Professor Rakesh Nigam obtained his MS and Doctoral degree from Stanford, with a  thesis on Solar Oscillations. He carried out postdoctoral research at Stanford in the field of Computational Biology studying Herpesviruses and Metabolic Networks. His research interests are in applying techniques from Data Science and Statistics  to diverse fields such as Economics, Finance and Medicine. Prior to joining Krea University, he served as the dean and principal of the  Data Science, MBA and PGDM programs at the  Madras School of Economics Business School.

Professor Nigam has held visiting professorships at the Chennai Mathematical Institute,  Claremont Graduate University, IIM Calicut, and was a visitor at the  International Computer Science Institute in Berkeley. He worked as a senior research scientist at AOL R&D  in the area of Machine learning and  Algorithms where he contributed to research and development efforts. 

Professor Nigam has taught a wide range of courses from Medical Analytics,  Mathematics, Physics, Computer Science and Finance at several institutions.

He is known for the Nigam’s formula of Solar Oscillations. In his spare time Professor Nigam likes to listen to classical music.

Research 

Professor Rakesh Nigam’s current research interests are in the application of data science to  economics and finance. Along with his colleagues and students, he has developed the state space approach to find patterns in panel data. This will complement many of the existing  econometrics and time series techniques. More specifically, he has applied the method to  study the impact of technology on the financial performance of Indian commercial banks,  and to understand the relationships between micro-level household behavior and macro level indicators for the Indian economy. This has also been applied to study the transfers  which the Centre provides to the Indian states. Also, a new algorithm for multi-variate time  series prediction using the state-space formalism has been developed and tested on several  datasets. 

In the past, he has worked in fluid mechanics, and wave propagation in the Sun. His major  contribution was to develop an asymmetrical fitting formula to estimate the observed  frequencies of solar p-modes. He has also worked on problems in the field of computational biology where he analyzed the DNA and protein sequences of the herpes virus family. Also,  his notable contribution was to develop optimization algorithms incorporating the second  law of thermodynamics for metabolic networks. During his brief stint in an industrial  research lab, he worked on problems of spam email detection. He also applied machine  learning and graph theoretic techniques for facial recognition.  

At Krea, he plans to develop techniques in data science and apply them to problems in  medicine such as neuro degenerative diseases. His current projects include studying flow  problems in generative AI, and recovering real world probabilities from option prices.

Publications

1. Shanmugam, K. R., & Nigam, R. (2020). Impact of technology on the financial  performance of Indian commercial banks: A clustering based approach. Innovation  and Development, 10(3), 433–449. 
2. Palit, B., Nigam, R., Perlmutter, K., & Perlmutter, S. (2009). Spectral face clustering.  2nd IEEE International Workshop on Subspace Methods (ICCV), Kyoto, Japan. 
3. Nigam, R., & Liang, S. (2007). Algorithm for perturbing thermodynamically infeasible  metabolic networks. Computers in Biology and Medicine, 37(2), 126–133. 
4. Nigam, R., Kosovichev, A. G., & Scherrer, P. H. (2007). Analytical models for  crosscorrelation signal in time-distance helioseismology. The Astrophysical Journal,  659, 1736. 
5. Nigam, R., & Liang, S. (2005). Perturbing thermodynamically unfeasible metabolic  networks. Lecture Notes in Computer Science, 3594, 30. 
6. Nigam, R., & Kosovichev, A. G. (1998). Measuring the Sun’s eigenfrequencies from  velocity and intensity helioseismic spectra: Asymmetrical line profile-fitting formula.  The Astrophysical Journal, 505, L51.

Teaching Modules 

1. Applied Cryptography 

Cryptography is the science of designing encryption and decryption algorithms for providing  a secure communication between parties over an insecure channel. In the present age,  cryptography has gained importance and is widely used in many disciplines. This is a first  course in cryptography, and the focus will be on the algorithmic foundations of real world  cryptosystems, with an aim to balance theory and application. In this class we will deal with  the basic mathematics that is needed to better understand various cryptographic protocols,  algorithms and attacks. This will provide a sound understanding of the practical issues of  applying cryptographic techniques to design secure systems. 

2. Application of Matrices to Arts and Sciences 

Matrix analysis has become a very important subject in data science which connects data  to various fields. In today’s world, computers are inexpensive and fast, and it is easy to  procure large amounts of data, and many outcomes are dependent on the interplay between  several disciplines and data. This course, therefore, takes an interdisciplinary approach and  attempts to connect a wide range of subjects, from economics and finance to biology, using  the language of matrices. It will provide students with a sound understanding of many  practical issues faced in data science. 

3. Probabilistic Models in Finance 

Probabilistic models are widely used in finance and provide the framework for pricing  derivatives, constructing portfolios, and understanding market dynamics. This course  focuses on discrete probabilistic models applied to finance. It covers basic topics such as  the principle of no-arbitrage and replicating portfolios to well-known market models such  as the Glosten-Milgrom model and the Capital Asset Pricing Model. The course will equip  students with a knowledge in finance along with the underlying mathematics. 

4. Introduction to Statistical Learning 

This is an introductory course on statistical learning, that introduces students to various  statistical learning techniques such as linear regression and classification that are used in  supervised machine learning. In this course, several data-driven prjects will be assigned and  students have the freedom to analyze them using the techniques covered in class. 

5. Introduction to Deep Learning 

This is an introductory course in deep learning which will cover topics from convolution  neural networks (CNN) and recurrent neural network (RNN). We will analyze various forms  of data that can be in the form of text, image and video. The applications of this course will  span various fields from economics, finance to medicine. Finally, the course will also  introduce some aspects of reinforcement learning, which can be combined with deep  learning.