To help find answers, researchers at the Institute for Neuroscience, Neurotechnology, and Society (INNS) are using math, data, and AI to unlock the secrets of thought. Together they are helping turn the brain’s raw electrical “noise” into real insights about how people think, move, and perceive the world.
Fair warning: Prepare your neurons for the complexity of this brain research ahead.
What if artificial neurons in AI programs were arranged as they are in the brain?
AI programs would then help us understand why the brain is organized the way it is. This neuro-AI synthesis would also work faster, use less energy, and be easier to interpret. Creating such systems is the goal of Apurva Ratan Murty, an assistant professor of Psychology who is creating topographic AI models like the one above of three domains — vision, audition, and language inspired by the brain. In the near future, he predicts doctors might be able to use these patterns to predict the effects of brain lesions and other disorders. “We’re not there yet,” he says. “But our work brings us significantly closer to that future than ever before.”
How cats walk keeps Chethan Pandarinath on his toes. This biomedical engineer uses sensors to analyze how two sets of feline leg muscles — flexors and extensors — are controlled by the spinal cord. Understanding how that happens could help patients partially paralyzed from spinal cord injuries, strokes, or progressive neuro-degenerative diseases get back on their feet again. “My lab is using AI tools that allow us to turn complex spinal cord activity data into something we can interpret. It tells us there’s a simple underlying structure behind the complex activity patterns,” says the associate professor.
“The brain is like a symphony conductor,” says Simon Sponberg. “Individual instruments have some independent control, but most of the music comes from the brain’s precise coordination of notes among the different players in the body.” This physics professor studies the fantastically fast-beating wings of the hummingbird-sized hawk moth (Manduca sexta). Its agile flight movement comes as a result of spikes in electrical activity in 10 muscles. Sponberg found something that surprised him — the brain focuses less on creating the number of spikes than in orchestrating their precise patterns over time. To Sponberg, every millisecond matters. “We are just beginning to understand how the nervous system first acquires precisely timed spiking patterns during development,” he says.
Put a mouse in a maze with food far away, and it will learn to find it. But life for mice — and people — isn’t so simple. Sometimes they want to explore, only want water, or just want to go home. What’s more, animals make decisions based on their history, not just on how they feel at the moment. To dig deeper into the decision-making process, Anqi Wu, an assistant professor in the School of Computational Science and Engineering, is giving mice more options. By using a new computational framework called SWIRL (Switching Inverse Reinforcement Learning), her findings have outperformed models that fail to take historical behavior into account. “We’re seeking to understand not only animal behavior but also human behavior to gain insight into the human decision-making process over a long period of time,” she says.
Connectivity shapes cognition in the cerebral cortex, a layered structure in the brain. The visual cortex, in particular, processes visual data from the retina relayed through the Lateral Geniculate Nucleus (LGN) in the thalamus, and directs it to the correct cognitive domain in the brain. How it does this is the mystery that computational neuroscientist Hannah Choi wants to solve. “The big question I’m interested in is how network connectivity patterns in the architecture of the LGN are related to computations,” says this assistant math professor. To find answers, she shows mice repeated image patterns such as flower-cat-dog-house and then disrupts the pattern. The goal? To grasp how the thalamus’s nonlinear dynamical system works. If scientists and doctors better understand how brain regions are wired together, such knowledge could lead to better disease treatment.
This story was originally published through the Georgia Tech Alumni Magazine. Read the original publication here.
]]>News and Media Contact: Audra Davidson
]]>His foresight in nurturing junior researchers and stimulating practically relevant research has been pivotal in shaping the event's objectives. This year's symposium, hosted by Georgia Tech, marked an exceptional turnout, surpassing recent year attendance records and underscoring the growing enthusiasm in this field.
The event kicked off with a warm welcome remark by event organizing committee member Prof. Shihao Yang, ISyE School Chair Prof. Edwin Romeijn, and Chair Professor Roshan Joseph, emphasizing the collective efforts of the three pivotal institutions: The University of Georgia, Georgia Institute of Technology, and Emory University.
