Anyone with children knows that while controlling one child can be hard, controlling many at once can be nearly impossible. Getting swarms of robots to work collectively can be equally challenging, unless researchers carefully choreograph their interactions — like planes in formation — using increasingly sophisticated components and algorithms. But what can be reliably accomplished when the robots on hand are simple, inconsistent, and lack sophisticated programming for coordinated behavior?

A team of researchers led by Dana Randall, ADVANCE Professor of Computing and Daniel Goldman, Dunn Family Professor of Physics, sought to show that even the simplest of robots can still accomplish tasks well beyond the capabilities of one, or even a few, of them. The goal of accomplishing these tasks with what the team dubbed "dumb robots" (essentially mobile granular particles) exceeded their expectations, and the researchers report being able to remove all sensors, communication, memory and computation — and instead accomplishing a set of tasks through leveraging the robots' physical characteristics, a trait that the team terms "task embodiment."

The team's simple BOBbots, or "behaving, organizing, buzzing bots" were named for granular physics pioneer Bob Behringer," explains Randall. "Their cylindrical chassis have vibrating brushes underneath and loose magnets on their periphery, causing them to spend more time at locations with more neighbors." The experimental platform was supplemented by precise computer simulations led by Georgia Tech physics student Shengkai Li, as a way to study aspects of the system inconvenient to study in the lab.

Despite the simplicity of the BOBbots, the researchers discovered that, as the robots move and bump into each other, "compact aggregates form that are capable of collectively clearing debris that is too heavy for one alone to move," according to Goldman. "While most people build increasingly complex and expensive robots to guarantee coordination, we wanted to see what complex tasks could be accomplished with very simple robots."

Their work, as reported April 23, 2021 in the journal Science Advances, was inspired by a theoretical model of particles moving around on a chessboard. A theoretical abstraction known as a self-organizing particle system was developed to rigorously study a mathematical model of the BOBbots. Using ideas from probability theory, statistical physics and stochastic algorithms, the researchers were able to prove that the theoretical model undergoes a phase change as the magnetic interactions increase — abruptly changing from dispersed to aggregating in large, compact clusters, similar to phase changes we see in common everyday systems, like water and ice.

"The rigorous analysis not only showed us how to build the BOBbots, but also revealed an inherent robustness of our algorithm that allowed some of the robots to be faulty or unpredictable," notes Randall, who also serves as a professor of computer science and adjunct professor of mathematics at Georgia Tech.

 

The collaboration is based on experiments and simulations also designed by Bahnisikha Dutta, Ram Avinery and Enes Aydin from Georgia Tech, as well as on theoretical work by Andrea Richa and Joshua Daymude from Arizona State University, and Sarah Cannon from Claremont McKenna College, who is a recent Georgia Tech graduate.

This work is part of a Multidisciplinary University Research Initiative (MURI) funded by the Army Research Office (ARO) to study the foundations of emergent computation and collective intelligence.

Funding: This work was supported by the Department of Defense under MURI award no. W911NF-19-1-0233 and by NSF awards DMS-1803325 (S.C.); CCF-1422603, CCF-1637393, and CCF-1733680 (A.W.R.); CCF-1637031 and CCF-1733812 (D.R. and D.I.G.); and CCF-1526900 (D.R.).

This story was first published on EurekAlert! by Georgia Tech. 

One College of Sciences student is a rising fourth-year, but she spent some of her non-research hours helping incoming first year students navigate the newness of Georgia Tech. Another became the Georgia Tech hockey team’s first woman player in school history. (Yes, Georgia Tech has a hockey team.) Yet another is helping a professor build an organic meteor database.

The six College of Sciences students honored for end-of-school-year awards and scholarships offer a wide variety of accomplishments. Join us in congratulating these winners of the 2021 College of Sciences Student Awards: 

2021 A. Joyce Nickelson and John C. Sutherland Undergraduate Research Award

Presented to a student studying physics and mathematics.
Endowment gift of Jen Nickelson and John Sutherland. 

Luojia Zhang

 

2021 Roger M. Wartell and Stephen E. Brossette Award

Presented to a student who studies at the physics/math-biology interface. Endowment gift of Stephen Brossette.

