The human brain, composed of about 86 billion noisy neurons, is a reliable, durable, complex, and cryptic biological supercomputer. A community of multidisciplinary researchers at Georgia Tech is decrypting that neuronal chatter, which may hold the key to better treatments for disease and addiction, advanced robotics and artificial intelligence (AI), and even global energy efficiency.

These researchers work in the realm of computational neuroscience, a branch of neuroscience that uses mathematical models, computer simulations, and theoretical analysis of the brain to gain a deeper understanding of the nervous system.

"We want to understand the brain and the important data that we gather from this amazing, mysterious organ,” said Chethan Pandarinath, assistant professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. “But for a long time, we really didn’t have the adequate tools.”

Basically, the ability to look at the brain and gather large amounts of data from it has advanced rapidly — faster than our ability to understand it all.

“There has been an explosion of technology over the past five or 10 years,” Pandarinath said. “So, we’re moving into a different space in the ways we approach the brain, and the ways we think about it.”

Much of that explosion — manifested at Georgia Tech and its partner institutions, like Emory, in the form of additional academic offerings as well as research interest — has been fueled by the BRAIN Initiative, launched by President Barack Obama in 2014. That global research program has identified computational neuroscience (among other things) as an area where progress is most needed.

The wish list includes improvements in machine learning, AI, and crowdsourcing approaches to translating the massive volume of data being gathered from human brains. And Georgia Tech researchers have their hands in every area.

Pandarinath’s lab, for example, is using AI tools and the insights gained from the brain’s neural networks — and support from the National Institutes of Health —  to develop revolutionary assistive devices for people with disabilities or neurological disorders.

He also spearheaded the Neural Latents Benchmark Challenge on GitHub. These competitions attracted a diverse range of teams that created new models for analyzing large data sets of neural activity.

“This was our effort to accelerate progress at the intersection of neuroscience, machine learning, and artificial intelligence,” Pandarinath said.

The challenge illustrated a need for multiple perspectives and disciplines in computational neuroscience — the winning team of the first competition in January was a firm called AE Studio, a software development, data science, and product design company that doesn’t ordinarily focus on neuroscience but develops potent mathematical and machine-learning tools.

“This field is multidisciplinary and collaborative by nature and necessity,” said Tansu Celikel, chair of Georgia Tech’s School of Psychology, who wants to understand complex behaviors controlled by the brain in order to make smarter, more intuitive robots.

“It begs for biologists, psychologists, mathematicians, physicists, data scientists — people from the machine learning and AI worlds — to come together and advance the state of computation and brain research," Celikel added. "With that in mind, Georgia Tech is in an excellent position to make a significant impact and become a global center for this kind of research.”

Celikel and Pandarinath are just two of the researchers at Georgia Tech in computational neuroscience, a wide-ranging field that relies on collaborations between data scientists, experimentalists, and clinicians, who are forming partnerships across schools, colleges, universities, and disciplines. A small sampling of the people connecting the brain’s neuronal dots and expanding this body of research at Georgia Tech include:

 

Hannah Choi

• Assistant Professor, School of Mathematics

• Research Group in Mathematical Neuroscience

Neuroscience wasn’t part of Choi’s plans. But while working toward her Ph.D. in applied ­mathematics, “I got really interested in nonlinear dynamical systems, a big topic in applied mathematics,” she said. Such systems seem to be chaotic, unpredictable, and counterintuitive — pretty much like most systems in nature. “I soon realized the brain is the most exciting nonlinear dynamical system, and that I could apply my mathematical tools and develop computational theories to better understand the brain.”

Earlier this year, Choi’s work in applying math to neuroscience earned her a prestigious 2022 Sloan Research Fellowship, which goes to the nation’s most promising young scientific researchers. Since launching her lab at Georgia Tech in January 2021, Choi has continued her collaboration with the Seattle-based Allen Institute in studying how information is processed in neural networks of many different scales, while starting partnerships with several Georgia Tech and Emory researchers, including Simon Sponberg, Anqi Wu, Nabil Imam, Chris Rodgers, Ming-fai Fong, and Dieter Jaeger, working in the sprawling computational neuroscience world.

Like some of her Georgia Tech colleagues, Choi also wants to address the problem of the environmental footprint being made by all of this computation and AI in her chosen field. “The idea is to apply what we have learned about our very energy-efficient brains to the development of better, more efficient artificial neural networks.”

