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Intro/Outro (00:00:01):
Welcome to the Microscopists, a Bitesize Bio podcast, hosted by Peter Oall, sponsored by Zeis Microscopy. Today on the microscopists,

Peter O'Toole (00:00:14):
Today on Microscopists, I'm talking to Aron Samp from U C L A. We talk about the important communicating research with the general public.

Aydogan Ozcan (00:00:23):
We need to protect science, and for that, there's nothing better than scientists communicating their major results as opposed to just communicating those results to their peers.

Peter O'Toole (00:00:34):
The need to take ownership of your research,

Aydogan Ozcan (00:00:37):
That letter, that papered article is, uh, is your commitment to science. And it it's going to hopefully be read by someone, uh, to inspire that scientist, uh, with new ideas, new directions, new things,

Peter O'Toole (00:00:52):
And how advances in imaging could change the way we do security scans.

Aydogan Ozcan (00:00:58):
What if you could, uh, build an imaging system that could only image the weapons, metal, whatever is the target and everything else, the body shape, et cetera, is filter That, and, and those kinds of ideas are possible

Peter O'Toole (00:01:14):
All in this episode of the Microscopies.

Peter O'Toole (00:01:24):
Hi, I'm Peter Atol from the University of York, and today on the Microscopies I'm joined by Awin Acan from U UCLA and H H M I A Awin. How are you today?

Aydogan Ozcan (00:01:35):
Doing good. Doing good. How are you?

Peter O'Toole (00:01:37):
I'm really good. Thank you. Thank you so much for joining me today. Uh,

Aydogan Ozcan (00:01:40):
Pleasure. Thanks for having me.

Peter O'Toole (00:01:42):
I've, I guess my research some years ago moved into the world of label free imaging, uh, with typography. So it's not, not holography. Uh, and we've never personally met pastorally, cuz my diary's absolutely shocking to get to the meetings I've been invited and wanting to go to. But the film seems really collegiate. And how is that really the case? Because you'll know the film much better than I do. Uh, how is the field of pornography and how, how does that interact together? It's a small market, so I can imagine there's lots of competition cause there's not many of us in there. But how is it in reality?

Aydogan Ozcan (00:02:19):
Um, yes, it's a very collegial field and it's very international. Um, and I learned this firsthand by, you know, by entering holography field, um, from scratch. Basically, my PhD was, uh, nothing to do with geography, nothing to do with microscopy, and nothing to do with imaging actually. And nothing to do with biophotonics. So you can imagine how, you know, um, I'm kind of an orphan, um, trying to find my own way, you know, my own lab and everything. And, um, I, I think pornography, field label free imaging, qpi, quantitative phase imaging field, uh, is a very open-minded, um, uh, you know, um, field with a lot of international leaders. Um, uh, kind of embracing new ideas, newcomers. Uh, and uh, and that certainly helped me set my stage. And I could feel that as I was bringing some new insights, new ways of doing things, uh, they, they were kind of, um, opening a space for me and, uh, including me, uh, in, in specific conferences, uh, kind of like helping me, um, move forward with my career.

(00:03:33):
Uh, and I first firsthand observed how collegial the, the environment, the field, uh, is and still is. And, and it's very international at the same time you have, yeah. You know, uh, researchers from the us, the, you know, from Europe, Asia, uh, different parts of the world, different universities, different continents. Uh, it's beautiful. It's really bringing the joy of science because, um, otherwise without the human factor, without, without, uh, this, uh, you know, uh, friendship that you built over years, it's very stressful. It's just competition after competition, you know, getting scooped, uh, scooping others, you know, science, uh, has some competition to it. And that's, that's natural. And it's, uh, it, you know, kind of gets the best out of every research lab, right? It brings some fresh air, but it must be balanced with, with friendship. And, uh, you, you need to really feel, um, the, the environment being, being open to newcomers with new ideas.

(00:04:44):
That's the only way to, uh, stop self beating. It's very dangerous to have, you know, uh, certain labs dominating the field with their own proteas. And, you know, I've seen fields like that, but fortunately in general, geography and label free, uh, imaging is far away from that. And it's a blessing for me. It's been a blessing for me. And I think it's a blessing for everybody because it promotes innovation. It promotes newcomers to come with unique perspectives. And it makes it fun, honestly, uh, to attend conferences, learn from each other, collaborate, establish new collaborations. And still there's competition. And competition is extremely healthy if it's balanced with, with such an environment, with friendship and everything. So I can certainly say that is the case for, uh, label free imaging choreography. And it's been a blessing for,

Peter O'Toole (00:05:38):
Yeah, I think, yeah, when I look at publications, there's a few megastar in the world of digital pornography, label free imaging, of which sure. One Gabby was another park, another, I'm not gonna name everyone cuz otherwise I'll offend someone by not using them. But what also amazes me, some of the conferences are co-organized by the same people. So although they're competing, they're also coming together to bring the community together.

Aydogan Ozcan (00:06:03):
Yeah. And go was fantastic in that actually. Um, uh, Goby, um, uh, is a big loss for optics and holography and label three imaging. Um, he was a great person. Um, he was very deep, um, in theory, uh, when you visited his office, you could clearly see, uh, papers that he was driving, things like, you know, he was behind, uh, all the, uh, theoretical frameworks, um, of the major instruments that, uh, his lab pioneered in QPI field. And he was very rigorous and deep as a scholar at the same time. A great colleague, uh, friendly, um, you know, he would be critical of, of certain things that he doesn't understand, but, but he would feel that it's out of curiosity and, and his scholar scholarship depth. But at the same time, he would be open, welcoming and, um, helping you kind of, um, with, with, uh, with, with your new approaches.

(00:07:08):
Um, when I was, uh, first working on, um, partially coherent holographic microscopy on a chip for ultra high throughput, uh, you know, sensing microscopy, imaging pathology, telemedicine type of applications, ultra compact cost effected, but at the same time some beautiful math behind it in terms of partial coherence, spatially, and engineering. You know, he was one of the first to kind of appreciate it. Um, and, uh, i, I guess scholarly support. Um, and I'm sure without knowing, uh, he must have, or his team must have reviewed some of our papers as well and making them through the review process perhaps better. Uh, I'm just guessing because he's, he's a prominent name and, and we publish quite a bit, um, sometimes 20 papers a year, right? So, uh, it would go to some of, some of the colleagues. So overall, um, it's a big loss. I wanna remember here.

