"Connecting Generations" with Ruzena Bajcsy and Eshika Saxena | Video Playback


2020 Collegiate Award Winner Eshika Saxena interviews 2020 NCWIT Pioneer Award Winner Ruzena Bajcsy in an inspiring conversation, connecting generations. They both discuss their unique pathways, interests, and accomplishments.



TERRY: Welcome to NCWIT Conversations for Change, an online thought leadership series. My name is Terry Hogan, and I'm the President and CEO at NCWIT. It is my pleasure to welcome you to this series which features speakers with a diverse range of opinions and provocative ideals and world views.

We would not be here today without the support of our sponsors and I want to thank the viewing audience in advance for our patience should we experience any bandwidth or any other technical issues.

For today conversation, one of our esteemed 2020 Collegiate Award Winner, Eshika Saxena will interview NCWIT award winner Dr. Ruzena Bajcy. In an inspiring conversation connections generations.

They will discuss their unique pathways interest interests implements and the attendees will have an opportunity to answer questions.

We will answer as many questions as possible. Please feel free to pose questions as you think them throughout the interview.

Dr. Bajcy, I was born in 1933 and born in Czechoslovokia, and shortly that after was invited to study the new discipline of computer science at Stanford University. For the next 30 years she worked at the University of Pennsylvania where she was Professor and Chair of Computer Science and Engineering as well as founder and Director of the University of Pennsylvania general robotics and active sensory perception laboratory. After leaving Penn she headed to the national — engineering director, and in 2002 she arrived at UC Berkeley where she is currently a Professor and NEC chair of computer science. College of engineering. Her research continues to focus on modeling people through robotic technology. Welcome, Dr. Bajcy.

Eshika Saxena is a freshman at Harvard University, she developed an early interest in technology through U.S. Robotics and continued in summer through Washington's ubiquitous computing lab. Stanford outreach summer program, apple engineering, technology camp, and research science institute. She gained hands-on experience and a deep foundation in artificial intelligence in machine learning. She is passionate about solving real-world problems, especially in the healthcare field.

With the power of technology. She has developed several innovative solutions that have been recognized nationally and internationally with awards from a wide range of organizations.

Welcome, Eshika.

We’re honored to have these two amazing women with us today. And we will begin with opening remarks from Dr. Ruzena Bajcy.

RUZENA: Thank you very much, first of all I would like to the organization, and the pioneering award.

I am deeply honored to be among the trail-blazer women who accomplishes and the IT technology and brought us to what we have today. Without that we could not enjoy all the Zoom technology we have been enjoying.

I am 87 and have been in the field more than 60 years. But let me just say that who I am is not accidental, and it reflects the history that I lived so I was born in 1933 when Hitler was elected. And I should remember that he was elected. My family was Jewish, although not really Jews. So — nevertheless, the Hitler and anti-Semitism affected my family although I, as a child was not very much aware of all the horrors that were committed between 1933 and 1945. My parents were intellectuals, and my father was a civil engineer, and my mother was a medical doctor. So — I was kind of protected. But grew up in this intellectual family, which cherished knowledge and learning and curiosity-driven research. And so forth.

Regrettably, in 1944 my parents were killed by the Nazis, and I served by the kindness of some people in Slovakia in an orphanage of Red Cross.

So that's the impact of the early childhood I had. Between 45-67, as many of you may know, between — Czechoslovokia after 45 got a liberal democracy as most of the western and central European countries. Unfortunately, in ’68, we lost that and thanks to Stalin and the soviet invasion, so to speak, imposed upon us, the communist regime, and the effect of communist regime was that as a scientist, we were very much isolated from the rest of the world. And — I remember how frustrated I was in the engineering school where I studied in the Slovak Technical University we have no access to the western literature. We could only read the Russian papers, and so — the isolation was really very frustrating.

