Jay Shah: Awesome. Awesome. Okay, cool. Um, welcome song when Watson for the 15th episode of machine learning for beginners.
Jay Shah: It's really nice to have a long way over here. And I remember, I remember longer because we met for the first time in last CVPR meeting.
Jay Shah: We were attending together and we had a very nice nice right from here, Arizona to California. Long Beach.
Jay Shah: And that's when that's when I got to know about your research work with Professor Liang, and your lab group. And I remember actually reading that paper unit plus plus.
Jay Shah: During the during my stay over there overnight Airbnb with what cell. And I was just reading that paper and down the line. I wanted to work on medical image analysis. So,
Jay Shah: I really got intrigued and right now when I see the scholar profile of yours unit plus plus has now like maybe more than 300 and I don't know if it might be increasing as we speak.
Jay Shah: So congratulations to you and your team and Professor Liang, for that very nice paper.
Jay Shah: But I guess we will talk about your unit plus plus paper in a while. In this video, but
Jay Shah: Before we start on. I'll just quickly, I'll just quickly say a few words about this standard for all those people who are new, new to this video on this channel for on this video.
Jay Shah: I create these videos on machine learning for beginners.
Jay Shah: With the aim to help people who are starting of their career in machine learning to understand what the machine learning stands for and what really lies ahead in the in terms of industry and research.
Jay Shah: And for the very first time I have invited zone where who has like maybe on the Potomac for research daily looks like.
Jay Shah: Because he is he, he would be graduating next year from this PhD and he has a stellar profile in research. So I'm really intrigued to understand what research looks like.
Jay Shah: And before we move on. I'll just quickly introduce myself. I'm Jay. I'm a PhD student at Arizona State University working in biomedical informatics and a letter, but so long. So, introduce himself for this call.
Vatsal Sodha: Yeah. Hi, I am what
Vatsal Sodha: I am like just graduated with my thesis from Arizona State University in medical image analysis. I worked with its own way for two years and like it's been it's been a very good journey. So, and I'm even excited about to see the other side or somewhere.
Vatsal Sodha: So let's see.
Vatsal Sodha: Some
Jay Shah: And as long as I can you tell us a little bit about yourself. Like, can you tell us something about your research interests and who are you, what are you currently working on
Zongwei Zhou: Hey. Hey. My name is song way, Joe, I'm a from China and I'm now a PhD student at ASU. This is my fourth year I work with Dr. Jim Elian is also a Chinese and also I also work very happy with us. So we work our summer good publication out
Zongwei Zhou: I get my bachelor degree in computer science from the university. And then I joined Dr. The answer lab.
Zongwei Zhou: So firstly, I get rejected from the PhD, because my TOEFL, you know English. It's terrible. And the ASU decided, hey, why not improve your English first. Okay then. But the doctor dancer really client that he
Zongwei Zhou: recruited me as a visiting scholar for the first year, so that the English not so crucial then I joined and from from the 2016
Zongwei Zhou: Then I start working on the deep learning on medical imaging analysis and now on my thesis topic is pretty settle down. So my main focus is annotation effect effective deep learning for medical imaging
Zongwei Zhou: So I think it's a pretty exciting topic that I am really fortunate to get a guide to this topic and I also get some publication like surrounding this focusing on this topic, so that that's a brief introduce myself. Yeah.
Jay Shah: That's that's really great to know. I mean, yeah, I didn't knew like you had a red flag from ASU on the first go but yeah I mean now when he so it looks back. It's really bad decision on their part, but yes.
Jay Shah: Can you tell like we all, we all see a lot of applications of AI in lot of topics right now. Like, it can be
Jay Shah: It's used in a lot of domains self driving cars medical domains and all those things. And was there a reason you picked out biomedical informatics to be your research focus or
Jay Shah: Like what really was the thing that really motivated you and how do you see that domain right now. Like, do you see
Jay Shah: Great amount of research being done in this domain was that the key thing or maybe something you really liked being vulnerable and like, okay, I want to do something in medical domains. What was the key factor motivating factor for you.
Zongwei Zhou: Yeah, it's a deep learning. It's a tool. So how do you use deep learning is mostly based on your interest. If you interest in
Zongwei Zhou: Natural language, our lecture natural language processing, then you can go to an LP if you're interesting agriculture that you can apply to the agriculture. If you interesting astronomy, then you can use the plenty to find a stock. So yeah, it's based on different people's
Zongwei Zhou: Interest. So I select a medical imaging. It's not initially, not my decision. So I started film row with the planning since 2015 my last year in undergraduate
Zongwei Zhou: So that was my birthday, and I have really boring last year in college and I need to find something to work on. Then I just contacted one of the professor in
Zongwei Zhou: A minute department, computer science, and he is. He is working on medical image analysis that hey, then let's work on the medical image, then
Zongwei Zhou: Then that's the time I started working on it. But now, as the time goes, I just think oh the planning. It's a really powerful tool so
Zongwei Zhou: It can specifically beneficial for something that's a human really hate to do it because of something. Maybe it's a really boring tedious time consuming, like a lot of
Zongwei Zhou: dirty job and deep learning at the same time also good at some some job and like a human cannot do very well like calculating some pixel wise to analyze the human eyes may not be capable so
Zongwei Zhou: So by understanding this to advantage I think medical image analysis may be a good application for deep learning. One of the greater best application because
Zongwei Zhou: radiologists, the everyday so busy and a lot of small things detailed things they don't want to do, then you can keep learning
Zongwei Zhou: And it's something like a radiologist that cannot tell like maybe need a biopsy results and the different hopefully if deep learning can do it. It's also very good. So that's mostly to reason I select the medical imaging access
Vatsal Sodha: Yeah, I have a question for you. So I recently checked your profile and you and I'm part of this paper. So we recently won Dominica young scientist or 2019 so congratulation on that.