(00:08:08):
Uh, Goby, pop Poco and his legacy, not only an amazing scholar, very productive scholar and entrepreneur, startup companies commercializing his instrumentation, uh, trying to create a community around him, uh, around qpi QPI instrumentation, trying to convince biologists, uh, pathologists, uh, and, and, uh, uh, you know, especially Sal biologists on the benefits of qpi. Uh, while doing so, he was also a great instructor, um, co author of, um, of, uh, an important book, uh, and organizing conferences, inviting, uh, you know, uh, different people around him to form a community, a welcoming community. Um, so it's a big loss, but at the same time, you quite a bit, um, from his legacy of how, how science evolves and how, you know, science benefits from leaders like him. Um, and at the same time bringing some humor, some, some human insights, human human nature, uh, as part of the scholarship to make it welcoming and at the same time, entertaining, enjoyable, uh, during this, you know, scientific exploration as a field.

Peter O'Toole (00:09:21):
So, awin, that, that leads a, there's a couple of questions I want to ask on the back of that. Uh, the first one, I think I, I'm gonna go down the direction of you. We're all trying to push label free. I, I'm quantitative phase imaging and, and in towards a life science market, because it's got a huge potential there. Mm-hmm. , but it still doesn't seem to be hugely utilized yet. Now, I, I've certainly got my ideas why it's not massively, I, I've gotta say, York has embraced it quite well. So we have a couple of instruments and they are saturated in use, which is good, but it's, that's not common, uh, across all of academia. Do you have any feeling why, what, what are your thoughts or why it hasn't yet become the next confocal microscopy, for example?

Aydogan Ozcan (00:10:10):
Um, so it's, it's a great question. Um, part of it is, um, I think, well, it's, it's very, um, let's, let's put it this way. A fair analysis of this, uh, needs some sophisticated research uhcustomers on, on the users, we call it, for commercialization of technology, um, customer discovery. Yeah. So I think that's one of the, um, most important things that academicians, uh, we're, we're obsessed with, with, you know, novelty, with capabilities, innovation, instrumentation, you know, those kinds of things that are driving engineers and scientists as, you know, as the goal, but impact translation of impact into a domain where there are other key players like Sal biologists, let's say, or pathology market, let's say. It's a very different game. It's actually, you need to understand the landscape of the users, what they need, uh, and money, like, you know, the market, who's, who's selling those instruments, who's reimbursing them, uh, distribution channels, manufacturers, those, those kinds of things dictate success.

(00:11:32):
Um, and, and I think someone has to study, um, label free imaging for cell biology, uh, from the perspective of users. Um, how, how does it compare to fluorescence based methods that, that are currently employed? Uh, what are the advantages? Uh, what would it take to translate? Um, uh, you should also think about, uh, new instrumentation. Um, how, how, what's the cost point of a new instrumentation to disrupt the existing technologies? How much, uh, is a lab, for example, in Europe, willing to pay for a new instrument? Is it, you know, at a level where they can dump their existing solutions and entirely go go through that? What are the advantages of label three approaches? Uh, that would be, uh, you know, the massive markets for pathology is a cell biology for pathology. It's entire new animal. So one of them is as an orange, the other one's an apple, and you have to study them separately.

(00:12:34):
The lessons will not translate pathology. It's a very conservative field. And when you, is that exceptionally conservative, exceptionally conservative, exceptionally conservative. I, I I like that word. Yes. Um, it is, and, uh, the therefore there, the rule of the game will be, you know, throughput. Uh, so things like ultra fast scanning. So you'll be competing for the pathology market, let's say you'll be competing with, uh, systems that can in, in a couple of minutes, scan five centimeter square with defraction, limited resolution. These are the feris of pathology microscopes, whole slide scanners. Yeah. Uh, so, so all in all, I think, um, the field, uh, of QPI choreography will need to first understand the, uh, most valuable product and the value proposition in entering a domain that they want to disrupt with, with the, uh, with the, with the instrument. Uh, and for that, that starts with a very careful analysis of advantages, disadvantages, and who's gonna, who's gonna be paying for the advantages?

(00:13:54):
Uh, and at what point, and, and what can you achieve as a, as a scientist engineer to meet those needs for geography in general, reconstruction engines in, you know, speaking of creating Q P I, there is the problem with, with commercial success and impact, I think is also little bit hampered by the revolution of the digital background of how do you get to the Q P I, let alone the hardware, the instrument is constantly upgrading itself in the, in the, you know, the next, uh, you know, five years. Uh, it's gonna upgrade itself, transform itself, let's say, compared to the previous five years. Because in the middle of whole, you know, uh, uh, Q P I revolution, deep learning came, and all of a sudden a lot of the reconstruction engines were also getting some transformative advances, uh, in, in the way that are done. This is great, but, uh, you know, a startup company, uh, there's a joke for startups, right? If you wanna succeed, at some point, you have to kill the engineer, because if the

Peter O'Toole (00:15:05):
Stop developing and sell,

Aydogan Ozcan (00:15:07):
Exactly. So if the product keeps getting better and better, it's cool from science perspective, but impact perspective, you need to kinda stop the growth at some point and, uh, start the development and sales, uh, with a device that is locked, uh, you know, ready to go.

Peter O'Toole (00:15:26):
So it isn't that like publishing though, even in the research lab, that there's people who always see the next thing and want the next thing before they publish. So instead of thinking, okay, that's my paper, let's publish, they think, oh, but just one more experiment will add more and then more, and then more. And then this work is no longer relevant.

Aydogan Ozcan (00:15:41):
Yes. That, that,

Peter O'Toole (00:15:42):
A similar philosophy,

Aydogan Ozcan (00:15:44):
A absolutely, it's similar, but it doesn't hurt the paper. The paper, uh, will, can be two papers put together. Supplementary can have a lot of those relatively irrelevant older things to support certain claims, but not the main claim of the paper. And you can get away with it with a very long paper, uh, if you carefully drafted some of that earlier development can be hidden, the supplement. But market success with instrumentation, with startups that take QPI or other kinds of biophotonics interventions, inventions into kind of next step. You know, it's gonna hurt if you keep doing that because then the target is changing and you burn a lot of cash and do, do not deliver. So, uh, for pathology, you cannot afford doing that because you need to start validating your technologies with biomedical stuff, with clinical studies. And if it keeps changing, FDA won't let you do anything, right?

