Farzaneh Najafi

Farzaneh Najafi
fnajafi3@gatech.edu
Najafi Lab Website

Overview:
Our brain not only processes sensory signals but also makes predictions about the world. Generating and updating predictions are essential for our survival in a rapidly changing environment. Multiple brain regions including the cerebellum and the cortex are thought to be involved in the processing of prediction signals (aka predictive processing). However, it is not clear what circuit mechanisms and computations underlie predictive processing in each region, and how the cortical and cerebellar prediction signals interact to support cognitive and sensorimotor behavior. Our lab is interested in figuring out these questions by using advanced experimental and computational techniques in systems neuroscience.

Assistant Professor
Phone
2672519137
Office
IBB 3314
Additional Research

Research Interests: Systems and behavioral neuroscience; Computational neuroscience; Predictive processing; Brain area interactions; Cortex and cerebellum; Population coding

IRI and Role
Bioengineering and Bioscience > Faculty
Bioengineering and Bioscience
University, College, and School/Department
Georgia Institute of Technology > College of Sciences > School of Biological Sciences

Vidya Muthukumar

Vidya Muthukumar
vmuthukumar8@gatech.edu
ECE Profile Page

Vidya Muthukumar is an Assistant Professor in the School of Electrical and Computer Engineering and H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Dr. Muthukumar’s broad interests are in game theory, online and statistical learning. She is particularly interested in designing learning algorithms that provably adapt in strategic environments, fundamental properties of overparameterized models, and the foundations of multi-agent decision-making.

Dr. Muthukumar received the B.Tech (with honors) degree from the Indian Institute of Technology, Madras and the Ph.D. degree in Electrical Engineering from University of California, Berkeley. She interned at IBM Research in the summer of 2018 as a Science for Social Good fellow. Before joining Georgia Tech, she spent a semester at the Simons Institute for the Theory of Computing as a research fellow for the program “Theory of Reinforcement Learning.” She is the recipient of an Amazon Research Award, NSF CAREER Award, Adobe Data Science Research Award, Simons-Berkeley Google Research Fellowship, and the UC Berkeley EECS Outstanding Course Development and Teaching Award.

Dr. Muthukumar has served/is serving as an Area Chair for COLT (2021-2024), NeurIPS (2023 and 2024), ALT 2024 and UAI 2024. She also co-organizes several mentorship workshops as part of the Learning Theory Alliance team, and organized the ITALT symposium in 2024.

Assistant Professor
Additional Research

Statistical signal processingGame theorySequential decision-making

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Engineering > School of Electrical and Computer Engineering
Georgia Institute of Technology > College of Engineering > School of Industrial Systems Engineering
Google Scholar
Related Websites
Personal Website

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

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computer Science
Georgia Institute of Technology > College of Engineering > School of Electrical and Computer Engineering
Google Scholar
LinkedIn

Yunan Luo

Yunan Luo
yunan@gatech.edu
CoC Faculty Profile Page

I am an Assistant Professor in the School of Computational Science and Engineering (CSE), Georgia Institute of Technology since January 2022. I received my PhD from the Department of Computer Science at the University of Illinois Urbana-Champaign, advised by Prof. Jian Peng. Prior to that, I received my bachelor’s degree in Computer Science from Yao Class at Tsinghua University in 2016.

I am broadly interested in computational biology and machine learning, with a focus on developing AI and data science methods to reveals core scientific insights into biology and medicine. Recent interests include deep learning, transfer learning, sequence and graph representation learning, network and system biology, functional genomics, cancer genomics, drug repositioning and discovery, and AI-guided biological design and discovery.

Assistant Professor, Computational Science and Engineering
Additional Research

Deep learning Transfer learning Sequence and graph representation learning Network and system biology Functional genomics Cancer genomics AI-guided biological design and discovery

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computer Science
Google Scholar

Ling Liu

Ling Liu
lingliu@cc.gatech.edu
Website

Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale big data systems and analytics, including performance, availability, security, privacy and trust. Prof. Liu is an elected IEEE Fellow and a recipient of IEEE Computer Society Technical Achievement Award (2012). She has published over 300 international journal and conference articles and is a recipient of the best paper award from numerous top venues, including ICDCS, WWW, IEEE Cloud, IEEE ICWS, ACM/IEEE CCGrid. In addition to serve as general chair and PC chairs of numerous IEEE and ACM conferences in big data, distributed computing, cloud computing, data engineering, very large databases fields, Prof. Liu served as the editor in chief of IEEE Transactions on Service Computing (2013-2016), on editorial board of over a dozen international journals. Ling’s current research is sponsored primarily by NSF and IBM.

Professor
IRI and Role
Data Engineering and Science > Affiliated Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology

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
IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing
Google Scholar

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.

