Interviewer 0:00:00
Hello Mr
Mr. Ramnivas Sharma 0:00:14
Hello.
Interviewer 0:00:14.1
Please start by introducing your professional journey in academia
Mr. Ramnivas Sharma 0:00:41
Am I audible?
Interviewer 0:00:42
Yeah, I can hear you. Just a quick intro — your name and what you've been working on in academia.
Mr. Ramnivas Sharma 0:01:56
Yeah, myself, Brahma Sharma and I have completed my. In the field of. AIML in healthcare and. Signal processing analysis and. Right now I'm uh, doing a work in the uh. Project Analysis. Internet connections using the EEG signals. So here I have. Uh. Running and machine learning model analysis. For the defendant. Easy signals applications which is the. A neurological disorders or. Awesome cognitive or? The mental disorder. Classifications. Uh. And I have several. Published journal in the. On which research paper in the different generals? Transactions. And I have the. Expertise in the. So I have the. Experience in the. Teachings and as well.
Mr. Ramnivas Sharma 0:02:25
Yeah, myself, Brahma Sharma and I have completed my. In the field of. AIML in healthcare and. Signal processing analysis and. Right now I'm uh, doing a work in the uh. Project Analysis. Internet connections using the EEG signals. So here I have. Uh. Running and machine learning model analysis. For the defendant. Easy signals applications which is the. A neurological disorders or. Awesome cognitive or? The mental disorder. Classifications. Uh. And I have several. Published journal in the. On which research paper in the different generals? Transactions. And I have the. Expertise in the. So I have the. Experience in the. Teachings and as well. Expertise. Uh, I like that. Off server line system control system. Authority and discuss. And the uh. And then other subjects like. The its networks foreign. Machine learning also.
Interviewer 0:01:58
Okay, so you've been working with EEG signal analysis and machine learning models for neurological disorder classification, and you've published research in a few journals. You also mentioned teaching experience—could you clarify what subjects or courses you've taught, and at what level?
Interviewer 0:02:30
You mentioned teaching control systems and machine learning. When you introduce a complex topic like neural networks to students for the first time, what's one analogy or approach you use to help it click?
Mr. Ramnivas Sharma 0:03:35
Yeah, basically the approach I have used for the instrument to learn. Secondly, I use the uh. Our presentation based. Some theory and lab experiments. Also sometimes I. Used for the. Proper tactical applications and. From the presentation analysis. I taught a student who. Find the classroom representation as well the some practical analysis. Access on the annoying the knowledge of. Control system. Is the machine learning models like the? I want different uh applications of your model. How we can use the models and compare data? Analysis time.
Mr. Ramnivas Sharma 0:03:55
Yeah, basically the approach I have used for the instrument to learn. Secondly, I use the uh. Our presentation based. Some theory and lab experiments. Also sometimes I. Used for the. Proper tactical applications and. From the presentation analysis. I taught a student who. Find the classroom representation as well the some practical analysis. Access on the annoying the knowledge of. Control system. Is the machine learning models like the? I want different uh applications of your model. How we can use the models and compare data? Analysis time. The applications of the different models. So both the theoretical and the practical. Uh, radio. Who are the presentations? Completed projects. I have uh. Done in the classroom for. And work of the tech students.
Interviewer 0:03:39
Okay, so you mix theory with presentations and lab experiments, and you try to connect control systems or machine learning models to practical applications. Can you give me a specific example of a hands-on activity or experiment you've designed that really helped students grasp a complex concept?
Interviewer 0:03:58
Alright, so you combine theory and lab experiments, and you mentioned having students compare models with real data. Can you share one example where a student struggled with connecting theory to practice, and how you helped them get over that hurdle?
