Dr. Kashi Sai Prasad is an associate professor and head of the CSE AIML department at MLRIT. He has eight years of experience in the field.
Dr. Kashi Sai Prasad holds a Ph.D. in Computer Science and Engineering and has expertise in computer science. He has published numerous research papers in various journals and conferences, covering topics such as deep learning, image classification, disease diagnosis, data mining, artificial intelligence, and machine learning. Some of his notable publications include papers on histopathological image classification, kidney disease diagnosis, brain stroke detection using MRI scans, and real-time data streaming using Apache Spark.
In terms of teaching, Dr. Kashi Sai Prasad has taught a wide range of subjects related to computer science and engineering, including cloud computing, big data analytics, predictive analytics, artificial intelligence, machine learning, and natural language processing, among others.
Apart from his academic contributions, Dr. Kashi Sai Prasad has also authored books on topics such as cryptography and network security, video-based abnormal driving behavior detection using machine learning, and deep learning approaches for analyzing traffic congestion.
Dr. Kashi Sai Prasad has been associated with MLRIT since December 11, 2015, and he plays a regular role in the institution.
As you have been in the education sector for over 8 years now, what is that one thing that made you keep connecting with you to walk with MLRIT in your journey?
Since 2008, I've been associated with MLRIT, and I’ve learned many things. MLRIT lays a strong foundation for students and faculty by upskilling them in the latest technologies through Center of Excellence labs and partnerships with industry for advanced training programs. I've been part of the Bigdata COE since 2016 to learn Hadoop through an MOU with Techybees and Virtusa. That industry-oriented training program has helped us work on real-time projects and helped students achieve good placements.
As the Head of the CSE (AI&ML) department, what are your primary goals and vision for the department's growth and development?
As current industry and research revolve around artificial intelligence, there are some challenges in adopting the latest technologies. The primary goal is to upskill students and faculty in areas like generative AI, prompt engineering, NLP, computer vision, etc. For the growth of the department, we are striving hard to implement real-time projects and case studies. The vision is to make students progress in the areas of AI to meet industry needs.
Some of the things I want to ensure for the growth of the department are:
Curriculum Enhancement: Continuously update and enhance the curriculum to reflect the rapid developments in AI and ML. By offering specialized courses that cover emerging topics such as generative AI, deep learning, natural language processing, prompt engineering, ethical AI, and reinforcement learning. Incorporate practical, hands-on projects to ensure students gain real-world skills.
Student Engagement: Create opportunities for students to engage actively in AI and ML-related activities. Support student-led clubs, hackathons, workshops, and conferences to nurture their passion and expand their practical skills.
Organize Seminars and Training: Organize seminars, webinars, and public lectures to raise awareness about the potential of AI and ML as well as their societal impact.
Industry Partnerships: Forge strong partnerships with industries across various sectors to facilitate internships, research collaborations, and job placements for students.
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How do you keep up with the rapidly evolving field of AI and ML to ensure that the curriculum and teaching methodologies are up-to-date and relevant for students?
Keeping up with the rapidly evolving field of AI and ML as the head of the department is crucial to ensuring that the curriculum and teaching methodologies remain up-to-date and relevant for students. Here's how I would approach this challenge:
Continuous Learning: This is the success mantra for anyone. As the department head, I would prioritize my own continuous learning by attending conferences, workshops, and seminars related to AI and ML. Staying connected with the latest research and industry trends will enable me to make informed decisions about curriculum updates.
Faculty Development: Provide resources and incentives for faculty members to engage in professional development activities. Encourage them to attend conferences, participate in online courses, and collaborate with industry professionals to bring real-world insights into the classroom.
Research and Collaboration: Encourage faculty members to actively engage in research and collaborate with industry partners. Research projects often lead to new insights that can be incorporated into the curriculum. Collaboration with industry ensures that students learn the most relevant and practical skills. This is followed at MLRIT across all departments.
Industry Partnerships: Industry is the backbone of any academic organization. Foster strong relationships with AI and ML companies and startups. These partnerships can provide insights into the latest tools, techniques, and applications in the field. Guest lectures, internships, and joint projects can help bridge the gap between academia and industry.
Teaching Innovation: Encourage faculty to explore innovative teaching methodologies, such as project-based learning, flipped classrooms, and experiential learning. These approaches can better prepare students for the dynamic nature of AI and ML.
By actively engaging in these strategies, I would ensure that the department's curriculum and teaching methodologies stay current and relevant in the ever-evolving fields of AI and ML, ultimately preparing students to excel in their careers and contribute to the advancement of the field.
How do you ensure that students get practical exposure to real-world applications of AI and ML technologies during their academic journey?
Providing students with practical exposure to real-world applications of AI and ML technologies is essential for preparing them to succeed in their careers and contribute meaningfully to the field. Here are several strategies to ensure students receive practical experience during their academic journey:
Hands-on Projects: Incorporate hands-on projects into the curriculum that challenge students to apply AI and ML concepts to solve real-world problems. These projects can range from basic to advanced, allowing students to gradually build their skills. We are providing in-house projects by giving live data from our Arundathi hospital. This helps students understand real-time problems in analysis and new approaches.
Industry Collaborations: Establishing partnerships with industry organizations, startups, and established companies Collaborative projects, internships, and co-op programs can provide students with opportunities to work on actual AI and ML projects under the guidance of industry professionals.