The highlight of the opening was the keynote address by Professor Samuel Kou from Harvard University, who captivated the audience with insights on 'Catalytic Prior Distributions for Bayesian Inference'.
GSD 2023 was remarkable not just for its record attendance but also for the diversity of its participants. Attendees ranged from undergraduate students to seasoned faculty members, representing various universities and stages in their academic and professional journeys. What was particularly noteworthy was the breadth of research presented: 12 oral presentations and 30 poster presentations provided a platform for a rich exchange of ideas and the latest advancements in statistics and data science.
The event wasn’t just about presentations; it also recognized the hard work and talent of its participants, with an award ceremony celebrating outstanding student contributions, where the best poster award was presented to ISyE PhD candidate Tianjiao Li.
This emphasis on mentorship and recognition is a testament to the supportive academic culture we nurture here at Georgia Tech.
This remarkable gathering was made possible through the generous support of several sponsors from within our institution, including the H. Milton Stewart School of Industrial and Systems Engineering, the College of Engineering Dean’s Office, the Office of Undergraduate Research, and the Provost’s Office, along with external sponsors such as Wells Fargo, the ASA GA Chapter, and Georgia CTSA.
As GSD 2023 concluded, it was clear that the symposium had not only celebrated current achievements but had also laid a path of intellectual rigor and collaborative spirit for the years ahead. With GSD 2024 slated to be at Emory University and GSD 2025 at the University of Georgia, the anticipation is palpable.
We are confident that the tradition of excellence will continue, and we wholeheartedly pass the baton, excited to see how these peer institutions will further enrich this vibrant academic dialogue. Our collective success is a shared one, and we look forward to contributing to and celebrating future achievements in our field, together.
]]>The Box Medal is named after George Box, the late British-American statistician who is considered one of the greatest statistical minds of our time. Box was extremely influential on Wu’s work during his formative years as a young academic at the University of Wisconsin, Madison, where Box was also a professor.
In a 2015 interview with Professor Hugh Chapman (Acadia University) and Professor Roshan Joseph (ISyE), Wu affirmed that Box was a tremendous influence: “[Box] was a great scholar and a great lecturer. His opinions and passion for work were contagious … I respected him a lot.”
According to the ENBIS website, the Box Medal honors the legacy of George Box and is awarded each year to “an extraordinary statistician who has remarkably contributed with his work to the development and the application of statistical methods in European business and industry.”
The ENBIS press release announcing Wu as this year’s Box Medal recipient stated that “with the medal, the link between two great statisticians is strengthened even further.”
The press release also specified that Wu was chosen for his many contributions to the study of statistics, as well as “his ability to clearly explain complex concepts … and for systematically passing on his knowledge.” Wu has supervised 45 Ph.D. students in the course of his career, many of whom are active researchers in the statistical sciences.
Wu will accept the Box Medal at the ENBIS conference, held from September 9-14, 2017, in Naples, Italy. While there, he will also deliver a keynote speech on September 12, 2017.
About Jeff Wu
Wu earned a bachelor of science in mathematics from National Taiwan University in 1971, and a Ph.D. in statistics from the University of California, Berkeley in 1976. He has been a faculty member at the University of Wisconsin, Madison; the University of Waterloo; the University of Michigan; and currently is the Coca-Cola Chair in Engineering Statistics and professor in ISyE.
He is known for his work on the convergence of the EM algorithm; resampling methods; nonlinear least squares; sensitivity testing and industrial statistics, including design of experiments, robust parameter design and computer experiments; and has been credited for coining the term “data science” as early as 1997.
Wu has received several awards, including the COPSS Presidents’ Award (1987), the Shewhart Medal (2008), the R. A. Fisher Lectureship (2011), and the Deming Lecturer Award (2012). He is an elected member of Academia Sinica (2000) and the National Academy of Engineering (2004), and has received many other awards and honors, including an honorary doctorate from the University of Waterloo.