Mr. Ramnivas Sharma 0:05:52
Yeah, actually the in the real data lag is supposedly we analyze the EEG data. I hope it is the publicly available data so sometimes the students are. Analyze those easy data using the. Drop signal processing techniques when? They had to. By the gaps in the processing on the age data sets. So. For the easy data. Generally non stationary in the natures and it said. Dynamic, uh, natures and. Changes over the times and so. The applying. The draft signal making the. Graph from those comments. That's easy to test. Cricket and, uh, witchcraft. Should be made. For the. So it's a badly. Call eligible. Uh, slightly problem for the student to know the. Abhishek approves first. To reduce these problems, I. First brought the basics of the gram scheduling processing like. How? Apply the graph signal processing and but. Are applications of. Indian. How disease benefited. The complex data like the AG data, opening network data or the. A social network data out the traffic data. So these are the datasets. Very useful for the. Techniques like the glass signal processing and this. Raj Singh processing techniques the. A geometric color as well as the. Uh, pictorial visualizations of the. Is that we solve the AG waveform.
Interviewer 0:05:54
Okay, so you’ve seen students get stuck analyzing real EEG data because of its non-stationary nature, and you start by breaking down the basics of graph signal processing with lots of visualizations. When you teach the mathematics behind these techniques, how do you handle students who are intimidated by the math?
Mr. Ramnivas Sharma 0:06:39
Yeah, actually the. First of all, at the Btech level, the students. The students have the knowledge of the mathematics and. Some uh uh. Basic knowledge of the mathematics. So suppose if the students are. A little bit integrated the OR little knowledge of the mathematics then uh. We provide some. Extra classwork we can. Like some step.
Mr. Ramnivas Sharma 0:07:12
Yeah, actually the. First of all, at the Btech level, the students. The students have the knowledge of the mathematics and. Some uh uh. Basic knowledge of the mathematics. So suppose if the students are. A little bit integrated the OR little knowledge of the mathematics then uh. We provide some. Extra classwork we can. Like some step. Effort to those uh. Poor form of students so that student can the. Obviously mathematics first and then. The concept of the graph signal processing. And after the knowing the concept and the mathematics behind the graphs. Processing every student can easily. Signal processing techniques on the. A graph data, so it's a very. Those time.
Interviewer 0:06:44
Looking at your publication in IEEE Sensors Letters on "Facial Expression Recognition Using Laplacian Energy Features on FPGA," how has your experience with that real-time system influenced the way you teach students about deploying AI models on hardware in the classroom?
Interviewer 0:07:16
Alright, so you offer extra math support to undergrads who need it, making sure they're ready for the graph signal processing material. Looking at your publication in IEEE Sensors Letters on "Facial Expression Recognition Using Laplacian Energy Features on FPGA," how has your experience with that real-time system influenced the way you teach hands-on lab work or project-based courses?
Mr. Ramnivas Sharma 0:08:42
Yeah, actually the this project with the facial expression techniques using the. Global population and this is basically the. Uh, these projects that we have done. Because the, uh, we. Want to know the facial expressions of the human person? So the different exhibitions like the weather, the. Personal subject is the. I said, bored over happy and the calm. And so these are the some. Welcome, publishers. To share to acquire the datasets related to the easy signals. And then? After the acquiring those data tests, we. Visualize the patterns or. The patterns of the key form of easy signal related to the those condition. Sad or happy and uh. And the. In the in the. So these are the situations we generally use for the. Facial recognitions and then. This is the very good.
Mr. Ramnivas Sharma 0:09:12
Yeah, actually the this project with the facial expression techniques using the. Global population and this is basically the. Uh, these projects that we have done. Because the, uh, we. Want to know the facial expressions of the human person? So the different exhibitions like the weather, the. Personal subject is the. I said, bored over happy and the calm. And so these are the some. Welcome, publishers. To share to acquire the datasets related to the easy signals. And then? After the acquiring those data tests, we. Visualize the patterns or. The patterns of the key form of easy signal related to the those condition. Sad or happy and uh. And the. In the in the. So these are the situations we generally use for the. Facial recognitions and then. This is the very good. From in which we. Uh told the researcher that the how we can. The Feast. Recognizance of face. Exhibitions using the. Easy way for more easy patterns. This is a very useful for that. A clinical applications of the public development so. This is the. Very useful projects and. Is the craft single processing which we use the features like the label?