Vatsal Sodha: Thank you.
Vatsal Sodha: So I wanted, I have seen your like struggle, like from the first person who you like how did, how much you work hard and how much effort were done in developing this award winning method.
Vatsal Sodha: And so I have a question like, can you just tell the viewers how you came up with an idea and what it's an idea about and how it aligns with your long term goal of the thesis.
Zongwei Zhou: Yeah, so the paper bustle was mentioned its name, the model of Genesis. It's a very cool name like a name from my advisor. So this paper it's doing one job that
Zongwei Zhou: Provide a 3D pre trained model for the imaging community for medical imaging specifically. So, oh, so this paper get good attention. I think it's because
Zongwei Zhou: People really need it. So let's think about the 2D image and vision task. So if you want to train a model and
Zongwei Zhou: You will naturally just download the model patron model front imaging that because it's pretty powerful. And you can like translate use transfer learning to translate this knowledge to a lot of different a task.
Zongwei Zhou: But now the question is if you feel if you face a 3D problem. Like most likely happen is the medical image analysis because the CT, MRI ultrasound most likely dominate modality image and type it in 3D, so that if you want to use the 2D image ad model. It's not so
Zongwei Zhou: Straightforward. I mean, it will have some gap. So we realize this gap so that that's the motivation. We want to do this thing we want to build a model.
Zongwei Zhou: Like so when people ask, Oh yeah, I have a 3D image fantastical would your suggestion. Yeah, my suggestion is a download that model Genesis and stuff from there it will give you promising results at least
Zongwei Zhou: Better than live from scratch. So that's, that's why people like it. It's very straightforward. So I provide a tool that
Zongwei Zhou: A lot of researchers can use for their own job. So it's a, like a fun basic tool for deployment for deep learning and another same like people may may like it because
Zongwei Zhou: This framework. It's very, very simple framework. It's actually not very new, but
Zongwei Zhou: It's a first time to use in 3D imaging. So it's a very simple framework that everybody like a read the paper or even not read the paper, read the abstract or the
Zongwei Zhou: Figure Eva, they can like suddenly understand. Oh, just the image restoration path you mess up the image and the restore it. That's it.
Zongwei Zhou: So that's easy. And another advantages that this, this method really work. So it's a really magic like simple method, but it really working.
Zongwei Zhou: So this makes the not a so many gap, like the beginner, even for the machine learning beginner, they can learn. It's not like a deep learning. It's like a
Zongwei Zhou: Philosophy philosophy engineering thing, it's a really like easy to understand that you can really design your own framework based on it.
Zongwei Zhou: So, and another thing. Maybe it's become pop like people like it is we make it public available because
Zongwei Zhou: Nowadays, deep learning, if you make it public available, then you just download it and the UK reproduce other people's work and you can
Zongwei Zhou: Translate to your own work so public available. It's very important. And the most important part is the writing. So as I said my writing is not very
Zongwei Zhou: Good. So at the beginning my writing is even terrible. So I'm very fortunate to get very good at supporting from my seniors our student from our lab.
Zongwei Zhou: It's a NEMA he is very helpful in the writing the paper and actually we just work closely and the right model like versions to our funnel finalize this
Zongwei Zhou: MCI paper. So our. That being said, the submission papers submission and the final camera ready version to the public that has other people can view. It's a totally different to paper. So one paper it's about
Zongwei Zhou: More technical orientated writing like what this is. What do we do, this is what parameter another the now's the version that you can see it's more like a high level.
Zongwei Zhou: View. Like, why this is important. And what's the advantage of our technology. What's the property of our advantage of our
Zongwei Zhou: Technology and what's the observation from the so many work. So the writing also very important. That's, that's what I figured out to get maybe why the reason people like it.
Vatsal Sodha: Okay, so, yeah, that was all the good stuff. You know, like once you develop the method, but what I am interested in, like, how was your journey.
Vatsal Sodha: How much time you spent in just developing one method and like realizing the importance of the question like what do you think, what are the key factors in like before you jump into your the research problem. What should you look for and you can use this paper is an example.
Zongwei Zhou: Right. So the question is how I started it started with class are this idea. Yeah, it's a little bit
Zongwei Zhou: Starting idea. It's always the most difficult part like the idea is not always like, come to your mind and it's not
Zongwei Zhou: The first problem is you don't really know what the clinical neither. So what's the direction you need to think about and the second after you know the clinical problem and lack of what a physician.
Zongwei Zhou: Needed and how to come up with a good solution. So that's this two directions all both a very challenging, in my opinion, so
Zongwei Zhou: The first solution for the question, what's a good problem usually based on my advisor, so he has more experience that he knows. So what's the
Zongwei Zhou: research community. Now, what's the state of art and what is the missing. That's what a PhD do right so we figured out what what's the problem.
Zongwei Zhou: And then after he advised me, hey, we need this kind of pre trained model, then the job. It's a how to get to the good idea how to train. How to retrain this model, how to make it a habit.
Zongwei Zhou: Then, to be honest, how to make it happen. I don't really have a very clear like mine, the trail. So, so basically I was in, I was eating hamburger than the idea just to come up. So, uh, yeah.