Now, luckily, in 1967, I got the opportunity — there was this — spring from 1968 — that allowed me to leave and go to Stanford at the invitation of Professor John Ricarti who was leaving one of the three artificial intelligence labs at that time. Sponsored by the American — it was the advanced — the different department research organization and sponsored three labs, one was at Stanford, and one was at MIT, and one was — so — when I — now the reason they allowed me to come was that I left two children as a hostage. And that was a really, very painful decision on my part. And then in 1968, my original intent was of course to go back to my family but 1968 when the Russians — in Czechoslovokia, I decided not to return, and I was separated from my children for about 20 years. So that of course big prize personally. Nevertheless — I kind of burned or — buried myself in science and John McCarti was very kind and inspiring adviser. And he was a mathematician. Logician, actually. And from him I learned quite a bit about things that I didn't have an opportunity to learn in Czechoslovokia, nevertheless, he directed me being an elected colleague — Computer Vision Studies, and I worked on basically computer vision for my PhD thesis from Stanford. And then in 1972, I for various reasons I accepted a job as an Assistant Professor at the University of Pennsylvania in Philadelphia. Which was actually the burst of the first computer in the — in the whole world actually. It was the first digital computer in the world. And then — there were some very prominent colleagues who in particular in natural language processing like — and was a graduate of University of Pennsylvania.

Anyway, I've been in computer vision I realized very quickly that if you observe biological system vision it's only one of the many sensors, so I 1979, I started investigating for — sensors, and then that led me to robotics, an establishment of the — lab which I am very proud today is — nourishing, and of course completely new leadership of Professor Omar — the dean of the engineering school, and George P. — and the other junior faculty who are really broadly — lab to now hitting heights.

As mentioned in my bio, I went to the National Science Foundation because I am really firm believer that besides doing research our responsibility is to serve the community. And the reason I went with NSF to really serve the computer science community and after that I came to work and I'm still here. So that's my story.

ESHIKA: Thank you so much that's really an inspiring story, and I also want to say thank you to NCWIT for giving me this opportunity to speak with you today. And one of the things you mentioned that I definitely can relate to was the importance of your mentor, Dr. John Mcfarthi, and I definitely found in my experience my research mentors were not only like guiding me with my research but also just general role models, and how a lot of importance in my life. And it seems like it's the same for you. So I was wondering if you could talk more about maybe the importance of having mentors, and just elaborate on the role of mentors in your life.

RUZENA: Well, this is very closed problem to my heart because indeed, you know — as much as many of you might think that science is solitary, but — actually it certainly is engineering science is not solitary. It really depends on very much the community we are surrounded in. So initially of course you have your Professors and in fact even in Czechoslovokia, I was very lucky to have some very prominent people inspired me to really not to just learn you know — especially but dig deeper in mathematics and later on engineering computer science. McCarti had a very deep influence on me because he showed me a much broader horizon of what at least in those days artificial intelligence was in his mind. And he emphasized basically that the AI from his point of view was all about given that you have all this experiences in the world, and how you — is what you retain in your head. And this was what's called a presentation, and it's not — it's a reflection of — it's not completely the reality but it's a reflection of the reality. And that remained my guide in my work. But then later on as I said, when I came to Penn, you know I interacted quite a bit with — who looked at the world from the linguistics point of view. And then, of course, I also learned I was very fortunate from my students and my colleagues so — I learned quite a bit of mechanics from BJ Kumar, who joins it grass lab later on, and max mince, and so forth and the students, I have been very fortunate. I had some brilliant students who really knew more than I did, which is okay. That's what it's a give and take kind of — and today I have to tell you, I am very fortunate, I have colleagues from — I still learn you know here at Berkeley, Claire Tomlin, and — and a few others — but also my students. They teach me. Because there is so much going on in this area today that for one individual is not possible to really comprehend everything. And so I'm very fortunate. I have currently I have five PhD students who are all women as it happens. By accident. And they all teach me about all kinds of things about how robots can interact, control, measure, model, you know you name it we look at every possible aspect. And my job is sort of just kind of challenge them and help them to grow up to bigger and aim to a gigger horizon.

ESHIKA: Yeah and building off that once you get to learn from your mentors, even more rewarding when you get to mentor others. So could you talk about maybe some advice that you have for especially women who are starting out in the field or just beginning their careers?

RUZENA: Okay. So I have a few rules in my book. So to speak. First of all, when you are young, learn as much mathematics and physics as you can. It is never enough of that knowledge. Here I am at age 87, and I'm still learning more things about probability, and information technology, and you name it. Temporal logic didn't exist when I was young. So — you really should learn as much as you can. Then later on, as you are looking at problems, ask yourself you know — you have to focus. Because if you go too broad, you don't — you'll pick here and there, and so forth. So you have to really focus, especially if you are in the PhD program. You have to focus. So you have to find some problem where you can ask questions that other people perhaps didn't ask. Okay? So that's number one.