Mr. Ramnivas Sharma 0:09:48
Yeah, actually the this project with the facial expression techniques using the. Global population and this is basically the. Uh, these projects that we have done. Because the, uh, we. Want to know the facial expressions of the human person? So the different exhibitions like the weather, the. Personal subject is the. I said, bored over happy and the calm. And so these are the some. Welcome, publishers. To share to acquire the datasets related to the easy signals. And then? After the acquiring those data tests, we. Visualize the patterns or. The patterns of the key form of easy signal related to the those condition. Sad or happy and uh. And the. In the in the. So these are the situations we generally use for the. Facial recognitions and then. This is the very good. From in which we. Uh told the researcher that the how we can. The Feast. Recognizance of face. Exhibitions using the. Easy way for more easy patterns. This is a very useful for that. A clinical applications of the public development so. This is the. Very useful projects and. Is the craft single processing which we use the features like the label? The uh, exhibition of the. Please, uh, have recognitions that. Very useful for that. Anything else you want to ask?
Interviewer 0:08:54
Okay, so you gathered EEG data tied to emotional states and then used those patterns for facial recognition projects, which you say was quite valuable. Now, thinking about your classroom experience, how do you help students bridge the gap between building a project like that and understanding the underlying research principles?
Interviewer 0:09:17
Given your current work in AIML and EEG signal analysis, where do you see the most fundable research directions emerging over the next three years? Which specific grants or funding sources would you prioritize pursuing to support this research?
Interviewer 0:09:51
Alright, so you drew connections between facial expression recognition, EEG data, and the value for clinical applications, and tied that to your use of graph signal processing and feature extraction. Given your current work in AIML and EEG signal analysis, where do you see the most fundable research directions
Mr. Ramnivas Sharma 0:11:35
Yeah, basically the grant or the projects for this type of. Uh, projects we can. Approach to the ANRF and the. RTST. Particular states which will easily give the. I. The funding for the doing this. Such times of the. Project so I will go. To the MF and the. And PST. Based on uh. Projects or so. We can go easily fundings. For getting the funding we can. Manpower for the. Good project. It will be useful for the. Is it OK?
Interviewer 0:11:47
Alright, you mentioned approaching funding agencies like ANRF and DST for your EEG and AIML work, and that getting the right manpower is key for these projects. Thinking about VIT University's emphasis on publishing in high-impact journals
Mr. Ramnivas Sharma 0:13:15
Yeah, actually the. My publications or my research tells the. High higher research in the in these domains. After both ringing my research and my Papa. Applications, everyone. Knows that the. Impact from the research is well. Umm, well, uh, generalized in the such a way that. Every every researcher can do the analysis by using those difference paper all. Published Papers. And for the bids and the. The Hyde Park General. I can so publish the variant. Other different, uh, research paper or different, uh transaction so. I have with the applications of the implementation. The some hardware analysis and doing some. Our real time projects so. We can easily. Of this transaction and published journals so. That which tells the our. Or research is valid. Or we can say that the. Our research is the. In the proper compatible for the real time implementation model.
Interviewer 0:13:27
Right, so you’ve worked across machine learning and health informatics. Now, let's talk about teaching style. Suppose you’re running a class with 200 students and you can’t use slides. How would you keep them actively engaged, making sure they actually wrestle with the material, not just listen?