Sophia Wiesenfeld

 

2021 Robert A. Pierotti Memorial Scholarship

Presented to a top graduating senior in the College. Endowment gift of the family and friends of former dean Robert Pierotti

Kalen Patton

 

2021 Mehta Phingbodhipakkiya Memorial Scholarship

Presented to a top junior in the College of Sciences. Endowment gift of Maranee Phing

Jennifer Kim

 

2021 Virginia C. and Herschel V. Clanton Jr. Scholarship

Presented to a top pre-medical student in the College of Sciences.
Endowment gift of Herschel V. Clanton.

Suraj Modi

  • Mountain View High School, Lawrenceville, graduate
  • Biology major
  • Research with Frank Rosenzweig, School of Biological Sciences 
  • Studying the integration of nutrient-sensing and pheromone-sensing pathways in Saccharomyces cerevisiae
  • Ambassador, Stamps Health Services; Biology Student Advisory Council

 

2021 Cynthia L. Bossart and James Efron Scholarship

Presented to a top out-of-state junior in the College of Sciences.
Endowment gift of Cynthia Bossart and James Efron.

Eva Erickson

Seven College of Sciences faculty members from five schools are winners of annual awards from Georgia Tech’s Center for Teaching and Learning.

The Center for Teaching and Learning, part of the Office of the Vice Provost for Graduate Education and Faculty Development, enhances the learning and teaching environment at Georgia Tech by encouraging a fully engaged, sharing community with communication networks, resources, and innovative programs for faculty, postdoctoral scholars, and graduate students. It recognizes tenured and non-tenured faculty with end-of-school-year awards honoring the work and innovation Georgia Tech educators bring to their classrooms.

This year's list includes a pair of School of Mathematics educators: Stephanie Reikes, a lecturer in the School of Mathematics, is the winner of Georgia Tech’s 2021 Undergraduate Educator Award. Professor Dan Margalit is one of two winners of the 2021 Eichholz Faculty Teaching Award.

2021 Undergraduate Educator Award 

Stephanie Reikes, School of Mathematics

Reikes’ award was offered for the first time in 2009, recognizing the outstanding contributions that non-tenure track faculty make to student education. It reflects Reikes’ unique role at Georgia Tech, with responsibilities in the School of Mathematics and the Tutoring & Academic Support unit at Georgia Tech. She is responsible for teaching all of the Institute’s pre-calculus mathematics courses, including Support for College Algebra, College Algebra, and Pre-Calculus. She specializes in working with student of all backgrounds, including at-risk students, students with disabilities, and student-athletes. 

In addition to leading improvements in this challenging area, she has strengthened the cooperation and collaboration between Tutoring & Academic Support and the School of Mathematics, and introduced an innovative Learning Assistants program. She also directs the Math Lab.

2021 Eichholz Award Faculty Teaching Award

Dan Margalit, School of Mathematics 

The Eichholz Award, which includes a $3,000 prize, was established in 2005 through a gift from School of Mechanical Engineering's Regents’ Professor Emeritus Geoffrey Eichholz. It was created to reward senior faculty members who made a long-term contribution to introductory undergraduate education and were outstanding teachers for students taking freshman and sophomore core courses. It was recently broadened to recognize faculty at any point in their careers who excel in teaching core and general education courses, and who help students establish a solid foundation for their education at Georgia Tech.

Margalit’s math research lies at the intersection of low-dimensional topology and geometric group theory. He focuses on mapping class groups of surfaces, also called the the symmetries of surfaces. The author/editor of three books, Margalit hosts several workshops and discussion groups centering not just on topology and the advanced geometry he teaches, but mentorship and support for undergraduate and graduate students.

CTL/BP Junior Faculty Teaching Excellence Award ($3,000 each award)

Young Jang – School of Biological Sciences

This award, offered through the joint support of the Center for Teaching and Learning (CTL) and BP America, provides Georgia Tech with the opportunity to highlight the excellent teaching and educational innovation that junior faculty bring to campus. 

Jang, an assistant professor, researches stem cell biology and its impact on the aging process. Jang’s lab uses multi-disciplinary approaches to study muscle stem cell biology and develops bioactive stem cell delivery vehicles for use in regenerative medicine.

Faculty Award for Academic Outreach ($3,000)

Chandra Raman – School of Physics

This award rewards faculty members for productive academic outreach in which they go beyond their normal duties to enrich the larger educational community with their subject matter knowledge. Initiatives may involve furthering the learning of K-12 students, teachers, or other educational stakeholders in Georgia.