No.
Zongwei Zhou: Not a wisdom you, you know. So if you want a good idea come to your mind and I usually be alone. You know, and
Zongwei Zhou: Do some relaxing stuff. But the key is, don't forget to think about it. If you forget to think about and nothing will come. If you yeah you are eating hamburgers and you think about it, then
Zongwei Zhou: It may come if you are lucky. So that is basically how it is start, but what kind of research problem. I will select like the the future. I usually just a will like
Zongwei Zhou: Estimate my my capability like this thing can I can I can I do it.
Zongwei Zhou: Like, do I have a capability to to accomplish it. If I cannot, I will not select this problem and another fact is I do have the resource to make this happen. Like if I don't have data set.
Zongwei Zhou: To it and then forget about if I don't have a GPU, then don't do it. And the third factor. I wouldn't say it's once this problem get solved. Other people should care about it if other people don't care about it then.
Zongwei Zhou: Of course, the some research degree are some research a problem. They do some very advanced work, but for myself, I will not do it. So I it have to be like
Zongwei Zhou: People really care about this problem. So before I started for example model Genesis, I will most delay. Like, do some small like a baby experiment, like a design some
Zongwei Zhou: Tiny concept and do some proof of concept job to really saying, Okay, this method may be promising, of course, a lot of good idea come in, but not all idea works. So I need to first
Zongwei Zhou: Spend a little amount of time to prove this idea, at least to some degree. It helps, then I may like a devoted devoted most my time on
Vatsal Sodha: Yeah, I remember you told me that you should reject 99% of the idea
Vatsal Sodha: Yeah and this thing about one 1% of the idea, the word their methods on it. Right.
Zongwei Zhou: Yeah.
Zongwei Zhou: Yeah, make one idea work and the push to the end it's need a lot of time. It's not like oh
Zongwei Zhou: This month I work on this idea and come out next month, I can change another. So, for example, model Genesis I from the start to the end, I spent like a
Zongwei Zhou: Almost a two year from the conference, like a front the idea comes and the to the conference publication and to to the journal publication. So it's a long journey. So you need to really investigate. Is it worth to do it if it's not worth to do it, don't, don't touch that with time. Yeah.
Vatsal Sodha: And there's another thing I want to add is like I have seen you personally for two years and it was not not like the idea came over night. Right. Like, first you had some another idea.
Vatsal Sodha: You will donate you try to publish it. But then you just discarded it. I don't want to take the name of the idea
Vatsal Sodha: So, but then the idea you use there's a pre processing for the current was and right so my point is like sometimes that failure if you have just stopped there. The first idea.
Vatsal Sodha: Okay, this is not working right, then you won't get an award winning paper.
Vatsal Sodha: But in fact, that first idea was used as a pre processing to do for our paper. Right. So how do you want to say anything on that, like, I'm going to stop and how what motivated you to just keep on going for one year continuously just developing a method right
Zongwei Zhou: Yeah, so most of us that most the idea from my mind is discarded because of a lot of reasons. Most. The reason it's the idea itself doesn't work. Another reason is I didn't dig into it. Maybe it is a good idea, but I just
Zongwei Zhou: Don't throw it away. Yeah. That's also possible. So the management is the reason I pushed to the end. It's a first, as I said, the task, it's easy. Not easy like I can do it like
Zongwei Zhou: Because there's not many competitor, you know,
Zongwei Zhou: As I said a 2D image and it is the dominant, it's a it's a standard for the 3D. Hey, if you ask a question, 3D printer model. Yeah. Maybe you need to think about a five minute to get some some answer that you don't know. So this is
Zongwei Zhou: Like a, a new direction so that at the stage of that I think as a PhD student, if I can do it. It's very good. So not very competitor. That's why I think, yeah, I should
Zongwei Zhou: Like continue this, no matter the idea how good it is in which level. I still need to make it a published because
Zongwei Zhou: Maybe people can read my paper and the lesson about a really good idea. It's a passport right so i think is a worse to do it and also my
Zongwei Zhou: Suggest a master mother Genesis also look very like a promising. We did a lot of larger scale experiments on it as well. So it's a significant delay. Better than learning 3D model from scratch. So that's why I think
Zongwei Zhou: This is a good topic that I want to spend like two years or or even more.
Vatsal Sodha: Yeah.
Jay Shah: Yeah, that's, that's really interesting. I mean, learning about the motivations and why this is very personally helpful to me because I'm just starting off my PhD journey. So this would be kind of a very useful advice for me but
Jay Shah: What was that like I really understand what will they go how what said mentioned like under understanding and never giving up like it. It could be the next award winning paper for anyone else.
Jay Shah: But are talking about the technical aspects of research, like for example your paper on unit plus plus. I'm wondering the, how do you approach a problem from a technical aspect that
Jay Shah: That you decided that, okay. Let's use an ensemble techniques of unit models or skipped connections in general are making or using resonate models, instead of the already pre trained models. How do you, how do you like is it totally the adjust the literature survey or like
Jay Shah: Or like, how do you define like what models to use for that particular application. Is that something. What have you learned over the time like
Jay Shah: How to Choose models. How to when to redesign went to think of a new architecture or maybe just start something from scratch. How do you, how do you, what have you learned over that.
Jay Shah: For years of your peers.