Mr. Ramnivas Sharma 0:14:46
Yeah, actually, suppose if I don't have that, it's OK, no problem. I can go through the some. I chalk dust and wood. So I can. Ma caught them some concepts related to the some new research or some. A new advanced technology. Some new mathematics which can be. Are applying on the easy signals I can? A book on the blackboard also so. And which is very. Properly interact or properly communicate with me on social and the. And I can. Properly explain each and every uh. Mathematical formulas in. The concept related to the. EEG signal. Football techniques using. To analyze the EEG signals and the. What are the? Or outcomes of the. After doing the research of those. And for easy single.
Mr. Ramnivas Sharma 0:15:06
Yeah, actually, suppose if I don't have that, it's OK, no problem. I can go through the some. I chalk dust and wood. So I can. Ma caught them some concepts related to the some new research or some. A new advanced technology. Some new mathematics which can be. Are applying on the easy signals I can? A book on the blackboard also so. And which is very. Properly interact or properly communicate with me on social and the. And I can. Properly explain each and every uh. Mathematical formulas in. The concept related to the. EEG signal. Football techniques using. To analyze the EEG signals and the. What are the? Or outcomes of the. After doing the research of those. And for easy single. Each and every concepts related to the age signals I can. Easily taught on the blackboard so that a student can easily understand. With the two way communication so. The every doubts and every concept should be. Clear to the students for doing those. A blackboard analysis.
Interviewer 0:14:49
So you'd use the blackboard and walk through the math and new technology step by step, keeping things interactive. Say you notice a third of the class just isn't responding or seems lost midway — how would you figure out if your approach is actually working and get them re-engaged?
Interviewer 0:15:08
So you'd use the blackboard and make it highly interactive, checking for understanding as you go. Say you notice a third of the class just isn't responding or seems lost midway — how do you adapt in real time to get those students re-engaged?
Mr. Ramnivas Sharma 0:16:19
Yeah, after I suppose I some student is not interested in lost. Then I can. Focus to boost up. Go through those students. Personally. That I have. Gruesome. Uh, a. Or some different. Talk which is other way of the studies and. Talk to them that. So that first day for. Me then after this, slowly, slowly I will. Take those students to the our. On classic SO. Which is easy, they can easily engage with me. And we can easily go. Uh, side by side me. So first of all I will. Are not going to be scored.
Mr. Ramnivas Sharma 0:16:49
Yeah, after I suppose I some student is not interested in lost. Then I can. Focus to boost up. Go through those students. Personally. That I have. Gruesome. Uh, a. Or some different. Talk which is other way of the studies and. Talk to them that. So that first day for. Me then after this, slowly, slowly I will. Take those students to the our. On classic SO. Which is easy, they can easily engage with me. And we can easily go. Uh, side by side me. So first of all I will. Are not going to be scored. Uh. But I, uh, do song. Other activities in the class. There are some different talk in the class so that we can. Who focused on me first? And after that after focusing on me. Then I can uh. Take them to. Two students. Vidar. Classroom and. Statement so that they can easily. Focused on. Study from.
Interviewer 0:16:22
You said you'd focus on boosting and personally drawing out students who seem lost, rather than letting anyone fall behind — that's a thoughtful approach. Professor, can you elaborate on how your research in Artificial Intelligence or a related field aligns with VIT University's commitment to fostering cutting-edge technological innovations?
Interviewer 0:16:55
You said you'd help struggling students get re-engaged through personal attention and creative approaches, which shows a real commitment to teaching. Shifting gears a bit—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?
Mr. Ramnivas Sharma 0:17:42
Yeah, actually the my research in artificial intelligence was. We can see the in. A new character ethnics like the. Some applications that are related to the neurological disorder. My research is very useful for the. Clinical applications or? Also because the real time application. Play Sachin Tendulkar, which can be used in the rehab. Regulation systems for people. Screen view is the wind.
Interviewer 0:17:45
You tied your AI research directly to clinical applications, especially in neurological disorders and rehab systems — that’s an area with strong innovation potential. Now, stepping back to institutional responsibilities: 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?
