Several employees at the Office of Technology Transfer extend their expertise in the technology industry to outside professional organizations.

Among these employees are Laura Fritts, Director of Patent and License Strategy and Chief Intellectual Property Officer; Kimberly Dunn, Compliance Associate; Linda Kesselring, Operations Director; Kevin Lei, Director of Faculty and Start-Up Services; Patrick Reynolds, Assistant Director of Faculty and Start-up Services; Quentin Thomas, Marketing Manager; and Sarah Wilkening, Licensing Associate.

The majority of the above OTT employees volunteer with the Association of University Technology Managers (AUTM). AUTM is a nonprofit organization that supports individuals involved in technology transfer through education, professional development, and advocacy, among other activities. Through these methods, the organization provides knowledge of technology transfer beyond Emory and forges connections between members, which is why many employees opted to join.

Each involved OTT member contributes to AUTM in a different capacity, ranging from assisting with marketing to hosting instructional webinars. For example, while Fritts serves on the AUTM Public Policy Legal team, Dunn previously served on the Distance Learning Committee organizing speakers for AUTM-hosted webinars and was Chair of the Intellectual Property Portfolio Management Committee. As chair, Dunn helped develop and manage an annual course for three years before recently joining AUTM’s TOOLs Committee.

Having been an AUTM member for over 20 years, Kesselring currently serves as chair of the website committee. As chair, she works with AUTM staff focused on marketing and communications and organizes work, activities, and conference calls to “meet objectives for the AUTM website.” This is done through tasks like supporting the website’s redesign and providing regular statistics.

Reynolds chairs AUTM’s Better World Project committee, a group that selects stories submitted by technology transfer offices about how their technologies “make the world a better place.”

“It’s easy to get in the cycle of only knowing what is happening in your own office,” he said. “The Better World Project allows me to see the great work that institutions and [technology transfer offices] around the world are doing.”

Thomas, another AUTM volunteer, occasionally serves as a presenter on Annual Meeting webinar sessions and recently left the Marketing Course Committee after working with this group for three years. He noted that his work with AUTM increases exposure of Emory’s OTT office and fuels additional opportunities, while also allowing him to learn new skills and gain knowledge from colleagues at other institutions.

In addition to those who volunteer with AUTM, several OTT employees cite benefits from volunteering with outside organizations. Lei volunteers with Certified Licensing Professional (CLP), Inc. as a member of The CLP Exam Development and Maintenance Committee. The CLP program certifies professionals that demonstrate experience and proficiency in the licensing and commercialization of intellectual property after they take and pass a 150 multiple choice question exam. When the organization recruited volunteers to revise exam questions in 2019, Lei took this opportunity.

“I thought it would be important to be involved and help maintain the high standard of the CLP program, because it takes dedicated volunteers to develop a quality certification program,” he said.

Wilkening primarily volunteers with the Patent Agents of Georgia, an organization she co-founded under the Georgia Intellectual Property Alliance. This group aims to foster community among Georgia’s past, present, and future patent agents and coordinates networking events for “science-lovers that want to stay at the frontlines of research without having to do the research.”

“I wish I would have known about this career path earlier in my life … Being a patent agent can be a rewarding career opportunity,” she said. “This organization has brought together technology transfer and patent professionals from all over Georgia, and I have had the honor to help branch those networking opportunities.”

Fritts has volunteered as a lawyer with the Executive Committee for Atlanta’s IP Inn of Court and the Advisory Board for the United States Patent and Trademark Office (USPTO) Georgia PATENTS. As a volunteer, she has helped inventors file patent applications, saying, “The benefits to the organizations, my office, and me personally far outweigh the burden of the work.”

Dunn also volunteered with the Georgia Association of Paralegals’s Atlanta Legal Aid Society. With this organization, she assisted domestic violence victims in filing appropriate documents involving scenarios like temporary restraining orders and coordinating temporary housing. She listed obtaining a “diverse network of knowledge and experience” among several benefits she received from volunteering.

“Volunteering is like continuing education: we constantly learn something new,” she explained. “The most important benefit is the feeling of being a part of something bigger than yourself.”

Telemedicine is the use of available telecommunications technologies to diagnose and provide care to patients from a remote location. Although frequently mentioned in the news lately due to COVID-19, telemedicine is a field that has been around for more than 50 years.

