Artists and scientists in today’s world often exist in their own disciplinary silos. But the Laboratory Art in Practice Bass Connections team hopes to rewrite this narrative, by engaging Duke students from a range of disciplines in a 2-semester series of courses designed to join “the artist studio, the humanities seminar room, and the science lab bench.” Their work culminated in “re:process” – an exhibition of student artwork on Friday, April 28, in the lobby of the French Family Science Center. Rather than science simply engaging artistic practice for the sake of science, or vice versa, the purpose of these projects was to offer an alternate reality where “art and science meet as equals.”
The re:process exhibition
Liuren Yin, a junior double-majoring in Computer Science and Visual and Media Studies, developed an art project to focus on the experience of prosopagnosia, or face blindness. Individuals with this condition are unable to tell two distinct faces apart, including their own, often relying on body language, clothing, and the sound of a person’s voice to determine the identity of a person. Using her experience in computer science, she developed an algorithm that inputs distinct faces and outputs the way that these faces are perceived by someone who has prosopagnosia.
Yin’s project exploring prosopagnosia
Next to the computer and screen flashing between indistinguishable faces, she’s propped up a mirror for passers-by to look at themselves and contemplate the questions that inspired her to create this piece. Yin says that as she learned about prosopagnosia, where every face looks the same, she found herself wondering, “how am I different from a person that looks like me?” Interrogating the link between our physical appearance and our identity is at the root of Yin’s piece. Especially in an era where much of our identity exists online and appearance can be curated any way one wants, Yin considers this artistic piece especially timely. She writes in her program note that “my exposure to technologies such as artificial intelligence, generative algorithms, and augmented reality makes me think about the combination and conflict between human identity and these futuristic concepts.”
Eliza Henne, a junior majoring in Art History with a concentration in Museum Theory and Practice, focused more on the biological world in her project, which used a lavender plant in different forms to ask questions like “what is truthful, and what do we consider real?” By displaying a live plant, an illustration of a plant, and pressings from a plant, she invites viewers to consider how every rendition of a commonly used model organism in scientific experiments omits some information about the reality of the organism.
Junior Eliza Henne
For example, lavender pressings have materiality, but there’s no scent or dimension to the plant. A detailed illustration is able to capture even the way light illuminates the thin veins of the leaf, but is merely an illustration of a live being. The plant itself, which is conventionally real, can only further be seen in this sort of illustrative detail under a microscope or in a diagram.
In walking through the lobby of FFSC, where these projects and more are displayed, you’re surrounded by conventionally scientific materials, like circuit boards, wires, and petri dishes, which, in an unusual turn of events are being used for seemingly unscientific endeavors. These endeavors – illustrating the range of human emotion, showcasing behavioral patterns like overconsumption, or demonstrating the imperfection inherent to life – might at first glance feel more appropriate in an art museum or a performing arts stage.
But the students and faculty involved in this exhibition see that as the point. Maybe it isn’t so unnatural to build a bridge between the arts and the sciences – maybe, they are simply two sides of the same coin.
Statistics and computer science double major Jenny Huang (T’23) started Duke as many of us do – vaguely pre-med, undecided on a major – but she knew she had an interest in scientific research. Four years later, with a Quad Fellowship and an acceptance to MIT for her doctoral studies, she reflects on how research shaped her time at Duke, and how she hopes to impact research.
Jenny Huang (T’23)
What is it about statistics? And what is it about research?
With experience in biology research during high school and during her first year at Duke, Huang toyed with the idea of an MD/PhD, but ultimately realized that she might be better off dropping the MD. “I enjoy figuring out how the world works” Huang says, and statistics provided a language to examine the probabilistic and often unintuitive nature of the world around us.
In another life, Huang remarked, she might have been a physics and philosophy double major, because physics offers the most fundamental understanding of how the world works, and philosophy is similar to scientific research: in both, “you pursue the truth through cyclic questioning and logic.” She’s also drawn to engineering, because it’s the process of dissecting things until you can “build them back up from first principles.”
At the International Society for Bayesian Analysis summer conference in Montreal
Huang’s research and the impact of COVID-19
For Huang, research started her first year at Duke, on a Data+ team, led by Professor Charles Nunn, studying the variation of parasite richness across primate species. To map out what types of parasites interacted with what type of monkeys, the team relied on predictors such as body mass, diet, and social activity, but in the process, they came up against an interesting phenomenon.
It appeared that the more studied a primate was, the more interactions it would have with parasites, simply because of the amount of information available on the primate. Due to geographic and experimental constraints, however, a large portion of the primate-parasite network remained understudied. This example of a concept in statistics known as sampling bias was muddling their results. One day, while making an offhand remark about the problem to one of her professors (Professor David Dunson), Huang ended up arranging a serendipitous research match. It turned out that Dunson had a statistical model that could be applied to the problem Nunn and the Data+ team were facing.
The applicability of statistics to a variety of different fields enamored Huang. When COVID-19 hit, it impacted all of us to some degree, but for Huang, it provided the perfect opportunity to apply mathematical models to a rapidly-changing pandemic. For the past two summers, through work with Dunson on a DOMath project, as well as Professor Jason Xu and Professor Rick Durrett, Huang has used mathematical modeling to assess changes in the spread of COVID-19.
On inclusivity in research
As of 2018, just 28% of graduates in mathematics and statistics at the doctoral level identified as women. Huang will eventually be included in this percentage, seeing as she begins her Ph.D. at MIT’s Department of Electrical Engineering and Computer Science in the fall, working with Professor Tamara Broderick.