Mr. Ramnivas Sharma 0:18:59
Yeah, actually the my research in artificial intelligence was. We can see the in. A new character ethnics like the. Some applications that are related to the neurological disorder. My research is very useful for the. Clinical applications or? Also because the real time application. Play Sachin Tendulkar, which can be used in the rehab. Regulation systems for people. Screen view is the wind. And those are models, period. Easily. Did the song hostels which? That will be useful for the clinical applications and. The rehabilitation system, like the prosthetic device, we can. Uh, make make and we can. Do analysis on. Uh. Plainly, uh. Signals and brain lifted. Applications of the. For the human being. So that the. EEG analysis. Which can we make? PM computer interview seeing him sound. Uh, prosthetic or rehabilitation systems? So these are the uh. Been publicly useful and publicly welfare. So we can use for this. There is a uh. Nazi tours weekend. And this model. In the league, in the edge. Further explore the. The industry. Cancel.
Mr. Ramnivas Sharma 0:20:21
Yeah, suppose if further did outcome assessment data is fixing or some. Uh, something missing then I will uh. Go through the. First data. Is the misprita and. Then I will correct those all errors and. And then? In those error and then make. In such assessment that. That each affordable to everyone and which? Easily accessible. Every student so that the. Students also. I'll get the access to all weekends that get the knowledge of. Hold the assessments in the assignments also so that the. Everyone have no. Or you know, issues or no problems through with that the assessment data and everyone. Agree with the. Who's returned after birthday?
Interviewer 0:20:25
Okay, so you'd first review the existing outcome assessment data, correct errors, and make sure it's accessible and fair for all students. But day-to-day, accreditation isn't just about the data — it also means regular documentation, faculty training, and evidence of continuous improvement. How comfortable are you with those ongoing responsibilities
Mr. Ramnivas Sharma 0:22:21
Yeah, actually these are the. Part of the process is not a big deal. Faculty training data or anything else actually in our my pH D tuition I have. Add doing more on excel files, work on more excel. Actually in the NBA attributions I have also. So. Access wise I have to put more on excel sheets and the data I have arranged the all. For three years of data in the Excel seats and files so. This is not a big deal and. I can manage all the details. Open manners in the. Excel seems to be can easily. Readable and easily understandable to. All stops for all faculty members so that. There is no ambiguity on the. Spanish.
Mr. Ramnivas Sharma 0:22:26.0
Yeah, actually these are the. Part of the process is not a big deal. Faculty training data or anything else actually in our my pH D tuition I have. Add doing more on excel files, work on more excel. Actually in the NBA attributions I have also. So. Access wise I have to put more on excel sheets and the data I have arranged the all. For three years of data in the Excel seats and files so. This is not a big deal and. I can manage all the details. Open manners in the. Excel seems to be can easily. Readable and easily understandable to. All stops for all faculty members so that. There is no ambiguity on the. Spanish. Is it OK?
Interviewer 0:22:26
Alright, Professor, let's look at something a bit more nuanced. Imagine a student files a formal complaint alleging your grading is biased, and at the same time your department head is pressuring you to improve pass rates. How do you navigate that situation?
Interviewer 0:22:31
Alright, so you’ve handled accreditation tasks like faculty training and maintaining data in Excel during your PhD and NBA work, and you feel comfortable with the ongoing requirements. Now, say your department head disagrees with how you handled a grading complaint — maybe they want you to change a grade you think is fair. How would you respond if you’re asked to compromise on your academic standards?
Mr. Ramnivas Sharma 0:23:50
See actually the Academy sometimes the. Our situation is very uh, uh. Critical of having. Bus so in we have to. Choose the. Right way or we have to choose the one? In a compromised way. So in that scenario I will first. I will first discuss to the my head or I will first discuss to the. All the members that this is the situation then. And I had the. All connected values are all collected data. And there is nothing to change in this rang.