Number two, you have to be realistic. What with the current technology can you verify? Because in engineering science it's not just writing an equations, but it's also build where you can validate your results. So — engineering science is in some ways more like a physics. It's less than mathematics. In mathematics if you prove a theory and you are done. In engineering, it's not exactly the same. So it has to be something that you can with the current devices and the current technology, and the current know-how, you can address.

ESHIKA: And yeah I think that's really great advice. I was wondering I think for me in my experience a lot of times for my research I was inspired by my personal experience. I worked on a project for detecting food quality analysis because I had an experience with food poisoning, and I wanted to do something to solve it. So — maybe could you — talk about how you get inspiration for your research, and what inspires you to start working on a project?

RUZENA: Well, being an engineer at heart, I really always looked at you know how can technology help people? That was my model with robots, and in fact my research in the medical area as well was how can we make things not just empirical, but predictable. Science has to be predictable, so it should be able to predict things. And also, science also has to define constraints. No one theorem, or one model will solve everything. This universality, you know has limits. Especially in engineering science. So you have to define you know — where you can do some good. And because everything that we do has some limits. And one of the things that worries me about in the AI that some people over-enthusiastically promote things that it can solve. You know general artificial intelligence. Well — some of the wiser people in this field say well we don't understand what general artificial intelligence. So that's good, because you really want to say, we have a solution in the constraint domain. Or in a — under constraint conditions. Okay? We can do that. It seems to me that that's very important to be truthful to what your signs or theory can explain. Can predict. And not be naive that it can do everything. There is no such thing that it can do everything.

ESHIKA: Yeah. I definitely agree with that. And — I think especially within AI as a field that's kind of blown up recently. That's definitely a challenge that we have to address. And I think one of the things that I kind of find concerning with AI is that there is especially with AI in the medical field a lot of ethical issues to consider. And — in fact I'm actually taking a class right now on the ethics of AI, and more specifically like — as an AI researcher what do you think the researcher should do to kind of ensure that we're developing responsible AI, or fair AI.

RUZENA: You are bringing out a problem close to my heart. To my mind, AI is in a similar position as the physicist. The nuclear physicists were in 1945, or 1944, 1946, and the difference, of course, is that at that time the nuclear reaction and the atomic bomb and all that goes with it was very localized. You could have — my husband was a nuclear physicists later on, and through him I really understood how the physics community was coming together to really protect the disarmament, and all that with it AI is at a similar stage in my opinion as the physicist were in ’45, except that it's a much more distributed system. So, in some ways, it's much harder to contain it to keep it so that it obeys some of the ethical values. People see it as a way of getting rich. Or — making a lot of money. That is dangerous. We have to understand what the implication — what the societal implications of all this AI technologies and I have colleagues who are looking at — in particular, and I think we collectively, those of us that have been working in this technology, we owe it to the society to make sure that A, we don't promise more than what we can seriously deliver, and B, whatever we do, that we take in account what the societal effects will be.

Now I am not saying that we have to stop doing what we are doing, no. I mean there are some profound possibilities to help people. To — you know, and you know help the elderly, help the disabled. Help the — even organized society, I mean — provide digital services, I mean it's a fantastic technology that can help people. But — you really have to understand the limits and you want to make sure that while you are servicing, you are not hurting. And society.

ESHIKA: Yeah. That's a great point. And — I think just by the challenges like I'm still very optimistic about the potential of AI. And especially now where we're living in a time where obviously there is so much going on. There is a lot of challenges that people are facing, have you worked on research that is kind of relevant to this Coronavirus epidemic? Or something — or just maybe something that you — or an example of how kind of AI can really be used to address these kinds of challenges?

RUZENA: Unfortunately at my old age it's much harder to get into these new areas. So no, and I think this — this Coronavirus is much more in the realm of the biologists and immunologists — so but technology can do — what technology can do actually is do some monitoring. Monitoring the databases and how people move. How you — and of course the privacy issues are tremendous here. So — you can monitor people but then also how do you keep the privacy? And I don't work in those things. However, I have been recently starting to think about it, since we are teaching remotely. You know through the zoom and all that. In the robotics this is a really problematic way of teaching, because in the robotics you really want to also convey some practical physical understanding what our force is, friction — talks and you know mechanical properties, interacting with the real world. And that is missing from all of this simulations. And just doing things — so I have been thinking and looking at somehow how can we design some small inexpensive devices where the students would have some physical experience — if you can take the digital data and display it as a physical surface — or physical property that is reflecting the digital data — then the students can get some better education about the physics so I'm kind of concerned about this virtual education.