“When I was younger, I always thought that successful and smart people in academia were white men,” Huang laughed. But that’s not true, she emphasizes: “it’s just that we don’t have other people in the story.” As one of the few female-presenting people in her research meetings, Huang has often felt pressure to underplay her more, “girly” traits to fit in. But interacting with intelligent, accomplished female-identifying academics in the field (including collaborations with Professor Cynthia Rudin) reaffirms to her that it’s important to be yourself: “there’s a place for everyone in research.”
At the Joint Statistical Meetings Conference in D.C with fellow researcher Gaurav Parikh
Advice for first-years and what the future holds
While she can’t predict where exactly she’ll end up, Huang is interested in taking a proactive role in shaping the impacts of artificial intelligence and machine learning on society. And as the divide between academia and industry is becoming more and more gray, years from now, she sees herself existing somewhere in that space.
Her advice for incoming Duke students and aspiring researchers is threefold. First, Huang emphasizes the importance of mentorship. Having kind and validating mentors throughout her time at Duke made difficult problems in statistics so much more approachable for her, and in research, “we need more of that type of person!”
Second, she says that “when I first approached studying math, my impatience often got in the way of learning.” Slowing down with the material and allowing herself the time to learn things thoroughly helped her improve her academic abilities.
Being around people who have this shared love and a deep commitment for their work is just the human endeavor at its best.
Jenny huang
Lastly, she stresses the importance of collaboration. Sometimes, Huang remarked,“research can feel isolating, when really it is very community-driven.” When faced with a tough problem, there is nothing more rewarding than figuring it out together with the help of peers and professors. And she is routinely inspired by the people she does research with: “being around people who have this shared love and a deep commitment for their work is just the human endeavor at its best.”
Post by Meghna Datta, Class of 2023
(Editor’s note: This is Jenny’s second appearance on the blog. As a senior at NC School of Science and Math, she wrote a post about biochemist Meta Kuehn.)
If you’re a doe-eyed first-year at Duke who wants to eventually become a doctor, chances are you are currently, or will soon, take part in a pre-med rite of passage: finding a lab to research in.
Most pre-meds find themselves researching in the fields of biology, chemistry, or neuroscience, with many hoping to make research a part of their future careers as clinicians. Undergraduate student and San Diego native Eden Deng (T’23) also found herself plodding a similar path in a neuroimaging lab her freshman year.
Eden Deng T’23
At the time, she was a prospective neuroscience major on the pre-med track. But as she soon realized, neuroimaging is done through fMRI. And to analyze fMRI data, you need to be able to conduct data analysis.
This initial research experience at Duke in the Martucci Lab, which looks at chronic pain and the role of the central nervous system, sparked a realization for Deng. “Ninety percent of my time was spent thinking about computational and statistical problems,” she explained to me. Analysis was new to her, and as she found herself struggling with it, she thought to herself, “why don’t I spend more time getting better at that academically?”
Deng at the Martucci Lab
This desire to get better at research led Deng to pursue a major in Statistics with a secondary in Computer Science, while still on the pre-med track. Many people might instantly think about how hard it must be to fit in so much challenging coursework that has virtually no overlap. And as Deng confirmed, her academic path not been without challenges.
For one, she’s never really liked math, so she was wary of getting into computation. Additionally, considering that most Statistics and Computer Science students want to pursue jobs in the technology industry, it’s been hard for her to connect with like-minded people who are equally familiar with computers and the human body.
“I never felt like I excelled in my classes,” Deng said. “And that was never my intention.” Deng had to quickly get used to facing what she didn’t know head-on. But as she kept her head down, put in the work, and trusted that eventually she would figure things out, the merits of her unconventional academic path started to become more apparent.
Research at the intersection of data and health
Last summer, Deng landed a summer research experience at Mount Sinai, where she looked at patient-level cancer data. Utilizing her knowledge in both biology and data analytics, she worked on a computational screener that scientists and biologists could use to measure gene expression in diseased versus normal cells. This will ultimately aid efforts in narrowing down the best genes to target in drug development. Deng will be back at Mount Sinai full-time after graduation, to continue her research before applying to medical school.
Deng presenting on her research at Mount Sinai
But in her own words, Deng’s most favorite research experience has been her senior thesis through Duke’s Department of Biostatistics and Bioinformatics. Last year, she reached out to Dr. Xiaofei Wang, who is part of a team conducting a randomized controlled trial to compare the merits of two different lung tumor treatments.
Generally, when faced with lung disease, the conservative approach is to remove the whole lobe. But that can pose challenges to the quality of life of people who are older, with more comorbidities. Recently, there has been a push to focus on removing smaller sections of lung tissue instead. Deng’s thesis looks at patient surgical data over the past 15 years, showing that patient survival rates have improved as more of these segmentectomies – or smaller sections of tissue removal – have become more frequent in select groups of patients.
“I really enjoy working on it every week,” Deng says about her thesis, “which is not something I can usually say about most of the work I do!” According to Deng, a lot of research – hers included – is derived from researchers mulling over what they think would be interesting to look at in a silo, without considering what problems might be most useful for society at large. What’s valuable for Deng about her thesis work is that she’s gotten to work closely with not just statisticians but thoracic surgeons. “Originally my thesis was going to go in a different direction,” she said, but upon consulting with surgeons who directly impacted the data she was using – and would be directly impacted by her results – she changed her research question.
The merits of an interdisciplinary academic path
Deng’s unique path makes her the perfect person to ask: is pursuing seemingly disparate interests, like being a Statistics and Computer Science double-major on the pre-med, track worth it? And judging by Deng’s insights, the answer is a resounding yes.