 

Eva Dyer

• Assistant Professor, Wallace H. Coulter Department of Biomedical Engineering

• Neural Data Science (NERDS) Lab

Dyer leads a diverse team of researchers in developing machine learning approaches to analyze and interpret massive, complex neural data sets. Winner of a McKnight Technology Award and BRAIN Award in recent years, Dyer’s interest in the brain is rooted in her love of music — being keen on how we perceive sound at the neuronal level.

As a postdoctoral student she developed a cryptography-inspired strategy for decoding neural conversations. Now one of the leading young researchers in computational neuroscience, Dyer directs a lab that routinely presents research at high-profile conferences like NeurIPS. Her team is “essentially interested in how the coordinated activity of large collections of neurons are being modified or changing in the presence of something like disease,” she said.

“Ultimately, with the information we gather and analyze, we hope to discover biomarkers of Alzheimer’s and other diseases,” added Dyer, who worked with Pandarinath to develop the Benchmark Challenge. “The idea is to catch changes in neural activity that are happening before we actually see the cognitive deficits.”

 

Dobromir Rahnev

• Associate Professor, School of Psychology

• Perception, Neuroimaging, and Modeling Lab

Rahnev uses a combination of neuroimaging and computational modeling to reveal the mechanisms of perception and decision-making in humans.

A recipient of the American Psychological Association Distinguished Scientific Award for an Early Career Contribution to Psychology and the Vision Science Society Young Investigator Award, Rahnev has already made important contributions to our understanding of how people perceive the world and make decisions. He has recently started to investigate how deep neural networks — which have established themselves as state-of-the-art computer vision algorithms — can serve as excellent models for the perceptual and decisional processes in the human brain.

“One of my strongest passions is to make science more open in every sense of the word,” said Rahnev, who organized the Confidence Database, the largest field-specific database of open data in the behavioral sciences. “It’s important for me to be involved in efforts to attract and retain people from underrepresented groups in cognitive and computational neuroscience.”

 

Chris Rozell

• Professor; Julian T. Hightower Chair, School of Electrical and Computer Engineering

• Sensory Information Processing Lab

Rozell describes his lab’s focus on computational neuroengineering as a combination of data science, neurotechnology, and computational modeling, with the goal of advancing the understanding of brain function, leading to the development of intelligent machine systems and effective interventions for disease.

“One of the projects in our lab that is really compelling right now is a novel therapy for patients with treatment-resistant depression,” said Rozell, whose lab is partnering with a clinical team to improve this experimental treatment for patients who have not responded to any currently approved therapy. “So, no drugs help them. No psychotherapy. No electroconvulsive therapy. We’re using deep brain stimulation.”

The results have been positive for patients, and the researchers are, “getting an objective readout, for the first time, of what’s happening in their brains,” Rozell said, thanks to a new generation of machine-learning tools called “explainable AI.” “With these new approaches, we can gain a deeper understanding of the disease, which can lead to more personalized therapies.”

 

Lewis Wheaton

• Associate Professor, School of Biological Sciences

• Cognitive Motor Control Lab

When he isn’t helping to lead the city of Smyrna as a city councilman, Wheaton is leading a research effort that could lead to user-friendly prosthetic devices and improved motor rehabilitation training, particularly for people with upper limb amputation.

“There are a lot of beautifully developed upper limb prostheses available right now, but one of the big challenges is they’re just not heavily used by individuals — partly because they’re really, really expensive, but also because they’re such an easy thing to not use,” said Wheaton. “It’s very easy to just take it off and never wear it at all.”

Which is why much of Wheaton’s research is focused on acquiring and studying data that shows what upper limb amputees are thinking or feeling while using, or trying to use, a prosthesis. Integrating a patient’s neural activity with observations of behavior and gaze patterns, the team is “gathering data that’s never really been acquired before,” Wheaton said.

“This will help us gather more information that is helpful in developing new rehabilitation protocols for persons learning how to use prostheses. A better understanding of how rehabilitation efforts are influenced by different types of prostheses can also inform engineers and the marketplace on the type of prostheses we should be developing.”

 

Anqi Wu

• Assistant Professor, School of Computer Science and Engineering

• BRAin INtelligence and Machine Learning Laboratory

One of Georgia Tech’s newest computational neuroscientist faculty members, Wu is building her research enterprise around building advanced machine-learning models for neural and behavioral analyses.

