Divya Mahajan

Divya Mahajan
divya.mahajan@gatech.edu
Personal Website

Divya is an Assistant Professor in School of ECE and Computer Science. Divya received her Ph.D. from Georgia Institute of Technology and Master’s from UT Austin. She obtained her Bachelor’s from IIT Ropar where she was conferred the Presidents of India Gold Medal, the highest academic honor in IITs.

Prior to joining Georgia Tech, Divya was a Senior Researcher at Microsoft Azure since September 2019. Her research has been published in top-tier venues such as ISCA, HPCA, MICRO, ASPLOS, NeurIPS, and VLDB. Her dissertation has been recognized with the NCWIT Collegiate Award 2017 and distinguished paper award at High Performance Computer Architecture (HPCA), 2016.

Currently, she leads the Systems Infrastructure and Architecture Research Lab at Georgia Tech. Her research team is devising next-generation sustainable compute platforms targeting end-to-end data pipeline for large scale AI and machine learning. The work draws insights from a broad set of disciplines such as, computer architecture, systems, and databases.

Assistant Professor
Additional Research
Computer ArchitectureSystems for Machine LearningLarge Scale Infrastructure for AI and Data Storage
Google Scholar
https://scholar.google.com/citations?hl=en&user=HmBa_6gAAAAJ&view_op=list_works&sortby=pubdate
LinkedIn

Yingyan (Celine) Lin

Yingyan (Celine) Lin
celine.lin@gatech.edu
EIC Lab Website

Yingyan (Celine) Lin is currently an Associate Professor in the School of Computer Science at the Georgia Institute of Technology. She leads the Efficient and Intelligent Computing (EIC) Lab, which focuses on developing efficient machine learning systems via cross-layer innovations from algorithm to architecture down to chip design, aiming to promote green AI and enable ubiquitous machine learning powered intelligence. She received a Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017. 

Prof. Lin is a Facebook Research Award (2020), NSF CAREER Award (2021), IBM Faculty Award (2021), and Meta Faculty Research Award (2022) recipient, and received the ACM SIGDA Outstanding Young Faculty Award in 2022. She was selected as a Rising Star in EECS by the 2017 Academic Career Workshop for Women at Stanford University. She received the Best Student Paper Award at the 2016 IEEE International Workshop on Signal Processing Systems (SiPS 2016), and the 2016 Robert T. Chien Memorial Award for Excellence in Research at UIUC. Prof. Lin is currently the lead PI of multiple multi-university projects, such as RTML and 3DML, and her group has been funded by NSF, NIH, DARPA, SRC, ONR, Qualcomm, Intel, HP, IBM, and Meta. Her group’s research won first place in both the University Demonstration at DAC 2022 and the ACM/IEEE TinyML Design Contest at ICCAD 2022, and was selected as an IEEE Micro Top Pick of 2023

Associate Professor
Research Focus Areas
University, College, and School/Department
Google Scholar
https://scholar.google.com/citations?hl=en&user=dio8IesAAAAJ&view_op=list_works&sortby=pubdate

Joy Arulraj

Joy Arulraj
jarulraj3@gatech.edu
Personal Website

Joy Arulraj is an assistant professor in the School of Computer Science at Georgia Institute of Technology. His research interest is in database management systems, specifically large-scale data analytics, main memory systems,  machine learning, and big code analytics. At Georgia Tech, he is a member of the Database group.

Assistant Professor
Additional Research
Data Systems
Research Focus Areas
University, College, and School/Department
Google Scholar
https://scholar.google.com/citations?hl=en&user=rp8dOfAAAAAJ&view_op=list_works&sortby=pubdate

Giri Krishnan

giri@gatech.edu

Dr Krishnan is research professor in the Georgia Tech’s Interdisciplinary Research Institute, Institute for Data Engineering and Science, School of Computational Science and Engineering, College of Computing. He is an associate director of the Center for AI in Science and Engineering. His current interest is in developing AI methods for computational science problems across many domains. He is a computational neuroscientist by training, with past work spanning across a wide range of computational modeling and AI methods. His group's current focus is on generative methods for computational workflow, neural approaches for accelerating compute intensive problems and applying interpretable methods to scientific AI for advancing scientific understanding.

