WiDS Conference 2024 | Chen Zhang & Bing Ma | ASML

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More from WiDS Conference 2024

Chen Zhang

Principal Software Engineer

ASML

Bing Ma

Software Engineer

ASML

play_circle_outline Bing works on image processing and quality assurance in chip production.

play_circle_outline Chen works on computational lithography and building accurate models using data.

play_circle_outline The intersection of academia and industry in data science careers.

play_circle_outline Empowering Women in Data Science: Insights from ASML Representatives Bing and Chen

play_circle_outline The Impact of Mentors and Role Models at Medical School and ASML: Empowering Women in Tech

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Chen Zhang & Bing Ma | ASML

Chen Zhang

Principal Software Engineer ASML

Bing Ma

Software Engineer ASML

search

(light intro music)

>> Good afternoon, data science community, and welcome back to Stanford University. We're here with theCUBE's live coverage all day long at Women in Data Science Worldwide Annual Summit. My name's Savannah Peterson, delighted to be bringing you a power-packed lineup all day. My next two guests are from ASML. Bing and Chen, thank you so much for being on the show, welcome.

>> Thank you. >> Thank you.

>> It's very nice to have us here.

>> Yes, are you enjoying yourself so far at the show?

>> Of course, yeah.

>> Yeah. >> Yes.

>> You mentioned you'd been here before the pandemic. Now things are scaling. Does it feel like there's more and more women in data science?

>> Yeah, I'm so glad to see the community is growing so fast. Back to a few years ago, I'm one of the maybe 200-ish ambassadors. And now we know that there are over 1,000 ambassadors.

>> Oh my goodness, a thousand ambassadors. 150,000 people in the community, super impressive. Really glad to have you here. You both have very interesting jobs, and ASML is more than just a semiconductor company, more than just a silicon company. So I am going to have you tell us a little bit about those. Bing, let's start with you.

>> Okay, so in ASML, we make the most complex machines to print chips. So for most people will think that most logic just hardware, but actually we also have a lot of software engineers to work behind this. And so for me specifically, I'm working on the image processing part of the production line. So we do detect. We do, sorry, inspection and metrology. So for inspection, basically means that after we get the chips that we want to find the anomalies on the chips, so that we can quantify this chip, sorry.

>> No, you're doing great. It's essentially quality assurance. It's QA for the lithography and the actual making sure that everything that goes into these machines is going to do what we expect it to do.

>> Exactly, and for metrology also. So they are basically taking measurements of the printed patterns. So if the measurements are within the given range of the specifications, we know that these patents are printed well. the chip actually will work as we designed. But if it is out of the range, obviously, we need to rework this chip. So for both inspection and metrology, actually we are doing the quality assurance, yes.

>> It's got to be a tough job. I mean, we're talking about things on the micron layer, too. We're talking about very, very tiny nuance and detail here.

>> Exactly, so we are at the resolution of nanometers.

>> Amazing. >> Yes.

>> Wow, that's super cool. And Chen, you also have a very exciting job.

>> Yeah, so I work with Bing in our Shenzhen office. So my job is slightly different. I work on the modeling side inside computational lithography. So you know that our machine keeps pushing the physics boundary and engineering boundary in every generation of our new machines. So the computational simulation part is really important. So what I'm working on is that trying to build highly accurate model twin sub nanometer level to predict how the optical, physical, chemical, and how the physical, chemical process is happening on the paper at this highly accuracy. How do we do that? Is actually we leveraging a large amount of, I should say, exponentially growing amount of data, actually through metrology and inspection to build highly accurate models. And this large amount of data actually how to enable us to unleash the power of machine learning or even deep learning and our production lines, yeah.

>> You would have to. Otherwise, you couldn't process all that data to be able to-

>> Yeah.

>> I mean, we can't see what you're doing. It's such a detailed level. Yeah, it's so exciting. So I'm curious, this whole show is a real intersection of academia, of industry, of curiosity even there's folks here just coming to check things out. What are some of the career paths in academia for data science? So I'll start with you Chen, and then I'll bring it to you, Bing.

