Interview with Dr. Sunil Bhutada
Dr. Sunil Bhutada
Professor at Sreenidhi Institute of Science and Technology
Hyderabad, Telangana, India
Q1. Please share your educational and professional journey with us?
My educational and career graph has been a straight line but with lot of thought process rolling mind about my future. After becoming a Computer Science Engineering Graduate, I straight away jumped into a job at Aditya Birla Organization and was working as a Systems Engineer.
Family being a major priority I quit that job and came back to my home town. Being Jobless was very difficult. Then I joined into teaching. After that I realized that if I have to be in this field I need to upgrade myself. After which I completed my M.Tech and PhD.
Looking back at my journey, I always feel proud and grateful for everything I’ve achieved so far.
Q2. What is Data Mining and where it is useful? How do you see the Data Science Domain for the future as a career for students?
Data Mining is used to extract valuable data from an enormous amount of available data by using multiple softwares. It used in multiple fields like Healthcare, Banking, Research, Fraud Detection, Ecommerce and many more. Business houses uses Data Mining to learn more about market and its needs. They use effective techniques to make a better decisions to get good results.
Data Scientists demand is currently very high in the market. This tide is due to huge avability of data. Every business is using Data Scientist to make their business more profitable.
Q3. How do you excel students in their academics and career?
I don’t force students to by-heart things. Never ever try to mug up. Study as much as you can. I explain them the concept and then give them a case study or a scenario where they need to come up with the solution for the same. And believe me this really works. I ask them not to run study slowly but understand it and try to solve the questions.
Q4. What did attract you towards teaching instead of any corporate job?
Teaching is one profession which creates all other professions. Initially when I shifted my job from industry to teaching, it wasn’t easy for me. But slowly and gradually it became my passion. One of the reasons for becoming a teacher is to contribute to your community in a meaningful way. Teaching is one of the most direct ways to make an impact, and if you are driven by the desire to help those around you, being a teacher is an invaluable contribution.
Q5. What was your Thesis topic in Ph.D? Please share some brief about it?
TOPIC MODELING USING VARIATIONS ON LATENT DIRICHLET ALLOCATION
In this growing information cyber age, it is important to have effective search methodologies which usually depend on the keyword based indexing. But in the later part, the systems need to provide the information specific to a particular topic or concept. In general, the topic model concentrates on the hidden themes, which are termed as topics within a specific collection of documents. There are many searching models proposed by various researchers in the recent past which were specific to identify the topic out of the given set of documents. In the present text mining and information processing, there are latent knowledge models namely Latent Semantic Indexing (LSI), Probabilistic Latent Semantic Indexing (PLSI) and Latent Dirichlet Allocation (LDA). Latent Dirichlet Allocation (LDA) model, which was originated in 2003 by David Blei et.al, is widely used to solve text classification, text annotations, and other issues. Its applications have also widened to images, audio, video and other data fields. In text mining, it is very difficult to correlate the sub-topics in a group of documents.
For extracting the necessary topics Latent Dirichlet Allocation (LDA) models of discrete dataset, and treats the documents as the probability distribution of topics and simplifies the generative process of text which helps to handle the huge scale of text sets effectively. Initially multilevel classification of documents was carried by using Topic Modelling which identifies major topics and sub topics from a large set of document collection. However, it works well only for 2-3 iterations and does not work with the synonyms of the topic keywords generated by the modelling technique.
This PhD research work is aimed at providing the effective semantic topic modelling. A new model termed as Semantic Latent Dirichlet Allocation (SLDA) is developed for automatic topic extraction. The modification is carried in conventional LDA in a semantic way. The semantic handling is also tedious in dynamic databases. In order to handle the dynamic insertion of documents in static dataset as well semantic handling, in this research work a new model is introduced called Dynamic Semantic Latent Dirichlet Allocation (DLSDA). The research work aims at checking the integrity and performance of the model when the dynamic data is appended. Further the performance of SLDA model was improved by using optimization technique was applied to obtain a fully-fledged semantic based topic modeling. Optimization is the process of finding the best solution for problem under given limitations. In these research genetic optimization algorithms is used for relevant topic identification along with semantic handling of text documents. These issues are delineated and solutions have been obtained by introducing a new model known as Optimized Semantic Latent Dirichlet Allocation.
Q6. Which one thing do you want to change in yourself domain and why?
Everything is good. But one thing I would like to change is the life of present students, where they face a sudden paradigm change immediately after graduation and getting in job. They find it difficult to immediately shift over from studies to job. So I believe students should be made to work during their third year of studies.
Q7. You had both academic and engineering experience in your career. Which one do you like most and why?
Both! Each of them is having its own challenges and both goes hand in hand. If some new technology comes into the market we as academicians need to understand first. Teaching is lifelong learning.
Q8. Tell us about one of the moments which changed your perspective towards life?
For me, most of life-changing moments have come in the past 14 years or so, the time since I post graduated and entered into the field of research. There I understood that teaching can become better only if you do research and find new things, ideas etc. This is when I took research as a challenge and became a Doctorate.
Q9. Which one skill do you like most about yourself?
I am very quick learner. I adapt to changing technologies easily. My strength is meeting deadlines and trying to do innovative things.
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