At Duke, she says, “I’ve been challenged by many things that I wouldn’t have expected to be able to do myself” – like dealing with the catch-up work of switching majors and pursuing independent research. But over time she’s learned that even if something seems daunting in the moment, if you apply yourself, most, if not all things, can be accomplished. And she’s grateful for the confidence that she’s acquired through pursuing her unique path.
Moreover, as Deng reflects on where she sees herself – and the field of healthcare – a few years from now, she muses that for the first time in the history of healthcare, a third-party player is joining the mix – technology.
While her initial motivation to pursue statistics and computer science was to aid her in research, “I’ve now seen how its beneficial for my long-term goals of going to med school and becoming a physician.” As healthcare evolves and the introduction of algorithms, AI and other technological advancements widens the gap between traditional and contemporary medicine, Deng hopes to deconstruct it all and make healthcare technology more accessible to patients and providers.
“At the end of the day, it’s data that doctors are communicating to patients,” Deng says. So she’s grateful to have gained experience interpreting and modeling data at Duke through her academic coursework.
And as the Statistics major particularly has taught her, complexity is not always a good thing – sometimes, the simpler you can make something, the better. “Some research doesn’t always do this,” she says – she’s encountered her fair share of research that feels performative, prioritizing complexity to appear more intellectual. But by continually asking herself whether her research is explainable and applicable, she hopes to let those two questions be the North Stars that guide her future research endeavors.
At the end of the day, it’s data that doctors are communicating to patients.
Eden Deng
When asked what advice she has for first-years, Deng said that it’s important “to not let your inexperience or perceived lack of knowledge prevent you from diving into what interests you.” Even as a first-year undergrad, know that you can contribute to academia and the world of research.
And for those who might be interested in pursuing an academic path like Deng, there’s some good news. After Deng talked to the Statistics department about the lack of pre-health representation that existed, the Statistics department now has a pre-health listserv that you can join for updates and opportunities pertaining specifically to pre-med Stats majors. And Deng emphasizes that the Stats-CS-pre-med group at Duke is growing. She’s noticed quite a few underclassmen in the Statistics and Computer Science departments who vocalize an interest in medical school.
So if you also want to hone your ability to communicate research that you care about – whether you’re pre-med or not – feel free to jump right into the world of data analysis. As Deng concludes, “everyone has something to say that’s important.”
What are the trials and tribulations one can expect? And conversely, what are the highlights? To answer these questions, Duke Research & Innovation Week kicked off with a panel discussion on Monday, January 23.
The panel
Moderated by George A. Truskey, Ph.D, the Associate Vice President for Research & Innovation and a professor in the Department of Biomedical Engineering, the panelists included…
Claudia K. Gunsch, Ph.D., a professor in the Departments of Civil & Environmental Engineering, Biomedical Engineering, and Environmental Science & Policy. Dr. Gunsch is the director of the NSF Engineering Research Center for Microbiome Engineering (PreMiEr) and is also the Associate Dean for Duke Engineering Research & Infrastructure.
Dr. Claudia Gunsch
Yiran Chen, Ph.D., a professor in the Department of Electrical & Computer Engineering. Dr. Chen is the director of the NSF AI Institute for Edge Computing (Athena).
Dr. Yiran Chen
Stephen Craig, Ph.D., a professor in the Department of Chemistry. Dr. Craig is the director of the Center for the Chemistry of Molecularly Optimized Networks (MONET).
Dr. Stephen Craig
The centers
As the panelists joked, a catchy acronym for a research center is almost an unspoken requirement. Case in point: PreMiEr, Athena, and MONET were the centers discussed on Monday. As evidenced by the diversity of research explored by the three centers, large externally-funded centers run the gamut of academic fields.
PreMiEr, which is led by Gunsch, is looking to answer the question of microbiome acquisition. Globally, inflammatory diseases are connected to the microbiome, and studies suggest that our built environment is the problem, given that Americans spend on average less than 8% of time outdoors. It’s atypical for an Engineering Research Center (ERC) to be concentrated in one state but uniquely, PreMieR is. The center is a joint venture between Duke University, North Carolina A&T State University, North Carolina State University, the University of North Carolina – Chapel Hill and the University of North Carolina – Charlotte.
PreMiEr – not to be confused with the English Premier League
Dr. Chen’s Athena is the first funded AI institute for edge computing. Edge computing is all about improving a computer’s ability to process data faster and at greater volumes by processing data closer to where it’s being generated. AI is a relatively new branch of research, but it is growing in prevalence and in funding. In 2020, 7 institutes looking at AI were funded by the National Science Foundation (NSF), with total funding equaling 140 million. By 2021, 11 institutes were funded at 220 million – including Athena. All of these institutes span over 48 U.S states.
Athena, or the Greek goddess of wisdom, is a fitting name for a research center
MONET is innovating in polymer chemistry with Stephen Craig leading. Conceptualizing polymers as operating in a network, the center aims to connect the behaviors of a single chemical molecule in that network to the behavior of the network as a whole. The goal of the center is to transform polymer and materials chemistry by “developing the knowledge and methods to enable molecular-level, chemical control of polymer network properties for the betterment of humankind.” The center has nine partner institutions in the U.S and one internationally.
MONET, like French painter Claude Monet
Key takeaways
Research that matters
Dr. Gunsch talked at length about how PreMiEr aspires to pursue convergent research. She describes this as identifying a large, societal challenge, then determining what individual fields can “converge” to solve the problem.
Because these centers aspire to solve large, societal problems, market research and industry involvement is common and often required in the form of an industry advisory group. At PreMiEr, the advisory group performs market analyses to assess the relevance and importance of their research. Dr. Chen also remarked that there is an advisory group at Athena, and in addition to academic institutions the center also boasts collaborators in the form of companies like Microsoft, Motorola, and AT&T.