>> Yeah, interestingly, I have both a career in the academia and I shifted to industry, so I'm a-

>> Ah, perfect person to ask. (both laughs)

>> So I'm a physics major in undergrad, and I took my physics PhD in atomic molecular and optical physics. And then I did one year postdoc in the Lawrence Berkeley Lab just up north 60 miles and so on. So after that, I realized that I really want my research, my work, and my skill sets to be applicable, to contribute to real world impacts. So that a few friends referred me to ASML. After I entered ASML, my job in the research division in ASML and now production engineering division in ASML, I really find that ASML is the best place because it combines all of my, I would say, academia background, postdoc experiences, as well as my programming skills, physics skills, and so on, combines everything contributing to most challenging machine in the world. At the meantime, I met many brilliant colleagues from multidisciplinary, for example, Bing is electrical engineer background, and many of other my colleagues are physics, chemistry, mathematics, and mechanical engineer, and so on. So it's really, how to say, I should say that people who are having academia, doing studies, researchers in academia, they will have a very huge amount of opportunity in industry, yeah.

>> Savannah: Yeah.

>> And nowadays, all of our, how to say, pretty much every industry requires multidisciplinary contributions from all the backgrounds. I will say that if you're willing to enter a new interest in industry, you're willing to learn, there will be tons of opportunities from my experiences, yeah.

>> It's definitely been a theme of the show is this intersectionality, multiple disciplinary, cross team collaboration. Sounds like you were a dream fit for ASML and you two are dream teammates in general. What sort of paths have you noticed, Bing?

>> Okay, so my background is a little bit different from Chen. So even at the beginning of my undergrad study, I started major in image processing. So even starting from then, I have started dealing with massive data. And later on, I also got my PhD in image processing and statistical data analysis.

>> Savannah: Just a little bit of education.

>> Yeah. >> Sitting at this table with me right now, (all laughs) I'm feeling uneducated by a mile and I couldn't be more proud. It's great, I love it.

It's an honor. >> No, academic, yeah.

>> Yeah.

>> And also after I finished my degrees, actually, I worked in the university for quite some years. And at that time, I actually was doing clinical imaging studies, so I was doing the cancer treatment

or tracking >> Oh, great. following for the medical school in University of Michigan. And so through all these years in the academic that I'm vigorously trained with all the theoretical background and also in academia that most of the time, we are trying to develop a novel algorithm and models to solve to get a specific metric optimized.

>> Right. >> So that's the goal to get novel technologies. And so that need a lot of theoretical background. And that's very useful when I switched my career from academia to industry because in... Okay, I say in the company, most time, we are in encountered with real world problems. So the data messier, let's say messier. And the problems are usually at the beginning, they are less well defined as in academic background, but with the real good training there-

>> That's a really good point you just made. You're saying that when we're in academia, there's a lot more research that's maybe led up to that moment or things coming together to intersect. So it's a little almost more scripted versus when you're in an industry business solution you're just pulling together parts off the shelf and trying to make it up as you go

and build >> Exactly. what you need to build.

>> Exactly. You are overwhelmed with the data that you have.

>> Yeah, yeah. >> You have to make the decisions how to pre-process this data, how to clean data and how to formulate your problem to a model or to a real well defined problem. Yeah, so with the training and all the experiences you get in the academia and that will have you grid to start and to get the solution to... Sorry, to get the formulation of the problem in the most efficient time. So that's the advantage that we had transferring from academia to industry.

>> Well, I think that that's also, I mean, there's clearly an overlap between academia and industry. I think also we're at a really interesting time just in the state of AI, ML a lot of what's happening right now, we're going to have new folks entering the space. What you both just said is your background in whatever your background is, paired with some education in this space means anything's possible. You could be working on really interesting projects and that background, that interdisciplinary background could be really compelling. Are there any very specific, and you've both touched on this a little bit, but I'm curious if there's some that come to mind. Are there any particular challenges in the semiconductor industry that are uniquely solved by data science or technology today that maybe would've been significantly harder to do in the past before we were at where we're at?