The need to treat patients without seeing them in person arose mostly because of people in rural areas, who were unable to travel long distances to see a doctor or go to a hospital. To solve this issue, doctors would talk to patients on the phone and even use the telegraph to send and receive medical information. Nowadays, modern video-conferencing is widely used for telemedicine, providing an easy and convenient way to share information with providers. Since file sharing is easier than ever, doctors can electronically send their patients their lab results, X-Rays, and other types of diagnostic information. Furthermore, doctors can share patient records with each other, enabling them to provide better collaborative care to their patients.

Since the use of smart devices has increased in the past few years, many doctors now have remote access to important health information, such as their heart rate, activity levels and even their blood pressure. That way patients can receive medical advice from the comfort of their home, without unnecessary travel and long wait times at doctor’s offices. Telemedicine helps doctors with office-related costs while also saving them precious time and allowing for more appointments. As evidenced during this pandemic, an additional benefit of telemedicine is the reduced risk of exposure to contagious diseases that are frequently found in hospitals and doctor’s offices, therefore protecting both patients and medical personnel.

The telemedicine industry is rapidly growing, with more and more tech companies creating HIPAA-compliant video platforms and e-health environments. With the inevitable future developments in the medical device sector, it is very likely that telemedicine is here to stay as a regular part of medical care.

More resources on telemedicine:

• National Institute of Biomedical Imaging and Bioengineering. https://www.nibib.nih.gov/science-education/science-topics/telehealth

• Centers for Disease Control and Prevention. https://www.cdc.gov/phlp/publications/topic/telehealth.html

• World Health Organization. https://www.who.int/gho/goe/telehealth/en/

Experimental drugs can be a lifeline for those with conditions for which conventional treatments are not working or readily available. At the same time, doctors and patients want to have confidence that they are making safe and informed choices when requesting the use of drugs that have not have fully completed the clinical trial process.  With many doctors turning to experimental treatments for COVID-19 patients, this topic has recently gained renewed attention. Two processes in particular: compassionate use and Right to Try, give patients with serious or terminal conditions the ability to gain access to treatments which have not received FDA approval. These treatments can be potentially lifesaving for those whom conventional treatment is either unavailable or not working.

Compassionate use, formally known as Expanded Access, allows patients access to experimental treatments with approval from their doctor, the FDA, and an Institutional Review Board (IRB). The patient must meet several criteria, including having a serious disease or condition for which no comparable satisfactory treatment is available and being unable to enroll in clinical trials for the drug. If patients decide to pursue compassionate use, they must first speak to their physician, who then files a request to both the FDA and IRB. Eligibility is granted by the FDA if the agency determines that both (1) the benefits of using the experimental treatment outweigh any risks and (2) that its use will not jeopardize ongoing clinical trials. The FDA must respond to such a request within thirty days, and approval has historically been granted in 99% of cases. Secondly, the use must be approved by a registered IRB. Only once both of these steps are complete is the patient allowed access to the drug.

In May 2018 Congress passed the Right to Try Act, establishing a second process for patients to gain access to experimental treatments. Under Right to Try, patients with life-threatening conditions no longer need the approval of the FDA and IRBs to gain access to certain experimental drugs. Those with a terminal illness can submit a request directly to the manufacturer of an experimental treatment with the consent of their doctor. They may then obtain the treatment if the manufacturer approves. Similar to with compassionate use, patients using the Right to Try process must have exhausted all available conventional treatments and be unable to participate in clinical trails involving the experimental drug. The medication must also have completed Phase 1 clinical trials, an additional requirement not present in the compassionate use process. Right to Try has proven controversial in the scientific community since its conception. Proponents claim it cuts red tape patients for terminally ill patients in obtaining treatment, but critics point to the lack of reporting requirements and say that Right to Try removes safeguards in the compassionate use process that ensure patient safety.

While compassionate use and Right to Try have different processes, the programs share several key limitations. Importantly, manufacturers have no obligation to provide experimental drugs under either process, and the FDA has no power to compel companies to do so. Many manufacturers are reluctant to provide patients access to experimental drugs due to concerns that adverse reactions could lead to negative publicity or interfere with the FDA approval process.  Additionally, insurance companies are under no obligation to cover experimental treatments. The fact that many patients must pay out-of-pocket for experimental treatments has led to a stratified system where low-income patients are unable to obtain experimental drugs.

Despite these constraints, both compassionate use and Right to Try continued to be utilized by doctors and patients across the country. Many individuals with severe or terminal illness can’t afford to wait the seven to twelve years it takes for drugs to be approved by the FDA. Through compassionate use and Right to Try, these patients with few options left get a renewed chance.