Prior to joining Georgia Tech, he was research scientist at UC San Diego and his research involved developing large-scale modeling of the brain to study sleep, memory and learning. In addition, he has contributed towards neuro-inspired AI and neuro-symbolic approaches. He is broadly interested in the emergence of intelligent behavior from neural computations in the brain and AI systems. 

Dr Krishnan has more than 50 publications and his research has been supported by multiple grants from NIH and NSF. He is passionate about open-science and reproducible science and strongly believes that progress in science requires reproducibility.

Associate Director, Center for Artificial Intelligence in Science and Engineering (ARTISAN)
Principal Research Scientist
Phone
404.894.2132
Office
CODA Building
Additional Research
AI : Deep learning, Neuro-symbolic ApproachesGeosciences.Molecular DynamicsNeuroscience : Theoretical and computational modeling
Research Focus Areas
Google Scholar
https://scholar.google.com/citations?hl=en&user=IGsdszkAAAAJ&view_op=list_works&sortby=pubdate

Abigale Stangl

Abigale Stangl
abigale.stangl@design.gatech.edu
Personal Website

Dr. Abigale Stangl is a design researcher specializing in the development of systems that promote inclusive design practices and enhance the accessibility of products and information. With expertise in human-centered design, human-computer interaction, accessibility, and sensory AI, her interdisciplinary research encompasses universal design principles and prioritizes disability-first innovation. Abigale's current research goals focus on expanding tactile media availability through in-depth investigations of tactile design practices, interaction techniques, and the optimization of multimodal and multisensory systems. She actively collaborates with individuals with disabilities, ensuring their perspectives and needs drive innovation. Abigale also cultivates students' abilities as allies and co-designers, fostering an inclusive design community that embraces diverse perspectives.

Additional Research
AccessibilityCreativity Computer visionInclusive Design
University, College, and School/Department
Google Scholar
https://scholar.google.com/citations?hl=en&user=55redA8AAAAJ&view_op=list_works&sortby=pubdate
School of Industrial Design Profile Page

Zahra Mobini

Zahra Mobini
zahra.mobini@scheller.gatech.edu
Scheller Profile Page

Zahra Mobini is an Assistant Professor of Operations Management at Scheller College of Business. Her research interests revolve around the design and analysis of human-centric solutions to operations management problems, with a focus on healthcare operations. Using empirical and analytical methods, she studies how advancements in technology, regulations, and clinical protocols influence provider and patient behavior, and how to align their incentives for optimal outcomes. Her research has been supported by the Work in the Age of Intelligent Machines (WAIM) Research Fellowship with funding from the NSF's Future of Work at the Human-Technology Frontier Initiative. Her contributions have been recognized by the INFORMS Decision Analysis Society and POMS College of Healthcare Operations.

Zahra completed her PhD in Management Science - Operations Management at the UT Dallas Jindal School of Management and was a George Family Foundation postdoctoral fellow at Georgia Tech’s ISyE before joining Scheller.

Assistant Professor
Additional Research
Behavioral and Human-Centric Operations Management Healthcare Operations Health Analytics
Research Focus Areas
University, College, and School/Department
Google Scholar
https://scholar.google.com/citations?hl=en&user=f7zaX8QAAAAJ&view_op=list_works&sortby=pubdate

Pan Li

Pan Li
panli@gatech.edu
Personal Website

Pan Li joined Georgia Tech in 2023 Spring. Before that, Pan Li worked at the Purdue Computer Science Department as an assistant professor from the 2020 fall to the 2023 Spring. Before joining Purdue, Pan worked as a postdoc at Stanford Computer Science Department from 2019 to 2020. Pan did his Ph.D. in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. Pan Li has got the NSF CAREER award, the Best Paper award from the Learning on Graph Conference, Sony Faculty Innovation Award, JPMorgan Faculty Award.

