Teegala Krishna Reddy Engineering College, HOD(AI&ML): Dr. Vadivelan Natarajan Interview

Dr. Vadivelan Natarajan is a Professor and the Head of the Department of Artificial Intelligence and Machine Learning at the Teegala Krishna Reddy Engineering College, Meerpet, Hyderabad. He received his B.E. in Computer Science and Engineering from Anna University in 2008 and his M.Tech. in Computer Science and Engineering from Dr. MGR University in 2011. He was awarded a PhD in Computer Science and Engineering from St. Peter’s University in 2017. Dr. Natarajan’s research interests include WSN, Deep Learning, IoT, Big Data Analytics and Networking and she has published many articles in reputed journals.

What is the latest program that you are offering in Teegala Krishna Reddy Engineering College which will help students outperform and stand apart from the crowd?

We get our students to enroll in the credit-based NPTEL (National Program on Technical Enhanced Learning) program. Under this, they can learn not only the theory but also acquire the necessary practical application-based skills. So, as soon as students complete the credits they are awarded the B.Tech. degree. Along with the theory, there is a demonstration session with open-source tools. The content delivery is done by the college faculty and industry experts. Choice based system is one of the best practises, in which students are given the freedom to choose the subjects they want to study as well as the faculty under whom they would like to study.

What are the factors that make this program the best for the students to opt?

Artificial Intelligence and Machine Learning is a versatile discipline. AI and Machine Learning are used in computing and space-based fields; it is also used in industries closely related to the common citizens, such as healthcare, automobiles, banking and finance. AIML is a rapidly growing field with a lot of potential for innovation. If you are interested in developing intelligent machines that can learn and adapt, then AIML is a great choice. AIML graduates can find jobs in a variety of industries, such as healthcare, finance, and manufacturing. A program should provide opportunities for students to work on real-world projects, use industry-standard tools and frameworks, and gain hands-on coding experience. A program should offer flexibility in terms of course formats (online, in-person, or hybrid), class schedules, and the ability to pace one's learning.

What will you say the “best practices” in the course you’re offering?

Our students have a strong understanding of linear algebra, calculus, probability, and statistics. These are essential for understanding the algorithms and concepts in AI/ML. We also consider python to be crucial for implementing AI/ML algorithms and working with popular libraries like Tensor Flow and Py Torch. We also emphasize hands-on projects throughout the course. Real-world application of AI/ML concepts is essential for good understanding. We teach students how to choose the right algorithms and models for different types of problems and explain the trade-offs between different approaches. Our curriculum includes data preprocessing, data cleaning, feature engineering, and data scaling, which are all crucial steps. Low-quality data can lead to poor model performance. Moreover, we teach cross-validation techniques to assess model performance accurately and avoid overfitting. We also focus on discussing ethical issues in AI/ML, including bias, fairness, and transparency. Encourage responsible AI development.

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What are some valuable insights of the program that you are offering at your institute?

The valuable insights of the programme come from our strong curriculum. Our programme has a comprehensive curriculum covering key topics in AI and machine learning, including fundamentals, deep learning, natural language processing, computer vision, and more. We also try to keep up with the latest trends and technologies in the field. We look for programmes with experienced and knowledgeable faculty members who are actively involved in research and industry collaborations. Faculty expertise can greatly impact the quality of education. We believe programs that offer opportunities for students to engage in research projects or internships in collaboration with industry partners can provide valuable practical experience. Our programme with strong ties to industry can help students access internships, job placements, and networking opportunities. Guest lectures, industry-sponsored projects, and job placement assistance are all valuable. We provide access for our students to state-of-the-art labs, computing resources, and software tools relevant to AI and machine learning. These resources are crucial for hands-on learning. We promote diversity and inclusion create a more inclusive learning environment and often lead to better outcomes for all students.

How does the program ensure that students are being prepared for the future?