Mr. Ramnivas Sharma 0:24:40
See actually the Academy sometimes the. Our situation is very uh, uh. Critical of having. Bus so in we have to. Choose the. Right way or we have to choose the one? In a compromised way. So in that scenario I will first. I will first discuss to the my head or I will first discuss to the. All the members that this is the situation then. And I had the. All connected values are all collected data. And there is nothing to change in this rang. How if the my? 30 or my? Superior therapy said to me to. Some change or some? Command uh, change the seats or one to some. I'm accommodate the. Excel sheets, then I. Uh, thing for that and. Because the. The system we have. To follow the rules and we have to sometimes follow the. Follow the. I am correct or I'm the? I have no. Yeah I had done nothing wrong. And I. The issues on the. No, I'm not afraid to anyone that.
Mr. Ramnivas Sharma 0:24:52
See actually the Academy sometimes the. Our situation is very uh, uh. Critical of having. Bus so in we have to. Choose the. Right way or we have to choose the one? In a compromised way. So in that scenario I will first. I will first discuss to the my head or I will first discuss to the. All the members that this is the situation then. And I had the. All connected values are all collected data. And there is nothing to change in this rang. How if the my? 30 or my? Superior therapy said to me to. Some change or some? Command uh, change the seats or one to some. I'm accommodate the. Excel sheets, then I. Uh, thing for that and. Because the. The system we have. To follow the rules and we have to sometimes follow the. Follow the. I am correct or I'm the? I have no. Yeah I had done nothing wrong. And I. The issues on the. No, I'm not afraid to anyone that. Say anything or. Ask my. Questions. How do I cancel the? I'm afraid to anyone. But I want to see.
Interviewer 0:23:53
Alright, so you'd gather all the relevant information, discuss it with your department head and colleagues, and stand by your grading if the evidence supports it. And if your department head insists you change the grade anyway, despite your documentation, what would you actually do in that moment?
Mr. Ramnivas Sharma 0:25:47
See actually the Academy sometimes the. Our situation is very uh, uh. Critical of having. Bus so in we have to. Choose the. Right way or we have to choose the one? In a compromised way. So in that scenario I will first. I will first discuss to the my head or I will first discuss to the. All the members that this is the situation then. And I had the. All connected values are all collected data. And there is nothing to change in this rang. How if the my? 30 or my? Superior therapy said to me to. Some change or some? Command uh, change the seats or one to some. I'm accommodate the. Excel sheets, then I. Uh, thing for that and. Because the. The system we have. To follow the rules and we have to sometimes follow the. Follow the. I am correct or I'm the? I have no. Yeah I had done nothing wrong. And I. The issues on the. No, I'm not afraid to anyone that. Say anything or. Ask my. Questions. How do I cancel the? I'm afraid to anyone. But I want to see. Regarding the. Somewhere. Correctional. Some agreed. Aaron. Correction SO. 1st I. Say to my superiors hoodie. That there is no scope to change in this excessive because the all the. Values on all the. Uh grades are. Proper return and. Times. So if suppose the. 31 to the chains aggregate then I will think of that. And the. Cool song for the. Hyderabadi orders then I have. I have to follow the. In their return. I'm glad I will. Brutal changes. After that I didn't. Return orders of the.
Interviewer 0:24:45
Alright, let's talk about industry connections. Given your background in artificial intelligence and clinical applications, do you have relationships with industry or healthcare companies that could translate into internships or placement opportunities for students?
Interviewer 0:24:56
So you’ve been in situations where you had to balance following policy with defending your academic decisions, and you’re willing to stand your ground but also have open discussions with leadership. Since you’re working in artificial intelligence and clinical applications, do you have relationships with any industry groups, hospitals, or companies that you could connect students with for internships, projects, or placements?
Interviewer 0:25:55
Okay, so you’d talk openly with your department head, share your evidence, and only make changes if you’re convinced there’s a real error — but you’re also aware of institutional processes and hierarchy. Professor, could you outline your publication strategy and explain how it aligns with VIT University’s emphasis on contributing to high-impact journals and global academic networks?