(00:16:41):
So there, there is a lot of, uh, a lot of things that are lumped into impact when it comes to biomedical space, cause of the regulations, cause of the customers not being engineers and they're skeptical. They don't want to change their tools, uh, if they're working well. So that means, uh, Q P I has perhaps little bit more time to mature, to settle. Um, and the, the techniques in terms of hardware and software that are the most competitive, that are being currently translated into spinoffs and into, you know, products or co-development by engineers and biologists, we, we'll see how it goes. There will be a filter of time, but through that time, I'm very hopeful. I'm, I'm, I'm believe there are going to be home runner applications in QPI for pathology, for biomedical diagnostics, for sensing for cell biology. We will just have to ba wait a bit more for the time to kind of filter the most competitive approaches that meet the demand at the same time, the right team taking it with the business, with the engineering team. So I think that's my take on it. I'm very optimistic overall, but, uh, it's, it's fair to say that there's a lot going on, and in general, computational imaging, computational imaging field is transforming and it's gonna continue transforming.

Peter O'Toole (00:18:08):
Yeah, I, I, I, I couldn't agree. Obviously, I couldn't agree more ,

(00:18:15):
But I think, yeah, quantitative phase imaging, one of the other problems is it's still relatively new to the, to the cell biology field that a cell biologist and not microscopists, and they're built into their grant. The use of fluorescent dyes and the application to do it at the moment, quantitative phase Q P I, is still a bit of a novelty and they're not sure what they're going to get at. They're not asking the questions that they can ask with it, cuz it enables us to ask utterly new questions of our samples that we weren't before. That I say your, you know, it is taken time, you know, we've been since 2010, I think, working with, uh, typography, so not holography, but a form of quantitative phase imaging. And it's taken a while to get the users to appreciate, understand as soon as they do it, it's, yeah, they, they use it a lot and start publishing, which is, which is great news.

(00:19:10):
And I think you said about the digital and pathology and the, the difficulties of getting it to market. And I, I, there's certain companies I work with, not just in the quantitative phase place, but in, in other technologies too, there comes a point, you're better just to sell to the research market to get an income coming in and then fine tune it to get it to the clinical markets. At least you've got an in in income coming in. Now we should very, I did say I'll get too geeky in this one, so I've gotta be very careful. The other part of the question you mentioned earlier, you said how Gabby, you, you went in his office and there was lots going on, lots of different ideas there. You, I think that's a bit rich coming from you who published these maybe 20 publications a year, which is a huge amount.

(00:19:56):
And it's not just in quantitative phase imaging. It's not just in holography. You, you yourself have tons of work going on, uh, publishing q and actually what, this is a really key bit that I think is a really good message for people to understand. It's not just the publications, there's a lot of commentary that you do. So you'll see articles in new scientists, in newspapers, articles, showing the relevance to society today. Firstly, how big is your group? Secondly, how on earth do you publish 20 papers a year? And three, how important is communication outside of the scientific field? So to, to get it broader. Yeah.

Aydogan Ozcan (00:20:40):
So lemme start with the last one. Um, I learned that actually, um, uh, when I was a junior faculty, um, the, the power of press releases, uh, or the power of communicating your science in short pieces with one figure simple, uh, lay language, uh, that news, uh, reporters are accustomed to writing. And maybe at most 700, uh, a thousand words, like, kind of like news articles. It, it, it's so powerful, the storytelling aspect of it. Um, I, I learned that, um, when I was visiting National Geographic, um, this was the, the year, the years that I was into mobile microscopy for a developing world. Uh, and, and using the mobile phone as a, as a vehicle to bring more advanced sensing and, uh, microscopy and in general measurement tools to kind of resource limited settings. So that was kind of the story initially that I had.

(00:21:43):
It was matching, uh, a poor startup package because it's, it must work in resource limited settings. So good. Uh, you know, you don't spend too much time and money to, you know, to create very sophisticated devices that, uh, take a whole bench. You kind of, it also matches your junior faculty budget. But anyways, I was working on mobile inexpensive microscopy, and, um, was invited to national, you know, national Geographic to talk about it. And also, uh, they, they were honoring kind of like this new direction that, that I was working on. And there they were kind of also educating, uh, a bunch of us, um, about what they do, about, about the importance of storytelling. Uh, there were instructors that, uh, you know, were educating us in short, uh, sessions about storytelling. That was the first time that I heard about storytelling from the context of science.

(00:22:37):
And it was like, wow, you know, that, that, you know, during my PhD or postdoc, I never, uh, understood that the importance of that, the significance of that, and it coming from National Geographic kind of helped me kind of realize the importance of that, uh, and think more about it. And through the whole process, uh, of, of, you know, learning and kind of like un understanding the importance of it, I developed this kind of, uh, habit of, whenever I have a major result, 20 pages of , uh, publication, lots of equations, figures, graphs, where's different kinds. I always try to summarize it to one figure and something like 500 to 700 words of impact statement that is a draft of a press release. And, and, and that was priceless for many reasons, because it helped me disseminate, uh, the key important points to the public.

(00:23:37):
It's great for funding agencies, because that's the taxpayer money everywhere globally. It's the taxpayer money that pays our research and our training and, and, and publishing mission scholarship, mission training, next generation. That's done through taxpayer money. You have to educate the public that this was funded by, let's say, the National Science Foundation. And that's how that foundation taking billions of dollars, dollars of tax money giving the feedback university so essential. And, and that's, that's number one. Number two, it's also bringing faith in science. Uh, science is under attack globally, unfortunately. Uh, it's, it's accelerated over the last 10 years that I was seeing, um, from at least where I, where I stand. We need to protect science. And for that, there's nothing better than scientists communicating their major results as opposed to just communicating those results to their peers, which is very important. But it's equally, in my opinion, especially in this era, to communicate your science and how it is going to make life better.

(00:24:49):
It's very easy for engineers to, to talk about this. I believe cause engineering at scale is making life better and easier for people at, at scale. And, and that story is already evident, uh, in a lot of our work. I was very fortunate, uh, I had a, a communication channel with the public through the mobile phone. Cause mobile phones are addictive, right? Uh, especially for the teenagers. I used that as a way to kind of understand firsthand the power of it. And, and from that point on, I, I, I, I, I, I started to kind of have a workflow for that. Um, in fact, it also helped me bring funding, believe it or not. Um, there was once a case, this is very kind of funny, um, through a press release that we had from ucla, a foundation contacted me and told me, would you be interested in working on Lyme disease?