“I want to help experimental neuroscientists to understand their data and draw scientific conclusions,” she said, pointing out that these collaborators are collecting larger and larger populations of neurons across the whole brain, as well as more naturalistic animal behaviors. “How to integrate these big data sets and extract multilayer knowledge to understand different perspectives of the brain is a very challenging problem.”

Wu, who came to Georgia Tech in Spring 2022, aims to develop sophisticated latent variable models to address those issues. These computational models are essentially used to project high-dimensional data from large neural populations across large brain areas into useful, low-dimensional (i.e., interpretable) information that experimental neuroscientists can use.

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The human brain, composed of about 86 billion noisy neurons, is a reliable, durable, complex, and cryptic biological supercomputer. A community of multidisciplinary researchers at Georgia Tech is decrypting that neuronal chatter, which may hold the key to better treatments for disease and addiction, advanced robotics and artificial intelligence (AI), and even global energy efficiency.

These researchers work in the realm of computational neuroscience, a branch of neuroscience that uses mathematical models, computer simulations, and theoretical analysis of the brain to gain a deeper understanding of the nervous system.

"We want to understand the brain and the important data that we gather from this amazing, mysterious organ,” said Chethan Pandarinath, assistant professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. “But for a long time, we really didn’t have the adequate tools.”

Basically, the ability to look at the brain and gather large amounts of data from it has advanced rapidly — faster than our ability to understand it all.

“There has been an explosion of technology over the past five or 10 years,” Pandarinath said. “So, we’re moving into a different space in the ways we approach the brain, and the ways we think about it.”

Much of that explosion — manifested at Georgia Tech and its partner institutions, like Emory, in the form of additional academic offerings as well as research interest — has been fueled by the BRAIN Initiative, launched by President Barack Obama in 2014. That global research program has identified computational neuroscience (among other things) as an area where progress is most needed.

The wish list includes improvements in machine learning, AI, and crowdsourcing approaches to translating the massive volume of data being gathered from human brains. And Georgia Tech researchers have their hands in every area.

Pandarinath’s lab, for example, is using AI tools and the insights gained from the brain’s neural networks — and support from the National Institutes of Health —  to develop revolutionary assistive devices for people with disabilities or neurological disorders.

He also spearheaded the Neural Latents Benchmark Challenge on GitHub. These competitions attracted a diverse range of teams that created new models for analyzing large data sets of neural activity.

“This was our effort to accelerate progress at the intersection of neuroscience, machine learning, and artificial intelligence,” Pandarinath said.

The challenge illustrated a need for multiple perspectives and disciplines in computational neuroscience — the winning team of the first competition in January was a firm called AE Studio, a software development, data science, and product design company that doesn’t ordinarily focus on neuroscience but develops potent mathematical and machine-learning tools.

“This field is multidisciplinary and collaborative by nature and necessity,” said Tansu Celikel, chair of Georgia Tech’s School of Psychology, who wants to understand complex behaviors controlled by the brain in order to make smarter, more intuitive robots.

“It begs for biologists, psychologists, mathematicians, physicists, data scientists — people from the machine learning and AI worlds — to come together and advance the state of computation and brain research," Celikel added. "With that in mind, Georgia Tech is in an excellent position to make a significant impact and become a global center for this kind of research.”

Celikel and Pandarinath are just two of the researchers at Georgia Tech in computational neuroscience, a wide-ranging field that relies on collaborations between data scientists, experimentalists, and clinicians, who are forming partnerships across schools, colleges, universities, and disciplines. A small sampling of the people connecting the brain’s neuronal dots and expanding this body of research at Georgia Tech include:

 

Hannah Choi

• Assistant Professor, School of Mathematics

• Research Group in Mathematical Neuroscience

Neuroscience wasn’t part of Choi’s plans. But while working toward her Ph.D. in applied ­mathematics, “I got really interested in nonlinear dynamical systems, a big topic in applied mathematics,” she said. Such systems seem to be chaotic, unpredictable, and counterintuitive — pretty much like most systems in nature. “I soon realized the brain is the most exciting nonlinear dynamical system, and that I could apply my mathematical tools and develop computational theories to better understand the brain.”

Earlier this year, Choi’s work in applying math to neuroscience earned her a prestigious 2022 Sloan Research Fellowship, which goes to the nation’s most promising young scientific researchers. Since launching her lab at Georgia Tech in January 2021, Choi has continued her collaboration with the Seattle-based Allen Institute in studying how information is processed in neural networks of many different scales, while starting partnerships with several Georgia Tech and Emory researchers, including Simon Sponberg, Anqi Wu, Nabil Imam, Chris Rodgers, Ming-fai Fong, and Dieter Jaeger, working in the sprawling computational neuroscience world.