Zongwei Zhou: Right for for the model architecture that's actually a very engineering topic so that you don't really know
Zongwei Zhou: Previous when you get a new task, you don't really know. Hey, which architecture. It's a bad always the work the always best and yeah so that
Zongwei Zhou: So, this area is different from model Genesis model genitive, it's like
Zongwei Zhou: You ask the question people. Not sure what's the answer. And this question is you ask, Oh, which model, I can use people has a lot of answers. You know, the wrath net
Zongwei Zhou: Net the retina. The next Allah lot of a bunch of architectures, so that this area is more competitive, like it's a risky, like you, you
Zongwei Zhou: propose a new architecture, maybe tomorrow it get beat by other architecture. So this is really compact competitive area, you might, in my opinion, so I developed
Zongwei Zhou: A unit, plus, plus, because the first as my logic. I think I can do it because the unit. It's a well as established the architecture for the imager.
Zongwei Zhou: Segmentation. It's like a image that is my rule is that if people ask it to the first answer is always the best like if you answer. What's for images segmentation in medical imaging unit. Don't tell me other things. Okay, then that's a moment, I decided to okay
Zongwei Zhou: Put my foundation on the unit. And then I studied the unit paper I studied the unit architecture. So understand what each component is doing and
Zongwei Zhou: So I think it may be more promising instead of like come up with brand new architecture, like a really, really novelty like
Zongwei Zhou: So stand upon the unit shoulder. If you want to say. And yeah, so the motivation of unit plus plus is a deep in general the deep neural network. It's a very deep. That's why a lot of deeper layers. It's like a lasagna or
Zongwei Zhou: Kilometer, you know. And the problem is people don't really know how how deep you want to go like, yeah, you go deeper. But how deep so we
Zongwei Zhou: To answer this question, we do some toy example like like I introduce you before when I start a project, I have a toy example. So the toy. The employees that we want to understand
Zongwei Zhou: Yeah, so people wanted to take 34 which deep, it's the most useful enough so we select data set like a sale segmentation and we test on it like we put one layer.
Zongwei Zhou: Two layer three layer four layer and it to see what's the performance and we say, oh, for this data set three layer is the best and we change the data set and we found the hey this data set.
Zongwei Zhou: Five layers past and sometimes another data set to layer is good enough give you better performance. If you deeper it's go worse. Okay, now the conclusion that observation, there is no conclusion observation is
Zongwei Zhou: We are the different layer can help for different application or can help for different the disease detection second patient so
Zongwei Zhou: Then we say why not just utilize every layers in information and that led to the model itself to decide which layer. I want to go, so that's
Zongwei Zhou: Why we developed with some nasty the layer. It's not very complicated, it just
Zongwei Zhou: Oh, I consider layer one if you Sinclair wines good just to go, how to use your layer one if you think a layer fourth good go ahead with layer for so
Zongwei Zhou: We we push that we throw this is a difficult problem to the model itself let model to optimize which that is good. So that's why in your, you can see our new net plus, plus it's nested look like very complicated, but actually make the model decided itself, which one I want to go.
Jay Shah: Right. Right. Yeah. And this is, this is really cool. I mean, yeah, this is like I can see some of these aspects are creeping into my my research work so
Jay Shah: Maybe even offline. I would be keeping in touch with you to have some really good advice of how to moving forward and not making
Jay Shah: Huge pitfalls for me. But yeah, this is, this is really interesting but deviating a little bit out of the hole deep research book and maybe addressing what I frequently get asked this question a lot and maybe I on so as this question a lot to myself before choosing for a PhD is
Jay Shah: How, how, if a person is really a graduate student, or maybe even an undergraduate student
Jay Shah: How does he or she decide that maybe PhDs right for him or her or not, like, maybe the guy, the person is interested in doing research, but his PhD something really
Jay Shah: Necessary for him or her or choosing an industry job of research could be done. So did you did you did you
Jay Shah: Question yourself all of these things like how do you like, what would you say or for whom PhDs really
Jay Shah: Really fruitful versus for whom industry would be really fruitful. Like, what are your honest reflections of your four years of PhD and how has how ESP has helped you and
Jay Shah: Maybe joining an industry would not have been a good option or would it have been a good option for you to join industry. What are your thoughts on that.
Zongwei Zhou: Right, so the question is what's good about a PhD and what's good about the industry. So, to be honest, I don't have much experience in the industry. I have some internship, but I haven't really like a focus on that place. So when I applied for PhD, I asked myself some questions like, okay.
Zongwei Zhou: So my if my long term goal is to become professor. So this pretty much answer your own question like,
Zongwei Zhou: If you want to become professor, go get a PhD, otherwise you cannot get professorship. That's it. Okay, that's the first question I answer. And the second question is I'm
Zongwei Zhou: I'm an international student. I'm from China. So if I want to, like,
Zongwei Zhou: Walk in America or late or even living America the summer family. Then what should I do which degree. Should I go go PhD or Ms. So I'm not sure if you familiar with the some policy.
Zongwei Zhou: Like PhD has a more chance to get the green card. So that's another very important
Zongwei Zhou: Factor for the international because the in this area, like an AI area. A lot of international students like us. So when you to decide. Like, oh master or PhD. So we want to if we want to live. If you want to go back to like China or India is fine. So, but I think
Zongwei Zhou: At least get a green card is can give me freedom. Maybe one time I decided to
Zongwei Zhou: Work in China. One time I decided to work in America. So at least I can choose. But if I don't get a PhD then pretty much I just go back to China. So I don't want to be myself to be restrict to
Zongwei Zhou: Some degree. So, I think, get a Ph. D also helpful. So this too. It's a very, I like a motivated. The reason that I choose PhD. And another thing is
Zongwei Zhou: I found a PhD compare with the working in the company that company. Usually, you've got a boss right but a PhD, you got the advisor. So advisors name is a much like a gentle. It's not like about hey you do this this week.