(00:25:53):
At that point, I didn't know how Lyme disease was, you know, transmitted and what are the ways to diagnose it. As I was speaking to the foundation, I was trying to kinda understand more about the disease. So they found us, uh, through a press release, and they thought we could be a, you know, good engineering group to work online disease to transform, you know, new kinda create new kinds of point of peer diagnostic tests. That kinda an interaction, um, enabled me to bring a large team at UCLA working on Lyme disease for the last five, six years. Multimillion dollar. Uh, fast forward, we have, uh, a multiplexed Lyme disease point of care test, uh, read by a mobile phone, very inexpensive and better than the currently available FDA approved technologies, in our opinion, published on it. Um, but that was something where literally they found us. And the next week, me and my colleague took Uber from ucla, uh, to a Hollywood villa. Uh, the, the founders of the foundation were, were there, and with, with a box, uh, like solicitors. I was carrying mobile, phone based microscopes and different things. Um, got out of the Uber and I thought, well, before security kicks us out because, uh, they would think we're solicitors, let's quickly go in there. And that, that's how it was launch. And that was purely PR helping. So that was a long answer, but that's act actually very important.

Peter O'Toole (00:27:30):
That's kinda the dream, isn't it? It's kinda the dream is to do a press release and someone realize the importance of the work and help sponsor it, help speed up the development. And, and in your case, actually slightly better than that. Cause it's also given another biological application that has Absolut, you

Aydogan Ozcan (00:27:46):
Know, absolutely.

Peter O'Toole (00:27:47):
Point of patient care. Yeah,

Aydogan Ozcan (00:27:49):
It, it helped me, uh, establish a team through that. Um, I started the long-term collaboration with the microbiology group at ucla. Uh, since then we're, you know, weekly meeting and formed a core team and expanded, uh, our ideas. It was so challenging that at some point I had to invent a new way of doing it. That was fantastic because that helped me bring some new insights because to sensing, in my opinion, because land was so challenging that we were failing in the first two years and like spinning wheels. And we said, you know, let's, let's do it different way. And it worked. And, uh, that's catalyzed by, by, by the pr. And and I tell this every, every time to new generation of students that come to my lab because, um, they should understand the importance of this communication. Internet has a memory, and those PR pieces that you publish will help, uh, disseminate science.

(00:28:47):
Its impact funding agencies impact your own labs, impact your trainees impact, and will bring confidence to science and hopefully help you kind of maintain, uh, a healthy, uh, funding environment for what to do. Going back to your original question about, you know, number of papers and, and you know, what, what, what, what does it take? So I take my papers quite seriously. A lot of people, um, perhaps, um, with, uh, these papers are not short papers. These papers will have, uh, quite a, a lengthy supplementary material. And if you put them together, some of them will be 50 pages with supplementary. So, um, it's a lot of work. And, um, scholarship in my opinion is, is, um, is your communication channel with the future. Uh, and for centuries, science has been propagated to us. And the next generations through written scholarship. Written scholarship to me is the most important way of talking to the future, you know, new generations.

(00:29:58):
And I love that. That's, that's my passion. That communication is the storytelling. Not to the public, to the peers, to the peers that will perhaps meet me when I'm close to my retirement, maybe after retirement. That's my letter to the future. And, and I take it that way. And, and that's why it's so important for me. Every figure, every subsection, every method, section, those are really things that I craft. And even the proofs I take very serious. Cause I don't like, uh, in inaccurate science to propagate. And that's why even the proof stage, I'm deeply involved and it's not stamping for me. It's not just like you, you write the paper and we put our names, it's far away from that. It, it must be, uh, your signature. And as, as a result, it's your ki kind of like your, your, your legacy. And you must make sure that it, that letter, that paper, that article is, uh, is your commitment to science.

(00:30:57):
And it, it's going to hopefully be read by someone, uh, to inspire that scientist, uh, with new ideas, new directions, new things. And that's what I love about science. I mean, think about a scholarship that you can talk to 10 years from now, the new generation. And, uh, you know, 20 years from now, the new generation, sometimes in conferences, you must have the same thing, right? You get approached, uh, by people junior, maybe the first time attending a conference, they look at your badge, and then they, they realize your name, and then it triggers that they don't know your face, but it triggers a certain paper that they read. And, you know, you start communicating with the previous generation, like 20 years younger than you. That's, that's why I do science. That's actually exactly, um, my letter that is, uh, published maybe 15 years ago, finding it's, uh, correspondent.

(00:31:49):
And, uh, we, we talk about it briefly and exchange ideas, but that person must have read it and is doing something useful with it that I didn't do, or I didn't even anticipate at that time. And that's why I take it very serious. Uh, and, and I think the volume is not important. Uh, everybody has a different flavor. Some, some labs are, uh, more focused, uh, on one theme. Some are, uh, more diverse. In my group, there are three different core areas. If you divide them separately, they could sustain as a separate lab. One of them is computational imaging. The other one is, uh, sensing, point of care, sensing, mobile sensing. And the third one is optical computing. Um, recently emerging area. So these three play out and, and I have groups that, but they're also sustaining themselves. So that's why kinda the volume might be larger. But at decor, um, I have this passion for, for, uh, for, for those papers, the, the excitement cause of the story. And again, the, the communication channel to the future.

Peter O'Toole (00:32:56):
You mentioned your three areas of research. Uh, so obviously you've got your lens list and your holography side. And actually Laura Waller, uh, a recent podcast guest in a very similar area, sort of probably different target markets, uh, of where these applications are being developed for. And you talked about the sensing, but you talk about Optum computing very briefly. What is opticum computing?

Aydogan Ozcan (00:33:21):
So, um, so I, I mean the, the, the, the theme that I'm working on is a, a specific, uh, class of optical computing where it's, um, structured material, um, using deep learning, uh, so that the material itself, uh, think of a, uh, uh, of a volume of a material where it's composed of layers. Each layer is transparent, let's say. But at the wavelength scale or sub wavelength scale, it has a code in it, um, which defs light with certain phase layer, the wavelength scale. But there are, uh, thousands of individual phase elements that are grade each layer. So, uh, this kind material, we call it defractive network, which means, uh, it's a passive material that is engineered at the wavelength scale, layer by layer, very thin, maybe layered layer separation is, uh, a few lambda to a few tens of lambda in the visible. One of these things would be like a stamp.