Raman, a professor, lists Bose-Einstein condensation and quantum atomic sensors as his research interests. His lab is an experimental atomic physics group that prepares atomic vapors from room temperature down to the microKelvin temperature regime, and seeks to exploit their unique capabilities for applications in quantum photonics, sensing, and many-body physics.

Innovation in Co-Curricular Education ($3,000 shared--$1,000 each)

Paul Verhaeghen—School of Psychology

This award is open to full-time faculty of any rank who increase student learning outside the traditional curriculum and help Georgia Tech achieve its strategic goal of graduating global citizens who can contribute to all sectors of society. Initiatives may involve formal or informal out-of-class learning experiences that engage undergraduate and/or graduate students in opportunities to develop respect for other cultures, explore the leadership qualities and ethical behaviors necessary to contribute to society, and/or build on their innovative and entrepreneurial talents in order to have a positive impact on local, state, national and/or international arenas. 

Verhaeghen, a professor, researches cognitive aging and working memory in the School of Psychology. He has also conducted scientific research into mindfulness meditation, and has published a book on his findings, “Presence: How Mindfulness Shapes Your Brain, Mind, and Life.” In late 2020 he was awarded a two-year, $200,000 grant from the Mind and Life Institute.

Scholarship of Teaching and Learning Award ($3,000 shared--$1,500 each)

Michael Evans, Carrie Shepler, School of Chemistry and Biochemistry  

This award, offered in 2018-2019 for the first time, provides Georgia Tech with the opportunity to acknowledge the value of scholarship of teaching and learning articulated by Boyer’s Scholarship Reconsidered (1990), and exemplified by the Carnegie Academy for the Scholarship of Teaching and Learning. This award is intended to encourage and support the work of faculty whose scholarship focuses on the instructional mission of the institution.

Evans is a senior academic professional who serves as the Freshmen Chemistry Laboratory Coordinator. As he writes in the Chemical Education section of his biographical profile, “Our advanced labs have focused on how to keep students engaged and allow them to see the relevance of lab work to their career paths.”

As Director of Instructional Activities and Student Experience in the School of Chemistry and Biochemistry, Shepler’s responsibilities include co-chairing the Freshman Chemistry Committee, providing administrative supervision and support, planning of assessment and feedback, pedagogical development, and coordination and training of teaching assistants in the freshman program in addition to teaching freshman program courses. Shepler also serves as an academic advisor. 

At the end of every semester at Georgia Tech — after weeks of faculty grading the work of students — the tables are flipped, and students get to evaluate their teachers and their class experiences using the Course Instructor Opinion Survey (CIOS). 

Faculty members with exceptional scores and response rates are presented with the Center for Teaching and Learning’s (CTL) Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award. This year, 40 College of Sciences faculty and instructors are receiving awards and honors for their work from spring through fall 2020 semesters.

The challenges of teaching classes during Covid-19 necessitated a new recognition from the CTL: The Honor Roll, which includes 32 College of Sciences faculty on its inaugural list.

“Teaching during the pandemic has required everyone to pivot to new ways of teaching, and faculty appreciate hearing that students value their efforts,” says Joyce Weinsheimer, CTL director. The criteria for Honor Roll selection are the same as for the Class of 1934 Award.

The following are the College of Sciences faculty named to both the Class of 1934 and Honor Roll Awards (groups broken up into small and large classes): 

Class of 1934 Award

Small Classes

Mirjana Milosevec Brockett, senior academic professional, School of Biological Sciences

Lutz Warnke, assistant professor, School of Mathematics

Large Classes

Hector Daniel Cervantes Banos, postdoctoral researcher, School of Mathematics

Dan Margalit, professor, School of Mathematics

Dobromir Rahnev, assistant professor, School of Psychology

Amit Reddi, associate professor, School of Chemistry and Biochemistry

Carrie Shepler, professor, Director of Instructional Activities and Student Experience, School of Chemistry and Biochemistry

Alonzo Whyte, academic professional, School of Biological Sciences (Neuroscience)

Honor Roll

Small Classes 

School of Biological Sciences — Mirjana Brockett, senior academic professional; Colin Harrison, academic professional

School of Earth and Atmospheric Sciences — Heather Chilton, lecturer;  Zachary Handlos, academic professional

School of Mathematics — Lutz Warnke, assistant professor

School of Psychology — Richard Catrambone, professor; Michael Hunter, assistant professor; James Roberts, associate professor

Large Classes 

School of Biological Sciences:

