Aug 16, 2023
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How are computer vision and contactless techniques spotting signs of autism much earlier in children? What is augmented cognition and how can you use AI to make data models work, even with small datasets? We'll find all that out and more in this episode of Research in Action. Hello and welcome back to Research in Action, brought to you by Oracle for Research.
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I'm Mike Stiles, and today we have with us Dr. Sarah Ostadabbas, an Associate Professor in the Electrical & Computer Engineering Department Northeastern University, where she's also director of the Augmented Cognition Laboratory (ACLab), which works at the intersection of computer vision, pattern recognition and machine learning. Before joining Northeastern, she was a postdoctoral researcher at Georgia Tech and got her Ph.D. at the University of Texas at Dallas.
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Her research looks at how we can enhance human information processing capabilities by designing adaptive interfaces based on rigorous models using machine learning and computer vision algorithms. With over 100 peer reviewed publications. Professor Ostadabbas has received recognition and awards from government agencies like the National Science Foundation, the Department of Defense and several private industries. In 2022, she received an NSF career award to use AI for early detection of autism, and she's working on that with Oracle for Research.
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Dr. Ostadabbas, thank you so much for being with us today. Thanks for having me. I'm excited to be here and feel free to call me Sarah. Well, listeners, get ready because we're going to get all into computer vision, machine learning, augmented cognition and wherever else I can get nosy about. But first, let's hear about you, Sarah, and your background.
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Your passion for technology and physics kind of started back in childhood, right? Yes, that's correct. Actually, physics was my favorite subject in middle school and high school. I was so passionate about it that I even went through the whole volume of Fundamentals of Physics by David Halliday and Robert Resnick in I believe it was in 10th year of my high school, and I was seriously considering to pursue the continuous PhD in physics even before graduating from high school.
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And alongside my love for physics, I was always also fascinated by technology, especially computers and programing. I started coding in a language called Basic, which some of your audience may not even heard about that. Why I was in middle school and loved it. Data Analytics capabilities of computer and how computers are giving advanced processing power to human no matter where they are.
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I was still living in Iran at the time and experiencing technological advances at that time, such as Internet and cell phone, and they were all very much interesting. And fast forward, all of this led me to pursue a natural combination of my interests, which was an electrical and computer engineering degree with a double majoring in biomedical engineering. And now when I look back, it's actually heartwarming to see one that one seemed to be diverse.
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Interesting collection of interests now have shaped my academic journey so far. Was it unusual for someone, you know, at your age, at that early age of middle school, to be coding and thinking about technology and physics and looking that far into the future? I was actually going to date if school, middle school and high school at that time was designed for for math and science.
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So no, I had a lot of of my classmates going and exploring different science topics. So it wasn't unusual. I mean, it was unusual when I was taking these heavy books to my gathering at parties, at my family, but not at the school. So I'm glad. And it was 200 of us, 200 girls at and now all of us are all around the world.
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Most of us have PhDs. And yeah, it wasn't unusual, but it, it was something that I cherish. Yeah, it's great that you had a school that focused on things like that. So let's kick things off with your NSF CAREER Award focused on developing machine learning algorithms towards the early detection of autism. Tell me if I get this wrong.
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But this is about using computer vision to predict autism a lot earlier in children. And what does what does that research involve and what does Oracle for Research have to do with it? You're certainly right. As I mentioned, my academic background revolves around electrical and computer engineering, focusing on data processing. And these data sources can be signals, images and videos.
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How might a specific focus a work on computer vision began when I joined Northeastern University as an assistant professor in 2016. As you may know and have heard of over the past decade, deep learning models have been driving advancements in many AI topics, including computer vision. But these algorithms often require a large amount of training data. They are very data hungry.
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So my National Science Foundation CAREER Award aims to leverage this advancement in computer vision for a specific health related domain that suffera from limited data. And I'm in particularly focusing on detecting autism in infant even before the first birthday. And this is true processing videos that is collected from them when they are doing daily activities, which is not a lot of things that they do.
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They are sleeping, playing or eating. And as I mentioned, my algorithm, they are designed to be data deficient because I'm working on the area that the there are not a lot of data due to this privacy and security reason, but adapting these complex networks, these complex neural networks which are which are building blocks of deep learning necessitates powerful computing resources.
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And that's where our collaboration with Oracle become highly valuable, allows me to make this model adapted to this specific application. So you have videos, video cameras, monitoring the kids and kind of like an in the wild get capturing of data. And then the computing power is needed to crunch all that video and that pulls out certain patterns that reveal autism earlier.