(00:34:31):
So the question is, how do you, how do you design these, uh, these stamps, volumetric defractive, uh, elements, and what do you do with them? What can, uh, what can light waves as they propagate through these defractive, uh, elements compete for you? So, um, these are task specific, all optical computers that you design using, uh, deep learning. But once the design plays in a computer is over, you go to the next phase and fabricate them using 3D printing, lithography your favorite tool. You build these layer by layer, uh, defractive systems to create a passive material. This passive material, you can think of it as a super set to, uh, an imaging lens. So, um, for example, you can build, uh, a, a defractive camera, which, um, does computational imaging behind a diffuser. So, as you know, uh, seeing through walls, seeing through fog, or seeing through the skin diffusive elements, random, uh, diffusive elements is, you know, seeing through them is a challenging problem.

(00:35:42):
And there are different ways of tackling this as an example. Um, but all of them are computer based. What if, uh, you could design a defractive camera, which solves, um, this inverse problem of seeing behind the complete diffuser with a new volumetric, uh, processor that processes the wave that is scattered by the phase diffuser? The rule of the game for these kinds of inverse problems, to be all optically performed with passive materials is to model, um, this VA propagation with thousands of examples of random diffusers. What we do is, in a computer, we actually show many examples of objects and random diffusers with a certain correlation leg with a certain grain size. We constantly change them. And there is a defractive volume impact. We're trying to adjust the phase values, uh, within layer by layer design until all optically, the image behind the, the, the defractive layers is, uh, preserving the features of the unknown object behind the diffuser.

(00:36:49):
So this training cycle, uh, continues for many epox. And at the end, you show, let's say a couple of thousand examples of random diffusers, then that volumetric, uh, system that you've learned after you fabricate. It generalizes to actually see unknown objects through unknown random diffusers, um, by a very elegant, uh, principle where it actually, um, if you look at what kind of a defractive element you have converged at the end, it's composed of tiny phase islands per layer, like micro lenses Yeah, that are, that are forming, uh, relatively dense, uh, array, which is axially aligned to the next layer. So the communication of the defractive layers is happening, um, be between the defractive layers is happening through this array of micro lenses, but that's the imaging backbone. The space between the micro lenses is, uh, surrounded by rapid face perturbations. And those face perturbations are rejecting the modes of the phase diffuser to site like radiation.

(00:37:59):
Radiation modes, kind of like leaky modes. It acts like a filter, a random filter that is, uh, understanding the modes of the random diffuser. And in the meantime, passing the wave coming from, uh, the, uh, the object plane. It's a good example of an inverse imaging problem that is solved at the speed of light, which means, uh, it's a passive computer task specific. Uh, it doesn't consume power because it's transparent material that is structured. Um, it's scalable. We can make it wider to compute in parallel. And the most important thing is it doesn't digitize anything, or it doesn't store any memory of an image to be inversed, uh, to be, uh, inverted because the solution of the object is appearing at the speed of light behind the deactive system. So these kinds of cameras are, uh, uh, possible with defractive computing using base degrees of freedom in materials to process waves and com compared to electronic computer vision systems.

(00:39:09):
The biggest advantage that I see in this line of research is this. Today's computer vision. Um, you have a scene. It contains a wave that has the object information in the face channel, like QPI systems or amplitude channel spectrum polarization, you name it. If you want to act on that information, rich information in front of you, you digitize it, you pre-process it and use GPUs. That's the traditional machine vision system. It requires a, a, a setup, digitization, storage, cloud, and GPUs. These kinds of processors, defractive processors, I think are uniquely positioned to take the advantage of the information in the analog domain within a certain aperture and compute defraction limited transformations, defraction limited, uh, uh, you know, computation on the flow. In fact, this framework we've shown is a universal linear transformer. It can perform with being any input output, input a plane, and an output plane.

(00:40:13):
It can, uh, perform any arbitrary, spatially varying points per function at the defraction. So think of a spatially varying complex points per function between an input and output aperture or arbitrary at every point, an arbitrary points with function. You can program it to do universal linear transformations in the complex domain, uh, which also translates into matrix of vector multiplication, not just convolutional any kind. So, so it's a, it's a, it's a very interesting framework that we have been playing to build, um, you know, defractive cameras or various different kinds. Um, solving the, uh, imaging through the future kind of a problem. A recent work showed that it can be useful for building plus specific cameras, which means another machine vision problem that we have today is we digitize everything. Uh, we, we, we capture everything and then sort it out. Uh, what if you could have actually a camera that rejects photons if the ordinary is within your field of view and only lets the photons and the image pass through.

(00:41:24):
If it's of the class that you want an image, we call it class specific imaging. Think of an industrial GL application where security screening, every time you go through the security screening, you know, every part of your buddy is imaged with terror earth scanners. But we're, it makes us uncomfortable because, you know, that's not what they're looking at. Why aren't they our buddy? What if you could, uh, build an imaging system that could only image the weapons, the metal, whatever is, is the target and everything else, the body shape, et cetera, is filtered down. And, and those kinds of ideas are possible. In fact, a lot of these kinds of defractive cameras, we built them in terra hertz waves and, um, cause the fabrication, um, we're using a 3D printer for a lot of these ideas to be tested experimental in, in, in terra hertz plastic is like gloss, it's transparent.

(00:42:18):
And 3D printers are having sufficient resolution at terra hertz waves. So we built, uh, actually I have one of these next to me. This is actually, um, let me see. This is a defractive, um, uh, system. It's got three different layers to it. Uh, if I bring closer, maybe you can see this has three different layers to it. Uh, you don't see, you only see the outer layer. It's actually a defractive processor for seeing through diffusers where there, there are, there are thousands of lamb over, two features half away. One features here that together does a certain, um, transformation in this case, uh, for, uh, oh, this was, uh, class specific imaging. This was, uh, certain data clause, era, everything. So yeah, that's, that's the of optics. It's got so many interesting degrees of freedom to it that you can play and, um, find yourself in, in a playground and entertain.