Zongwei Zhou: Don't see me if you don't finish it. So PhD is the more like educate you to prepare you instead of a push you like if you don't do it I fire you.
Zongwei Zhou: It's not like that. So are you can meet some good mentor or even more, more than one good mentor your life. This is a very important I think. So instead of, okay, you just
Zongwei Zhou: Go to the industry and like start walking from the 20s or 30s. I think everybody will become an employee. He in some age.
Zongwei Zhou: I really like most people will have an employee experience like work for somebody, but it's not a very are you usually like you spend the five years, four years, or even 10 years with one mental P i and developer really do some folks on some topic and
Zongwei Zhou: Do something. It's a. So I just want to experience some different things. So yeah, I will experience employee, but maybe in my 20s, I will like
Zongwei Zhou: Build a somewhat good education myself. So that's, that's, I think, like a PhD gave me the advantage. So first, the two is very solid. And third, maybe. Yeah, yeah, people that grew the mentor, mentee. But on the other side. So if you become a PhD, which means you cannot be really rich in your 20s.
Zongwei Zhou: Because that company usually like have a really good
Zongwei Zhou: Package for you and you can develop a mate started developing your own life like at earlier than PhD Student So PhD student every year, every month and maybe just the renting and
Zongwei Zhou: Renting renting house and eating. That's all, that's all the costs that then I have no money at the end.
Zongwei Zhou: So that's one thing but it's not a big deal for me. So, it's okay. And the second is PhD means you need to focus on one thing, like for a long circle is not like
Zongwei Zhou: A product that you just finished your, your own part, then that's it you can work on another part. So, but a PhD you like
Zongwei Zhou: For me, I really need to lead one publication to the end, like, as I said, the one publication circle. It's a pretty long, like maybe for a
Zongwei Zhou: conference paper I may need a one year. And if I pushed to the end to the journal. Maybe I need a two year or even more. So this is a pretty like a bigger commitment for me so that like it's a
Zongwei Zhou: I don't see maybe nowadays the good company also do this like they also want to publish good paper and
Zongwei Zhou: Let the employee really become a good researcher, but I think a more straightforward away it's if you really want to become a researcher or Professor adjust the jumping into the PhD spend like
Zongwei Zhou: Five years and it gets some good publication. It will also benefit for the job hunting.
Zongwei Zhou: In the end, I think.
Jay Shah: Yeah, yeah, this is really interesting. And would you would you have any tips on like what are the obvious pitfalls of any PhD student. I mean, because I'm realizing that the whole
Jay Shah: The process of PhD has very little incentives to offer, and it's really hard to hold on to PhD in really be excited about it. So would you have, what do you see any tips on that, like,
Jay Shah: Like some kind of advice that okay always stay away from this kind of things, or maybe like maybe kind of a red flag whether any Netflix that you discovered that okay I shouldn't have done that because I was talking to what some
Jay Shah: A few weeks back, and he explained to me that. Whoa.
Jay Shah: Correct me if I'm wrong, like maybe the modern Genesis PayPal you submitted it to few conferences or maybe journals. I don't remember the exact details but it got rejected maybe
Yeah.
Vatsal Sodha: Plus plus
Jay Shah: Yeah, sorry unit plus plus or yeah unit and it got rejected maybe twice or thrice and
Jay Shah: Only after the third try, you realize
Jay Shah: You publish it got published and now it has like it has a standard like score to it so
Jay Shah: Like any tips on like maybe understanding that, okay, well, like do you did you have any boundaries that okay
Jay Shah: If maybe I get rejected four times. I would not devote enough time to the unit plus plus project.
Jay Shah: Or maybe I shouldn't be doing this or should I shouldn't be doing that because I frequently asked myself a question. I'm a computer science student
Jay Shah: Should I be doing a lot of medical imaging projects. If I want to make a diverse profile. I don't want my profile to be narrowed down to a medical imaging
Jay Shah: Person I still want to be able to work in Tesla or something like that. Like, who works and other competition aspects. So how like these kind of boundaries have you have. Do you have any kind of advice on that that. Okay. Stay away from doing these kind of projects or maybe try try to like
Vatsal Sodha: Maybe take on three projects.
Jay Shah: Not just one project doing a PhD or like some some kind of a safety mechanism do
Jay Shah: That
Jay Shah: You might have developed for yourself during your PhD. Do you have any tips on
Zongwei Zhou: Right, so, you know, plus a lot of getting rejected four times. Actually, it's mostly my fault. So
Zongwei Zhou: The English really terrible that time.
Zongwei Zhou: Like a two years before my English writing is pretty naive and the worthy and sometimes the grammar. I mean, mostly grammar issue. And yeah, so the review just honestly point out the writings no good, but the experiment is okay. And yeah, that's a time I actually you needed this kind of
Zongwei Zhou: This
Zongwei Zhou: Disappointment so that you can
Zongwei Zhou: Realize how bad you are. And yeah, that time I was thinking, hey, how can I improve my writing in a short span of time.
Zongwei Zhou: But actually, it cannot because that's the first advice. So don't think that the writing skill can be improved, like a one month or two months.