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Is that how it works? Yeah. I mean, you can say that you put that on the simpler words. Yes, exactly. I'm a simple man. No, no, no. I'm just it's a good I mean, it's a good, good way to describe that. Yes, that's correct. So what we do, we actually leverage these computer vision techniques and contactless video processing algorithm to predict autism, as I mentioned, from daily activities.
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And these are daily activities captured by commercial video recording messages. Imagine like a baby monitor or even parent's cell phone cameras. Every parent's love to record videos from the day of their child. So they focus on this specific developmental sign. How will that that relates to motor function, which means that relates to infants posture, muscle tone, body symmetry, and they balance and range of movement.
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So these are specific markers that actually has been shown to be early visible warning signs of more developmental disorders such as autism. And they appear actually interestingly, long before the core feature of autism that you may have heard of and these are actually very known, such as social or communication difficulties as well as repetitive behavior. So we are focusing on these early signs.
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However, currently the standard approach to monitor this motor function is through visits to child doctor, pediatrician and how is it, unfortunately, over half of these visits are missed. You could imagine often due to the lack of transportation, for parents, it's hard to take time off from work and also lack of child care for other other kids set at home.
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So half of these visits are missed and a lot of this early sign has been overlooked. So to address this in equitable access to actually to clinical assessment and a lot of practical constraints, we are trying to to make a home based a I guided in monitoring tools that can track early motor function development very unobtrusively, like just a video that is watching like a baby monitor is rolling and then be the process this video on the back end and track this specific developmental sign and hopefully be we help for the early detection of autism.
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I want to also point the fact that it's actually important, very important and crucial to have timely detection in the autism case, because early intervention, it's actually shown that is most effective before the age of four. Yet the average age of autism diagnosis is still around four and a half. So we are hoping to make a clear detection tools better intervention outcome.
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It's really interesting to me that body symmetry is a hallmark of development. I guess my question is why would that be and how is Body Cemetery being addressed in your research? That's a very good question. So we are as I mentioned, a motor development is very important. If early signs offer any visible sign of something that may not working out right.
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So one interesting aspects of motor function that has been identified as an indicator of neurodevelopmental health is body symmetry. You can imagine that symmetrical movements and posture are crucial for supporting independent movements such as sitting, crawling and walking, especially infant. Then an infant is typically developing movement posture. Actually, you start asymmetric and then gradually they become more symmetrical as our sensorimotor coordination develops.
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And during the first year of life, infants could go through the various milestones, such as days rolling over, sitting up, standing so little by little watching, and all of these movement progressed from less symmetric to more symmetric movement and then also study, they have been looking at the infant movement. They have a map showing that the position is symmetry in their movement can be indication of disorders like autism.
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However, if we want to have motor functional function assessment in infant, especially body symmetry in larger scale for a long period of time, our for health care provider is going to be very expensive. I mean, somehow impossible and very challenging because imagine if you have 10 hours of videos, how long does it take for you to watch that?
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10 hours. I mean, it's going to take 10 hours. But what we want to do, we want to have these computer vision tools apply on these videos to automatically evaluate them all to a function and is start having something in home that people can use and start escorting to one of the mutual developmental indicators, escorting them the symmetry.
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So the idea is that we are actually using infant pose estimation algorithms that we have already developed in the lab to assess postural asymmetry based on differences in joint angle between opposing the arms, between the left side and right side. So the effect the the difference is more than 45 degrees, which has been suggested by Esposito in this study in 2009, in the we can call it asymmetric.
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We have also come up with our own measure, which is a data learned based assessment on using Bayesian assets to collect aggregation that we could actually come up with two different angles. But how that these are all allows us to do to process the beat you automatically. And then the video is called the whole movement of the infants based based on all of this processing symmetric or asymmetry.
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And then physicians can look at that and see that it is something alarming or not. And then as the process of the science and research goes on, well, I've talked to enough researchers to know that recruiting is usually a challenge for any experiment. But with this, the target population is children like babies. How did you manage to get your patient population?
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Were there any privacy, access or ethical concerns? It's a very good question and also absolutely an important matter. When recruiting for our experiment, we noticed that the challenge of targeting infants subject under the age of one, parents are already overworked, sleep deprived, and imagine asking them to to be part of yet another task. So it's very hard, however, to be able to overcome this this problem.
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We leverage the fact that many parents already are using baby monitoring systems, so they just want to wash them. I mean, a lot of these baby monitors, even the one that they call smart, they don't do anything. It's just a trigger. If the mat the baby's crying or they are moving. So we are aiming to develop this normal system that not only allow the parents to observe the child, but also offers this long term monitoring capability to track the child's developmental process and provides alert if some abnormalities are detected.