Like some of her Georgia Tech colleagues, Choi also wants to address the problem of the environmental footprint being made by all of this computation and AI in her chosen field. “The idea is to apply what we have learned about our very energy-efficient brains to the development of better, more efficient artificial neural networks.”

 

Eva Dyer

• Assistant Professor, Wallace H. Coulter Department of Biomedical Engineering

• Neural Data Science (NERDS) Lab

Dyer leads a diverse team of researchers in developing machine learning approaches to analyze and interpret massive, complex neural data sets. Winner of a McKnight Technology Award and BRAIN Award in recent years, Dyer’s interest in the brain is rooted in her love of music — being keen on how we perceive sound at the neuronal level.

As a postdoctoral student she developed a cryptography-inspired strategy for decoding neural conversations. Now one of the leading young researchers in computational neuroscience, Dyer directs a lab that routinely presents research at high-profile conferences like NeurIPS. Her team is “essentially interested in how the coordinated activity of large collections of neurons are being modified or changing in the presence of something like disease,” she said.

“Ultimately, with the information we gather and analyze, we hope to discover biomarkers of Alzheimer’s and other diseases,” added Dyer, who worked with Pandarinath to develop the Benchmark Challenge. “The idea is to catch changes in neural activity that are happening before we actually see the cognitive deficits.”

 

Dobromir Rahnev

• Associate Professor, School of Psychology

• Perception, Neuroimaging, and Modeling Lab

Rahnev uses a combination of neuroimaging and computational modeling to reveal the mechanisms of perception and decision-making in humans.

A recipient of the American Psychological Association Distinguished Scientific Award for an Early Career Contribution to Psychology and the Vision Science Society Young Investigator Award, Rahnev has already made important contributions to our understanding of how people perceive the world and make decisions. He has recently started to investigate how deep neural networks — which have established themselves as state-of-the-art computer vision algorithms — can serve as excellent models for the perceptual and decisional processes in the human brain.

“One of my strongest passions is to make science more open in every sense of the word,” said Rahnev, who organized the Confidence Database, the largest field-specific database of open data in the behavioral sciences. “It’s important for me to be involved in efforts to attract and retain people from underrepresented groups in cognitive and computational neuroscience.”

 

Chris Rozell

• Professor; Julian T. Hightower Chair, School of Electrical and Computer Engineering

• Sensory Information Processing Lab

Rozell describes his lab’s focus on computational neuroengineering as a combination of data science, neurotechnology, and computational modeling, with the goal of advancing the understanding of brain function, leading to the development of intelligent machine systems and effective interventions for disease.

“One of the projects in our lab that is really compelling right now is a novel therapy for patients with treatment-resistant depression,” said Rozell, whose lab is partnering with a clinical team to improve this experimental treatment for patients who have not responded to any currently approved therapy. “So, no drugs help them. No psychotherapy. No electroconvulsive therapy. We’re using deep brain stimulation.”

The results have been positive for patients, and the researchers are, “getting an objective readout, for the first time, of what’s happening in their brains,” Rozell said, thanks to a new generation of machine-learning tools called “explainable AI.” “With these new approaches, we can gain a deeper understanding of the disease, which can lead to more personalized therapies.”

 

Lewis Wheaton

• Associate Professor, School of Biological Sciences

• Cognitive Motor Control Lab

When he isn’t helping to lead the city of Smyrna as a city councilman, Wheaton is leading a research effort that could lead to user-friendly prosthetic devices and improved motor rehabilitation training, particularly for people with upper limb amputation.

“There are a lot of beautifully developed upper limb prostheses available right now, but one of the big challenges is they’re just not heavily used by individuals — partly because they’re really, really expensive, but also because they’re such an easy thing to not use,” said Wheaton. “It’s very easy to just take it off and never wear it at all.”

Which is why much of Wheaton’s research is focused on acquiring and studying data that shows what upper limb amputees are thinking or feeling while using, or trying to use, a prosthesis. Integrating a patient’s neural activity with observations of behavior and gaze patterns, the team is “gathering data that’s never really been acquired before,” Wheaton said.

“This will help us gather more information that is helpful in developing new rehabilitation protocols for persons learning how to use prostheses. A better understanding of how rehabilitation efforts are influenced by different types of prostheses can also inform engineers and the marketplace on the type of prostheses we should be developing.”