The healthcare industry generates a lot of data. X-rays, pathology slides, patient vitals, clinical trial information; we have mountains of information accessible at the touch of a button. But it’s costly and time-inefficient for humans to manually pour over it. So what do we do with all of this data?

The field of intelligence (AI) allows not only to let us analyze all our data, but to find subtle and complex patterns in them. Machine learning algorithms are particularly responsible for these advancements. Engineers have developed software that’s able to look at a dataset, find relationships between a bunch of variables, and then make mathematical models that we can use to predict behaviors in the future or analyze other sources of data: images, patient records, etc.

What does that mean in the real world? Computer scientists can make systems that can analyze an image and detect a disease better than humans can. They can make programs that are able to predict the notoriously unpredictable process of how proteins fold, or even make drugs which are currently in human trials. Let’s learn a little more about how machine learning works, and see some of the current applications.

How machine learning systems work

Machine learning systems are complex algorithms. Algorithms are methods of “treating” data: a computer receives an input, some sort of data. The computer recognizes that data and sorts it. For example, let’s say I have a program that can tell me if the color of an animal I’m thinking about makes sense. if I type in the phrase “blue horse,” there might be something in the code that recognizes the word “blue” and the word “horse.” The code then might have some information about the colors a horse can be, and would have an instruction to compare “blue” with “horse.” If “blue” isn’t in the “horse” database, then the computer would have an instruction to tell me “No, horses can’t be blue.” To make it simple, algorithms are sets of instructions that do something with information we give it.

A simple algorithm for calculating interest. Courtesy of Edraw.

They get way more complex than that example. Specific algorithms can take ridiculous amounts of data and look for statistical relationships between them. Machine learning systems are able to make new models by using what are called learning algorithms. There are a bunch of different types, but the most conceptually simple ones involve supervised learning. These learning algorithms are exposed to a “training set” of data with a bunch of examples with known “right answers” — for example, a bunch of CT scan images from patients whom we know have cancer or not. The algorithm “doesn’t know” which patients have the disease. It keeps changing the instructions inside of its algorithm to try to — if the computer sees a white spot in a patient’s liver, it says yes, but this turned out to be wrong, so the next time the computer doesn’t care about that spot when saying yes or no.

Do this a bunch of times, and eventually the algorithm learns what data points lead to the correct output: the machine learns. Over time, these algorithms get very accurate, and we can use them for specific applications.

Radiology and imaging

Image analysis is a very natural application of this sort of software. The cancer example above isn’t science fiction: Google’s already developed a system that’s able to detect breast cancer. A very recent study published information about a deep learning model that’s able to diagnose forms of common hip arthritis at an accuracy rivaling radiologists’ analysis.

But there are concerns about the current state of radiology AI and bringing these technologies to the clinic. Analyzing images is a very complex process, critics argue, and we don’t always know how these machine learning systems actually work. Although applications are rapidly developing, it’s clear that radiologists are still very necessary.

A picture of an image where an AI system found cancer in a human breast. Courtesy of Northwestern University via The New York Times.

Drug discovery

UK-based startup Exscientia became the first company to develop a small-molecule drug entirely designed by artificial intelligence. Small-molecule drugs are tiny chemicals that generally “stick” to proteins of interest. It is very hard to know what sort of molecules will stick and treat a disease and which ones won’t. The scientists at Exscientia use a learning algorithm that takes data from human-run tests to optimize the structure of the chemical they’re making, reducing the amount of time it takes to get a working drug. The compound, titled DSP-181 and targeted toward treating obsessive-compulsive disorder, is set to enter clinical trials in Japan soon.

Point-of-care solutions

AI doesn’t just help us make drugs; it can help us deliver them, too. Electronic health record systems are growing more and more. Nurses say that bureaucratic work cuts their time spent with patients by about 25 percent. Doctors report that less than half of their patients are what they’d describe as “highly engaged” in their course of care. Engineers are already developing systems that can not only manage data, but use electronic information to make predictions about how patients might comply with certain treatments.

From point-of-care solutions and radiology to drug discovery and beyond, it’s clear that artificial intelligence will play an increasingly large role in analyzing healthcare data in the future in ways both known and unknown to us now. New applications of machine learning systems are rapidly developing as engineers aim to create new machine learning algorithms that can analyze and find complex data. Used in tandem with the expertise of doctors, radiologists, and other healthcare professionals, these algorithm-based technologies are already modernizing our healthcare system and have the potential to improve public health on an even larger scale in the future.