Peter O'Toole (00:43:13):
So awin, how, uh, so I get on that side just about get that side, but I can see actually quite an, uh, industrial application for that and, uh, materials engineering type applications. Yes. And yet, so many of your that, that I'm familiar with, applications are actually around the biomedical Yes. Side. You said at the very start, your PhD was not in microscopy. It was not in the very, what was your PhD? Gotta be careful, certainly. What was your PhD roughly?

Aydogan Ozcan (00:43:50):
Yeah, my, my PhD was on, uh, nonlinear optics. Um, I was, uh, working on silica and, um, amorphous silica and, um, trying to introduce, um, second order non-linearity, um, through the rectification of kit three processes, so that there is a kit two with built an electric field, uh, rectification of kit three. So kind of like with the motivation of building electro optic modulators in fiber optic cables. So that's what I've done. Um, but it's

Peter O'Toole (00:44:24):
Not very in the engineering side,

Aydogan Ozcan (00:44:26):
Yes, applied physics engineering. Yes.

Peter O'Toole (00:44:31):
How'd you get into biomed med?

Aydogan Ozcan (00:44:34):
. So, yeah. So, um, the, the tools that I created to look into materials, um, so you were creating non-linear materials, right? But the, this was not a standard non-linear material, it was non uniform. So I had to invent new, new techniques to image what I was, uh, creating. So I built some non-linear, uh, material characterization tools with some math behind it. Then I realized the math was a applicable to optical coherence tomography. So you can think of appalled glass, where you, in this certain non-uniform electric field, very strong frozen electric field to charge distribution that rectifies, K three, that's actually, um, like imaging tissue in a sense with similar ideas. You, you, uh, understand the nomine coefficient that is, uh, spatially non uniform. And I realize that entire math that I was working on was applicable to optical Co. And, uh, let's have in the final, you know, half a year of my PhD, I looked into ot, little bit OT literature, uh, published a paper on it.

(00:45:41):
Yeah, I think journal and conference, OT and applications, and looked into the space. And that was the time that I was looking for a postdoc and moved into Harvard, Harvard Medical School to work in optical queer tomography and other biomedical imaging modalities from literally, uh, engineering, uh, from Stanford engineering. I went to a hospital, mass General hospital in Boston and started to eat where patients and doctors, uh, were eating every day with, with all the smell of the, uh, hospital and everything. So that's how I got, uh, into Biophotonics Field and, uh, uh, microscopy imaging. That was like, uh, you know, in my first year at, in post, uh, at Harbor.

Peter O'Toole (00:46:25):
Okay. So I, I'm gonna take you outta work a minute. It's pretty intense. You, you've got a big team, you've got lots going on. You've got three mainstreams in the lab. You almost got three different labs working on different aspects. When you go home in the evening, how'd you chill out? What are your hobbies?

Aydogan Ozcan (00:46:45):
So, um, yeah, my life is also a faculty. So , you know, even if I wanna, I wanna completely, um, uh, detach myself. It's not always possible. Cause um, uh, we sometimes collaborate. So at home, I have another scientist that I collaborate and publish with. So, you know, detaching entirely from science is harder. Cause even if I detach, you know, it's the likelihood of two small numbers multiplying each other, right? Mm-hmm. , . So that's how it goes. Um, so, but I'm pretty good in turning off my mind if I want. So if I, if I, for example, take a 20 minute walk from my office to get coughing, uh, if I want, I can completely turn off my mind and just enjoy, um, you know, la la weather and, um, buildings and the, the, the trees around me and people. I, I like observing people.

(00:47:42):
So it's easy for me kind of to kind of relax, uh, because if I'm not working, uh, uh, on anything, and if I'm not thinking about a problem in mind, uh, sometimes I like to kinda turn off my computer and just think about a problem, uh, with a piece of paper and a pen. And those are the times where I sometimes come up with ideas. And a lot of my students get these pictures where I literally, you know, sometimes in napkin I did napkin based, you know, uh, ideas that turned into good papers. Uh, sometimes, you know, simple, uh, a four papers, if I'm not one of those days where I, I, I, I feel this, right? So sometimes you bombard yourself with a lot of papers writing this and that, right after a while you say, okay, look, I'm depleting my new ideas. I have to plug myself off and just take a piece of paper, just a paper and a pen, and think about, you know, what should I do next? Like, what is a good stepping stone? If I'm not in one of those modes or working on something I like, um, cooking, um, um, all kinds of food. Uh, and this

Peter O'Toole (00:48:50):
Is cooking, uh, main or dinner type cooking, snack type cooking or, uh, pudding type, cooking

Aydogan Ozcan (00:48:57):
Dessert. I, I w I wish pudding, but I can, I, I cannot have as much dessert. I was cooking, uh, you know, desserts, but, uh, uh, you know, it was not, it was not good for my health. So no dessert cooking, but dinner type of cooking. Um, at some point in my life, I, I was literally kind of having a cookbook and follow the recipes there to kind of learn how advanced cooking is. Mm-hmm. , I did that and it was very instructive. Uh, but then I gave up on it. I, I kind of, uh, went with my own. So I like cooking the same thing in 10 different ways every time, depending on what my fridge, uh, has. And, you know, trying to kind of, um, new dishes. Uh, but in general, I like cooking and, and my wife at least doesn't complain. Um,

Peter O'Toole (00:49:45):
What's your favorite style of cooking?

Aydogan Ozcan (00:49:48):
Um, Italian. So, uh, I think my main ingredient that I cannot cook without is tomato. Yeah. And olive oil. I think tomato and olive oil can make any display. So I'm a big fan of, uh, those two ingredients. That's why I like a lot of Italian style and also like feta cheese and, uh, tomato combination to me, to me, like, um, there are certain things that are paired, uh, to, you know, that are created like, uh, to pair with each other. Like coffee and milk is one of those, uh, tomato and, um, feta cheese, in my opinion, like in salads, uh, and, um, um, and olive oil with, you know, with , this is another one. Yeah. So yeah, in general I like Italian cuisine lot.

Peter O'Toole (00:50:39):
Okay. So some quick fire questions to be moving on to the, the food side. So I'm gonna ask some quick fire. Uh, I already know this answer cuz I know in the UK right now when we are recording, it's early morning, and for you it's really late. So are you an early bird or an itel?

Aydogan Ozcan (00:50:55):
Yeah, I like nights more than, uh, early, early mornings. Uh, that's where I probably get also little more creative.

Peter O'Toole (00:51:03):
Okay. PC or Mac?