 

Anqi Wu

• Assistant Professor, School of Computer Science and Engineering

• BRAin INtelligence and Machine Learning Laboratory

One of Georgia Tech’s newest computational neuroscientist faculty members, Wu is building her research enterprise around building advanced machine-learning models for neural and behavioral analyses.

“I want to help experimental neuroscientists to understand their data and draw scientific conclusions,” she said, pointing out that these collaborators are collecting larger and larger populations of neurons across the whole brain, as well as more naturalistic animal behaviors. “How to integrate these big data sets and extract multilayer knowledge to understand different perspectives of the brain is a very challenging problem.”

Wu, who came to Georgia Tech in Spring 2022, aims to develop sophisticated latent variable models to address those issues. These computational models are essentially used to project high-dimensional data from large neural populations across large brain areas into useful, low-dimensional (i.e., interpretable) information that experimental neuroscientists can use.

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As we begin Fall Semester, College of Sciences faculty, graduate students, and staff are invited to join us the afternoon of Wednesday, August 31 for our virtual CoS Fall 2022 Plenary on Microsoft Teams — followed by an in-person reception in the Petit Institute (IBB) Atrium, hosted by Dean Lozier.

Please check your email and Outlook calendar for the Microsoft Teams link to join the virtual Plenary — as well as a separate Outlook calendar invitation for the in-person reception (RSVP required). Search your inbox for "College of Sciences Fall 2022 Plenary" and "CoS Fall Plenary Reception" to find each email/calendar invitation.

We hope you will join us for both components of this event, and also welcome those who may only join the online Plenary or the in-person reception:

Agenda:

College of Sciences Fall 2022 Plenary and Reception

Wednesday, August 31, 2022

3:00 - 3:45 p.m. ET — Plenary on Microsoft Teams
Presentation to include leadership and community news; updates on our teaching, research, and service missions; equity and inclusion; the College's financial outlook; and community engagement updates from our staff, student, academic faculty, and research faculty councils.
Virtual. No RSVP required. Please check your email for Teams join link/calendar invitation.

4:00 - 5:00 p.m. ET — In-person Reception in Petit IBB Atrium
Light bites (including vegetarian, gluten-free, and nut-free options) will be provided.
In-person. RSVP required. Please check your email for email/calendar invitation.

We look forward to seeing you soon.

Sincerely,
College of Sciences Office of the Dean

Event Details

Bill Ballard, Ph.D.
La Trobe University

Livestream via Zoom

ABSTRACT
The Australasian dingo has been argued to be a functional intermediate between wild wolves and domesticated breed dogs. Here we link a high-quality de novo long read chromosomal assembly with epigenetic footprints and morphology to establish a dingo “archetype” specimen for future research into domestication theory, canine genomics and Australasian wildlife ecology. The generated assembly uses a combination of Pacific Bioscience, Oxford Nanopore, 10X Genomics, Bionano, and Hi-C technologies to assemble a high-quality chromosome-level reference genome (Canfam_ADS). Compared to the previously published Desert dingo assembly, there are structurally important rearrangements on Chromosomes 11, 16, 25 and 26. Phylogenetic analyses of chromosomal data from the Alpine dingo and nine previously published de novo canine assemblies show dingoes are monophyletic and basal to domestic dogs. Network analyses show that the mtDNA genome clusters within the southeastern lineage, as expected for an Alpine dingo. Comparison of regulatory regions identified two regions that are unmethylated in the Alpine dingo genome but hypermethylated in the Desert dingo. These were regulatory regions within glucagon receptor GCGR and histone deacetylase HDAC4 genes. Morphological data, comprising geometric morphometric assessment of cranial morphology and magnetic resonance imaging of brain tissue, situate this female within population-level cranial variation for Alpine dingoes and suggest a larger cranial capacity than a similar-sized domestic dog.

Host: Dr. Greg Gibson

Event Details

Alison Feder, Ph.D.
Assistant Professor
Genome Sciences
University of Washington

Livestream via Zoom

Host: Dr. Joe Lachance

Event Details

Nancy J. Cox, Ph.D.
Mary Phillips Edmonds Gray Professor of Genetics
Vanderbilt University

Livestream via Zoom

SPEAKER BIO
Nancy J. Cox, PhD, is Professor of Medicine and Director of the Division of Genetic Medicine within the Department of Medicine at Vanderbilt University Medical Center.