Dr. Chen presenting on Athena’s partner institutions at Monday’s talk.
Commonalities in structure
Most research centers, like PreMiEr, Athena, and MONET, organize their work around pillars or “thrusts.” This can help to make research goals understandable to a lay audience but also clarifies the purpose of these centers to the NSF, other funding bodies, host and collaborating institutions, and the researchers themselves.
How exactly these goals are organized and presented is up to the center in question. For example, MONET conceptualizes its vision into three fronts – “fundamental chemical advances,” “conceptual advances,” and “technological advances.”
At Athena, the research is organized into four “thrusts” – “AI for Edge Computing,” “AI-Powered Computer Systems,” “AI-Powered Networking Systems,” and “AI-Enabled Services and Applications.”
Meanwhile, at PreMiEr, the three “thrusts” have a more procedural slant. The first “thrust” is “Measure,” involving the development of tracking tools and the exploration of microbial “dark matter.” Then there’s “Modify,” or the modification of target delivery methods based on measurements. Finally, “Modeling” involves predictive microbiome monitoring to generate models that can help analyze built environment microbiomes.
A center is about the people
“Collaborators who change what you can do are a gift. Collaborators who change how you think are a blessing.”
Dr. stephen craig
All three panelists emphasized that their centers would be nowhere without the people that make the work possible. But of course, humans complicate every equation, and when working with a team, it is important to anticipate and address tensions that may arise.
Dr. Craig spoke to the fact that successful people are also busy people, so what may be one person’s highest priority may not necessarily be another person’s priority. This makes it important to assemble a team of researchers that are united in a common vision. But, if you choose wisely, it’s worth it. As Dr. Craig quipped on one of his slides, “Collaborators who change what you can do are a gift. Collaborators who change how you think are a blessing.”
In academia, there is a loud push for diversity, and research centers are no exception. Dr. Chen spoke about Athena’s goals to continue to increase their proportions of female and underrepresented minority (URM) researchers. At PreMiEr, comprised of 42 scholars, the ratio of non-URM to URM researchers is 83-17, and the ratio of male to female researchers is approximately 50-50.
In conclusion, cutting-edge research is often equal parts thrilling and mundane, as the realities of applying for funding, organizing manpower, pushing through failures, and working out tensions with others sets in. But the opportunity to receive funding in order to start and run an externally-funded center is the chance to put together some of the brightest minds to solve some of the most pressing problems the world faces. And this imperative is summarized well by the words of Dr. Craig: “Remember: if you get it, you have to do it!”
I didn’t even know what an AirTag was until I attended a cybersecurity talk by Nick Tripp, senior manager of Duke’s IT Security Office, but according to Tripp, AirTag technology is “something that the entire Duke community probably needs to be aware of.”
An AirTag is a small tracking device that can connect to any nearby Apple device using Bluetooth. AirTags were released by Apple in April 2021 and are designed to help users keep track of items like keys and luggage. Tripp himself has one attached to his keys. If he loses them, he can open the “Find My” app on his phone (installed by default on Apple devices), and if anyone else with an Apple device has been near his keys since he lost them, the Bluetooth technology will let him see where his keys were when the Apple device user passed them—or took them.
According to Tripp, AirTags have two distinct advantages over earlier tracking devices. First, they use technology that lets the “Find My” app provide “precise location tracking”—within an inch of the AirTag’s location. Second, because AirTags use the existing Apple network, “every iPhone and iPad in the world becomes a listening device.”
You can probably guess where this is going. Unfortunately, the very features that make AirTags so useful for finding lost or stolen items also make them susceptible to abuse. Therearenumerousreports of AirTags being used to stalk people. Tripp has seen that problem on Duke’s campus, too. He gives the example of someone going to a bar and later finding an AirTag in their bag or jacket without knowing who put it there. The IT Security Office at Duke sees about 2-3 suspected cyberstalking incidents per month, with 1-2 confirmed each year. Cyberstalking, Tripp emphasizes, isn’t confined to the internet. It “straddles the internet and the real world.” Not all of the cyberstalking reports Duke deals with involve tracking devices, but “the availability of low-cost tracking technology” is a concern. In the wrong hands, AirTags can enable dangerous stalking behavior.
As part of his IT security work, and with his wife’s permission, Tripp dropped an AirTag into his wife’s bag to better understand the potential for nefarious use of AirTags by attackers. Concerningly, he found that he was able to track her movement using the app on his phone—not constantly, but about every five minutes, and if a criminal is trying to stalk someone, knowing their location every five minutes is more than enough.
Fortunately, Apple has created certain safety features to help prevent the malicious use of AirTags. For instance, if someone has been near the same AirTag for several hours (such as Tripp’s wife while there was an AirTag in her bag), they’ll get a pop-up notification on their phone after a random period of time between eight and twenty-four hours warning them that “Your current location can be seen by the owner of this AirTag.” Also, an AirTag will start making a particular sound if it has been away from its owner for eight to twenty-four hours. (It will emit a different sound if the owner of the AirTag is nearby and actively trying to find their lost item using their app.) Finally, each AirTag broadcasts a certain Bluetooth signal, a “public key,” associated with the AirTag’s “private key.” To help thwart potential hackers, that public key changes every eight to twenty-four hours. (Are you wondering yet what’s special about the eight-to-twenty-four hour time period? Tripp says it’s meant to be “frequently enough that Apple can give some privacy to the owner of that AirTag” but “infrequently enough that they can establish a pattern of malicious activity.”)