Aydogan Ozcan (00:51:07):
Pc

Peter O'Toole (00:51:09):
McDonald's or Burger King?

Aydogan Ozcan (00:51:12):
Neither of them. I've never had any, uh, did I have any McDonald's burger? No, I think I didn't. Same with Burger. Maybe at an airport. I don't know. But no, no, I, I, I, I'm against, uh, all, all everything that they're after. So it's, it's so, so, um, fair

Peter O'Toole (00:51:33):
Coffee or tea,

Aydogan Ozcan (00:51:35):
Obviously coffee. There's, uh, yeah, there's a lot more fun and coffee,

Peter O'Toole (00:51:40):
Short coffee or long coffee?

Aydogan Ozcan (00:51:43):
Um, I, for me, cappuccino, uh, it must be always with milk and, uh, uh, for me. And, and, uh, there's a certain distribution of, you know, form and air greases that must be in my coffee, which is hard to find outside. That's why I, I like having my own cappuccinos made.

Peter O'Toole (00:52:05):
So, so you have your own coffee machine, you froth your own milk? Yes,

Aydogan Ozcan (00:52:08):
Yes. I, I, I, I, you know, I have my own way of having cappuccino and it's very difficult for me to kinda enjoy, uh, the Starbucks of the world.

Peter O'Toole (00:52:18):
Good man. Not, not, not, not, not because of the Starbucks. Just good back actually having the, the art to be good barista and get your milk just as you want it.

Aydogan Ozcan (00:52:25):
Exactly. That's very important. I think the density of the foam, uh, is the, is the main ingredient to make successful cappuccino important.

Peter O'Toole (00:52:35):
I've got a son that's currently a barista and yeah, he, he can do it better. Ah, so frustrating. Cause I've always had the coffee machines always steam my own milk. Uh, he can do it better than me. And I'm so frustrated . So, so, and he give him any steam arm and he can make that steam arm work much quicker than I can learn how to use that steam. Cause every steam arm, you have to relearn how to get it. Yeah,

Aydogan Ozcan (00:52:56):
Yeah, yeah.

Peter O'Toole (00:52:59):
Anyway, I, I'm digress again. Beer or wine?

Aydogan Ozcan (00:53:03):
Um, honestly, I'm not a big fan of, uh, I don't drink, um, uh, alcohol, but, but I guess, um, because of the fruitful flavor, uh, I would say wine.

Peter O'Toole (00:53:16):
Okay. Chocolate or cheese?

Aydogan Ozcan (00:53:19):
Huh? That's a good question. Um, I don't like chocolate cake or chocolate cheesecake. Sorry. Yeah. So the combination is not a good thing for me. Uh, I love both of them separately. I cannot, you know, pick one. I, I love cheese and I love chocolate, but not the combination.

Peter O'Toole (00:53:39):
Okay. If you were to have, if you were to go to a conference and someone was to put a meal in front of you, which you quite often is on involuntary, you get given new food, what would be the best food that they could put in front of you?

Aydogan Ozcan (00:53:52):
Since it's probably lunch, I would love to get a, um, sweet salad with very good fresh tomatoes. Like the lettuce once good cucumber and good fe cheese and olive oil

Peter O'Toole (00:54:06):
Of, of course you've got tomatoes, you got, of course.

Aydogan Ozcan (00:54:09):
Yeah.

Peter O'Toole (00:54:10):
What would be the worst thing that they could put in front of you?

Aydogan Ozcan (00:54:15):
Pasta with, uh, no tomato in it and overcooked, and not even cheese on it. So plain pasta, overcooked, uh, with just not even olive oil. Think of just, you know, greasy other kinds of oil. That would be the worst because, uh, you know, I'm not a big fan of it's overcooked pasta.

Peter O'Toole (00:54:39):
Okay. TV or book?

Aydogan Ozcan (00:54:43):
Uh, book.

Peter O'Toole (00:54:44):
Okay. And what, what are you reading at the moment, or what, what genre do you like to read when you do get a chance to read a book that isn't scientific literature?

Aydogan Ozcan (00:54:52):
Um, so, uh, I think, um, um, I mean, novels certainly like, um, uh, uh, from, from the classics would be great. Um, like, you know, noble series, like, you know, Albert Camu and, you know, writers like that which have the, uh, um, the psychology behind the human, uh, those would be, uh, would be kinda interesting, uh, for me to kinda relax and detach from, from the modern times.

Peter O'Toole (00:55:31):
Okay. And do you have a favorite film movies?

Aydogan Ozcan (00:55:36):
Uh, yes I do. Um, it's maybe, so how can I say? Um, I mean, I like Godfather quite a bit. Um, uh, I don't, I'm

Peter O'Toole (00:55:47):
Not series,

Aydogan Ozcan (00:55:48):
Not the fir No, just the first one. Um, because I think Marlon Brando is a, is an amazing, uh, was an amazing actor. And I think the first one where, you know, he's playing, uh, it's amazing. Um, you nailed it. Done Amazing. Wells

Peter O'Toole (00:56:08):
, we, oh my, okay. This is gonna have to run just over the hour, so I apologize to the listeners of viewers, my fault. Uh, but I, there's more questions I have to ask You quite clearly. Love your job and what you are doing. When you were a young child, 10, 11, 12, what was the first career that you aspired towards?

Aydogan Ozcan (00:56:33):
Um, without knowing what it is, I think I was intrigued, uh, by the word, um, uh, nuclear scientists, uh, nuclear engineer. I think without knowing what it is, I, I, I, I felt fascinated by, uh, you know, by, by that, uh, notion of having enormous energy and control of energy, uh, uh, with, with, you know, breaking of the atom without, again, understanding what they do. That was kind of like the science element of it was very, um, appealing. But that was when I was probably four years old, five years old, just hearing the words Yeah. Uh, of that nature. But soon after, you know, when I, when I got introduced to, uh, math and science, it, it changed, uh, that I, I was interested at the intersection of math and physics.

Peter O'Toole (00:57:29):
Okay. So now, now I'm gonna take you forward to today or in a few years time, if there's any job that you could do for a day or week or a year, what other type of job would you like to go and sample or just to try out and see what it's like in that environment?