Dr. Cox completed her PhD at Yale University and conducted postdoctoral research at Washington University and the University of Pennsylvania. She joined the faculty at the University of Chicago, where she spent her academic faculty career until she was recruited to Vanderbilt in 2015 to lead the new Vanderbilt Genetics Institute (VGI).

As Founding Director of the VGI, Dr. Cox is focused on recruiting world-class genetics and genomics scientists to the Institute, with the primary goal of making Vanderbilt’s DNA databank, BioVU, into an unparalleled engine for discovery and translation in human genetics and genomics. She initiated the first large-scale genetic/genomic consortium in type 2 diabetes, and she has active research and advisory roles in many of the top genomic consortia that provide the foundation for current studies in human genetics and genomics.

Dr. Cox has an active research program in data integration, particularly in the integration of functional genomic information to aid in discovery and interpretation of associations of genome variation with common disease. Her lab was the first to show that most of the common variant associations to common human diseases and complex human traits appear to be regulatory in function.

Dr. Cox has more than 280 peer-reviewed publications and was a co-winner of the 2008 American Association of Cancer Research Landon Award and winner of the 2010 Leadership Award in Genetic Epidemiology. She was named a Pritzker Scholar in 2012 and a Distinguished Faculty from the Biological Sciences Division at the University of Chicago in 2013. She is former editor of Genetic Epidemiology (2006-2011), a former member of the Board of Directors for the American Society of Human Genetics, and is Member-at-Large for Biological Sciences in the American Association for the Advancement of Science.

Host: Dr. Greg Gibson

Event Details

Mark A. Lyle, Ph.D., PT.
Assistant Professor
Department of Rehabilitation Medicine
Emory University

Livestream via Zoom

SPEAKER BIO
Dr. Lyle received his BA in Zoology (Neuroscience Minor) from Miami University (Oxford, OH) and his MS in Physical Therapy from Simmons College (Boston, MA). He subsequently worked in an outpatient physical therapy clinic for 4 years primarily helping persons with musculoskeletal injuries. He then pursued a PhD in Biokinesiology at the University of Southern California, under the supervision of Dr. Christopher M. Powers. His research examined factors that contribute to anterior cruciate ligament injuries in young athletes. He received additional training studying the functional role of proprioceptive feedback during a NIH supported Postdoctoral Fellowship with Dr. T. Richard Nichols at Georgia Institute of Technology.

Currently, he works as Assistant Professor of Physical Therapy at Emory University, and serves as director of the ReAMP Lab.

Host: Dr. Richard Nichols

Event Details

Nathan Kraft, Ph.D.
Associate Professor
Department of Ecology and Evolutionary Biology
University of California, Los Angeles

Livestream via Zoom

SPEAKER BIO
Dr. Kraft completed his graduate work at Stanford University and UC Berkeley. He spent several years as a Biodiversity Postdoctoral Fellow at the University of British Columbia in Vancouver, before moving to a faculty position, first at the University of Maryland, College Park, and more recently at UCLA.

Host: Dr. Lin Jiang

 

Event Details

John Wallingford, Ph.D.
Professor
Department of Molecular Biosciences
The University of Texas at Austin

Livestream via Zoom

SPEAKER BIO
John Wallingford is a professor of molecular biosciences at The University of Texas at Austin. Wallingford studies the intricacies of embryonic development at the intersection of gene expression and specialized cell behaviors. His research seeks to understand lethal birth defects through an approach that combines systems biology and bioinformatics with novel strategies for imaging living organisms.

Host: Dr. Shuyi Nie

Event Details

Lorin Crawford, Ph.D.
Principal Researcher
Microsoft Research Lab, New England

Livestream via Zoom

SPEAKER BIO
Lorin Crawford is a Principal Researcher at Microsoft Research New England. He also holds a faculty position as an Associate Professor of Biostatistics at Brown University with an affiliation in the Center for Computational Molecular Biology. Prior to joining both MSR and Brown, Lorin received his Ph.D. from the Department of Statistical Science at Duke University where he was formerly co-advised by Sayan Mukherjee and Kris C. Wood. As a Duke Dean’s Graduate Fellow and NSF Graduate Research Fellow he completed his Ph.D. dissertation entitled: "Bayesian Kernel Models for Statistical Genetics and Cancer Genomics." Dr. Crawford received his Bachelors of Science degree in Mathematics from Clark Atlanta University.

Host: Dr. Joe Lachance

Event Details

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