AIML programmes can teach students essential skills in AI, Machine learning, and Natural Language Processing, which are increasingly in demand in various industries. These skills are highly relevant in a world where automation and AI technologies are becoming more prevalent. Learning AIML often involves solving complex problems and building AI applications. This fosters critical thinking and problem-solving abilities that are valuable not only in AI but in various other fields. AIML programmes often require students to have knowledge in mathematics, statistics, computer science, and domain-specific areas. This interdisciplinary knowledge prepares students to work in diverse and dynamic fields. AIML programmes typically involve staying updated with the latest advancements in AI and technology. This fosters adaptability, a crucial skill in a world where technology is rapidly evolving. Many AIML programmes include hands-on projects that simulate real-world scenarios. These projects help students apply their knowledge and gain practical experience.

What are you most proud of in your career so far?

I'm proud of assembling and leading a talented team of AI/ML researchers and engineers. Building a cohesive and innovative team is crucial for driving forward the department's objectives. Our department has made significant contributions to the field of AI and ML, publishing groundbreaking research papers and advancing the state of the art in various domains. These contributions have not only expanded our understanding of AI but have also had practical applications in industries such as healthcare, finance, and technology. Nurturing the growth and development of our team members is another source of pride. We have invested in training and mentorship programs to help our researchers and engineers thrive in their careers and make meaningful contributions to the field.

What strategy do you employ for building an efficient work team?

We start by defining the team's goals and objectives. Ensure that everyone on the team understands what they are working towards and why it’s important. We make sure to carefully choose team members with the skills, expertise, and personalities that complement each other and align with the team's goals. We consider diversity in skills, experiences, and perspectives to promote creativity and innovation. Additionally, we clearly define each team member and roles and responsibilities to avoid confusion and duplication of effort. We also ensure that roles are aligned with individual strengths and expertise.

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What was the most challenging task that you faced in your career so far, and what did you learn from it? 

One of the most significant challenges is keeping up with the fast-paced advancements in AI and ML. The head of the department may need to continually learn about new techniques, tools, and technologies. Attracting and retaining skilled faculty and researchers can be challenging in a competitive field like AIML. From this challenge, the head of the department can learn the importance of effective recruitment strategies, nurturing talent, and fostering a positive work environment. Allocating resources effectively, securing research funding, and managing the department's budget can be significant challenges. The head of the department can learn financial management skills and strategic planning to ensure resources are optimally utilized. Building and maintaining strong partnerships with industry for research and internship opportunities for students can be challenging. This can teach the head of the department the importance of networking, negotiation, and aligning academic programmes with industry demands. The increasing focus on AI ethics and regulations can pose a challenge. Learning to navigate ethical considerations and regulatory compliance is essential for responsible AI research and development. Ensuring students' success, particularly in a field known for its complexity, can be challenging. The head of the department may learn to provide additional support and resources to help students excel.

What do you find most difficult about being Head of Department? 

Department heads often need to strike a balance between leading their team and managing departmental operations. This involves setting a strategic vision while also handling day-to-day tasks, budgets, and administrative responsibilities. People management can be challenging, as it involves working with diverse personalities, addressing conflicts, motivating team members, and ensuring a productive and harmonious work environment. Effectively allocating and managing resources, including budgets, personnel, and equipment, is a critical responsibility for department heads. Deciding where to invest resources and how to prioritize projects can be difficult. Department heads are typically responsible for aligning their department goals with the organization’s overall objectives. This can be challenging when there are conflicting priorities or limited resources.

Any insights or Advice that you would like to give to the current youth or the aspiring students?

They should have a strong understanding of the fundamentals of the areas that are crucial for success in AIML. Theory and practical experience are equally important. Work on projects, participate in coding competitions, and explore real-world applications to apply what you learn. AIML students should stay updated with the latest research papers, technologies, and trends by reading academic journals, attending conferences, and following industry news. Students of AIML must attend meetups, conferences, and webinars. They should meet professionals and researchers in the AIML field so that they get good opportunities for internships and collaborations. Develop a strong understanding of the ethical implications of AIML. Responsible AI practises are becoming increasingly important and being ethically aware is a valuable skill. AIML can be challenging, so students should be persistent in their efforts. Learning from failures is an integral part of growth. AIML teams benefit from a diverse skill set. So, students should develop skills in data engineering, data visualization, or domain expertise to complement their AIML skills. If possible, find a mentor in the field who can provide guidance and advice based on their experience.