>> Ah, so what do you mean? Is that how the problem with data science technologies from data science or inspired by data science methods we can solve some problem better, right?

>> Yes, yeah. >> Yeah, okay.

>> And specifically in semiconductors.

>> Yeah, definitely. So nowadays many of our data are coming from as metrology inspection. And many of our data are coming from the how the machine log datas. So those data are massive. So before maybe even five years ago, we can only do down sampling of those data to extract very limited amount of information to build positive empirical models with a few parameters. I should say back to that time, we wasted a lot of data simply due to the computational power not sufficient or model is too complicated to finish the simulation we see in production needed timeframe. So that means we are limited back to that time. However nowadays we can leverage distributed computing, we can leverage GPU technologies and so on this hardware computing powers as well as many data science methods, which were not well known before. However, now it's getting much more popular. For example, cluster technologies, image processing technologies, video processing, audio processing, and so on. So all these, I should say data science make all these technology much easier accessible even to industrial applications and for both ourselves and our customers to be more open to try these new technologies and generate more values for them and for ourselves.

>> So pretty much every aspect of it, to some degree.

>> Right, yeah. >> Yeah.

>> For us, let's say before we do metrology and inspection, that will be scanned using a electron beam. And okay, I talked about that these days all the resolution is either nanometer scale, so the images are very, very targeted.

And imagine >> Very much. with the electron beams that the noise of the images they are much higher than what we deserve to be. So the first step is that we need to actually pre-process these images to have better quality. And now with current data science technologies, we can use all the data that we have, like Chen mentioned, we don't have to reduce a lot of the data or done sampling. We can use all of them so we can get the best benefit of it.

>> Yeah. >> Yeah.

>> It's all about making the world better and making our jobs easier.

>> Yeah, let me add one more thing.

>> Yeah, please by all means.

>> Yeah, yeah. >> I'm here to learn. So I have a very recent example in my work, although I couldn't disclose the details, but I used one example, so maybe five years ago, our computational capability can only afford a few hundred training examples. And now we can easily afford up to million training examples. That means our models competency back that time should be limited, right? But now we can leverage deep planning models in our productions because the growing computational power and growing data amount.

>> I'm really glad you brought that up because what we're able to do now before even four years ago, 10 years ago is orders and orders of magnitude

more in terms of volume >> True. and speed so I think that's a really good point. You obviously know that sitting at the epicenter in a semiconductor company processing power is that focal point there. But I'm really glad you brought that up. We're here celebrating International Women's Day. We are all passionate women in our space. You both do a little bit of work there, don't you? For the working groups. So tell me a little bit about how you are empowering women in our field. Chen, I'll start with you.

>> Yeah, so both of us are inside a community called ASML Women in Silicon Valley, and it's part of the ASML women bigger community. So we established this community back to the year 2015, and I'm the third chairwoman and Bing worked with me very closely contributing to the community. So the community's mission is to build a supportive environment for people to learn, share, and lead. And these WiDS organization is definitely one of our channels that we believe resources from WiDS can be accessible to our members for their career growth, for their technology pathways and career pathways and so on improve all of them. That's why we come here and we not only introduce ourself, but meet the community who are either academia or other industries to join the bigger community and use that as our members growing resources.

>> I love that. And and you obviously get excited working with her on this.

>> Exactly, yeah. And also, like Chen mentioned that we came here and this is my first time at WiDS and I'm so excited to, okay, this morning, listen to all the talks and meet all the other peers, let's say.

>> Yeah. >> And this community, I think that it will do huge contributions to promote Women in Data Science. And we'll see, yeah, we're looking forward to all the brighter futures.

>> I was just going to say the future is bright.

>> One more thing I want to add is inside ASML there are many, many different departments. So mechanical engineering, electrical engineering, software engineering, and so on. Many of our colleagues does not even know how their work already involve a lot of data science elements. So that's why we-

>> Great point.