Aydogan Ozcan (00:57:46):
Um, so, uh, let's see. I have a passion for basketball. So, um, uh, you know, it, it's, uh, if you think about a basketball player, it's so competitive, but you, you know, you must work very hard, uh, to play, uh, at the top level game after game and without, you know, injuries. Um, but it must at the same time feel an amazing adrenal in like, with thousands of people, uh, uh, in the crowd. So I, I would love to, you know, face what it feels to be a professional basketball player and play at that level. Um, but I, I believe it must be very intense to maintain that kind of pace with all the travel and the training and everything. So nothing is very easy, but that would be something that would be cool for me because I, I really love basketball.

Peter O'Toole (00:58:44):
And who's your team?

Aydogan Ozcan (00:58:46):
Um, I, you know, I'm, I'm not supporting just one team. Um, I mean, when Kobe was playing Lakers, um, was, was a great team for me, uh, to support. Um, I like also European teams, uh, European basketball teams, um, they, they play quite different than nba. Their, um, their level is more, um, defense, uh, oriented and, and the level of basketball. I don't know if, if you follow European, uh, basketball league. Oh, okay. Okay. Yeah. Soccer is also pretty entertaining for me, but, but basketball, um, uh, I, I like European basketball a bit more than the nba. NBA is really show oriented, very little defense compared to Europeans. So, uh, especially over the last 10 years, the European Basketball League, uh, advanced quite a bit in terms of, uh, you know, entertainment and how fun. It's to watch these top teams compete.

Peter O'Toole (00:59:50):
Okay. I, we, we are actually just over the hour. Uh, do you know, I think I've got more questions left than I've ever had left at the end of a podcast. So I, I'm hoping that we can come back and some of these are more scientifically orientated questions. Actually, I hope we can come back maybe with another guest as well and talk more in depth about lens free microscopy, quantitative phase imaging and the future of that and the potential applications. But before I leave, I'm gonna ask you, is there anything you'd like to convey or say? Uh, is there anything we haven't touched on that you'd really like to communicate or have we covered the main points today?

Aydogan Ozcan (01:00:28):
Yeah, I think we've covered, uh, the, the main points. Uh, of course. Um, and there are so many things, as you said. Uh, maybe, maybe, maybe, yeah, maybe my answers were not as, uh, as directed and, and maybe they were longer than usual. Um, it did, yeah. I think, I think, I think, um, one, one area where, um, there's, there's also, um, quite a bit of effort that, um, different then for example, some of the others that, that I mentioned, which are at a lower technology readiness level in terms of their translation. There's still, you know, basic research ideas. Uh, and we're exploring it very much like the, uh, uh, the defractive computing that, that I've described. That's actually a beautiful, uh, scientific, uh, explore exploration for, for us to create new kinds of systems. They will have commercial applications, but at least I'm not pursuing them, uh, through technology transfer at the moment.

(01:01:22):
But there is one thing that perhaps, uh, you know, I should have, I should have mentioned when you talked about pathology, conservativeness of pathology field. And that is actually, I, I don't know if you, if you're aware of this, but, uh, virtual staining, uh, of tissue, um, is an area where I'm so proud about, you know, what we've achieved and we're actually commercializing it as we speak. Uh, right now we formed a, a, uh, you know, an effort to take, uh, some of the work on virtual staining of tissue using label-free imaging, but not necessarily, in this case a quantitative phase. Imaging. In fact, we're using autofluorescence of tissue, uh, uh, which is like, uh, no labels. Again, we're using endogenous fluro force of tissue and using those signatures, uh, as input to train neural networks to mimic what comes out of histology lab. And that one, we're exactly to do the same things, uh, in terms of, um, entry to pathology and entry to biomedical clinical, uh, stuff.

(01:02:27):
So what is the pyramid of opportunities, uh, in terms of maybe the research market? Cause there's a lot of staying done in research, maybe the pharma, uh, where for toxicology reports and, and, and, uh, animal studies, there's quite a bit of interest for steel staining of tissue. So we're exploring that. And, and, you know, perhaps we face the same challenge that QPI feel has been facing in terms of, uh, translation into commercial use. But, um, cuz we targeted pathology as a very conservative field. That's the core, uh, of our technology here in terms of the impact. But, but I'm seeing a change in pathologists mind mindset, uh, when I, when I kind of interface with them because they're seeing how their job is not threatened by technologies like this, in fact will make their job more efficient, more accurate. Uh, perhaps they will even enable to see more patients per hour than they could now cause of the bottleneck be becoming, for example, the histology lab.

(01:03:33):
Sometimes like, you know, the patient specimen, uh, that goes to theology lab comes too late, sometimes comes garbage, right? And those kinds of things, I think with new technology, uh, like virtual staining, using AI for stating of, uh, tissue without any chemicals, that's going to change. And I'm seeing the signs of pathologists actually working with us to convince the rest of the community on the benefits, uh, on the utility of, uh, technology like virtual stain. So I just wanted to mention that I forgot how, you know, to relate it from the Q P I angle, the challenge of the QPI to, uh, something at the intersection of ai.

Peter O'Toole (01:04:16):
Yeah. And I, I think for the future at diagnostics, this is gonna be huge. And I do, it won't not be now. Okay. So someone's gonna watch this back in 20 years, time goes off, he was wrong. , the potential is really there. And, and it just, you know, before the microscope what were pathologist doing at that point. You know, these are new tools. They're becoming very disruptive, but it only just gives them a new tool. It doesn't replacing just and better information, more accurate, better, I hope better personalized medicines. Cause the diagnostics can be far more personalized with information, but there's a lot to go into there. Yeah. Aon, thank you very much for joining me today. Everyone who's watched, listen, sorry I ran this over a little bit, but I hope you really enjoyed it. I hope you are Welcome back. Aon, next time we have a chat with him. And actually on this, it's not often I've indulged in doing a A A Q P quantitative phase imaging type, uh, talk. Uh, we had Laura and now we've had add one by coincidence quite close together. But their work is so inspirational. And I think what you, we've heard today from the importance of storytelling, from the importance of putting tomatoes with olive oil, , I think , really enlightly conversation. Thanks very much for joining me so late where you are.

Aydogan Ozcan (01:05:34):
Thank you for having me. It was wonderful. It was very fun.

Intro/Outro (01:05:39):
Thank you for listening to the microscopists, a Bitesize Bio podcast sponsored by Zeis microscopy. To view all audio and video recordings from this series, please visit bitesize bio.com/the microscopists.