Emory has approximately 600 technologies available to license at any given time. In particular, Emory offers a variety of live science resources such as therapeutics, diagnostics, and research tools that are marketed by Emory OTT.  

However, finding new technologies available at a university can be time-consuming and potentially frustrating for startups and established companies alike. Below are simple ways to find and remain up-to-date with technologies coming from Emory University.

1. Subscribe to TechFeed to receive email notifications about products: TechFeed is a notification system where users can sign up to receive emails about recently added technologies. It can be individually customized to get notifications based on what products you’re interested in and how frequently you want to be notified. 

2. Use our Technology Listings page: To get an improved searching experience with more accurate results, use our Google-powered search option. Through this, you can find non-confidential summaries of available technologies. Alternatively, if you’re looking for something around a specific indication or topic, click on Keywords in our word cloud and Technology Categories to get a list of these technologies.

3. Visit our Featured Innovations page: Using these articles, you can get more information about and see real-world applications of available technologies.

4. Contact our knowledgeable Marketing Associate, Quentin Thomas: Reach out to Quentin via email to request a hand-picked selection of technologies related to your needs and areas of interest. Quentin also encourages interested parties to set up a face-to-face conversation with him so he can guide the path from there. 

5. Subscribe to our RSS feed and follow us on Twitter @Emory OTT: By doing this, you can stay up-to-date with all of our new technologies as soon as they’re listed on the website.

Additionally, to find a one-stop shop to find technologies from Emory as well as universities worldwide, there are a number of third-party listing services available to search free of charge, including the Association of University Technology Managers (AUTM) Innovation Market (AIM).

Our goal as technology matchmakers is to simplify the process for industry colleagues to find our technologies. If you have any suggestions on how we can improve this process, please contact us.

Two important updates 

1.  All staff in the Office of Technology Transfer are now working remotely, telecommuting. The office is still open for business and fully functional. Please reach out and we are ready to help. To find a complete staff listing go to our staff page on our website.

2. Any new request for an Unfunded Research Contract should be sent to ott-mta [at] emory [dot] edu. Please fill out the appropriate questionnaire from our website to reduce processing time. The review of the agreement can not start until this information is submitted.

3. Submitting a disclosure form can now be done on-line with our new tool called IdeaGate. Please go to the IdeaGate website to submit.

4. The blog posts will slow down during this work from home period.

In 2011, Emory University was listed in the New England Journal of Medicine as the 4th largest contributor to drug discovery among public-sector research institutions. Even with this accomplishment, drug discoveries comprise about 35% of the 1,634 active technologies managed by the OTT. This indicates a powerful research program, where researchers also produce cutting-edge advances in diagnostic technology, medical devices, medical software, research tools, and more.

As such, the technologies the Office of Technology Transfer (OTT) markets and licenses are often life science technologies. This article exposits various non-medical technologies currently being marketed by OTT in an attempt to showcase work taking place in other corners of the university. Emory is known for strong research programs across each of its schools, from the College to the Rollins School of Public Health. The technologies below illustrate the work of Emory researchers outside of life sciences.

An Emory Information Security Scientist Improves Upon Institutional Phishing Prevention
With the roll-out of an award-winning Duo Two Factor authentication system, the Emory information security team has established itself as one of the leaders in its field. Naturally, its specialists would eventually turn their eye to “phishing” attacks – fraudulent emails purported to come from credible sources under the pretenses of extracting personal information. Recently, for those within Emory, you may have noticed “[External]” tags attached to emails sent from outside of the Emory network. This has been one method to weed out phishers and other scams. A few years ago, Elliot Kendall produced another method, this one meant to help build targeted education campaigns for Emory employees. His software improves upon the service provided by PhishMe, Inc., a phishing attack simulator meant to suggest how often employees provide sensitive information to illegitimate sources. Kendall found a way to combine PhishMe report data with institutional demographic data, such as email address or departmental building, in order to demonstrate where phishing-vulnerable employees were clustered. By understanding who exactly was falling for PhishMe’s fake phishing attempts, the information security team could directly work with affected respondents and provide them with the resources to avoid real phishing attacks in the future. Our office’s technology brief of the software can be found here.