>> we want to bring the knowledges, observations, learnings from this conference to our community members, our colleagues to let them know, look, your work is how they leveraging data science already and how about joining this community to learn more.

>> Yeah, Oh, I love that. Well, it sounds like everyone's invited into your community. On that note, a couple last questions to close this out. For women who are just about to embark on their journey or looking at you thinking how cool your jobs sound, what's your advice? Bing, I'll start with you. What's your advice to a young woman in data science right now when thinking about it?

>> Oh, okay, the first thing is that when... Okay, well, young ladies trying to get into this field of data science, I'm sure that she has the passion about it. So with passion, insight driving for you to get to learn new skills, to broaden your view. And so I'm thinking at the beginning, if you haven't been into this field yet, so just to explore your area, you know that you can talk to all the women. I want to specific say women, all the data scientists that you know and talk to know how their path was, how their professional path was. And so that will give you some insights that you can get to reach your goal maybe faster.

>> Beautiful, beautifully said. What's your advice, Chen?

>> Yeah, so one thing I want to emphasize, not specifically for women who want to enter data science career is actually for every women, is that they never be afraid. Be bold and be brave. Think about what we can do if we are not afraid. Once we feel safer, how they leveraging our potentials. We maximize our potentials best.

>> Beautifully said ladies. Alright, two more questions. What is your advice for someone who is looking to be an ally to women like us? They've got a young woman or any age woman in their life who's trying to fight the good fight and get in a career in STEM or in data science? What do you think, Chen? What would be your advice?

>> The question is about if someone wants to be ally, how they can contribute, right?

>> Yes.

>> So let me use one example I like most, I share with everybody. One of our executive sponsor is actually a male executive in our company. I should say he's Chris Ben and he's the best male ally I've ever had in my entire academic plus industry career. So what I observe is that he is champion, sponsor and supporter and mentor of all the female colleagues in our company and even globally. So I think to be a ally, you can feel free to take all these roles. And what else? Be brave enough to stand up to help women to advocate.

>> Well said.

>> Yeah. >> Yeah.

>> Any advice you'd like to add to that, Bing?

>> I think that she said too well. (laughs)

>> So that was very well said. >> Yeah.

>> So I just say that, I mean, for women that already in this engineering or data science specific data science world, that we need to more open to the, let's say newcomers. And because for them it's a little bit intimidate at the beginning, right?

It's kind of like. >> Of course it's intimidating. I was intimidated even when coming in here today because you're also smart.

Yeah, totally. >> It's a male dominated field.

>> Right? >> Totally.

>> We have to dominate, yeah. But we can also see that there are so many women engineers, women data scientists already there. And they have done all the work. Oh, how can I say? They have been through the path. I'm thinking when we are already in this field, we can get connected with the newcomers and help.

>> Yeah, always be willing to bring that person up.

>> Yeah, as much can we can. >> And water level rises with us all together. I think that's a really great note. Anyone who's helped you on your journey that you'd like to give a shout out to?

>> Yeah, I'll say that when I was working in medical school and my advisor, and he's my mentor. Okay he's a he, but he always very helpful for me that his name is Chuck Meyer.

>> Well, thank you. Thank you Chuck. And what about you? Anyone who's helped you on your journey?

>> Oh, so I had quite a few mentors inside ASML. Some of them are female role models. And some of them are male allies and some of them are peers. From all these mentors, I see genuine support attitude and they really want me to success. So they guide me and share me with many things. And later I become a mentor myself and mentoring a young lady in my company. So I really think that this type of... I asked one of my mentors, "Why do you want to mentor me since you're already very, very high level." He said, "I want you to mentor somebody else when you grow." So I want to say that to everybody that you are either in a growing journey or you are already well established or you are very green, very young in this field. Keep that mentality in mind. This community will grow, yeah.