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Engineering > School of Electrical and Computer Engineering
Google Scholar
Related Websites
ECE Profile Page

Wenke Lee

Wenke Lee
wenke@cc.gatech.edu
Website

Wenke Lee, Ph.D., is executive director of the Institute for Information Security & Privacy (IISP) and responsible for continuing Georgia Tech's international leadership in cybersecurity research and education. Additionally, he is the John P. Imlay, Jr. Professor of Computer Science in the College of Computing at Georgia Tech, where he has taught since 2001. Previously, he served as director of the IISP's predecessor -- the Georgia Tech Information Security Center (GTISC) research lab -- from 2012 to 2015. Lee is one of the most prolific and influential security researchers in the world. He has published several dozen, oft-cited research papers at top academic conferences, including the ACM Conference on Computer and Communications Security, USENIX Security, IEEE Security & Privacy ("Oakland"), and the Network & Distributed System Security (NDSS) Symposium. His research expertise includes systems and network security, botnet detection and attribution, malware analysis, virtual machine monitoring, mobile systems security, and detection and mitigation of information manipulation on the Internet. Lee regularly leads large research projects funded by the National Science Foundation (NSF), U.S. Department of Defense, Department of Homeland Security, and private industry. Significant discoveries from his research group have been transferred to industry, and in 2006, doing so enabled Lee to co-found Damballa, Inc., which focused on detection and mitigation of advanced persistent threats. Lee’s awards and honors include the “Internet Defense Prize” awarded by Facebook and USENIX in 2015, an “Outstanding Community Service Award” from the IEEE Technical Committee on Security and Privacy in 2013, a Raytheon Faculty Fellowship in 2005, an NSF Career Award in 2002, as well as best paper awards in the IEEE Symposium on Security and Privacy and the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Passionate about quality education, Lee serves on the advisory boards of the Faculty of Engineering at the Chinese University of Hong Kong and the board of trustees at Pace Academy in Atlanta. He received his Ph.D. in Computer Science from Columbia University in 1999.

Executive Director, Institute for Information Security and Privacy
Co-Executive Director, SEI
Professor
Phone
404.385.2879
Additional Research

Data Security & Privacy; Encryption; Internet Infrastructure & Operating Systems; Machine Learning; Cyber Technology

IRI and Role
Data Engineering and Science > Affiliated Faculty
Energy > Research Community
Data Engineering and Science
Energy
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computer Science

Srijan Kumar

Srijan Kumar
srijan@gatech.edu
Website

Prof. Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. His research develops data science solutions to address the high-stakes challenges on the web and in the society. He has pioneered the development of user models and network science tools to enhance the well-being and safety of people. Applications of his research widely span e-commerce, social media, finance, health, web, and cybersecurity. His methods to predict malicious users and false information have been widely adopted in practice (being used in production at Flipkart and Wikipedia) and taught at graduate level courses worldwide. He has received several awards including the ACM SIGKDD Doctoral Dissertation Award runner-up 2018, Larry S. Davis Doctoral Dissertation Award 2018, and best paper awards from WWW and ICDM. His research has been the subject of a documentary and covered in popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. He completed his postdoctoral training at Stanford University, received a Ph.D. in Computer Science from University of Maryland, College Park, and B.Tech. from Indian Institute of Technology, Kharagpur.

Assistant Professor
Additional Research

Online malicious actors and dangerous content threaten public health, democracy, science, and society. To combat these threats, I build technological solutions, including accurate and robust models for early identification, prediction and attibution, as well as social mitigation solutions, such as empowering people to counter online harms. I have conducted the largest study of malicious sockpuppetry across nine platforms, ban evasion/recidivism on online platforms, and some of the earliest works on online misinformation. I am the one of the first to investigate of the reliability of web safety models used in practice, including Facebook's TIES and Twitter's Birdwatch. My work is one of the first to study whole-of-society solutions to mitigate online misinformation.

IRI and Role
Data Engineering and Science > Affiliated Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology

Tushar Krishna

Tushar Krishna
tushar@ece.gatech.edu
ECE Profile Page

Tushar Krishna is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Tech. He also holds the ON Semiconductor Junior Professorship. He has a Ph.D. in Electrical Engineering and Computer Science from MIT (2014), a M.S.E in Electrical Engineering from Princeton University (2009), and a B.Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Delhi (2007). Before joining Georgia Tech in 2015, Krishna spent a year as a researcher at the VSSAD group at Intel, Massachusetts.

Krishna’s research spans computer architecture, interconnection networks, networks-on-chip (NoC) and deep learning accelerators – with a focus on optimizing data movement in modern computing systems. Three of his papers have been selected for IEEE Micro’s Top Picks from Computer Architecture, one more received an honorable mention, and three have won best paper awards. He received the National Science Foundation (NSF) CRII award in 2018, a Google Faculty Award in 2019, and a Facebook Faculty Award in 2019 and 2020.

ON Semiconductor Junior Professor, School of Electrical and Computer Engineering
Phone
404.894.9483
Office
Klaus 2318
Additional Research

Networks-on-Chip (NoC)Interconnection NetworksReconfigurable Computing and FPGAsHeterogeneous ArchitecturesDeep Learning Accelerators

IRI and Role
Data Engineering and Science > Faculty
Matter and Systems > Affiliated Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Engineering > School of Electrical and Computer Engineering
Google Scholar
LinkedIn
Related Websites
Personal Research Site
Research Areas
Matter and Systems
  • Computing and Communication Technologies
  • Frontiers in Infrastructure
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