Insecticide Applications from the Work of Emory Chemists
While searching for a novel therapeutic compound, Emory’s Erwin Van Meir, along with collaborator Binghe Wang from GSU, stumbled across a molecule capable of blocking mitochondrial complex I. “In the mitochondria, there is the electron chain transport that is part of how a cell makes its energy,” Dr. Van Meir told me. “The electron chain transport produces ATP through a process that goes through five different complexes. Complex I and II are generally seen as the root, where the others are dependent on their activity.” Cells can also make ATP through glycolysis, where glucose is converted to pyruvate, but only in small amounts. Furthermore, pyruvate sometimes converts to lactate, acidifying the surrounding environment and damaging other cells. This means that mitochondrial complex I is crucial for energy production, and its inhibition produces deleterious effects. In recent years, insecticide manufacturers have targeted the complex in their formulas, as its inhibition in insects would remove their presence from farmland. “Mitochondrial Complex 1 is really complex, comprised of roughly 50 different proteins, so it’s hard to know exactly where our molecule binds,” Van Meir furthered. “However, our molecule is also the most potent inhibitor that I’m aware of.” Greater potency allows for the deployment of lower doses, mitigating broader environmental consequences. Read our office’s technology brief for the compound here.

Air Detoxification Technologies Produced by Emory Researchers
As consumers pay more attention to environmentally harmful chemicals and their damaging impacts on human health, researchers have begun to search for ways to remove toxins from our surroundings. In a series of breakthroughs, Goodrich C. White Professor Craig Hill collaborated with postdocs and colleagues to develop various compounds that can detect, absorb, and detoxify harmful chemicals. One such technology is a polymer that transforms into a gel upon contact with toxins, entrapping and neutralizing them. Not only does the polymer change color upon detoxifying a substance, but its versatility allows for customization in selecting target chemicals. As referenced in our featured technology piece, Hill and his colleagues have continued to research the polymer and hope to perfect it further. Their development, however, is only the most recent in a line of other detoxification projects headed by Hill. Previously, Hill collaborated with Yurii Geletii, as well as a few postdocs, on a catalyst to neutralize nerve agents and other toxic substances. This technology, which is detailed further in our office’s brief, marked an innovation in solely requiring oxygen as a reagent. In the late 90’s, Hill worked with a graduate student to produce a fabric that could remove both gaseous and liquid contaminants from the ambient environment. Our office’s technology brief for the polymer can be found here.

Emory Researchers Inch Closer to a Hydrogen Economy
Another central focus of The Craig L. Hill Research Group is the development of materials for water oxidation. Our current mode of energy production burns hydrocarbons for fuel, which notoriously leaks carbon dioxide and other carbon pollutants into our atmosphere. As the combustion of hydrogen only produces clean water, hydrogen fuel has become a leading alternative for sustainable energy. This is also due, in part, to the known inefficiencies of solar power storage. Rather than rely on solar batteries, researchers have begun considering using sunlight to separate hydrogen molecules out of water and produce hydrogen fuel. This requires a water oxidation catalyst (WOC), a molecule that can perform the separation. Dr. Hill’s team at Emory debuted a polyoxometalate-based catalyst (technology brief here) that propelled the group to the forefront of their field. Not only were they the first research group to develop polyoxometalate for water oxidation, but they discovered that such a catalyst was more stable and efficient than any previous effort and highly cost-effective to produce. More recently, the Hill research group developed a new catalyst twice as efficient as their first WOC (technology brief here). These advances move towards a global-scale catalyst that could efficiently and sustainably shoulder humanity’s collective energy demand. No matter where the research community goes from here, if hydrogen fuel eventually becomes the global standard, it will be indebted to the work of Emory scientists.

How biomedical neuroengineering can make our collective future brighter 

Eva Dyer’s research centers on machine learning and neuroscience, and developing computational methods that uncover principles governing the organization and structure of the brain. Chethan Pandarinath’s research focuses on neuroengineering, deep learning, brain-machine Interfaces, and neural coding, as applied to devices that assist people with disabilities and neurological disorders.

Dyer and Pandarinath, both assistant professors in Emory and Georgia Tech’s Coulter Department of Biomedical Engineering, were awarded 2019 Sloan Research Fellowships honoring early career scholars, “with a unique potential to make substantial contributions to their field.”

We spoke with them about their research, the future of biomedical engineering, and what brains and birds have in common.

Both of your research lies at the intersection of biomedical engineering and neuroscience. What is the big question about the brain right now?