Annalise Paaby, assistant professor; William Ratcliff, associate professor; Raphael Rosenzweig, professor; Emily Weigel, academic professional

School of Earth and Atmospheric Sciences:

Samantha Wilson, academic professional

School of Chemistry and Biochemistry:

Meghan Benda, graduate student; Amit Reddi, associate professor; Carrie Shepler, professor, Director of Instructional Activities and Student Experience

School of Mathematics:

Alex Blumenthal, assistant professor; Hector Daniel Cervantes Banos, postdoctoral researcher; Klara Grodzinsky, Director of Teaching Assistants; Miriam Kuzbary, assistant professor; Gary Lavigne, professor; Wenjing Liao, assistant professor; Marissa Loving, postdoctoral researcher; Dan Margalit, professor; Gregory Mayer, Director of Online Learning; Stephanie Reikes, lecturer, Tutoring and Academic Support; Victor Vilaca Da Rocha, assistant professor; Zhiyu Wang, postdoctoral researcher

Neuroscience:

Mary Holder, academic professional; Alonzo Whyte, academic professional

School of Psychology:

Dobromir Rahnev, assistant professor

Congratulations go to Professor Dan Margalit, who has been awarded the Geoffrey G. Eichholz Faculty Teaching Award.

This award was established in 2005 through a gift from School of Mechanical Engineering's Regents’ Professor Emeritus Geoffrey Eichholz. It was created to reward senior faculty members who made a long-term contribution to introductory undergraduate education and were outstanding teachers for students taking freshman and sophomore core courses. Recently, the award has broadened to recognize faculty at any point in their careers who excel in teaching core and general education courses, and who help students establish a solid foundation for their education at Georgia Tech.

In addition to his success as an effective and engaging instructor, Prof. Margalit is recognized for his leadership on MATH 1553 Intro. Linear Algebra over the years, including coauthoring the online interactive textbook Interactive Linear Algebra which is the course textbook for MATH 1553. Every semester, this course is taken by many GT students from several key majors, and thus plays an important role in their educational foundation.

School of Mathematics Lecturer Stephanie Reikes has been recognized for her acheivements in undergraduate education with the bestowment of the Georgia Tech’s 2021 Undergraduate Educator Award.

This award, which was offered for the first time in 2009, recognizes the outstanding contributions that non-tenure track faculty make to the education of students on campus.

Lecturer Reikes has a unique role at Georgia Tech, with responsibilities in the School of Mathematics and the Tutoring & Academic Support unit at Georgia Tech, being responsible for teaching all of Tech’s pre-calculus mathematics courses, including Math 0999 Support for College Algebra, Math 1111 College Algebra and Math 1113 Pre-calculus. In addition to leading improvements in this challenging area, she has strengthened the cooperation and collaboration between Tutoring & Academic Support and SoM, and introduced an innovative Learning Assistants program. 

Along with the students who receive her constant efforts, we are very grateful for Lecturer Reikes' continued work in undergraduate education and specifically her work in the School of Mathematics, and congratulate her on this distinguished honor. 

This story first appeared in the Georgia Tech College of Engineering newsroom.

Contact tracing apps have become a crucial way for people to keep themselves and others safe during the Covid-19 pandemic. However, contact tracing is often a reactive rather than proactive way of monitoring the spread of disease. An alternative digital tool is an early warning system that anonymously alerts users as a Covid-infected individual approaches their social interaction circles – simply put, a Covid radar system. 

In an event co-hosted by the College of Engineering and the College of Sciences, Georgia Tech faculty members Matt Baker and Shannon Yee invited Po-Shen Loh, a professor of mathematics at Carnegie Mellon University, to speak about the innovative radar approach, as well as NOVID, an app pioneered by Loh and his team

In March 2020, Loh began the theoretical plans for the NOVID app. He wanted to find a way to apply his scholarly work in times of national emergency, which led him to examine mainstream contact tracing apps that emerged at the beginning of the pandemic. Using his expertise in network theory – the study of the way elements in a network interact – Loh and his team wanted to create an app that would empower people with information, including when to take extra precautions to mitigate exposure, such as wearing a more protective mask or avoiding optional social gatherings. To do so, they created what he describes as a Covid radar app that provides users with a self-defense mechanism to avoid infection, showing multiple degrees of separation from infected individuals.