>> I love that. And your legacy is the impact you have on other people. It's why we invest the time. Bing and Chen was so nice to have you here. Thank you for the work you're doing at ASML and within the WiDS community. Just brilliant, I'm inspired and I hope you're all inspired wherever you're tuning in live to theCUBE's coverage here at Stanford University. My name's Savannah Peterson, you're watching theCUBE, the leading network for empowering females in tech. (upbeat music)

Chen Zhang & Bing Ma | ASML

search

(light intro music)

>> Good afternoon, data science community, and welcome back to Stanford University. We're here with theCUBE's live coverage all day long at Women in Data Science Worldwide Annual Summit. My name's Savannah Peterson, delighted to be bringing you a power-packed lineup all day. My next two guests are from ASML. Bing and Chen, thank you so much for being on the show, welcome.

>> Thank you. >> Thank you.

>> It's very nice to have us here.

>> Yes, are you enjoying yourself so far at the show?

>> Of course, yeah.

>> Yeah. >> Yes.

>> You mentioned you'd been here before the pandemic. Now things are scaling. Does it feel like there's more and more women in data science?

>> Yeah, I'm so glad to see the community is growing so fast. Back to a few years ago, I'm one of the maybe 200-ish ambassadors. And now we know that there are over 1,000 ambassadors.

>> Oh my goodness, a thousand ambassadors. 150,000 people in the community, super impressive. Really glad to have you here. You both have very interesting jobs, and ASML is more than just a semiconductor company, more than just a silicon company. So I am going to have you tell us a little bit about those. Bing, let's start with you.

>> Okay, so in ASML, we make the most complex machines to print chips. So for most people will think that most logic just hardware, but actually we also have a lot of software engineers to work behind this. And so for me specifically, I'm working on the image processing part of the production line. So we do detect. We do, sorry, inspection and metrology. So for inspection, basically means that after we get the chips that we want to find the anomalies on the chips, so that we can quantify this chip, sorry.

>> No, you're doing great. It's essentially quality assurance. It's QA for the lithography and the actual making sure that everything that goes into these machines is going to do what we expect it to do.

>> Exactly, and for metrology also. So they are basically taking measurements of the printed patterns. So if the measurements are within the given range of the specifications, we know that these patents are printed well. the chip actually will work as we designed. But if it is out of the range, obviously, we need to rework this chip. So for both inspection and metrology, actually we are doing the quality assurance, yes.

>> It's got to be a tough job. I mean, we're talking about things on the micron layer, too. We're talking about very, very tiny nuance and detail here.

>> Exactly, so we are at the resolution of nanometers.

>> Amazing. >> Yes.

>> Wow, that's super cool. And Chen, you also have a very exciting job.

>> Yeah, so I work with Bing in our Shenzhen office. So my job is slightly different. I work on the modeling side inside computational lithography. So you know that our machine keeps pushing the physics boundary and engineering boundary in every generation of our new machines. So the computational simulation part is really important. So what I'm working on is that trying to build highly accurate model twin sub nanometer level to predict how the optical, physical, chemical, and how the physical, chemical process is happening on the paper at this highly accuracy. How do we do that? Is actually we leveraging a large amount of, I should say, exponentially growing amount of data, actually through metrology and inspection to build highly accurate models. And this large amount of data actually how to enable us to unleash the power of machine learning or even deep learning and our production lines, yeah.

>> You would have to. Otherwise, you couldn't process all that data to be able to-

>> Yeah.

>> I mean, we can't see what you're doing. It's such a detailed level. Yeah, it's so exciting. So I'm curious, this whole show is a real intersection of academia, of industry, of curiosity even there's folks here just coming to check things out. What are some of the career paths in academia for data science? So I'll start with you Chen, and then I'll bring it to you, Bing.

