Schedule

Abstract: TBA

Abstract: TBA

Abstract: Signs of borrower distress are appearing in the eastern and north-eastern states of India. With India’s checkered history with borrower distress, there is a clear and urgent need for systematic course-correction. In this presentation, we discuss the current level of visibility RBI has over the credit markets, and the prevalence of indebtedness in borrowers. We also discuss a Framework through which the RBI may better monitor the credit markets and detect the prevalence of over-indebtedness, simultaneously. In the second half of the presentation, we discuss an approach through which hidden variables like overindebtedness may be estimated using easily available data like “rates of default”.

Abstract: TBA

Abstract: Novel method and system for neuropsychological performance testing that can establish a link between cognition and emotion. It comprises a portable device used to interact with a cloud service which stores user information under username and is logged into by the user through the portable device; the user information is directly captured through the device and is processed by artificial neural network; and this tridimensional information comprises user cognitive reactions, user emotional responses and user chronometrics. The multivariate situational risk assessment is used to evaluate the performance of the subject by capturing the 3 dimensions of each reaction to a series of 30 dichotomous (Yes/No) questions describing various situations of daily life and challenging the user’s knowledge, values, ethics, and principles. In industrial application, the timing of this assessment will depend on the user’s need to obtain a service from a provider such as opening a bank account, getting a mortgage or an insurance policy, authenticating clearance at work or securing online payments, etc. Limitations of use have been observed in the current design for which we are proposing next steps innovation to remedy and scale up.

Abstract: TBA

Abstract: In the last decade, deep learning has revolutionized AI and is now well on its way to changing the face of Computer Science, in arguably the most profound change since the advent of the stored program computer. Fundamentally, CS is moving from the study of well-specified, algorithmizable functions over crisp, discrete data to the study of non-linear functions over noisy, uncertain, high-dimensional data. The functions are such that no human can write down their code (when was the last time you wrote out a program with 175 billion variables?). Instead, humans can specify a program sketch (program with holes) in such a way that standard algorithms can operate on (input, output) information about the function and find values for the holes which yield an approximation of the original function. When successful, the technique results in an approximation that can produce an acceptable result for a new, unseen input value. Notably, these techniques can be made to work not just for the transformational view of computing (as outlined above) but also the reactive view, where the goal of the computation is to maintain an interaction with an environment (via reinforcement learning).

Through Computer Science, these ideas are now changing many other fields, from engineering, to medicine and health-care to financial services. We will illustrate with examples from the analysis of financial documents, and the quantitative analysis of markets.