Chethan Pandarinath

CP: My area of research is neuroengineering, which focuses on repairing the nervous system in cases of injury or disease and also on developing new tools or quantitative approaches to study the brain and further expand our understanding. We bring in cutting-edge computational techniques from computer science and artificial intelligence to better understand the brain, and then try to use these insights to develop new therapeutic approaches to treat brain injury or disease. There’s no question to me—this combination of new tools is going to fundamentally change how we understand the complex processes in the brain that make us who we are, and if we can start to compare these processes between healthy individuals and cases of injury and disease, we can develop new strategies for repairing the brain.

ED: One of the major questions that is driving my research is a question of heterogeneity and variability. In particular, when we study disease models, we would like to learn signatures of disease from data. Because brains are so different in their structure, however, they all produce different measurements. I am fascinated by how we can build expressive models that capture this variability to build better decisions about brain state and disease. This trend toward personalization in health care could end up being critical in the study of brain disease as well. With all of these topics and projects, engineering is critical because we need to design new data analysis tools that enable discovery in large and complex datasets.

You each have a background in electrical engineering and now work in the BME field. What do you find exciting about BME right now and where do you see BME heading in the future?  

CP: I think this is an unbelievably exciting time for our field. For a long time, neuroscience has been really limited by a lack of tools. The brain is incredibly complex, made up of billions of individual neurons that are intricately connected into powerful networks. Yet, in terms of neuroscientific experiments, we’ve mostly been able to study the activity of one or a handful of neurons at a time. When you’re staring at the activity of one neuron, it’s extremely challenging to say anything concrete about what a billion-neuron network is really doing. There is a confluence of factors now that promise to transform our understanding of the brain. On the one hand, we’re getting new tools that allow us to monitor many thousands of neurons simultaneously, and these capabilities are growing exponentially. When you can monitor the activity of 10,000 neurons for long periods of time, you start to create truly massive datasets, which will hopefully allow us to ask fundamentally different questions about how neural networks in the brain really work. At the same time, our ability to process complicated data has transformed over the last 6-7 years, thanks to the rise of a field of artificial intelligence known as “deep learning.” Now we can build artificial neural networks that are capable of processing large and complex datasets in very sophisticated ways, allowing us to uncover relationships in data that we’d never be able to pull out in the past.

Eva Dyer

ED: As we continue to see exponential growth in data analysis and the computational sciences, it has become increasingly attractive to bring these tools to bear on problems in biomedical research. BME is becoming increasingly dependent on the use of data for discovery – and this trend seems to only be growing in BME and other areas. BME is really about combining engineering and biology, and thus, as we continue to see problems arise that require that integration of engineering and biology, biomedical engineers will be at the forefront of many of these integrative innovations. As the size of datasets that are being generated in neuroscience continue to grow, it will be even more critical that we use machine learning and data-driven methods to help us uncover the mysteries of the brain. As we scale up our learning methods, we will be able to apply the lessons learned from simple controlled experimental tasks and scale these ideas to more realistic and naturalistic settings which will not place constraints on behavior. Being able to record and interpret the activity of neural populations “in the wild” will likely contribute to major breakthroughs in our understanding of the brain.

Where do you see your own research headed?

ED: A lot of the projects in my lab now focus on learning variability in large-scale neural datasets—this could be individual differences, changes in brain structure and function due to disease, or even changes that occur during learning or development. The aim is to develop automated systems that can navigate through large datasets and identifying changes that could be due to any of these factors. With automated approaches to discover changes in brain structure, even in light of individual differences, this will enable a new class of approaches for diagnosing disease and hopefully catching small changes at early stages in brain disease to address the problem early. I am excited about the possibility of leveraging information about the brain’s architecture to build and inspire the design of the next generation of deep learning architectures.

CP: Neuroengineering holds tremendous potential for developing new methods to help treat brain injury and disease. Traditionally, the options we’ve had for treating brain disorders are either medication or surgery. There’s a growing recognition that biomedical engineering solutions can play an important role here. We already have a rich history of using electrical stimulation in the brain, including deep brain stimulation for disorders like Parkinson’s disease, and cochlear implants to restore hearing for people who are deaf. Now we’re seeing emerging applications of closed-loop brain stimulation to epilepsy, depression, psychiatric disorders, and memory. We also have a rich history here at Emory. Mahlon DeLong, W.P. Timmie Professor of Neurology at Emory School of Medicine, who has been at Emory for decades, has laid down much of the scientific basis for how deep brain stimulation might be effective in Parkinson’s disease. Neurologist Helen Mayberg, who was here for quite a while before recently departing for University of Southern California, has been a pioneer in applying deep brain stimulation to other disorders like chronic depression. So I’m really excited by the team we have at the Emory Neuromodulation Technology Innovation Center (ENTICe), which brings together researchers from neurosurgery, neurology, and biomedical engineering, and also partners with folks at the GT Neural Engineering Center. We have an amazing team in place to develop the next generation of neuroengineering therapies for brain disorders.