>> Yeah, interestingly, I have both a career in the academia and I shifted to industry, so I'm a-

>> Ah, perfect person to ask. (both laughs)

>> So I'm a physics major in undergrad, and I took my physics PhD in atomic molecular and optical physics. And then I did one year postdoc in the Lawrence Berkeley Lab just up north 60 miles and so on. So after that, I realized that I really want my research, my work, and my skill sets to be applicable, to contribute to real world impacts. So that a few friends referred me to ASML. After I entered ASML, my job in the research division in ASML and now production engineering division in ASML, I really find that ASML is the best place because it combines all of my, I would say, academia background, postdoc experiences, as well as my programming skills, physics skills, and so on, combines everything contributing to most challenging machine in the world. At the meantime, I met many brilliant colleagues from multidisciplinary, for example, Bing is electrical engineer background, and many of other my colleagues are physics, chemistry, mathematics, and mechanical engineer, and so on. So it's really, how to say, I should say that people who are having academia, doing studies, researchers in academia, they will have a very huge amount of opportunity in industry, yeah.

>> Savannah: Yeah.

>> And nowadays, all of our, how to say, pretty much every industry requires multidisciplinary contributions from all the backgrounds. I will say that if you're willing to enter a new interest in industry, you're willing to learn, there will be tons of opportunities from my experiences, yeah.

>> It's definitely been a theme of the show is this intersectionality, multiple disciplinary, cross team collaboration. Sounds like you were a dream fit for ASML and you two are dream teammates in general. What sort of paths have you noticed, Bing?

>> Okay, so my background is a little bit different from Chen. So even at the beginning of my undergrad study, I started major in image processing. So even starting from then, I have started dealing with massive data. And later on, I also got my PhD in image processing and statistical data analysis.

>> Savannah: Just a little bit of education.

>> Yeah. >> Sitting at this table with me right now, (all laughs) I'm feeling uneducated by a mile and I couldn't be more proud. It's great, I love it.

It's an honor. >> No, academic, yeah.

>> Yeah.

>> And also after I finished my degrees, actually, I worked in the university for quite some years. And at that time, I actually was doing clinical imaging studies, so I was doing the cancer treatment

or tracking >> Oh, great. following for the medical school in University of Michigan. And so through all these years in the academic that I'm vigorously trained with all the theoretical background and also in academia that most of the time, we are trying to develop a novel algorithm and models to solve to get a specific metric optimized.

>> Right. >> So that's the goal to get novel technologies. And so that need a lot of theoretical background. And that's very useful when I switched my career from academia to industry because in... Okay, I say in the company, most time, we are in encountered with real world problems. So the data messier, let's say messier. And the problems are usually at the beginning, they are less well defined as in academic background, but with the real good training there-

>> That's a really good point you just made. You're saying that when we're in academia, there's a lot more research that's maybe led up to that moment or things coming together to intersect. So it's a little almost more scripted versus when you're in an industry business solution you're just pulling together parts off the shelf and trying to make it up as you go

and build >> Exactly. what you need to build.

>> Exactly. You are overwhelmed with the data that you have.

>> Yeah, yeah. >> You have to make the decisions how to pre-process this data, how to clean data and how to formulate your problem to a model or to a real well defined problem. Yeah, so with the training and all the experiences you get in the academia and that will have you grid to start and to get the solution to... Sorry, to get the formulation of the problem in the most efficient time. So that's the advantage that we had transferring from academia to industry.

>> Well, I think that that's also, I mean, there's clearly an overlap between academia and industry. I think also we're at a really interesting time just in the state of AI, ML a lot of what's happening right now, we're going to have new folks entering the space. What you both just said is your background in whatever your background is, paired with some education in this space means anything's possible. You could be working on really interesting projects and that background, that interdisciplinary background could be really compelling. Are there any very specific, and you've both touched on this a little bit, but I'm curious if there's some that come to mind. Are there any particular challenges in the semiconductor industry that are uniquely solved by data science or technology today that maybe would've been significantly harder to do in the past before we were at where we're at?

>> Ah, so what do you mean? Is that how the problem with data science technologies from data science or inspired by data science methods we can solve some problem better, right?

>> Yes, yeah. >> Yeah, okay.