But despite these safety features, a highly motivated criminal could get around them. Tripp and his team built a “DIY Stealth AirTag” in an attempt to anticipate what measures criminals might take to deactivate or counteract Apple’s built-in security features. (Except when he’s presenting to other IT professionals, Tripp makes a point of not revealing the exact process his team used to make their Stealth AirTag. He wants to inform the public about the potential dangers of tracking technology while avoiding giving would-be criminals any ideas.) Tripp’s wife again volunteered to be tracked, this time with a DIY Stealth AirTag that Tripp placed in her car. He found that the modified AirTag effectively and silently tracked his wife’s car. Unlike the original AirTag, their stealthy version could create a map of everywhere his wife had driven, complete with red markers showing the date, time, and coordinates of each location. An AirTag that has been modified by a skilled hacker could let attackers see “not just where a potential victim is going but when they go there and how often.”
“The AirTag cat is out of the bag, so to speak,” Tripp says. He believes Apple should update their AirTag design to make the safety features harder to circumvent. Nonetheless, “it is far more likely that someone will experience abuse of a retail AirTag” than one modified by a hacker to be stealthier. So how can you protect yourself? Tripp has several suggestions.
Know the AirTag beep indicating that an AirTag without its owner is nearby, potentially in your belongings.
If you have an iPhone, watch for AirTag alerts. If you receive a notification warning you about a nearby AirTag, don’t ignore it.
If you have an Android, Tripp recommends installing the “Tracker Detect” app from Apple because unlike iPhone users, Android users don’t get automatic pop-up notifications if an AirTag has been near them for several hours. The “Tracker Detect” Android app isn’t a perfect solution—you still won’t get automatic notifications; you’ll have to manually open the app to check for nearby trackers. But Tripp still considers it worthwhile.
For iPhone users, make sure you have tracking notifications configured in the “Find My” app. You can go into the app and click “Me,” then “Customize Tracking Notifications.” Make sure the app has permission to send you notifications.
Know how to identify an AirTag if you find one. If you find an AirTag that isn’t yours, and you have an iPhone, go into the “Find My” app, click “Items,” and then swipe up until you see the “Identify Found Item” option. That tool lets you scan the AirTag by holding it near your phone. It will then show the AirTag’s serial number and the last four digits of the owner’s phone number, which can be useful for the police. “If I found one,” Tripp says, “I think it’s worth making a police report.”
It’s worth noting that owning an AirTag does not put you at higher risk of stalking or other malicious behavior. The concern, whether or not you personally use AirTags, is that attackers can buy AirTags themselves and use them maliciously. Choosing to use AirTags to keep track of important items, meanwhile, won’t hurt you and may be worth considering, especially if you travel often or are prone to misplacing things. Not all news about AirTags is bad. They’ve helped people recover lost items, from luggage and wallets to photography gear and an electric scooter.
“I actually think this technology is extremely useful,” Tripp says. It’s the potential for abuse by attackers that’s the problem.
The healthcare industry and academic medicine are excited about the potential for artificial intelligence — really clever computers — to make our care better and more efficient.
The students from Duke’s Health Data Science (HDS) and AI Health Data Science Fellowship who presented their work at the 2022 Duke AI Health Poster Showcase on Dec. 6 did an excellent job explaining their research findings to someone like me, who knows very little about artificial intelligence and how it works. Here’s what I learned:
Artificial intelligence is a way of training computer systems to complete complex tasks that ordinarily require human thinking, like visual categorization, language translation, and decision-making. Several different forms of artificial intelligence were presented that do healthcare-related things like sorting images of kidney cells, measuring the angles of a joint, or classifying brain injury in CT scans.
Talking to the researchers made it clear that this technology is mainly intended to be supplemental to experts by saving them time or providing clinical decision support.
Meet Researcher Akhil Ambekar
Akhil standing next to his poster “Glomerular Segmentation and Classification Pipeline Using NEPTUNE Whole Slide Images”
Akhil Ambekar and team developed a pipeline to automate the classification of glomerulosclerosis, or scarring of the filtering part of the kidneys, using microscopic biopsy images. Conventionally, this kind of classification is done by a pathologist. It is time-consuming and limited in terms of accuracy and reproducibility of observations. This AI model was trained by providing it with many questions and corresponding answers so that it could learn how to correctly answer questions. A real pathologist oversaw this work, ensuring that the computer’s training was accurate.
Akil’s findings suggest that this is a feasible approach for machine classification of glomerulosclerosis. I asked him how this research might be used in medicine and learned that a program like this could save expert pathologists a lot of time.
What was Akhil’s favorite part of this project? Engaging in research, experimenting with Python and running different models, trying to find what works best.
Meet Researcher Irene Tanner
Irene Tanner and her poster, “Developing a Deep Learning Pipeline to Measure the Hip-Knee-Ankle Angle in Full Leg Radiographs”
The research Irene Tanner and her team have done aims to develop a deep learning-based pipeline to calculate hip-knee-ankle angles from full leg x-rays. This work is currently in progress, but preliminary results suggest the model can precisely identify points needed to calculate the angles of hip to knee to ankle. In the future, this algorithm could be applied to predict outcomes like pain and physical function after a patient has a joint replacement surgery.
What was Irene’s favorite part of this project? Developing a relationship with mentor, Dr. Maggie Horn, who she said provided endless support whenever help was needed.
