exclusively organised for young people living in rural communities near SRM University-AP with an aim to develop the knowledge and experiences that will help them live happier, more fulfilling lives and improve their ability to make better decisions about their future. Courses are offered across various domains such as internet technologies, web development, MS office, and the like that have become essential tools in a technology-driven world.

The inaugural session is scheduled on August 29, 2022, at 05.15 pm. The event will be graced by the honourable Vice-Chancellor Prof VS Rao, Dean – SEAS Prof BV Babu, Associate Dean – SEAS & the Head of the Department of CSE – Prof T Ragunathan, and other university dignitaries. Classes will be conducted between 5.30 pm and 7.00 pm during the month.

Join us and empower yourself for a better tomorrow.

The Department of Physics is glad to announce that Assistant Professor Dr Soumyajyoti Biswas and his PhD scholar Ms Diksha have published their article “Prediction of imminent failure using supervised learning in a fiber bundle model” in the Q1 journal, Physical Review E. Prediction of breakdown in disordered solids under external loading is a question of paramount importance to the stability of buildings and bridges to earthquakes. The researchers used numerical simulations of a model of disordered solids and recorded the time series of the avalanche sizes and energy bursts. They propose that a systematic analysis of these time series using supervised machine learning can predict the time of failure. Interestingly, the most important feature for such predictions turns out to be the measures of how unequal the avalanche sizes are.

Applying external stress on disordered materials beyond their mechanical limits results in their fracture. Hence it is important to know the limit or how the material behaves as it approaches the limit and the factors that influence it. The failure properties of materials are very distinct from other properties such as elasticity, in the sense that their predictions are not always straightforward. Predicting the failure in driven disordered systems is a long-standing problem in physics, engineering, and earth sciences. So, for understanding the fracture process and predicting the failure properties of the materials, a mathematical model (fibre bundle model) has been used.

They introduced the disorder to the system and generated the time series of avalanche size and energy bursts. Some inequality indices i.e., Hirsch index (h), Gini index (g) and recently introduced Kolkata index (k) were measured for the response statistics of the driven systems. These social inequalities are usually represented by the Lorenz function L(p), where p fraction of the population (events) possesses L(p) fraction of total wealth (avalanche mass) when the population (avalanche events) are arranged in the ascending order of their wealth (size). Based on these time series, the machine learning algorithm can predict the prior failure time of the system. So, they have used a supervised machine learning algorithm (with the above-mentioned indices as some of the features) to predict the failure time of the model. They observed that these inequality measures play an important role in making predictions.

Prediction of imminent fracture has its implications in a wide range of disciplines, including stability of mechanical structures (buildings, aircraft, bridges etc.), extraction of oil (fracking) to the largest scale of mechanical failure i.e., earthquakes. Here a supervised machine learning approach is used to make such predictions in numerical models. However, with the important features identified here for such predictions, such research can carry out similar predictions for experimental data. A follow-up of this work is being carried out by Ms Diksha with a group in Spain regarding the experimental verification. Their future research plans include applications of the methods developed here to be applied to real-life physical structures for their stability analysis and predictions of impending catastrophes.

Illustration 1: A schematic diagram of the Lorenz function L(p)is shown, where L(p) denotes the cumulative fraction of the avalanche mass contained in the smallest p fraction of avalanches. If all avalanches were equal in size, this would be a diagonal straight line, called the equality line. The area between the equality line and the Lorenz curve (shaded area),therefore, is a measure of the inequality in the avalanche sizes. Two quantitative measures of such inequality are extracted from here, the ratio of the shaded area and that under the equality line (Gini index, g) and the crossing point of the opposite diagonal – from (0,1) to (1,0), shown in dashed line,and the Lorenz curve, giving the Kolkata index, k. 1 − k fraction of avalanches contain k fraction of the cumulative avalanche mass.