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So this may be a good incentive for for parents to take part in our study. And as one of the points that you mention about the privacy and ethical concern, we have taken several measures to make sure to address these concerns. We are collaborating with health care professional that they are more familiar with to dealing with the human subject.
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And also we are working closely with a Northeastern Institutional Review board known as IAB to make sure our data collection protocol has strict security and privacy standard. We make sure that the parents that they are participating in our study are fully informed about the purpose of the research. And also we get they consent to to use some some part of these data for public use and public release for scientific and technological advancement, because a lot of them these days, how to win is shared in other a study can be built on top of that.
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So but we make sure that parents are that the parents that they are part of this study, they are they are aware, fully aware of that. And I want to emphasize that our priority is to preserve the privacy and confidentiality of them, the participant to out the whole process, although they are looking and working on very important and impactful research.
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QUESTION But this is also very important at the top of our list. Yes, security and privacy data for data that is important. Is that why a tech concern like Oracle Cloud that obsesses over things like privacy and security kind of speeds up the research? That's very good. Good point that you brought up. That's true. As I mentioned, security and privacy of the data, especially in our field based on the sensitive nature of data that we are collecting, is important.
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We are working with them with personal health related information. So we required some sort of robust measure to to protect confidentiality and prevent unauthorized access. And working alongside part industry partners like Oracle ensures that we are actually having a huge safeguard on our sensitive information. The team that I am working with, Oracle has this huge expertise in data management and security practices, and this allows us to then when we are storing, processing and analyzing data in a in a protected environment, we can focus on our research objective while having a partner that gives us confidence in the security and privacy of the data that they are handling.
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So it's a very useful and necessary collaboration. So your lab Augmented Cognition Laboratory or the A.C. Lab works with Computer Vision and machine learning. How did that lab come to be and what exactly is augmented cognition? This is actually brings back many fond memories for me, I think. Tell you the story behind the name, Why I was interested in physics, computers, math, and even literature.
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I mean, this is specific. Interest by itself can be another podcast session, but not now. I always had a vision of becoming a university professor and leading my own research lab. I remember clearly that I wasn't seen earlier for my Ph.D. when I started to look at look for names for my future lab to reflect the into intersection of engineering inspired artificial intelligence because I was farming, doing school and data analytics.
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But also I wanted to emphasize the positive impact of A.I. in human life rather than replacing them. So I came up with the name Augmented Cognition. Augmented Cognition. I actually represent the core idea that I have about enhancing human information processing capability through the design of adaptive interfaces guided by A.I. algorithm, especially machine learning and computer vision. This is specific definition is actually opening of my my web page when I started at my my position at Northeastern University.
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This also highlights my focus on utilizing these advanced tools to augment human ability, especially in the data processing domain. Imagine what I'm doing here as part of my NSF CAREER and what I want to to give physician parents the power of processing hours and hours of data and then let them to extract the information that is needed to to make sure to make the informed decisions.
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I often have this phrase that at the ACLab we use artificial intelligence or AI to do human intelligence amplification or IEEE. So I do more Iot and A.I.. Your work relies a lot on machine learning and computer vision as tools to generate truly augmented intelligence solutions. How do you leverage the recent advancement of AI in your work?
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Because you've probably been watching it for years, but for most of the public, this A.I. thing came on like a tidal wave. So how does that get applied to computer vision? That's true. I mean, I it's the main wave, and I believe in my my opinion that the main a wave and also success is started from with the introduction of deep learning in 2012 2015 and the actually expand the recent advancement in AI to tackle challenges in understanding and predicting human behaviors from vision sources.
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As I said, images or videos, I am focused my my work focus on representation learning in visual perception problems such as object detection, tracking and action recognition and using all of these these tools, we want to estimate the physical, physiological or even emotional states of the individual under study. So to be able to do a robust estimation, the algorithms that we are developing at the Sea Lab utilizes this concept called Pose, which is a low dimensional embedding that captures the essential information in the state of the human that we are monitoring.
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For example, body pose, facial pose. You could imagine that you could from that to you can get body symmetry, you can get the emotional feeling of the the human. So help me that I want to emphasize the fact that many of these human data focus application that I work on belong to this small data domain. But the data collection and labeling are limited or restricted, such as healthcare application or even military application.
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So to address the data limitation, my algorithm also integrate explicit domain knowledge into the learning process through the use of a generative AI model. We actually built our genitive AI model that this model, they are all data efficient machine learning while incorporating valuable insight from domain experts. So this allows us to to use less data. But on the other hand, we have all of these backing from from the experts that allows us to to make our model work.