If you wanted to convince a former colleague to move to Atlanta, what would be your top reasons?

CP: In terms of science, I love the community. As a member of the biomedical engineering department that spans Emory and GA Tech, I get to interact with wonderful colleagues at two very different institutions. Everybody here is extremely friendly and supportive. People genuinely want to interact and build collaborations and community and there’s not even a hint of competitiveness.

ED: I feel lucky to be part of a thriving community of neuroscientists, neuroengineers, and data scientists in Atlanta. Being part of both Georgia Tech and Emory provides exposure to a wide range of perspectives, plenty of opportunities for growth and collaboration, and what I perceive to be a lot of momentum around computational approaches to neuroscience.

If you could ask any person, dead or alive, to join your lab for a day, who would you ask?

ED: Richard Feynman. He was not only a brilliant scientist but also a dedicated teacher and mentor. It would be so cool to see him in action. He also created these diagrammatic representations of the behavior of subatomic particles called Feynman diagrams. Would be cool to see how he might think about visualizing and interpreting the types of problems that we are working on.

CP: One of the concepts we use in our lab a lot is called “dynamical systems theory”, which is a mathematical framework for understanding how complex systems behave. I think these approaches are key to understanding how the complex networks of neurons in the brain work. So I’d probably get a world-expert in dynamical systems, someone like Professor Steven Strogatz at Cornell, and force him to listen to me drone on and on about neurons until he caves and just tells us how to solve the brain.

A bonus question for CP: You’ve compared the behavior of neurons to the flocking of birds. How would you describe your research to someone who had no background in BME or neuroscience but doesgo bird-watching?

CP: So the activity of neurons and flocking behavior of birds has a little more in common than you might think. When you look at a flock of birds, you see these beautiful patterns pop out, and with complex, coordinated behaviors. Yet, there’s no one leader coordinating all of their activity and telling them what to do. You just have a bunch of individuals doing their own thing, but through very basic interactions, the group as a whole does something that’s tightly coordinated. This is what’s called emergent behavior, where putting relatively simple elements together can result in complex and coordinated activity. Neurons are very similar! You have a bunch of individual neurons that behave in certain ways, but the fact that they’re all wired up together and interacting with each other results in this coordinated, emergent system that is capable of doing really profound things. Another similarity here is that if I only showed you what one bird in a flock is doing, you’d have a hard time saying what the group as a whole is doing. But as you zoom out and start to observe more and more elements, the underlying patterns become clear. We think (and hope!) the same is true of neurons. My lab is monitors the activity of lots of neurons. Rather than focusing on what any one individual neuron is doing, we try to understand the coordinated activity of the network, and we try to really concretely understand how that network-level activity relates the brain’s control of behavior.

— Courtney Shin

At the beginning of his past fall semester, Rain Tian ‘21BBA went to South Korea with his friends in what he thought would be a much-needed reprieve from the world of Emory’s campus. Once they arrived, however, Tian realized his friends were all tethered to the university – “Even when we were at home getting ready to go out, even when we were outside, they would be on their phone, messaging on Slack, working on EEVM stuff. I never saw that from any of the other clubs I’m in. When I’m having a lot of fun, I wouldn’t think of replying to a DM on Slack,” he said. Intrigued by this display of camaraderie, Rain joined EEVM, or Emory Entrepreneurship and Venture Management. In its promise of kinship, the club hasn’t failed – “Honestly, I resonate and vibe with the community so well… In EEVM, we post photos of our adventures. I went on Spring Break with a lot of EEVM people.”

If you’ve spent enough time on Emory’s campus, chances are you’ve spotted a student wearing a HackATL t-shirt. This business-minded hackathon is the flagship event of EEVM and regularly draws in hundreds of eager students from all over the country, expanding its yield each year. The momentum from HackATL has allowed EEVM to position itself as the central resource for aspiring entrepreneurs on campus. As told by Naam Srisaard ‘19BBA, former Director of HackATL and now an advisor to the executive board, “We are the only student-run entrepreneurship organization at Emory University. We run at the university level, so we are not under College Council or the Goizueta Business School. We serve the entire university and our mission is to provide entrepreneurial resources for any and every student that is interested in entrepreneurship, startups, pursuing their business idea, or even if they’re just interested in the industry.”