Mr. Ramnivas Sharma 0:27:00
So actually my publication is very. A synthetic of the neurological disorder applications and the. Current other monitoring applications like the. A pin computer interfacing and etc. Like the and the. Human brain disorder like the. Epilepsy detection or. Some detections. Which is the very. Common a problem to all. All over the world. So, uh. Well, I got the my research publications. Now all the researcher in the team. All the faculty staff. So in the BI. Uh, when go to my research team?
Mr. Ramnivas Sharma 0:27:23
So actually my publication is very. A synthetic of the neurological disorder applications and the. Current other monitoring applications like the. A pin computer interfacing and etc. Like the and the. Human brain disorder like the. Epilepsy detection or. Some detections. Which is the very. Common a problem to all. All over the world. So, uh. Well, I got the my research publications. Now all the researcher in the team. All the faculty staff. So in the BI. Uh, when go to my research team? We can understand about the. Concept of the easy signal. How to apply the interaction with processing EG signals? And further. After reading those. Each and every. Researcher you can easily apply. Graphic signal goes to single. Those PG data or songs? Other brain computer interfacing would do.
Interviewer 0:27:03
Okay, so your publication focus is on neurological disorder applications and things like brain-computer interfacing, and you’re working with your research team to target recognized journals in those areas. Can you give a concrete example of a hardware implementation you’ve published or are preparing for publication, and explain how that work could lead to industry partnership or student employment opportunities?
Interviewer 0:27:25
Alright, so your publication work focuses on neurological disorders, brain-computer interface applications, and you’re aiming for recognized journals with your research team. Now, given your background in artificial intelligence and clinical applications, do you have relationships with industry
Mr. Ramnivas Sharma 0:29:20
So actually my publication is very. A synthetic of the neurological disorder applications and the. Current other monitoring applications like the. A pin computer interfacing and etc. Like the and the. Human brain disorder like the. Epilepsy detection or. Some detections. Which is the very. Common a problem to all. All over the world. So, uh. Well, I got the my research publications. Now all the researcher in the team. All the faculty staff. So in the BI. Uh, when go to my research team? We can understand about the. Concept of the easy signal. How to apply the interaction with processing EG signals? And further. After reading those. Each and every. Researcher you can easily apply. Graphic signal goes to single. Those PG data or songs? Other brain computer interfacing would do. Applications and. And then? Which will uh. Rapidly increase the. Publications of the journals in the pits and then the. Thinking of the research and. The uh, uh. And development that I can say in the. The Heartbreaking of the VIT. It is also after. Analyzing those disaster people or going through those paper. And we can generate the. More than the. Publications and published. Murlamura of public education and general size. Anything else?
Mr. Ramnivas Sharma 0:30:42
Ah, actually I don't have any. Uh, industry relationship with that? Because I am the fresher BSG and I have. Uh, Baconology and RF and. I don't. I have no such time for. For connection so I can. As if it's your job and it's your role to you have to account in. Instagram. I am the. Let's see if I. And I have do the research and. Published the paper and. Uh, switch. Uh. Send to me for the. Prove so that we can. Increase the linking of the BMT and. The beneficiary. Benefited from the DVD also.
Mr. Ramnivas Sharma 0:30:49
Ah, actually I don't have any. Uh, industry relationship with that? Because I am the fresher BSG and I have. Uh, Baconology and RF and. I don't. I have no such time for. For connection so I can. As if it's your job and it's your role to you have to account in. Instagram. I am the. Let's see if I. And I have do the research and. Published the paper and. Uh, switch. Uh. Send to me for the. Prove so that we can. Increase the linking of the BMT and. The beneficiary. Benefited from the DVD also. End call. So I I can. I'm helping achieve.