Meet Researcher Brian Lerner
Brian Lerner and his poster, “Using Deep Learning to Classify Traumatic Brain Injury in CT Scans”
Brian Lerner and his team investigated the application of deep learning to standardize and sharpen diagnoses of traumatic brain injury (TBI) from Computerized Tomography (CT) scans of the brain. Preliminary findings suggest that the model used (simple slice) is likely not sufficient to capture the patterns in the data. However, future directions for this work might examine how the model could be improved. Through this project, Brian had the opportunity to shadow a neurologist in the ER and speculated upon many possibilities for the use of this research in the field.
What was Brian’s favorite part of this project? Shadowing neurosurgeon Dr. Syed Adil at Duke Hospital and learning what the real-world needs for this science are.
Many congratulations to all who presented at this year’s AI Health Poster Showcase, including the many not featured in this article. A big thanks for helping me to learn about how AI Health research might be transformative in answering difficult problems in medicine and population health.
Despite this, there are still very few places where one can make purchases directly using crypto. This means that in order to use cryptocurrency, people must first convert it back to US dollars, which can cost a lot due to transaction fees. Additionally, the exchange rate between any given crypto token and USD changes by the second, resulting in a lack of price stability.
(If you are unfamiliar with cryptocurrency or transaction (gas) fees please refer to my prior article here.)
Duke Alum, Joey Santoro, sensed this gap and saw an opportunity. Santoro graduated from Duke in 2019 with a major in Computer Science. There needed to be a volatility-free token with a stable valuation (i.e. matching the USD), to move between the worlds of crypto and fiat currency. This is also known as a stablecoin. While several were already in existence, Santoro wanted to create a more scalable and decentralized one.
Thus, in December of 2020, Joey founded the Fei Protocol. Fei is a stablecoin in Ethereum native decentralized finance (DeFi). Stablecoins are a type of token that aids in maintaining a liquid market by pegging the token’s value to the USD. Fei is able to achieve this through various stability mechanisms. Stablecoins can be used for real-life transactions while still benefiting from instant processing and the security of cryptocurrency payments.
When asked why he chose to work in crypto as opposed to Machine Learning (ML) or Artificial Intelligence (AI) Joey explained that it came down to how much impact he could have.
“The barrier for making an avenue of innovation in crypto is so much lower than something like a machine learning. Higher risk, higher reward.”
joey santoro
Santoro did not come to Duke with the plan of founding a web3 DeFi protocol. In fact, when he matriculated he was actually pre-med and originally only took CS 101 because it was a pre-requisite for the Neuroscience major.
However, it did not take long for Joey to realize he wanted to work in the crypto space. In his second semester, he joined the Duke Blockchain Lab and ended up teaching a blockchain course in his junior and senior years.
Because decentralized finance is still so new, no one completely knows what they are doing, which creates considerable opportunities for innovation. Additionally, because the crypto space is decentralized, it is inherently collaborative and community-driven.
“Being able to write code that’s immediately interoperable with dozens of financial protocols is the coolest thing ever,” Santoro said
Joey argues anyone can become an expert in a particular area in crypto in a couple of months. He said economists and mechanism designers are increasingly moving into the crypto space.
When the Fei Protocol launched in 2020 it was the height of a bull market for crypto and there was heavy demand for a decentralized stablecoin. While there were several other stablecoins in existence, USDC and tether were the most popular and they were both centralized, meaning they were owned by companies.
“What so important to me and why I do this is because I want people to be able to do whatever they want with their money.”
JOey santoro
The demand for a decentralized stablecoin created excitement around Feio but also a highly compressed timescale. The Fei Protocol ended up having the largest token launch for an Ethereum DeFi protocol in history, raising $1.25B. However, when it launched, the peg broke due to issues with the incentive mechanism and bugs in the code.
Santoro recalled the surreal and challenging experience of watching the protocol he spent countless weeks working on fall apart before his eyes. However, his team and investors decided to stick it through and try to salvage what they had built. It took over a month just to fix everything that had gone wrong. In the meantime, people were threatening Santoro and his team.
While the Fei protocol faced challenges while launching, Joey and his team were able to adapt, learn from their mistakes, and come back stronger. They recently conducted a multi-billion-dollar merge with Rari Capital and launched Fei version2 (V2).
Additionally, this is the first multi-billion dollar merger in DeFi meaning that the decision to merge was voted on by members of the respective Decentralized Autonomous Organizations (DAOs). This is a huge milestone in the world of DeFi and sets a precedent for the potential of decentralized business operations.
Joey Santoro Presenting at the ETHDenver Convention
Moving forward Joey explained, “I’m obsessed with simplicity now; I still move fast but more carefully.”
Unfortunately, AI has also magnified one of humanity’s least desirable traits: bias. In recent years, algorithms influenced by bias have often caused more problems than they sought to fix.
When Google’s image recognition AI was found to be classifying some Black people as gorillas in 2015, the only consolation for those affected was that AI is improving at a rapid pace, and thus, incidents of bias would hopefully begin to disappear. Six years later, when Facebook’s AI made virtually the exact same mistake by labeling a video of Black men as “primates,” both tech fanatics and casual observers could see a fundamental flaw in the industry.
On November 17th, 2021, two hundred Duke Alumni living in all corners of the world – from Pittsburgh to Istanbul and everywhere in between – assembled virtually to learn about the future of algorithms, AI, and bias. The webinar, which was hosted by the Duke Alumni Association’s Forever Learning Institute, gave four esteemed Duke professors a chance to discuss their view of bias in the artificial intelligence world.