ESHIKA: Yeah. And I think that's a great example of how not just the pandemic itself but there is so many other effects in all the fields that there is yeah we just have to adapt in so many ways. And what are your thoughts on — I think especially now the idea of interdisciplinary research like especially for me I've worked on AI in meds, and you have obviously had a lot of experience with different combining different disciplines. So could you just maybe talk about your experience with interdisciplinary research and where you think the potential lies in like combining different disciplines.

RUZENA: Well, that's a good question and it's a difficult question. Because — you don't want to be jack of all trades and master of none. You see — so you have to sort of decide you know where you really want to make an impact, but you also if you want to work and of course the discipline state are not anymore so single-minded. The days are more and more and especially in engineering there is more interdisciplinary problems that you really need to solve. So — what you really need to realize if you want to work in this boundary of two disciplines, let's say. That you have to make a time investment to learn about the second discipline as well. Maybe not in such a depth as your first discipline, but you have to know enough to really be relevant. So — when I worked on the anatomy atlas, first of all I had to learn quite a bit about how these what are the principles of these scanners, you know — the x-ray, has a different physics principle as opposed to MRI, or positron emission scan. Tomography, and — that of course determines what kind of data can you get from these imaging devices. Okay, so that's one thing I had to learn in order to really design something that reflecting the reality. The second thing I had to learn quite a bit about anatomy of the brain. Maybe not as much as a classical neuroscientist would know, but I had to know that the gross anatomy enough in order to really make it useful design an anatomy atlas that I made.

ESHIKA: That's super cool. And you mentioned that initially when you came to study at Stanford computer science was very new field. And — so I would love to know in your experience how you have seen the field kind of evolving and change over time.

RUZENA: Well, I can tell you when I was at Stanford, all the people were so enthusiastic. We can change the world. And John was — John McCarti was very supportive of that, and then — but we were terribly naive. You may have heard Professor Minsky said well computer will be solved in two months as a summer project. And here we are, we are still — some people not me anymore, but some people are still it's a big research project. So — naivety was tremendous. But the enthusiasm was matched with the naivety.

Now, how things developed was interesting, and I don't know whether our — get enough credit because you see — a lot of the new technology, especially in AI, the neuro-networks, were able to test things and develop a lot of the software because our hardware colleagues provided us with cheap computing, cheap memory and large scale. Now the other thing that was not there when I was there was the Internet. And the whole connectivity that people had ideas on that. I mean — and would serve this and the people — the fathers of the Internet they were dreaming about this, and I remember Jon Mccarty was also one of those who really had this vision of coming — of additional communication. But — (digital communication) but it took some time to develop this whole network, and it's interesting to note that it was a combination of hardware, but also the fiber networks so that you can make it. Because when I was at Stanford, the communicators were telephone lines, so it was slow and so forth, and so on. So you couldn't trans mitt a large amount of data and stuff like that but then, it was interesting that the software for making it easy to communicate was developed at in Geneva — where the high-energy physicist who had this need to collaborate were pushing for easy way of interfaces, and communication and so hence the software —

ESHIKA: And more specifically, how has your experience been as a woman personally in computer science and engineering — pursuing computer science and engineering?