Assistant Professor
Office
CODA Number S1219
Additional Research
Develop and analyze more expressive, generalizable, robust machine learning algorithms with graph and geometric data, using e.g., Graph neural networks, geometric deep learning, and equivariant models.  Build scalable analysis and learning tools for large-scale graph data, such as graph and hypergraph clustering algorithms, and large-scale graph machine learning.    Artificial Intelligence for Science: Interpretable and trustworthy graph machine learning for physics.
Research Focus Areas
Google Scholar
https://scholar.google.com/citations?hl=en&user=IroP0EwAAAAJ&view_op=list_works&sortby=pubdate
ECE Profile Page

Bo Dai

Bo Dai
bodai@cc.gatech.edu
Personal Website

Bo Dai is a tenure-track assistant professor at Georgia Tech's School of Computational Science and Engineering. Prior to joining academia, he worked as a Staff Research Scientist at Google Brain. Bo Dai completed his Ph.D. in the School of Computational Science and Engineering at Georgia Tech, where he worked from 2013 to 2018 with Professor Le Song. His research focuses on developing principled and practical machine learning techniques for real-world applications. Bo Dai has received numerous awards for his work, including the best paper award at AISTATS 2016. He regularly serves as a (senior) area chair at major AI/ML conferences, such as ICML, NeurIPS, AISTATS, and ICLR.

Assistant Professor
Office
CODA E1342A, 756 W Peachtree St NW, Atlanta, GA 30308
Additional Research
Reinforcement Learning Data-Driven Decision Making Embodied AI
Research Focus Areas
Google Scholar
https://scholar.google.com/citations?hl=en&user=TIKl_foAAAAJ&view_op=list_works&sortby=pubdate
CSE Profile Page

Nisha Chandramoorthy

Nisha Chandramoorthy
nishac@gatech.edu
Personal Website

Nisha Chandramoorthy is an assistant professor in the School of Computational Science and Engineering at Georgia Tech. Her research involves mathematical analyses and development of rigorous computational methods for better understanding and engineering nonlinear, possibly chaotic, dynamical systems. Some themes from her research are statistical response to perturbations, probability measure transport and high-dimensional Bayesian inference, and generalization of learning algorithms. These are motivated by fundamental scientific questions about nonlinearity as well as computational problems surrounding nonlinear systems. Both aims feed each other to improve our collective understanding of complex nonlinear processes, including in systems biology, climate studies and machine learning.

Prior to joining Georgia Tech, Nisha was a postdoctoral researcher at the Institute for Data, Systems and Society at MIT. She received her Ph.D. and master’s degrees from MIT in 2021 and 2016 respectively, and her bachelor’s degree from Indian Institute of Technology, Roorkee, in 2014.

Assistant Professor
Office
Rm:S1323, 756 W Peachtree St NW, Atlanta, GA 30308
Additional Research
Dynamical systems and ergodic theoryComputational statisticsComputational dynamics
Research Focus Areas
Google Scholar
https://scholar.google.com/citations?hl=en&user=7z8NqmUAAAAJ&view_op=list_works&sortby=pubdate
CSE Profile Page

Nabil Imam

Nabil Imam
nimam6@gatech.edu
Personal Website

Nabil Imam works on topics in machine learning and theoretical neuroscience with the goal of understanding general principles of neural coding and computation, and their technological applications.

Prof. Imam joined Georgia Tech faculty in January 2022.

Assistant Professor
Additional Research
Computational Neuroscience Neural Coding and Computation
Research Focus Areas
Google Scholar
https://scholar.google.com/citations?hl=en&user=DVK3S-AAAAAJ&view_op=list_works&sortby=pubdate
CSE Profile Page