Dr. Stacy Tantum, Bell-Rhodes Associate Professor of the Practice of Electrical and Computer Engineering, was the first to mention the instances of racial bias in image classification systems. According to Tantum, early facial recognition did not work well for people of darker skin tones because the underlying training data – observations that inform the model’s learning process – did not have a broad representation of all skin tones. She further echoed the importance of model transparency, noting that if an engineer treats an AI as a “black box” – or a decision-making process that does not need to be explained – then they cannot reasonably assert that the AI is unbiased.
Stacy Tantum, who has introduced case studies on ethics to students in her Intro to Machine Learning Class, echoes the importance of teaching bias in AI classrooms.
While Tantum emphasized the importance of supervision of algorithm generation, Dr. David Hoffman – Steed Family Professor of the Practice of Cybersecurity Policy at the Sanford School of Public Policy – explained the integration of algorithm explainability and privacy. He pointed to the emergence of regulatory legislation in other countries that ensure restrictions, accountability, and supervision of personal data in cybersecurity applications. Said Hoffman, “If we can’t answer the privacy question, we can’t put appropriate controls and protections in place.”
To discuss the implications of blurry privacy regulations, Dr. Manju Puri – J.B. Fuqua Professor of Finance at the Fuqua School of Business – discussed how the big data feeding modern AI algorithms impact each person’s digital footprint. Puri noted that data about a person’s phone usage patterns can be used by banks to decide whether that person should receive a loan. “People who call their mother every day tend to default less, and people who walk the same path every day tend to default less.” She contends that the biggest question is how to behave in a digital world where every action can be used against us.
Dr. Philip Napoli has observed behaviors in the digital world for several years as James R. Shepley Professor of Public Policy at the Sanford School, specifically focusing on self-reinforcing cycles of social media algorithms. He contends that Facebook’s algorithms, in particular, reward content that gets people angry, which motivates news organizations and political parties to post galvanizing content that will swoop through the feeds of millions. His work shows that AI algorithms can not only impact the behaviors of individuals, but also massive organizations.
At the end of the panel, there was one firm point of agreement between all speakers: AI is tremendously powerful. Hoffman even contended that there is a risk associated with not using artificial intelligence, which has proven to be a revolutionary tool in healthcare, finance, and security, among other fields. However, while proven to be immensely impactful, AI is not guaranteed to have a positive impact in all use cases – rather, as shown by failed image recognition platforms and racist healthcare algorithms that impacted millions of Black people, AI can be incredibly harmful.
Thus, while many in the AI community dream of a world where algorithms can be an unquestionable force for good, the underlying technology has a long way to go. What stands between the status quo and that idealistic future is not more data or more code, but less bias in data and code.
When people use apps or services like Netflix, Instagram, Amazon, etc. they sign, or rather virtually accept, digital user agreements. Digital agreements have been around since the 1990s. These agreements are written and enforced by the institutions that create these services and products. However, in certain conditions, these systems fail and these digital or service-level agreements can be breached, causing people to feel robbed.
A recent example of this is the Robinhood scandal that occurred in mid-2021. Essentially, people came together and all wanted to buy the same stock. However, Robinhood ended up restricting buying, citing issues with volatile stock and regulatory agreements. As a result, they ended up paying $70 million dollars in fines for system outages and misleading customers. And individual customers were left feeling robbed. This was partially the result of centralization and Robinhood having full control over the platform as well as enforcing the digital agreement.
Zak Ayesh Presenting on Chainlink and Decentralized Smart Contracts
Zak Ayesh, a developer advocate at Chainlink recently came to Duke to talk about decentralized Smart Contracts that could solve many of the problems with current centralized digital agreements and traditional paper contracts as well.
What makes smart contracts unique is that they programmatically implement a series of if-then rules without the need for a third-party human interaction. While currently these are primarily being used on blockchains, they were actually created by computer scientist Nick Szabo in 1994. Most smart contracts now run on blockchains because it allows them to remain decentralized and transparent. If unfamiliar with blockchain refer to my previous article here.
Smart contracts are self-executing contracts with the terms of the agreement being directly written into computer code.
Zak Ayesh
There are several benefits to decentralized contracts. The first is transparency. Because every action on a blockchain is recorded and publicly available, the enforcement of smart contracts is unavoidably built-in. Next is trust minimization and guaranteed execution. With smart contracts, there is reduced counterparty risk — that’s the probability one party involved in a transaction or agreement might default on its contractual obligation because neither party has control of the agreement’s execution or enforcement. Lastly, they are more efficient due to automation. Operating on blockchains allows for cheaper and more frictionless transactions than traditional alternatives. For instance, the complexities of cross-border remittances involving multiple jurisdictions and sets of legal compliances can be simplified through coded automation in smart contracts.
Dr. Campbell Harvey, a J. Paul Sticht Professor of International Business at Fuqua, has done considerable research on smart contracts as well, culminating in the publication of a book, DeFi and the Future of Finance which was released in the fall of 2021.
In the book, Dr. Harvey explores the role smart contracts play in decentralized finance and how Ethereum and other smart contract platforms give rise to the ability for decentralized application or dApp. Additionally, smart contracts can only exist as long as the chain or platform they live on exists. However, because these platforms are decentralized, they remove the need for a third party to mediate the agreement. Harvey quickly realized how beneficial this could be in finance, specifically decentralized finance or DeFi where third-party companies, like banks, mediate agreements at a high price.
“Because it costs no more at an organization level to provide services to a customer with $100 or $100 million in assets, DeFi proponents believe that all meaningful financial infrastructure will be replaced by smart contracts which can provide more value to a larger group of users,” Harvey explains in the book
Beyond improving efficiency, this also creates greater accessibility to financial services. Smart contracts provide a foundation for DeFi by eliminating the middleman through publicly traceable coded agreements. However, the transition will not be completely seamless and Harvey also investigates the risks associated with smart contracts and advancements that need to be made for them to be fully scalable.