He has published more than 170 peer-reviewed articles and two books. He was the second editor of Statistica Sinica.
]]>The Box Medal is named after George Box, the late British-American statistician who is considered one of the greatest statistical minds of our time.
]]>Stewart School of Industrial & Systems Engineering
404.385.4745
]]>Wu is an iconic figure in the field of engineering statistics. Throughout his career he has made fundamental contributions to the methodological and theoretical developments of a wide variety of statistical and application areas which include design and analysis of experiments (optimal, sequential, and factorial), computer experiments, robust parameter design, statistical computing, re-sampling methods, complex surveys, nonlinear least squares, and uncertainty quantification.
The conference brought together top scholars in these areas — some of whom were advised by Wu during their Ph.D. studies — from Asia, Europe, and the Americas to present their new research findings. WuFest also served as an opportunity for both speakers and attendees to collaborate and discuss their ideas and visions of emerging problems in their fields. Session topics included engineering statistics, statistical applications, design of experiments, fundamentals of experimentation and inference, optimal design, and uncertainty quantification.
“It was a great honor,” Wu said. “To see so many former students and colleagues that I respect at the conference was very special.” Wu is also thankful for the support of ISyE, Georgia Tech, and the WuFest planning committee.
One of the many highlights of the conference for Wu was a talk given at the banquet dinner by Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics at Harvard University and the founding editor-in-chief of the Harvard Data Science Review. In his talk entitled “Is Jeff Wu a Data Scientist?”, Meng deconstructed Wu’s famous 1997 lecture “Statistics = Data Science” in which Wu advocated that statistics be renamed data science. Wu’s family — including both of his children, their spouses, and his granddaughter — was also in attendance for the tribute.
“Jeff is a true leader in the field of engineering statistics,” said Roshan Joseph, one of Wu’s ISyE colleagues and chair of the WuFest planning committee. “He has done impactful work in both theoretical and applied problems, and his impact has multiplied through the work of his students, who have published more than 1,800 papers to date (without Wu being a co-author) in statistics and engineering.”
Wu’s professional achievements are unmatched. He is a member of both the National Academy of Engineering (2004) and Academia Sinica (2000). His is also a fellow of the Institute for Operations Research and the Management Sciences, the American Society for Quality, the Institute of Mathematical Statistics, and the American Statistical Association. Wu has also won numerous awards, including the Committee of Presidents of Statistical Societies (COPSS) Presidents Award in 1987, the Deming Lecture Award by American Statistical Association in 2012, the Fisher Lecture Award by COPSS in 2011, the Shewhart Medal by the American Society of Quality in 2008, the Pan Wenyuan Technology Award (Taiwan) in 2008, the Box Medal by the European Network for Business and Industrial Statistics in 2018, and the inaugural Akaike Memorial Lecture Award in 2017 (Japan).
Wu’s work is widely cited in professional journals and magazines. He has served as editor or associate editor for several major statistical journals and has published more than 175 research articles in peer review journals. He has supervised 49 Ph.D. students, out of which 21 are fellows of professional societies, and he has almost 200 “grand graduate students” that have been supervised by his former students.
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H. Milton Stewart School of Industrial and Systems Engineering
404.385.4745
]]>“AISTATS is an exciting conference that allows for engaging conversations and interactions at the intersection of machine learning and statistics. ML@GT is thrilled to be a part of this growing conference and we are looking forward to connecting with other researchers from around the world,” said ISyE's David M. McKenney Family Early Career Professor Sebastian Pokutta, associate director of ML@GT and a paper author.
ML@GT faculty members Le Song, Byron Boots, and ISyE Associate Professor Negar Kiyavash are 2019 area chairs.
Georgia Tech’s 12 papers:
Machine Learning Center
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Organizers:
“I am excited about this opportunity to serve the community as well as the general public,” said Huo.