RUZENA: Well I don't know what to tell you about it: I was the first woman faculty at Penn when I came there to the major league school. Actually, at that time I was the first but I was not really the first, because grace hopper, you heard probably about her, she used to come to Penn and worked on INIAC, she was in the — I think she was in the Navy, and she was a mathematician. So — and interesting note to put here, again here is that in those days, Mauchly and Eckert, these were the engineers that built the electronics — and but the programming was done by cables. You had to connect different parts of the INIAC in order to run something. And it was all built from cubes. Vacuum cubes. The computer flush but — my point is that the women were the programmers. And men were the engineers. You can't build anything if you are a woman. Now, after INIAC started the company, and that didn't go very far, and then IBM took over, the whole computing industry was IBM took over. And at that point, the first — the first it was assembly language programming, and then Fortran was developed. And it turns out that the IBM establishment realized that the programming was as important as the hardware. And they started to pay the programmers better salaries. The moment that happened, men took over. The programming. You know — and that is a fascinating story that when the salaries become higher, then men moved in. And it took some time before some equilibrium took place between men and women in this software, hardware sort of environment. So in 1972 — this is let's say 20 years later, right? Something like that — I was the first faculty and you know — I mean many of my older colleagues just didn't know what to make out of it because they found that I should at home taking care of some children and husband, and so forth. But there was this idea of that generation that even if the woman had higher education, even if she had a bachelor's degree or something, the reason she should use her knowledge is educate the children and be a proper conversant for her husband friends when they came to for their part so — the perception of the role of a woman, and this is another interesting point, that after the second world war, in 1945, because women were engaged in the war machine, it was accepted that woman could hold jobs like men. Okay? And that it was okay if a woman was working. However, after the war, when the economy picked up, this idea of women should be at home and husband should be the only provider has taken over and hasn't changed until I think until the late 70s or so.

ESHIKA: And yeah, I think we can see that well — some progress has been made there still a long ways to go. And what do you think are things we can do to well one encourage more women and minorities to pursue, especially technical fields, and also what can we do to ensure that even if they're interested they remain in these fields?

RUZENA: Well, how to encourage them you know that has to my mind — it has to start at home. The reason you are very wise because of your parents. The same in my family. My daughter, my granddaughter, they are all in the field. So — it really — I always felt that you really want to educate the mothers or the parents about this issue, rather — later on it can be done but it's much harder and this is both for minorities as for women. Treat the girls the same way as you treat to boys. Teach them, send them to schools, you know — give them toys that are not just dolls, but they are little robotic kids or whatever else it is. And so that's number one. So it has to start at home. Number two in terms of this how to deal with when you grow up, so to speak — and you have to juggle the home and the job, I think in developed countries it's a little bit easier because we have all kinds of services. And machines. And that's where technology can really help. You know — so that a lot of the work that used to be only women, should be doing is cooking, cleaning, you know, blah, blah, blah, it can be an automated, and B it can be shared. I think from my point of view I see the — your generation definitely at least in a certain class we have to be careful, it's not everywhere, but in a certain class it's taken for granted that the wife will work as much as the husband. And pursue her career.

TERRY: Thank you so much Dr. Bajcry and Eshika, we do have questions from the audience, I would like to ask a few of those if you don't mind.

RUZENA: No, not at all. Congratulations to the success of the patterns, 131 my goodness. Go ahead.

TERRY: One of the things you mentioned in one of your answers was how you were the first female faculty member during that time. At Penn, I wondered if you had any opinions on how universities could be recruiting and retaining more female faculty members in STEM as they are still underrepresented today?

RUZENA: Well, I think things are getting better. So — I am very optimistic how the universities are very conscious of this at least the universities that I am familiar with, they are trying to hire women and women are — so I'm not sure what else we can do. Maybe — you know one thing that I perceive as a possible problem is that universities should come up with some real plan for the Assistant Professors who plan to have children. Having leave and childcare, and stuff like that, that — because I know that some of my students I mentioned to you I have five female students they are — one is married, and one is thinking to have a family, and there is always this question. In what I hear is should I have a child as a graduate student? Should I have a child before I become an Assistant Professor, or should I have a child after I get tenure. These are the questions that women are asking. And — I know cases in both ways, and I don't know what the right answer is, honestly. Because if you really wait until you get tenure, sometimes you are too old maybe.

TERRY: I would certainly agree with you and hope that universities would be supportive of people who have children no matter when they —

RUZENA: Right, I think so. And I think that the top universities want to compete for the best minds. Nevermind women, and men. Has — are cognizant of this.

TERRY: Yes, absolutely. We have a question here about AI, and bias. The question is science has been used to prove or reinforce racial and gender bias. How do you think AI scientists and engineers need to address this issue at the point of creation?

RUZENA: Point of creation of what?

TERRY: The point of creation of the AI technologies.

RUZENA: Okay. Wow. Hmm. Is there a bias in the technology? Is that the implication?

TERRY: I think the person asking the question is saying that people are using science to sort of prove biases. I guess.