Founded only five years ago, the club now offers a startup accelerator program, a co-working space on the Clairmont Campus, an online publication, and more. Most importantly, the club offers a community to its members, who are united by a passion for innovation. Take Daniel Kessler ‘22C, whose knack for entrepreneurship was first uncovered in elementary school. “The NYC Sanitation Department ran a competition to see which elementary school could implement the best sustainability project.,” he told me. “I used Apple technology to create a database that graded each classroom’s sustainability and we actually won.” Kessler saw EEVM as a natural extension of his interests in “using entrepreneurship to better the lives of others and address social ills.” Since joining, he has ascended to the position of Director of Corporate Partnerships, along with Tian, and is responsible for securing sponsorships for HackATL and other EEVM events. In a testament to EEVM’s stature, the Student Programming Council recently contacted them for help in recruiting restaurants for this year’s Taste of Emory.

If EEVM is a community, it is organized around an ideology of self-actualization, which is considered best brought about in a collaborative environment. “We aim to promote entrepreneurship because we believe that everyone could have the next idea to change the world. We hope to give entrepreneurs the resources to realize their vision,” Tian told me. Entrepreneurship is cast as an inner awakening, one that transforms the world in the process, as expressed in Kessler’s view that “When I look at entrepreneurship or technology, I don’t see it as the implementation of accumulated knowledge. I see it as a comprehensive expression of who we are as people and how we can amplify our best selves using these tools.”

Srisaard echoed these sentiments, portraying entrepreneurship as future-minded and uniquely suited for the modern world – “Everyone is all about entrepreneurship because the traditional corporate business model is fading away. That alone calls for a more innovative mindset, design thinking, reiterative sprints of new ideas.” In her definition of entrepreneurship as “knowing how to identify problems and solve them,” she poignantly characterized entrepreneurship as an all-purpose model for diagnosis and treatment, the optimal road to meaningful change.

EEVM’s inclusive view of entrepreneurship removes it from the corporate board rooms and venture capitalist dreams one frequently associates it with. Anyone can be an entrepreneur and EEVM is ready to guide their journey. EEVM’s democratization of an industry often requiring connections and capital is reflected in the success of their events. Speaking on her experience competing in HackATL, Michelle He ‘22C said “I came into HackATL with the misconception that hackathons are limited to those with coding abilities or an interest in computer science. In the most wonderful way, HackATL proved me wrong… It has an incredibly accessible registration process (that is completely free!) and gives students a very holistic overview of the entrepreneurial process. I really enjoyed getting to work with other college students of vastly different strengths and interests to create a product that I am very passionate about.”

The club has similarly empowered its members, as Srisaard told me that “being in EEVM is like being in a startup. There isn’t a single lesson it taught me, but it gave me the experience of being in a fast-paced environment where everyone is just as driven as you. Someone might see we won’t have enough money for the future, so we go out hustling, selling ramen in the library. It teaches you that startup grind, that founder’s mentality where if you care about it, you get it done.” Next year, she will start work as a consultant at Ernest & Young, undoubtedly propelled as a candidate by her remarkable work for the club. Tian also found that “talking to sponsors has made me more confident about job interviews. My first networking call was supposed to go for thirty minutes, but I was so nervous it lasted less than ten. Now, the hardest part for me is actually waiting for a reply. Once I get it and can talk to a person, I immediately get into my comfort zone.” By creating a network of excited young entrepreneurs on campus, EEVM meaningfully trains students to engage in the career path of their choice.

As for the more distant future, Tian hopes to “increase member engagement so that people will be working with their friends with a common goal in mind.” Kessler yearns “to engage more meaningfully with the community around us, to use the skills of EEVM members to serve the broader Atlanta community in their entrepreneurial efforts, beyond just Emory students.” In this direction, the club is considering community service and non-profit organizations to affiliate with and exploring the possibility of a digital platform that would provide information and video resources to help aspiring entrepreneurs. Srisaard simply hopes that “EEVM can live up to its full potential and touch every student’s life, across every school at Emory, not just those currently interested in entrepreneurship.” In this world, the name of the game is growth.

Interested readers can access EEVM’s website, their Facebook page, or their Twitter.