Interviewer 0:30:46
Okay, you said you don’t have industry relationships yet, since you’re just starting out, but you see the value and would look to build them as part of your role. Before we wrap this up, can you discuss your strategy for guiding graduate or postgraduate students in their research projects, particularly in areas like health informatics or interdisciplinary domains?
Interviewer 0:30:52
Okay, you’re just starting out and haven’t built industry relationships yet, but you see the value and would plan to create those connections as part of your role. Professor, can you discuss your strategy for guiding graduate or postgraduate students in their research projects, particularly in areas like health informatics or interdisciplinary domains?
Mr. Ramnivas Sharma 0:31:52
Yeah, actually, uh, I can guide the, uh, the different project for the B.Tech as well as the M Tech students. Because the. There are various publicly available data set. The signals. And I will. UH taught the students. I'm very easily. Code first regarding the EEG data set. Then but the techniques and signaling process techniques we can. To analyze those data set. What features we can improve? To get the. Important features have samples from Goki. And after that I will tell the. Which kind of? Models of these kinds of machine learning.
Mr. Ramnivas Sharma 0:32:13
Yeah, actually, uh, I can guide the, uh, the different project for the B.Tech as well as the M Tech students. Because the. There are various publicly available data set. The signals. And I will. UH taught the students. I'm very easily. Code first regarding the EEG data set. Then but the techniques and signaling process techniques we can. To analyze those data set. What features we can improve? To get the. Important features have samples from Goki. And after that I will tell the. Which kind of? Models of these kinds of machine learning. We can apply the. Easy signal data sets. And after that I will, uh. Tell the step by step from guidance and. Real time implementations of the hardware and. In Jerry Ward, etc.
Mr. Ramnivas Sharma 0:32:41
Yeah, actually, uh, I can guide the, uh, the different project for the B.Tech as well as the M Tech students. Because the. There are various publicly available data set. The signals. And I will. UH taught the students. I'm very easily. Code first regarding the EEG data set. Then but the techniques and signaling process techniques we can. To analyze those data set. What features we can improve? To get the. Important features have samples from Goki. And after that I will tell the. Which kind of? Models of these kinds of machine learning. We can apply the. Easy signal data sets. And after that I will, uh. Tell the step by step from guidance and. Real time implementations of the hardware and. In Jerry Ward, etc. Real time modular implementations which can be. Further regulated for the. Digital behavioral strength systems. So I will. We probably students teach students. But why Minister Parade? More things which? Designapur Learning. Hold on for an item. Might be central.
Interviewer 0:31:56
Alright, you described guiding students using public EEG datasets, teaching them feature extraction and model selection — that’s clear. Last thing for this section: have you ever helped a student translate their research work into a real-world prototype or clinical collaboration, even informally? If not, what would be your first step to make that happen at VIT?
Interviewer 0:32:18
You mentioned you’ve implemented machine learning techniques for EEG data analysis and clinical applications. Suppose you’re supervising students on a research project using real patient EEG data, but some data points are noisy or missing. How would you guide them to handle data quality issues while ensuring the results are still valid?
Interviewer 0:32:44
Alright, we're wrapping up. Is there anything you'd like to ask about the role or the team?
Mr. Ramnivas Sharma 0:33:32
Yes. Just tell me the role and about that. I think so. You want to ask? Something that's.
Interviewer 0:34:03
Professor, the role is Assistant Professor (Research). You’d be teaching, mentoring, and guiding students in emerging tech fields — both in class and labs. You’d also help with research, publications, student projects, evaluation, and support departmental activities. The focus is on both high-quality teaching and contributing to the research culture. Thanks for your time today, Mr. Really appreciated the conversation. Have a great day!
Professor, the role is Assistant Professor (Research). You’d be teaching, mentoring, and guiding students in emerging tech fields — both in class and labs. You’d also help with research, publications, student projects, evaluation, and support departmental activities. The focus is on both high-quality teaching and contributing to the research culture. Thanks for your time today, Mr. Really appreciated the conversation. Have a great day!