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This means collaborating with professionals from various fields such as physicians, psychologists, even physicians and neuroscientists are very much important and ensures the practical relevance of many of the models that we are developing in the lab. I definitely see use cases for improving health care and data analysis and augmentation. But for the clinical space, are you a let's go for it person when it comes to AI or more of a cautious person and there is a responsible way to apply, I think that your question comes from all of these debates happening.
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Is AI for good or for bad? I mean, what we do, to be honest as a researcher working at the intersection of AI and health, I have been trying to keep a balanced perspective on this overall impact of AI. I am an optimistic optimist when it comes to the potential benefit of AI for health care, particularly for the data analysis and intelligence augmentation.
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As the name of my lab, we then come back. I believe that A.I. has the potential to change the healthcare and improve diagnosis, personalized treatment, enhancing patient care, and expanding access to care, as I mentioned. I mean, you can actually make an air power system at your home and get the monitoring and the diagnosis that that you need.
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And it can help clinician to make more accurate and timely decision leading to better outcomes for patient health. There is not that I'm just only say is the best and now we don't need to to think about other aspects. I also approach the use of AI in the clinical space, especially with caution. We have to be concerned and to address this concern related to privacy, security and ethical use.
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We have to be transparent and accountable and ensure that a AI system are fair, unbiased and trustworthy. These are useful for on on human subject. So proper validation and rigorous testing are necessary to make sure these models are reliable and robust. Also, it's very essential to involve health care professionals, patient and other a stakeholder in the development process.
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It cannot be inside AI sitting the lab and come up with something as okay, this is perfect. Let's so let's put that in every baby monitor around the world. We have to make sure the system is safe. A specific needs in inside the health care domain. So in one sentence, I believe that with responsible development and implementation, AI has the potential to significantly improve improved health care outcome.
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And I'm hoping this balance will that of you, especially in the clinical setting, allows us to to work more to make better and stronger and more robust AI model while addressing the concern and challenges that comes with its use in the clinical space. Well, I know based on what you said, and because I cheated and researched you before you came on the show, that you you believe that AI, as long as it's good, should be able to augment our capabilities.
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And again, you're saying not replace human capability, but augment capabilities. So as you mentioned, the average age of detection for autism is about four and a half years olds. How much and you mentioned about one year old, that's how much sooner than that you think the research could detect autism. And if you do detect it that much earlier, then what Can we actually improve developmental growth?
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So before I proceed, I want to make it clear that I don't have any formal academic training in the health care domain. Power through my extensive collaboration and engagement, I have come to understanding the significance of the early detection in neurodevelopmental conditions such as autism, and also how timely intervention can improve the developmental outcome. So as you mention and that's right, the current average age of autism detection is around four and a half years.
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But through our research, we want to aim to significantly reduces this age and we are hoping to make it on the age of one because we are able to detect this specific neurodevelopmental model signs unobtrusively, automatically and long term using our computer vision algorithm. And let's remember that the fact that the brain exhibits its highest level of neural plasticity during the first year of life.
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So intervening during this sensitive window can have profound impact on long term. So the sooner that we can catch some of these not neurodevelopmental disorder, then the rehabilitation can start. And also intervention can be much more accurate. Also detecting a system that can track and quantify infant development aside from autism can can be used to detect and test other hypotheses related to a motor function hypothesis that based on my collaboration with other health care professionals related to this, a liberal policy congenital tool to coalesce list out that all of this stuff that has some motor representations, but they are not catch early.
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You know, because infants are at home. Parents are especially new. Babies have a lot of work so they they missed a sign and then the number of visit is very limited if not missed. So by advancing the age of detection and enabling early intervention, I am not only hoping to have the individual outcome, but also the whole idea is studying other and testing other hypotheses in their developmental science.
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So hopefully that would be a tool that empower researcher, physician parents in the field to study these motor related developmental condition much earlier and less expensive and much more on up to the CV. Well, research does need data for exploration and reproducibility, but a lack of data sharing in the research community is kind of a hot topic. There are several people that just doesn't want our collective knowledge to collect.
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So why is data sharing vital to advancing science and getting to new discoveries and treatments? For sure, I'm not among those group that they don't share. I think I believe the data sharing plays a very important role in advancing scientific research. So essential for reproducibility, transparency and collaboration. So by sharing data research, it can not only validate what you have done, reproduce that, but also they can build upon your finding and start building new and new discoveries.
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So rather than everybody start from scratch. So sitting on your data and not sharing that, it's I don't see that is a scientific manner. This is very fundamental. We do, we do actually share the data on both the data and code in our lab, in the computer science and engineering field is is known that people share data. They could, but in the medical domain, this data is very protected.