Huo received the B.S. degree in mathematics from the University of Science and Technology, China, in 1993, and the M.S. degree in electrical engineering and the Ph.D. degree in statistics from Stanford University, Stanford, CA, in 1997 and 1999, respectively. Since August 2006, he has been an Associate Professor with the School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta. He represented China in the 30th International Mathematical Olympiad (IMO), which was held in Braunschweig, Germany, in 1989, and received a golden prize.
His research interests include statistical theory, data mining, and issues related to big data. He has made numerous contributions on topics such as sparse representation, wavelets, and statistical problems in detectability. His papers appeared in top journals, and some of them are highly cited. He is a senior member of IEEE since May 2004. He was a Fellow of IPAM in September 2004. He won the Georgia Tech Sigma Xi Young Faculty Award in 2005. His work has led to an interview by Emerging Research Fronts in June 2006 in the field of Mathematics - every two months, one paper is selected.
]]>The programs' principal homes are in the Stewart School of Industrial & Systems Engineering (ISyE) and are jointly affiliated with the School of Mathematics.
“The OR program in ISyE, especially the optimization component, has been well recognized internationally and viewed as the crown jewel of ISyE. Statistics received a big boost about 11 years ago and has quickly built its strength to reach international acclaim. ISyE is unique in that it is one of the only departments or schools in the country that has major programs in both operations research (OR) and statistics in the same unit,” commented Professor George Nemhauser, ISyE’s A. Russell Chandler Chair, who leads ISyE’s OR area.
Professor Jeff Wu, ISyE’s Coca-Cola Chair in Engineering Statistics, who leads statistics area added, “Both programs take pride in having done innovative and high impact research and also in educating a generation of doctoral students who have taken major positions in academia and industry. The synergy between the two disciplines is critical to the important emerging field of big data analytics in which we plan to play a major role.”
Georgia Tech is in good company in this ranking; the top five are Stanford University, Massachusetts Institute of Technology, University of California at Berkeley, Harvard University, and the Georgia Institute of Technology.
"Georgia Tech's statistics programs are highly regarded because of the efforts of many world-class faculty members working in a variety of areas, ranging from the theoretical and mathematical aspects through applications to engineering, science, and computing," said Doug Ulmer, chair of the School of Mathematics.
]]>Unhappy with the traditional books on the topic, Kvam and Vidakovic wrote the text to suit diverse groups of students with mathematical, programming, and statistical backgrounds. Their book is largely intended for use in graduate courses and as a reference text.
Interspersed with photographs and amusing biographical comments about statisticians, the book successfully provides a balance of the formal and informal. Kvam and Vidakovic include basic background information in order for the reader to effectively learn and apply methods of nonparametric analysis.
The bulk of the chapters consist of both Bayesian and Order statistics, various tests and experiments, estimation techniques, algorithms, and statistical learning. The last sections offer an overview of MATLAB, an interactive environment that allows the user to perform computational tasks and create graphical output, and WinBUGS, software used for constructing Bayesian statistical models and evaluating them using MCMC methodology.
Paul H. Kvam is a Professor at the Institute's Stewart School of Industrial and Systems Engineering. He has been an integral member of ISyE faculty since 1995. Dr. Kvam holds a PhD in Statistics from the University of California, Davis and is a fellow of the American Statistical Association. He has published over 50 articles in peer-reviewed journals in statistics and engineering.
Brani Vidakovic is a Professor at both the Georgia Tech's Coulter Department of Biomedical Engineering and Emory University's Department of Biostatistics. Dr. Vidakovic holds a Ph.D. in Statistics from Purdue University. He is a member of the American Statistical Association, Institute of Mathematical Statistics, and the International Society for Bayesian Analysis.
Nonparametric Statistics with Applications to Science and Engineering was published by John Wiley & Sons, Inc., Hoboken, New Jersey, in 2007.
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