Zongwei Zhou: So what I did is I first work. Work with more senior student, you know, because when I joined the lab. It's a I'm the I'm the youngest person at the
Zongwei Zhou: Same time as the oldest person because the senior graduate student or graduate after the year I grew up. I am a. So, that means the only advice feedback can can get it's my API so that
Zongwei Zhou: That are really like a become a too much gap. You don't want to have too much gap to work with other people like
Zongwei Zhou: What I do my best. And the to his eyes. It's like nothing so you don't want to do that. So what I found is, hey, I may seek help from the
Zongwei Zhou: Previous a student, which, yeah, maybe five years, different from me. And he has a more. He also goes through the PhD, he may give me more advice.
Zongwei Zhou: And the key also very good. He really helped me to improve my writing. He also helped me directly help me to write the paper and give me feedback which which part is wrong, which part it's
Zongwei Zhou: too wordy, which part, I need to elaborate more. So that's actually really helped. So that would I say the tape for the newbie for the PhD always like contact with your
Zongwei Zhou: Senior student and for another thing. How many projects you want to work on some your tenure, I wouldn't say if you end up like first the two year PhD, maybe just focus on one project, but can make sure you have
Zongwei Zhou: You have
Zongwei Zhou: Determination not determination, you have a belief that this project will do that even
Zongwei Zhou: Yeah, even though you just the you believe it's okay. Even though up. I don't believe it's okay so but you need to work on focus on one project and make it happen.
Zongwei Zhou: If you cannot like unit plus plus. I think it's a good attack knowledge, but the own the problem it myself. I cannot write it. Good. So, that's okay. That's a nod to the reason I give up this project.
Zongwei Zhou: But it's not projects for so I just figured out how to like make this my short calm.
Zongwei Zhou: Like to the average and then eventually to get published. So when you want to give up one project that you need to
Zongwei Zhou: Like evaluate yourself like, is this your for the, oh, it's a project for or it's the idea for or it's something if it's your fault is OK, it's not a big deal. You just forgot how to overcome it and improve yourself.
Zongwei Zhou: If it's the idea for that whatever experiment that you do. It's done the work they just didn't work just forget about it. You know, that's my opinion about the project when you need to give up when you need to.
Zongwei Zhou: Pursue
Jay Shah: Right, okay. And one more question that I had was, like, you know, you must have joined somewhere in 2015 or maybe I don't know 2014 or so in your PhD program and
Jay Shah: If we see the timeline of deep learning, like maybe it was it was on the sweet come over that we see that deep learning was the use of deep learning was
Jay Shah: getting much more frequent into not just medical domain, but a lot of other domains and great results for published great algorithms were coming in.
Jay Shah: And a lot of papers were getting published over the time of year for years and maybe in the niche domain of medical domain that you have been working on.
Jay Shah: How do you see this, though, how do you see this biomedical informatics domain really progress in like I
Jay Shah: For me personally, I see a lot of cool applications being done and deep learning has very promising results, but from a person like you have def definitely are longer than me into this whole domain than me.
Jay Shah: So what would you comment on that, like, what are the open challenges that you personally, I understand you, you don't know the whole domain. So I it's completely fine like us your opinion on this, but
Jay Shah: What are the, how have you for the first question is, how have you seen this domain, particularly particularly growing and what are the
Jay Shah: Promising things for any person who really is confused like very sure i i want to apply deep learning, but I need a domain, like, oh why he he or she should choose medical domain. And secondly, what, what, in your opinion, what are the open challenges that that maybe the whole
Jay Shah: research community in medical domain might be currently focusing on what are your views on. Oh.
Zongwei Zhou: Yeah so deep learning. It's a suddenly become famous recent like another recently 10 years in the recent attend. Yes. And it is really promising, I would say. So for the first time I meet the deep learning. I use it is pretty easy to get started because a lot of a good the
Zongwei Zhou: Two blogs and get half it just the hands on it and yeah I found this really promising and now as as Ryan's
Zongwei Zhou: Working on the medical field. I also find that, hey, the plan is a really like influence the medical domain as well. So let me just give you one simple example like Kobe 19 nowadays copy 19 it's a start from last year, December.
Zongwei Zhou: It's a very like a southern thing nobody can can foresee this and actually from the this year, January or even earlier, the child, the
Zongwei Zhou: The hospital in China already like a really actively see the help from the industry and academia for the help. Hey, deep learning, it's very
Zongwei Zhou: Promising given me. Give us some help, because of so many patients that come in come out, not many radiologist like really understand this, nobody this or really have time to like a
Zongwei Zhou: Like a do every patient to like carefully. So how to do it. First, as they seek the help from industry because industry usually hold a really
Zongwei Zhou: Promising tool because academia, maybe some revelation study by the industry, they usually just the welcome some already well established technology. So the first
Zongwei Zhou: Seek help from from the industry and
Zongwei Zhou: Even short span of time the deep learning algorithm based CT scan or other
Zongwei Zhou: CT scanner or other computer like interaction. It's already built into into the hospital and this device can help a helper like a radiologist or physician to
Zongwei Zhou: Do the diagnosis. When the PCR even not come out yet. So at the very beginning, like a Chinese government they decided to use a CT scan because it's more straightforward.
Zongwei Zhou: That time no PCR. Of course now have a PCR, then we don't need to go through the trouble. And another thing, the AI tool can help. It's a quantifies the disease. The region. The. In fact, the region in the body.