What specific steps does the department take to foster a supportive and inclusive learning environment for students of diverse backgrounds?
Fostering a supportive and inclusive learning environment for students of diverse backgrounds is crucial for creating an enriching educational experience. The steps the department can take to foster a supportive and inclusive learning environment for students of diverse backgrounds are:
Mentorship Programs: Establish mentorship programs that connect students from underrepresented groups with faculty mentors, industry professionals, and senior students. Mentorship provides guidance, support, and networking opportunities.
Inclusive Curriculum: Develop an inclusive curriculum that represents a wide range of perspectives and contributions. Ensure that course materials, examples, and case studies reflect diverse cultures, genders, and backgrounds.
Diverse Faculty and Staff: Actively recruit and retain a diverse group of faculty and staff members. A diverse teaching and administrative team helps students see role models who share their experiences and can relate to their challenges.
What unique opportunities does the MLR Institute of Technology offer to students interested in AI and ML beyond the regular curriculum?
MLRIT always encourages faculty and students to organize hands-on workshops, hackathons, training sessions, etc. We at MLRIT have a separate hub for AIML students called AIERC (Artificial Intelligence Exploration and Research Centre) in association with H-Bots Robotics. Students are open to working on real-time projects and learning multidisciplinary things.
20+ student patents on AI have been filed this year from this center, which is a remarkable achievement.
12 students are working as interns on a real-time project on automated supermarket product pricing in virtual mode for a US company called Sclanet. This is a one-of-a-kind achievement.
There's a separate club for students called the AIM Club, where students can work on their ideas and turn them into realities.
With the increasing popularity of AI and ML, how does the department address the challenges of student demand and faculty expertise to maintain quality education?
Addressing the challenges of student demand and faculty expertise in the context of the increasing popularity of AI and ML requires a strategic approach. Here's how we are managing these challenges while maintaining the quality of education:
Faculty Development: Providing professional development opportunities for faculty to stay updated with the latest trends in AI and ML, we have organized five such sessions in a year.
Hiring Strategy: Recruiting faculty members with diverse expertise in AI and ML subfields to cover a wide range of topics
Guest Lecturers and Industry Experts: Invite guest lecturers and industry experts to share their practical experiences and insights. It enhances the learning experience and bridges the gap between academic theory and real-world applications.
Online Resources: Curate and recommend high-quality online resources, tutorials, and MOOCs to supplement classroom teaching. This can help students learn at their own pace and reduce pressure on faculty.
Lab Facilities: Providing well-equipped AI and ML labs with access to state-of-the-art tools, software, and hardware improves hands-on learning opportunities for students.
Continuous Improvement: Regularly review and update the curriculum based on student feedback, industry trends, and faculty input. This ensures that the program remains relevant and of high quality.
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What are your thoughts on the future of AI and ML, and how does the department stay ahead in preparing students for upcoming trends and technologies?
The future of AI and ML is incredibly promising and dynamic. As these fields continue to evolve, they will likely shape numerous aspects of society, technology, and industry. To ensure that the department stays ahead in preparing students for upcoming trends and technologies, a proactive and adaptable approach is essential. Here are some thoughts on the future of AI and ML and how the department can prepare students:
Advanced Applications: AI and ML will find applications in increasingly complex and diverse domains, from healthcare and finance to autonomous vehicles and the creative industries.
Explainable AI and Interpretability: Develop courses that delve into the complexities of explainable AI and interpretable ML models, addressing the growing need for AI systems that provide understandable and transparent explanations for their decisions.
Real-world Projects: Prioritize experiential learning through industry collaborations, internships, and real-world projects. This bridges the gap between theory and practice.
Hackathons and Competitions: Organize AI and ML hackathons and competitions that challenge students to solve real-world problems, fostering creativity and innovation.
Research and Innovation: Encourage students to engage in research, publish papers, and attend conferences. Provide opportunities for them to contribute to cutting-edge advancements.
Faculty Expertise: Ensure faculty members are well-versed in the latest trends by promoting continuous learning through workshops, seminars, and research activities.
By embracing these strategies, the department can empower students to be well-prepared for the future of AI and ML, enabling them to drive innovation and make meaningful contributions to the field.
What advice would you give to students who are considering pursuing a career in AI and ML, and what qualities do you think are essential for success in this field?
As the head of the department, I would offer the following advice to students considering a career in AI and ML, along with the essential qualities for success in this rapidly evolving field:
Build Strong Foundations: Start by mastering the fundamental concepts of mathematics, statistics, and programming. A solid understanding of these basics will be crucial for advanced AI and ML studies.
Problem-Solving: AI and ML are about solving complex problems. Develop a keen ability to break down challenges, analyze data, and create innovative solutions.
Hands-on Experience: Engage in hands-on projects, internships, and research opportunities. Practical experience is invaluable for honing your skills and understanding real-world applications.
To conclude, for students aspiring to enter the AI and ML fields, I recommend building a strong foundation in math, programming, and statistics. Embrace lifelong learning, hands-on projects, and specialization while developing skills in communication and collaboration. Understand the ethical implications and stay curious, adaptable, and persistent to tackle challenges creatively.
Remember that AI and ML offer incredible opportunities to shape the future. By cultivating these qualities and embracing the advice provided, you can embark on a fulfilling and impactful career in this exciting field.
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