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And it's I understand all of their privacy consent. But in our data collection procedure, we make sure that we inform at the participant about the value of data sharing. So we get they consent to share these data is pieces of the video that they are collecting. And then I am hoping that collectively we can add best knowledge, at least address complex challenges related to data specific types of a question that we are addressing.
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And ultimately we want to improve human health and well-being well-being and enhance the quality of life for everybody. Do you think some of that reluctance has to do with concerns about intellectual property and researchers thinking about, you know, the marketability of what they're doing? Absolutely. Absolutely. That's the case. But I have a counter argument for that. So this is not 2000 years ago that we we come up with an idea and write it down and then buried so nobody can find it after after us.
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So I think by sharing with the acknowledgment of that there the research and who came up with that is important. But if we keep this strain of sharing thoughts, sharing ideas, sharing data, which data nowadays holds a lot of intelligence insight inside that, then we can actually build and everybody get into the training of the is Discovery new discovery.
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So if we want to keep that it's possible and then in industry because now the line between industry and academy is not as the strict as before because there are a lot of collaboration happen which we're very much I admire. But yeah, we have to to make sure to acknowledge both sides, industry and academics, to acknowledge their contribution, but then share the data and see and be happy on the growth, be happy about advancing the knowledge and the complex problem cannot be solved if we just keep it to ourselves.
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Well, our audience of researchers is pretty bright. So is there anything else you'd kind of like them to know or for them to think about that we haven't touched on yet? Just something that you wish people paid a little bit more attention to. Oh, thanks for asking. Yes, I think that this in this podcast you talk about my research related to the use of AI in computer vision for for autism.
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A study, as I said, that I don't have any any health care background. However, in my my lab doesn't only work on the autism patients, we are actually interested in developing computer vision and machine learning solution for a wide range of application dealing with the small data problem. The data, it's the the bread and butter of us because the intelligence, especially in the era of deep learning, it's all hidden in the data.
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So I work on the rehabilitation, animal monitoring, even autonomous driving scenarios that is hard to collect. Data is expensive, is dangerous to collect data or is impossible. Sometimes, for example, it's very hard to to collect data from animal in this specific pose or conditions. So that's one thing that's enabling these advancements, especially advancement in computation and machine learning in this small little domain is important.
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So rather than to do not be afraid or shy, if you think that, okay, this specific application needs a lot of detail, we don't have that. So let's not use let's abandon all of these advancement that we have because we don't have a lot of data. No, it's possible. And in our lab we are working on that to enable these advancement in the domain that rather than having millions and millions of sample, you have only 100 samples, you have only 20 samples of that in Central and all that.
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So in my lab we are looking at the problem time to size. First we want to see that if we can make our machine work with less amount of data as I mentioned earlier, how we can do that, we should actually make research a space for the parameters of the model, make it more constrained by bringing some outside domain knowledge inside the model.
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So rather than be say that, look, I don't want to hear anybody else's idea. I just want to look at the data and see what's happening. We only take them. They are data driven models. We are putting in some understanding of about the physics, about this specific phenomenal behind that, about the specific types of movement that we are looking for into the model.
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So to make the model work with a less amount of data. On the other hand, we we were thinking about this in digital expanded this data is called synthetic data generation. So we are looking at a lot of simulators, even game engines, to see that if we can use them and make an avatar of infant, for example, fall from the bit better than looking at videos or waiting for infant fall of the bit, we actually see that picture can be simulated.
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These data can be simulated driving in a very low trouble stability environment rather than asking actually a driver to go to do that. So these are also use of their simulators and synthetic data generation. So we expand the data as much as we can in the synthetic domain. And also we make our model to work with less amount of data.
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So hopefully in future we are not abandoning this specific application and the use of AI in there because we don't have data. And if our audience does want to learn more about you or your research or the lab, is there any way they can do that or get in touch with you? Yes. My email, I'm actually very fast and responding to email.
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You can find my email at my web page. And also you can find me a LinkedIn, send me a message there we we share our news in different platform but yeah the best way contacting me send me an email we do have them also even high schooler at our school right now that I'm talking with you Mike I have three high schooler they are collecting data from an avatar in fact in completely virtual world and they are just we are we want to use that to train our model to detect how intense to reach and grasp.
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Gosh, that's great. So, Sarah, thank you so much for being on the show with us today. And to help people find you, I'm just going to spell your last name for them. It's Ostadabbas. So that's the way you can look up Sarah. And if you are interested in how Oracle can simplify and accelerate your research, check out Oracle dot com slash research and join us next time on Research in Action.