Zongwei Zhou: Quantify what i mean quantify means a whole big it is and how bad it is, and how many burden estimate and even as they use this like a histogram to estimate
Zongwei Zhou: Like how many days you can live is a pretty crucial but they do this, they estimate to the burden estimated the are like after the treatment, how good you are.
Zongwei Zhou: gradually be this all this information. It's a PCR cannot give you because PC. I'll give you a binary decision, but this deep learning or even a CT CT scan. We can do this.
Zongwei Zhou: So, apart from the industry. The hospital also like really actively
Zongwei Zhou: Cooperate with the academia, like a university because you your university, a little bit slower than the industry. They need some funding, then you some project.
Zongwei Zhou: Settle down. So, but academia in this time urgent and also very actively response to this disease. For example, a lot of people just the
Zongwei Zhou: Group some public valuable data set on the website and they want to people like a collective effort to really focus on doing this. And another thing a lot of
Zongwei Zhou: Well well known journal and the conference. They have their own special issue on the AI to meet a copy of 19 it's another really good thing.
Zongwei Zhou: And
Zongwei Zhou: And the numberless the seminar. It's happened like us is to talk about
Zongwei Zhou: How, how far our group is and like a communicate each progress. So in summary, the industry and academia all very actively believe like a deep Lenny can do
Zongwei Zhou: It's very promising to do this because if it's not promising in this urgent time. Who will care about something like people and you know if it's not promising.
Zongwei Zhou: So, in my view. So this year, the domain growing really fast. So if you say five years ago. If you ask a doctor or physician or radiologist. Hey, what's your opinion about the deep learning
Zongwei Zhou: I'm not sure about this that maybe, maybe it's not as good as me and then people try to beat like I really come comparing like a whole AI will replace the doctor, or a I will over like a doctor.
Zongwei Zhou: Job will disappear or something and then now it's gradually people just a real life. It's just a tool that doctor also love to use AI as a tool to really like do some dirty job or to do some really
Zongwei Zhou: Difficult the job. So now it's a domain grows from like a competing like
Zongwei Zhou: I thought you you taught me I daughter, whom I don't know. And the truth really using this and make it a really a tool, not like a fantastic
Zongwei Zhou: Like attacking thing. So now it's a doctor just use AI. So it's a real to me. It's a very good achievement to like domain sheriff. The front the competing to the real application usage.
Jay Shah: Right.
Vatsal Sodha: Yeah, I want to add something. So I think
Vatsal Sodha: in near future, what's your thesis topic is cost effective medical imaging analysis. Right. I think that's the near future, for sure, but we don't want to use so much notation, you don't want to use so many label images because of printing labels is very expensive in terms of medical
Vatsal Sodha: So that's, yeah. I want to hear some opinion about that. And my second question is, in the long term.
Vatsal Sodha: Like
Vatsal Sodha: What I see right now in the field of just computer vision. I'm not talking about medical imaging in gentlemen I think the field is saturated. A little bit like last
Vatsal Sodha: Two years there is like there's so much incremental approach. You know, like in NLP people are talking right now about CPT three, but they are just increasing the parameters and it's becoming more like a GPU intensive tasks. So I want to get your V on vision side to like
Vatsal Sodha: What do you think of the long term, short term, I agree with your dissertation what you did. And it's fantastic. So both short term and long term. Can you tell me more about that.
Zongwei Zhou: Yeah, so every like a really novel thing is not like a happening happen every year is in the history like Einstein's
Zongwei Zhou: Theory and Newton theory, it's not like happened like every year this thing so usually
Zongwei Zhou: When these things happen like deep learning happens usually have a lack of 10 years or 15 years to have some incremental degree, like from zero to one. And from one to 11 it's it's usually so it's a it's it's a it's a helpful because
Zongwei Zhou: Like a really good good idea of deep learning at the very beginning, it's not very powerful, to be honest, like only can
Zongwei Zhou: Get the digital detection or recognition and and now it can add disease detection is a really good
Zongwei Zhou: Changes. So the pre like 10 years before the deep learning cannot do this. So the incremental innovation see important as well. And the for the long term. Yeah, I really hope that we get some more.
Zongwei Zhou: More
Zongwei Zhou: And better technology to come out like even shallow learning or what have I learned it is yeah I do help some good at technology that's
Zongwei Zhou: That's also the, the fact that I select my research problem. So I try not to like focus on deep learning
Zongwei Zhou: Or or SEM or random forest. I try not to focus on this kind of technology wise the problem, like how to improve this problem. I'm more care about a half, okay, you have a one to how to use it better. For example,
Zongwei Zhou: You have one technology which sample you wanted them. First let's look that's
Zongwei Zhou: Like a more general problem, like if the 10 years later, new, new architecture. Come, my, my research problems do you are people interested in. It's not like oh
Zongwei Zhou: This is 10 year before deplaning who cares. Now, so I tried to select a problem that is suitable for
Zongwei Zhou: A like a technology in general, for example. Another example, it's my thesis topic. The cost effective deep learning. So it can be cost effective other learning like
Zongwei Zhou: So my focus is not to get too deep learning better my focus is how you have, oh, you tell me you have a good idea. Okay.
Zongwei Zhou: My thesis to help you to make your idea more cost effective or more faster to get your perfect accuracy. So that's a research problem that I prefer so
Zongwei Zhou: Yeah you plenty may be replaced by other technology in the, in the future, but it is fine, we, we just see hopefully the get from me. The new technology. No.