Ultimately, there is a smart contract connectivity problem. Essentially, smart contracts are unable to connect with external systems, data feeds, application programming interfaces (APIs), existing payment systems, or any other off-chain resource on their own. This is something called the Oracle Problem which Chainlink is looking to solve.
Harvey explains that when a smart contract is facilitating an exchange between two tokens, it determines the price by comparing exchange rates with another similar contract on the same chain. The other smart contract is therefore acting as a price oracle, meaning it is providing external price information. However, there are many opportunities to exploit this such as purchasing large amounts on one oracle exchange in order to alter the price and then go on to purchase even more on a different exchange in the opposite direction. This allows for capitalization on price movement by manipulating the information the oracle communicates to other smart contracts or exchanges.
That being said, smart contracts are being used heavily, and Pratt senior Manmit Singh has been developing them since his freshman year along with some of his peers in the Duke Blockchain Lab. One of his most exciting projects involved developing smart contracts for cryptocurrency-based energy trading on the Ethereum Virtual Machine allowing for a more seamless way to develop energy units.
One example of how this could be used outside of the crypto world is insurance. Currently, when people get into a car accident it takes months or even a year to evaluate the accident and release compensation. In the future, there could be sensors placed on cars connected to smart contracts that immediately evaluate the damage and payout.
Decentralization allows us to avoid using intermediaries and simply connect people to people or people to information as opposed to first connecting people to institutions that can then connect them to something else. This also allows for fault tolerance: if one blockchain goes down, the entire system does not go down with it. Additionally, because there is no central source controlling the system, it is very difficult to gain control of thus protecting against attack resistance and collusion resistance. While risks like the oracle problem need to be further explored, the world and importance of DeFi, as well as smart contracts, is only growing.
If you walked across Duke’s Engineering Quad between 9AM on Saturday, October 23rd, and 5PM on Sunday, October 24th, the scene might’ve looked like that of any other day: students gathered in small groups, working diligently.
But then you’d see the giant banner and realize something special was afoot. These students were participating in HackDuke’s “Code for Good,” one of the most eminent social good hackathons in the country.
Participants have to “build something, not just an idea,” said Anita Li, co-director of HackDuke. Working in teams, students develop software, hardware, or quantum solutions to problems in one of four tracks: inequality, health, education, and energy and environment.
Participants can win “track prizes,” where $2,400 in total donations are made in winners’ names ($300 for first, $200 for second, $100 for third) to charities doing work in that track. There are other prizes too. Sponsors, including Capital One, Accenture, and Microsoft give incentives: if participants incorporate their technology or use their database, they’re qualified to win that sponsor’s prize (gift cards, usually, or software worth hundreds of dollars).
This year, Duke’s department of Student Affairs sponsored the health track, in hopes that participants might come up with ideas that could help promote student wellness here at Duke. “It’s a great space for thinking about these issues,” Li said.
Li told me they had more than 1,000 registrations, though there’s always a little less turnout. HackDuke is open to all students and recent graduates, so that “you get to see these cool ideas from everywhere.”
Just under half of this year’s participants were from Duke, almost 10% hailed from UNC, and the rest were from other universities across the US and the world. 30 percent of participants were women — a significant increase from the last HackDuke covered by the Research Blog, in 2014.
This year is “particularly interesting,” Li said, because of the hybrid model. Last year, everything was virtual. This year, about 300 (vaccinated) students attended in person, making HackDuke one of the few Major League Hacking events with an in-person component this year. With the hybrid model, talks, workshops, and demos are all livestreamed so that no one misses out.
Some social events also had online elements: you could zoom into the Bob Ross painting session as well as the open mic, which Li said quickly turned into karaoke night. The spicy ramen challenge was “a little harder over Zoom.”
I came across Sydney Wang and Ray Lennon, along with teammate Jean Rabideau, as they were building a web app called JamJar for the Education Track contest. In the app, students give real-time feedback to teachers about how well they’re understanding the material. There are three categories: engagement (you can rank your engagement along a scale from “mentally I’m in outer space” to “locked in), understanding (“where am I?” to “crystal clear”), and speed (“a glacial pace” to “TOO FAST!”). Student responses get compiled and graphed to show mean markers of understanding over time.
Lennon said he’s participating because “this is the best way to learn: to be thrown in the fire and have to learn as you go.” Wang felt the same way. She’s new to coding, and feels like she’s learning a lot from Lennon.
Like Lennon and Wang, many participants see HackDuke as an opportunity to learn. There are technical workshops where participants can learn HTML and CSS. There are talks where speakers discuss working in the coding and social good sector. The CTO of change.org, Elaine Zhou, flew to Durham to speak to participants about her experience. So there’s a networking opportunity, too — participants can meet people like Zhou doing the work they want to do, and professors and company representatives who can help them on their journey to get there.
There were challenges. Staying hydrated was one: by Sunday morning, they’d gone through seven cases of water, 16 cases of soda, and three cases of red bull. “It takes a lot of liquids,” Li said. And then there’s sleep — or lack thereof. When Li was participating in her freshman year, she slept for about three hours. Many people pull all-nighters, but “nap sporadically everywhere,” Li said. “It’s like finals season, with everyone knocked out.” She saw a handful of guys sleeping on the floor in Fitzpatrick. She gave them bed pads.
Li’s love for HackDuke is contagious. She loves to see participants focusing on social good and drawing on their awareness of what’s happening in the world. “People are thinking about things that are intense; they’re really worrying about issues facing certain communities,” Li said.
