Ratan obtained his PhD in Neuroscience from the Indian Institute of Science, Bangalore (India) with Prof. SP Arun and completed his postdoctoral research at the Massachusetts Institute of Technology with Profs. Nancy Kanwisher and James J DiCarlo.​ He leads the Murty Vision, Cognition, and Computation Lab at Georgia Tech.

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Ratan's research goal is to understand the neural codes and algorithms that support human vision.

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.

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.

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.

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.

Suresh Marru is a research professor dedicated to advancing science and engineering through AI and cyberinfrastructure. Over the past two decades, he has focused on accelerating and democratizing computational science. His work includes the development of science gateways and the pioneering of the Apache Airavata distributed systems framework.

In his current role as the Director of Georgia Tech's ARTISAN Center, his team is at the forefront of pioneering efforts to integrate AI into diverse scientific domains. His group is dedicated to bridging the gap between theory, experimentation, and computation by fostering open-source integration frameworks. These frameworks automate research processes, optimize complex models, and integrate disparate scientific data with simulation engines.

Collaboration is at the heart of Suresh’s ethos. He has had the privilege of working alongside brilliant scientists and technologists, contributing to groundbreaking research in domains such as geosciences, neuroscience, and molecular dynamics. These collaborations have not only accelerated scientific discovery but have also offered valuable insights into the potential of AI in scientific innovation.

Beyond his professional endeavors, Suresh is deeply passionate about open science and open-source software. He also believes in building synergies between academia and industry. He has played an instrumental role in a series of tech startups. Currently, he serves as the Chief Technology Officer at Folia, a company dedicated to unleashing the power of annotations.

Alexander Lerch is an Associate Professor at the School of Music, Georgia Institute of Technology. He received his "Diplom-Ingenieur'' (EE) and his PhD (Audio Communications) from Technical University Berlin. Lerch joined Georgia Tech in 2013 and teaches classes on music signal processing, computational music analysis, audio technology, and audio software engineering. Before he joined Georgia Tech, Lerch was Head of Research at his company zplane.development, an industry leader in music technology licensing. zplane technology includes algorithms such as time-stretching and automatic key detection and is used by millions of musicians and producers world-wide.       

Lerch's research focuses on teaching computers to listen to and comprehend music. His research field, Music Information Retrieval (MIR), positions him at the intersection of signal processing, machine learning, music psychology, and systematic musicology. His Music Informatics Group (http://www.musicinformatics.gatech.edu) creates artificially intelligent software for music generation, production, and consumption and generates new insights into music and its performance.

Lerch authored more than 40 peer-reviewed journal and conference papers. His text book "An Introduction to Audio Content Analysis" (IEEE/Wiley 2012) and the accompanying online materials at www.AudioContentAnalysis.org helped define educational practice in the field.

Dr. Bhasin's laboratory has developed strategies for analysis of transcriptome, epigenome, and proteomics data to perform multi-scale modeling of interaction among different cells molecular level and to identify novel biomarkers. He and his team are currently focusing on developing novel single-cell omics approaches to understand disease heterogeneity and the impact of treatments at single-cell resolution. He is involved in developing approaches for the analysis of multi-dimensional single-cell data by developing innovative approaches for single-cell sparsity, batch correction, annotation, and integration. Using these approaches, his group is working toward understanding: 1. Understanding heterogeneity and relapse mechanisms in pediatric hematological malignancies 2. Understanding heterogeneity and progression in multiple myeloma. 3. Development of molecular diagnostics platforms for cancer diagnosis and prognosis 4. Identification of biomarkers for early detection of pancreatic cancer, glioblastoma, and colon cancer 5. Artificial intelligence-based histopathology and radiology cancer image analysis approaches 6. Single-cell Atlas for Pediatric Cancers Additionally, our group is also developing Biomarkers associated with impaired healing of Diabetic Foot Ulcers using single-cell profiling and deep learning-driven wound image analysis. We are working collaboratively to develop innovative genomics and clinical data-driven drug repurposing approaches.

Juba Ziani is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering. Prior to this, Juba was a Warren Center Postdoctoral Fellow at the University of Pennsylvania, hosted by Sampath Kannan, Michael Kearns, Aaron Roth, and Rakesh Vohra. Juba completed his Phd at Caltech in the Computing and Mathematical Sciences department, where he was advised by Katrina Ligett and Adam Wierman.

Juba studies the optimization, game theoretic, economic, ethical, and societal challenges that arise from transactions and interactions involving data. In particular, his research focuses on the design of markets for data, on data privacy with a focus on "differential privacy", on fairness in machine learning and decision-making, and on strategic considerations in machine learning.

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.