RUZENA: Well, I am not aware of the gender bias in this technology. I can tell you that it used to be I should say — maybe I would say 20-25, 30 years ago, that women were somehow in their mind discouraged to go into robotics. I used to call it the screwdriver phenomenon. That screwdriver is dirty and it's not for women. And so if you go to computer science usually a database or theory or programming language but not robotics because you have to deal with real machines. Okay? And flush but I frankly this was definitely maybe 25-30 years ago, when I was at Penn I will tell you I had about 50 PhD or something like that, and out of those I had only two female students. Because there was this prejudice, and in fact one of them I was talking to her, and I said why don't you stay for PhD, and she said the reason I work so hard I don't want to do that. So that was another thing that they saw that the tenure — that was too difficult. I think that is not anymore the case. I honestly think that more and more women are going this route. And — I certainly as I said I didn't have time — difficulty to qualify- get a female — student, in fact I was not really even actively recruiting man or women, it just happened that a woman ended up in my lab.

TERRY: Thank you. We have a question from Eshika from the audience as well. The question is about the future and supporting the next generation. How can we help to support the next generation of computing innovators, and what technologies do you knowing will change our future?

ESHIKA: I definitely agree with Dr. Bajcsy about it starting in the home. And especially trying to develop or foster an environment where we are at least exposing kids to technology and its applications early on. And then beyond that I think especially in schools trying to incorporate more computer science education, more technical education, which ties to the idea that I with definitely think that technology is like artificial intelligence is changing our society as we speak. And it's definitely really, really important for kids to be able to learn about these things as early as possible. And — I think like — in my experience, exposing children to these technologies especially in kind of fun ways like for me it was Lego robotics, as one way to kind of keep them engaged, and especially interested at first. And so if we can do that, we can definitely help support the next generation.

TERRY: That's great, thank you. This next question is a question for both of you. What is the most important thing mentors and advocates have done for you in your lives?

RUZENA: Okay, I can answer. Encouragement and support. You know — even if you fail, I remember at Stanford my first qualifying exam I failed, and I was — I was devastated because I never failed an exam before, and my Professor said don't worry. Failure is not the end of the world. That's what I would say to everybody who listens.

TERRY: That's wonderful, thank you. How about you, Eshika.

ESHIKA: I agree, especially in research, I found that often the most successful research comes from failing multiple times and trying and trying again. And my mentor, especially my research mentors have really shown me that it's important to not give up. And especially try once even if you fail trying to approach a problem from different perspectives, or from trying a new approach can eventually kind of become fruitful.

TERRY: That's great, thank you. And so what could we all be doing to be better mentors and advocates for younger people today?

RUZENA: Try to tell them — well — learning should be fun. And you know — even in these days when you are locked in, there is Google. You have the world library at your fingertips. It didn't exist when I was young. So — learning can — you can whatever — your imagination desire. And encourage learning. And then — if you believe — if you learn things, skills, it will give you a better standard of living because you will have more skills to offer the society for which you can be re-numerated. So I mean if you come from a poor family, or a poor circumstances and you want to improve yourself, learning is the way to go.

TERRY: Thank you. Eshika, did you want to comment on that question?

ESHIKA: Yeah, I think definitely the most important thing a mentor can do is encourage others and also just providing opportunities, especially for those who may not have access to them. And in that way we can kind of help people grow just as we were able to grow because of our mentors.

TERRY: Wonderful. Thank you, well thank you Dr. Bajcsy, and Eshika. We all have much to learn from you Dr. Bajcsy, thank you for sharing your story and words of wisdom with us today. Eshika congratulations for winning the NCWIT collegiate award just one of your remarkable implements already. We look forward to hearing what your future holds. To our audience we invite you to send an electronic thank you card to someone who has inspired you, or perhaps been a mentor in your own journey. Please go to NCWIT.org/thanks and thank them today. To our sponsors who made the conversations for change series possible we are truly for change possible, we are truly appreciative. I especially want to thank Facebook, the sponsor of the pioneer and tech awards that we were honoring Dr. Bajcsy with today. Please join us for the next conversation, titled "Learning About Intersectionality" on Monday, May 11th, at noon Mountain time. Finally evaluation is a core practice for NCWIT and our member organizations. In that light, we encourage you to complete a short pop-up survey to provide feedback on this session. We use this information to assess and improve our work. Thank you again, Ruzena and Eshika. We are so grateful for your time. Eshika thank you Ruzena thank you.