>> And specifically in semiconductors.

>> Yeah, definitely. So nowadays many of our data are coming from as metrology inspection. And many of our data are coming from the how the machine log datas. So those data are massive. So before maybe even five years ago, we can only do down sampling of those data to extract very limited amount of information to build positive empirical models with a few parameters. I should say back to that time, we wasted a lot of data simply due to the computational power not sufficient or model is too complicated to finish the simulation we see in production needed timeframe. So that means we are limited back to that time. However nowadays we can leverage distributed computing, we can leverage GPU technologies and so on this hardware computing powers as well as many data science methods, which were not well known before. However, now it's getting much more popular. For example, cluster technologies, image processing technologies, video processing, audio processing, and so on. So all these, I should say data science make all these technology much easier accessible even to industrial applications and for both ourselves and our customers to be more open to try these new technologies and generate more values for them and for ourselves.

>> So pretty much every aspect of it, to some degree.

>> Right, yeah. >> Yeah.

>> For us, let's say before we do metrology and inspection, that will be scanned using a electron beam. And okay, I talked about that these days all the resolution is either nanometer scale, so the images are very, very targeted.

And imagine >> Very much. with the electron beams that the noise of the images they are much higher than what we deserve to be. So the first step is that we need to actually pre-process these images to have better quality. And now with current data science technologies, we can use all the data that we have, like Chen mentioned, we don't have to reduce a lot of the data or done sampling. We can use all of them so we can get the best benefit of it.

>> Yeah. >> Yeah.

>> It's all about making the world better and making our jobs easier.

>> Yeah, let me add one more thing.

>> Yeah, please by all means.

>> Yeah, yeah. >> I'm here to learn. So I have a very recent example in my work, although I couldn't disclose the details, but I used one example, so maybe five years ago, our computational capability can only afford a few hundred training examples. And now we can easily afford up to million training examples. That means our models competency back that time should be limited, right? But now we can leverage deep planning models in our productions because the growing computational power and growing data amount.

>> I'm really glad you brought that up because what we're able to do now before even four years ago, 10 years ago is orders and orders of magnitude

more in terms of volume >> True. and speed so I think that's a really good point. You obviously know that sitting at the epicenter in a semiconductor company processing power is that focal point there. But I'm really glad you brought that up. We're here celebrating International Women's Day. We are all passionate women in our space. You both do a little bit of work there, don't you? For the working groups. So tell me a little bit about how you are empowering women in our field. Chen, I'll start with you.

>> Yeah, so both of us are inside a community called ASML Women in Silicon Valley, and it's part of the ASML women bigger community. So we established this community back to the year 2015, and I'm the third chairwoman and Bing worked with me very closely contributing to the community. So the community's mission is to build a supportive environment for people to learn, share, and lead. And these WiDS organization is definitely one of our channels that we believe resources from WiDS can be accessible to our members for their career growth, for their technology pathways and career pathways and so on improve all of them. That's why we come here and we not only introduce ourself, but meet the community who are either academia or other industries to join the bigger community and use that as our members growing resources.

>> I love that. And and you obviously get excited working with her on this.

>> Exactly, yeah. And also, like Chen mentioned that we came here and this is my first time at WiDS and I'm so excited to, okay, this morning, listen to all the talks and meet all the other peers, let's say.

>> Yeah. >> And this community, I think that it will do huge contributions to promote Women in Data Science. And we'll see, yeah, we're looking forward to all the brighter futures.

>> I was just going to say the future is bright.

>> One more thing I want to add is inside ASML there are many, many different departments. So mechanical engineering, electrical engineering, software engineering, and so on. Many of our colleagues does not even know how their work already involve a lot of data science elements. So that's why we-

>> Great point.

>> we want to bring the knowledges, observations, learnings from this conference to our community members, our colleagues to let them know, look, your work is how they leveraging data science already and how about joining this community to learn more.