Combining Game and Network Theory

Here is where Loh’s application of game theory comes into play – the team had to find a way to align the user’s natural incentives with downloading the NOVID app and did so by offering opportunities for the user to be proactive and protect themselves. Loh started with a question – how a new methodology focused on game theory and networks could affect contact tracing apps – and ended up with an app that has seen more than 100,000 users.

Some contact tracing apps use smartphones’ ability to connect to inform users if they have been physically near an infected individual, measuring distance in terms of feet or miles, but Loh felt their approach needed a methodological shift. They went one level of abstraction up, measuring distance in terms of sustained physical relationships – mapping those smartphone interactions onto a network and measuring degrees of separation from infected individuals by network distance. His idea is extrapolated from network theory, where each node in the system is a person, and when two people spend a significant amount of time physically near each other, it forms a connection between those two nodes. The app can then measure the distance between a user and infection by measuring how many steps away on the network the user is from an infected individual.

A Shift in Approach

While most contact tracing apps notify a user to quarantine after physical exposures, Loh’s app gives users visual representation of how far away in terms of degrees of separation they are from an infected individual, and allows the user to become aware of their interactions and environment to make more proactive decisions.

During the event, Loh outlined three main problems he found in mainstream contact tracing apps that create a significant barrier to their success. One such problem is the threshold that most contact tracing apps are calibrated to: a six-foot distance from an infected person for a period of 15 minutes, measured via Bluetooth connections with other smartphones. The likelihood that the user has actually contracted the disease in the above situation is about 6%, according to findings in a UK study about contact tracing, and many users will not voluntarily self-quarantine for such a low chance of infection, or even decide to participate in contact tracing at all. To solve the problem of mistrust in these apps’ recommendation to quarantine, NOVID does not tell users to do so, but instead educates the user on how far away they are in the network from infection and recommends simple, temporary lifestyle changes, such as spending time with friends outside, not going out to eat, and other safety methods.

“Traditional contact tracing relies to a large extent on altruism, but the NOVID app flips the incentives around and relies on users’ desire to protect themselves,” said Baker. “It’s a marvelous idea and the potential applications are not limited to Covid-19, or even to epidemiology.”

The NOVID team continues to work with researchers and epidemiologists to analyze and improve the app, and they plan to release a new update that allows users to input vaccination status in a few weeks.

“I’m really intrigued by the whole project and interested to see where this app will go,” said Yee. “I chose to teach in-person this semester, and NOVID provides me peace of mind when I go home at night regarding the prolonged interactions I may have on campus. It’s great to have this app in the Georgia Tech toolkit to help stop the spread of Covid-19 in our community.”

Photo: (Left to right) Shannon Yee, professor in the George W. Woodruff School of Mechanical Engineering at Georgia Tech; Matt Baker, professor in the School of Mathematics and Associate Dean for Faculty Development in the College of Sciences at Georgia Tech; Po-Shen Loh, professor of mathematics at Carnegie Mellon University and NOVID app founder.

Congratulations go to Professor Dan Margalit, who has been awarded the Geoffrey G. Eichholz Faculty Teaching Award.

This award was established in 2005 through a gift from School of Mechanical Engineering's Regents’ Professor Emeritus Geoffrey Eichholz. It was created to reward senior faculty members who made a long-term contribution to introductory undergraduate education and were outstanding teachers for students taking freshman and sophomore core courses. Recently, the award has broadened to recognize faculty at any point in their careers who excel in teaching core and general education courses, and who help students establish a solid foundation for their education at Georgia Tech.

In addition to his success as an effective and engaging instructor, Prof. Margalit is recognized for his leadership on MATH 1553 Intro. Linear Algebra over the years, including coauthoring the online interactive textbook Interactive Linear Algebra which is the course textbook for MATH 1553. Every semester, this course is taken by many GT students from several key majors, and thus plays an important role in their educational foundation.

An assistant professor in the School of Mathematics is receiving a 2021 National Science Foundation Faculty Early Career Development Program (NSF CAREER) Award for research into promising aspects of statistical analysis, and for her outreach and mentorship plans for students and high schoolers from underrepresented communities.

Mayya Zhilova’s NSF project, “New Challenges in High-Dimensional and Nonparametric Statistics,” will “address challenging open questions in high-dimensional and nonparametric statistics motivated by practical applications in finance, engineering, and life sciences,” as Zhilova writes in her abstract.