Vatsal Sodha: Thank you will be keeping activity you like I will always review paper so
Zongwei Zhou: Thank you.
Jay Shah: But yeah that's that's it, that's a really interesting perspective and yeah but before I conclude I I normally ask this question a lot to all the speakers that I've invited, but most of them, they are
Jay Shah: They don't have a very similar profile in research. So feel free to edit the answer that you have to this question is
Jay Shah: In general for any, like, this is for the viewers, or maybe the students who are watching this video for any person who is really interested in research, but maybe just have started
Jay Shah: started learning about deep learning machine learning or in general, the whole domain of researching these mathematical models.
Jay Shah: What, what, what would be that one tip that you would give for him or hard to understand that. Okay. His research because I see a lot of people even even I had those incidents where I felt like okay maybe research is not for me. Like, this is really
Jay Shah: Boring. I would say because not a lot of people really enjoy mathematics behind the deep learning architectures.
Jay Shah: What would you have one tip or one advice for people. Any like they could be undergraduate student graduate students, obviously students for anyone who is actually planning to do research, but that
Jay Shah: They, they just don't find that interest or in the very start face because
Jay Shah: Because before you answer like a few words from me would be like it doesn't give you that gratification factor like a job, would they like you said.
Jay Shah: Previously job has a lot of incentives. You can make your own life but PST doesn't really allow you to build your own life.
Jay Shah: So, any, any just, just feel free to take this answer to any context that you, you want to, but what is one tip that you would say to any person who is genuinely interested in research meet a graduate student or a PhD student
Zongwei Zhou: Right, so the answer. So the advice will be
Zongwei Zhou: 321 okay
Zongwei Zhou: So it's about find a good mentor, you know, so you cannot do research by your own. You can find a mentor online. You can find a mentor. Like, but you need to find a mentor. So to my opinion I cannot accomplish this without any like a guidance or help from either my API or either a
Zongwei Zhou: Senior student or even the junior student or all the members in my lab. So you need some group. So if you go to the industry that fund the group. Maybe you get a group to work on one project. If you go to PhD than the lab. It's a good
Zongwei Zhou: And yeah, the reason it's a you want to expose to as much as possible. The good problem.
Zongwei Zhou: You, you really don't need to care about what's the solution, you need to like understand what is the good problem, like people really care. And then you figure out the solution.
Zongwei Zhou: Then you can't. You don't need a mentor you just get online see Sam related to work. And yeah, the first very challenging problem is you need to find a promising direction. So that's what mentor.
Zongwei Zhou: Meant to be. So yeah, my advice is don't do it alone, like a try to find somebody like that's a one of the
Zongwei Zhou: Advantage in PhD you you have to bond with one person like four years or five years. So he will he have to like help you or educate you. So that's a good opportunity.
Zongwei Zhou: Oh yeah, if you don't think all research is boring. Yeah. So that's his job to make it interesting and the combats you. What's the significance of the problem that we're working on.
Zongwei Zhou: Then you get them motivated because nobody learned some new things like a real they really know like a island piano when I'm
Zongwei Zhou: When I'm eight. Yeah, yeah, if my parents don't tell me it's a piano in the word, how can I come up with my own you know how comma at year a
Zongwei Zhou: Kid, like say, Hey, Dad, I want to play piano, so what what right so you need a mentor like introduce you. Some piano and then you can work on it.
Zongwei Zhou: gradually build some interest on it. So nothing. It's like interest you like a front. No way. You know, you still need some a lot have a voice and see a lot half for YouTube like to this channel, it will give you a lot of good views from different perspective. So, yeah, yeah, yeah.
Jay Shah: That's, that's a, that's a really great advice. And yeah, I can see that I'll be using that advice, personally, a lot. I don't know about on the people who are watching this video. But yes, that's, that's really
Jay Shah: Critical but yeah this this conversation has been great. I don't know, but still has any other questions, um,
Vatsal Sodha: Yeah.
Jay Shah: In that case, I'll just tank what sound and song we both of you for taking the time I'll thank what's up because he was the person who got in touch with song way for this particular video and
Jay Shah: And of course, thanks a lot longer for taking the time I, you know, it's, it's, I'm really fortunate because by the next year, I guess you would be joining at some University as a professor and would be really hard to get in touch with
Jay Shah: This kind of video but i'll be proud enough to say that okay I interviewed zone where you're back.
Jay Shah: So yeah but yeah but on a serious note, thanks. Thanks a lot. Be I really hope this video is really helpful to people who are just starting their
Jay Shah: Career into research on in general on deep learning
Jay Shah: Or any other machine learning domains.
Jay Shah: So thanks again. Thanks a lot. I'll leave
Jay Shah: If you don't mind, I'll leave a LinkedIn.
Jay Shah: Profile of yours, and the bottom for any people who are genuinely interested in getting in touch with you for any of
Jay Shah: Course, but like
Zongwei Zhou: Scan this
Jay Shah: Right here, right here.
Zongwei Zhou: Yeah, right here.
Vatsal Sodha: I think he has his own website is always good.
Zongwei Zhou: Yeah.
Zongwei Zhou: Yeah, don't wait though.com
Jay Shah: Yeah.
Zongwei Zhou: Check this off.
Jay Shah: Alonso Alonso put a link to your profile so that people can get in touch with any best possible. But yeah, sure. Thanks. Thanks a lot. And thank you. See you guys around and the campus.
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