>> Yeah, Oh, I love that. Well, it sounds like everyone's invited into your community. On that note, a couple last questions to close this out. For women who are just about to embark on their journey or looking at you thinking how cool your jobs sound, what's your advice? Bing, I'll start with you. What's your advice to a young woman in data science right now when thinking about it?

>> Oh, okay, the first thing is that when... Okay, well, young ladies trying to get into this field of data science, I'm sure that she has the passion about it. So with passion, insight driving for you to get to learn new skills, to broaden your view. And so I'm thinking at the beginning, if you haven't been into this field yet, so just to explore your area, you know that you can talk to all the women. I want to specific say women, all the data scientists that you know and talk to know how their path was, how their professional path was. And so that will give you some insights that you can get to reach your goal maybe faster.

>> Beautiful, beautifully said. What's your advice, Chen?

>> Yeah, so one thing I want to emphasize, not specifically for women who want to enter data science career is actually for every women, is that they never be afraid. Be bold and be brave. Think about what we can do if we are not afraid. Once we feel safer, how they leveraging our potentials. We maximize our potentials best.

>> Beautifully said ladies. Alright, two more questions. What is your advice for someone who is looking to be an ally to women like us? They've got a young woman or any age woman in their life who's trying to fight the good fight and get in a career in STEM or in data science? What do you think, Chen? What would be your advice?

>> The question is about if someone wants to be ally, how they can contribute, right?

>> Yes.

>> So let me use one example I like most, I share with everybody. One of our executive sponsor is actually a male executive in our company. I should say he's Chris Ben and he's the best male ally I've ever had in my entire academic plus industry career. So what I observe is that he is champion, sponsor and supporter and mentor of all the female colleagues in our company and even globally. So I think to be a ally, you can feel free to take all these roles. And what else? Be brave enough to stand up to help women to advocate.

>> Well said.

>> Yeah. >> Yeah.

>> Any advice you'd like to add to that, Bing?

>> I think that she said too well. (laughs)

>> So that was very well said. >> Yeah.

>> So I just say that, I mean, for women that already in this engineering or data science specific data science world, that we need to more open to the, let's say newcomers. And because for them it's a little bit intimidate at the beginning, right?

It's kind of like. >> Of course it's intimidating. I was intimidated even when coming in here today because you're also smart.

Yeah, totally. >> It's a male dominated field.

>> Right? >> Totally.

>> We have to dominate, yeah. But we can also see that there are so many women engineers, women data scientists already there. And they have done all the work. Oh, how can I say? They have been through the path. I'm thinking when we are already in this field, we can get connected with the newcomers and help.

>> Yeah, always be willing to bring that person up.

>> Yeah, as much can we can. >> And water level rises with us all together. I think that's a really great note. Anyone who's helped you on your journey that you'd like to give a shout out to?

>> Yeah, I'll say that when I was working in medical school and my advisor, and he's my mentor. Okay he's a he, but he always very helpful for me that his name is Chuck Meyer.

>> Well, thank you. Thank you Chuck. And what about you? Anyone who's helped you on your journey?

>> Oh, so I had quite a few mentors inside ASML. Some of them are female role models. And some of them are male allies and some of them are peers. From all these mentors, I see genuine support attitude and they really want me to success. So they guide me and share me with many things. And later I become a mentor myself and mentoring a young lady in my company. So I really think that this type of... I asked one of my mentors, "Why do you want to mentor me since you're already very, very high level." He said, "I want you to mentor somebody else when you grow." So I want to say that to everybody that you are either in a growing journey or you are already well established or you are very green, very young in this field. Keep that mentality in mind. This community will grow, yeah.

>> I love that. And your legacy is the impact you have on other people. It's why we invest the time. Bing and Chen was so nice to have you here. Thank you for the work you're doing at ASML and within the WiDS community. Just brilliant, I'm inspired and I hope you're all inspired wherever you're tuning in live to theCUBE's coverage here at Stanford University. My name's Savannah Peterson, you're watching theCUBE, the leading network for empowering females in tech. (upbeat music)