Contemporary problems concerned with analysis of complex and high-dimensional data sets require to address numerous questions about fundamental concepts in statistics, data science, and related fields. This is particularly relevant for high-dimensional and nonparametric statistics. In high-dimensional statistics, one studies problems involving data sets with a large complexity or dimensionality that can be much larger than an amount of available information. Methods that are used in nonparametric statistics typically impose much weaker assumptions on a statistical model than the parametric statistics does. In general, this leads to a smaller modeling error and to a broader range of applications, or real-life problems, where these methods can be used. 

Zhilova adds in her abstract: “The project is focused on development of new methods of statistical inference for complex data sets providing high accuracy and explicit theoretical guarantees. This includes (i) development of a novel framework for statistical inference that will considerably extend the range of applicability of some of the major statistical methods; (ii) studies of performance of resampling methods in a high-dimensional framework; and (iii) studies of intrinsic properties of high-dimensional models that ensure good performance of the statistical methods.”

The educational aspect of Zhilova’s NSF CAREER project includes mentorship of graduate and undergraduate students, summer camps in statistics and data science for STEM-oriented high school students, and a workshop/graduate school offering on high-dimensional statistics and learning theory for junior researchers. Zhilova notes that special attention will be given to supporting students and researchers from underrepresented minorities.

The NSF CAREER Program is one of the Foundation’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education, and to lead advances in the mission of their department or organization. The NSF notes that activities pursued by early-career faculty recipients should build a firm foundation for a lifetime of leadership in integrating education and research.

Zhilova began work at the School of Mathematics at Georgia Tech in 2016, and is an affiliate faculty member of the Center for Machine Learning at Georgia Tech and the Transdisciplinary Research Institute for Advancing Data Science (TRIAD). Before coming to Atlanta, Zhilova was a researcher at the Weierstrass Institute for Applied Analysis and Stochastics, and at the School of Business and Economics at the Humboldt University of Berlin. She received her M.S. from the Lomonosov Moscow State University, and her Ph.D. from the Humboldt University of Berlin.

An assistant professor in the School of Mathematics is receiving a 2021 National Science Foundation Faculty Early Career Development Program (NSF CAREER) Award for research into promising aspects of statistical analysis, and for her outreach and mentorship plans for students and high schoolers from underrepresented communities.

Mayya Zhilova’s NSF project, “New Challenges in High-Dimensional and Nonparametric Statistics,” will “address challenging open questions in high-dimensional and nonparametric statistics motivated by practical applications in finance, engineering, and life sciences,” as Zhilova writes in her abstract.

Contemporary problems concerned with analysis of complex and high-dimensional data sets require to address numerous questions about fundamental concepts in statistics, data science, and related fields. This is particularly relevant for high-dimensional and nonparametric statistics. In high-dimensional statistics, one studies problems involving data sets with a large complexity or dimensionality that can be much larger than an amount of available information. Methods that are used in nonparametric statistics typically impose much weaker assumptions on a statistical model than the parametric statistics does. In general, this leads to a smaller modeling error and to a broader range of applications, or real-life problems, where these methods can be used. 

Zhilova adds in her abstract: “The project is focused on development of new methods of statistical inference for complex data sets providing high accuracy and explicit theoretical guarantees. This includes (i) development of a novel framework for statistical inference that will considerably extend the range of applicability of some of the major statistical methods; (ii) studies of performance of resampling methods in a high-dimensional framework; and (iii) studies of intrinsic properties of high-dimensional models that ensure good performance of the statistical methods.”

The educational aspect of Zhilova’s NSF CAREER project includes mentorship of graduate and undergraduate students, summer camps in statistics and data science for STEM-oriented high school students, and a workshop/graduate school offering on high-dimensional statistics and learning theory for junior researchers. Zhilova notes that special attention will be given to supporting students and researchers from underrepresented minorities.

The NSF CAREER Program is one of the Foundation’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education, and to lead advances in the mission of their department or organization. The NSF notes that activities pursued by early-career faculty recipients should build a firm foundation for a lifetime of leadership in integrating education and research.

Zhilova began work at the School of Mathematics at Georgia Tech in 2016, and is an affiliate faculty member of the Center for Machine Learning at Georgia Tech and the Transdisciplinary Research Institute for Advancing Data Science (TRIAD). Before coming to Atlanta, Zhilova was a researcher at the Weierstrass Institute for Applied Analysis and Stochastics, and at the School of Business and Economics at the Humboldt University of Berlin. She received her M.S. from the Lomonosov Moscow State University, and her Ph.D. from the Humboldt University of Berlin.

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