I'm ready to get it. Do it.

Just a quick intro — your name and what you've been working on in academia so far?

I am Vanita. I'm having three years. Of.

College level. Teaching experience. And I am having four years of.

Alright, so with your three years in academia, what's one foundational concept or topic that you most enjoy teaching to students?

Alright, so you've got three years of college-level teaching experience and were about to mention four years of—could you finish that thought? What were those four years focused on?

Higher secondary level. Teaching experience.

Okay, so three years teaching at the college level and four years at higher secondary — that's a solid range. Out of all the topics you cover, which subject or concept do you find students struggle with most, and how do you approach making it accessible to them?

I had thought. Software engineering. Data structures. Data.

Mining.

Alright, so you've taught software engineering, data structures, and you were about to mention another topic. When you're teaching something like data structures, how do you help students who are struggling to connect the theory to practical use?

Alright, so you've taught software engineering, data structures, and data mining at both college and higher secondary levels. When you're teaching data mining, what's one practical example you use to make the core ideas clear to students?

My area of researching in. Stock market.

Okay, so your research is centered on machine learning applications in stock market prediction, and you've got experience teaching at both college and higher secondary levels. When you introduce a new machine learning concept to undergraduates, what's one way you help them connect theory to something practical?

Machine to behave like a. Human, that is the. Main process.

You mentioned your PhD was in data mining with a focus on machine learning applied to the stock market, and that you've published four articles. Can you walk me through one of those articles—maybe pick the one you felt had the most classroom impact—and explain how you made its core ideas accessible to your students?

We can say the sassafras. Practicing. Hit with the. Different types of. Animals to find out. Cat from that different types of. Animal images. Like that? We are training the. Machine with different types of. Data outputs. And that output output. Makes the machine to identify the. Result for the. New input. That is the main process behind this. Machine learning. When we are. Introducing this to the. This the student. Can learn. New technology. That has new idea. From the. Altering existing one. Because. The uh. Method is.

Actually. Trained with the. Raw data. That is huge amount of raw data. Are used for training. The machine. To predict a new.

Alright, so you use examples like training a machine to recognize cats among different animals to make machine learning concepts more accessible. In your stock market research, have you ever had to explain your findings to students without a technical background—how did you make it understandable for them?

Output. By that. If the system. Yes. Usually. In the. Ordinary process. The program is. Coded with rules. And we are finding the output. But here. The output. Put or feed into the system. System to find a new. The system may.

Alright, so you're explaining machine learning to students using analogies and real-world data examples. Now, you mentioned publishing four articles—can you tell me which journals or conferences those were published in, and what the core findings of one of those papers were?

Work on supervised learning data. Or unsupervised learning data. Or that is the supervised learning means the data set. Is available with the output. From the already. Existing output. The machine will learn. To predict the new output. That is the process of supervised learning. Whereas in the case of unsupervised learning. The data is not. Provided with the output. In that case. It will find out. A new pattern. Or a new types of. Clusters. Groups. To give. Precise meaning. From the already existing data. That is the process of. Unsupervised learning. Reinforcement learning is the process of doing. The test.

Alright, so you completed your PhD focusing on machine learning for stock market prediction, and you've also published some articles. Let me shift a bit—given your research area in AI and stock markets, where do you see the most fundable research directions over the next three years, and what specific grants or funding sources would you pursue to support that work?

Alright, moving to something a bit different. Professor, could you share an example of a student project you’ve guided that involved emerging technologies—maybe something in machine learning or multimedia—and how you ensured the project’s successful completion?

With some. Type of.

Punishment and it will be trained with that. The punishment may be giving some suggestions or you are giving some extra. Work to perform on that particular. Operation to retrieve a correct answer. That is the process of. Reinforcement learning. Like that we are training the student with different types of.

Right, let's shift focus to something broader. Accreditation cycles can be demanding—if you joined our department and found that outcome assessment data was inconsistent across courses, how would you approach fixing that?

Uh.

Alright, so you’re describing how reinforcement learning uses feedback—both positive and corrective—to shape behavior, similar to how you guide students with encouragement or extra practice when needed. Now, let's consider an ethical situation: suppose a student files a formal complaint that your grading was biased, but at the same time, your department head is urging you to raise the overall pass rates. How would you handle that conflict while maintaining academic integrity?

We are performing some operations and we are retrieving the hidden information. From the raw data. This is the process will be trained to them by giving some case studies. Or we can give them a simple task. To perform or you can give a simple. Uh, application. Building. And we will train them to find out. The result and make the students too familiar with. Machine learning.

Alright, so you focus on guiding students through real tasks and case studies, helping them extract meaningful patterns from raw data by actually building applications or running experiments. Let me give you a scenario, Professor: imagine a student files a formal complaint alleging your grading was biased, but at the same time, your department head is pressuring you to raise pass rates. How would you handle that situation?

When when they are having. For example. Here you mentioned this as a bias. The bias. Is because of.

Alright, Professor, shifting gears a bit. Given your experience in machine learning and stock market research, do you have any industry connections—like with financial firms or tech companies—that could help your students with internships or project placements?

Under fitting. The students should be equipped. And they have to gain the knowledge. By theoretical as well as. Practical. By implementing their. Uh, skill.

You drew a connection between grading bias and making sure students gain both theoretical and practical understanding, which is interesting. Let me check something more concrete—when you evaluate student lab work, how do you ensure your grading is fair and consistent across different students and batches?

Particular. Subject.

Without giving that. Particular.

So you've got a clear stance that just passing students isn't the real goal—you're focused on making sure they actually understand the core concepts, with plenty of practical exposure, and you support them through extra resources and small tasks. Let's shift for a moment—can you walk me through a specific time you guided a student project or research effort, and how you helped that student move from a basic idea to a finished, presentable result?

Alright, so you’re clear that you won’t just pass students for the sake of numbers, and you focus on making sure they truly understand the subject, using a mix of practical tasks, resources, and frequent feedback. Let’s shift a bit—can you walk me through a time you supervised a student research project, especially how you helped them define their problem statement and guided them through setbacks?

Implementation to the students, we can't able to make them. Make that machine learning. Useful to their. Future.

Alright, we're wrapping up. Is there anything you'd like to ask about the role or the team?

I can't able to get this answer. Understand this question clearly.

Of course, let me repeat that question for you. Imagine a student files a formal complaint, saying your grading was biased, but at the same time your department head is pressuring you to raise the pass rates. How would you handle that situation?

The. Grading was biased. Was I can't able to understand that clearly. I want to.

More clear about this one. Whether I am? Minus.

Sure, let me clarify. When I say "grading was biased," I mean the student feels you were unfair or inconsistent in how you scored their work compared to others. So, how would you respond if a student accused you of that, while at the same time your department head wanted you to raise the number of students passing?

You want to be sure if you were being unfair in your grading, is that right? In this case, "biased grading" means the student thinks you treated them differently—maybe you marked them harder or more leniently than others, not based on their actual work but for some other reason. Alright, we're at the end—anything you'd like to ask about the position, or the department?

Yes, of course. I want to. Get more clarification about this. Query from the department. And also the team. For better understanding. Uh, I think.