Munmun De Choudhury is an Associate Professor at the School of Interactive Computing in Georgia Institute of Technology. Dr. De Choudhury is renowned for her groundbreaking contributions to the fields of computational social science, human-computer interaction, and digital mental health. Through fostering interdisciplinary collaborations across academia, industry, and public health sectors, Dr. De Choudhury and her collaborators have contributed significantly to advancing the development of computational techniques for early detection and intervention in mental health, as well as in unpacking how social media use benefits or harms mental well-being. De Choudhury's contributions have been recognized worldwide, with significant scholarly impact evidenced by numerous awards like induction into the SIGCHI Academy and the 2023 SIGCHI Societal Impact Award. Beyond her academic achievements, Dr. De Choudhury is a proactive community leader, a persistent contributor to policy-framing and advocacy initiatives, and is frequently sought for expert advice to governments, and national and international media.

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.

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.

Yajun Mei is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.

Dr. Mei's research interests include change-point problems and sequential analysis in Mathematical Statistics; sensor networks and information theory in Engineering; as well as longitudinal data analysis, random effects models, and clinical trials in Biostatistics. 

He received a B.S. in Mathematics from Peking University in P.R. China, and a Ph.D. in Mathematics with a minor in Electrical Engineering from the California Institute of Technology. He has also worked as a postdoc in Biostatistics for two years in the Fred Hutchinson Cancer Research Center in Seattle, WA.

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.

Peter Kasson is an international leader in the study of biological membrane structure, dynamics, and fusion, with particular application to how viruses gain entry to cells. His group performs both high-level experimental and computational work – a powerful combination that is critical to advancing our understanding of this important problem. His publications describe inventive approaches to the measurement of viral fusion rates and characterization of fusion mechanisms, and to the modeling of large-scale biomolecular and lipid assemblies. He has applied these insights to the prediction of pandemic outbreaks and drug resistance, with particular attention to Zika, SARS-CoV-2, and influenza pathogens in recent years. See https://kassonlab.org/ for more information.

Lynn Kamerlin received her Master of Natural Sciences from the University of Birmingham (UK), in 2002, where she remained to complete a PhD in Theoretical Organic Chemistry under the supervision of Dr. John Wilkie (awarded 2005). Subsequently, she was a postdoctoral researcher in the labs of Stefan Boresch at the University of Vienna (2005-2007), Arieh Warshel at the University of Southern California (2007-2009, Research Associate at the University of Southern California in 2010) and Researcher with Fahmi Himo (2010). She is currently a Professor and Georgia Research Alliance – Vasser Wooley Chair of Molecular Design at Georgia Tech, a Professor of Structural Biology at Uppsala University, a Fellow of the Royal Society of Chemistry. She has also been a Wallenberg Scholar, the recipient of an ERC Starting Independent Researcher Grant (2012-2017) and the Chair of the Young Academy of Europe (YAE) in 2014-2015. Her non-scientific interests include languages (fluent in 5), amateur photography and playing the piano.

King Jordan is Professor in the School of Biological Sciences and Director of the Bioinformatics Graduate Program at the Georgia Institute of Technology. He has a computational laboratory and his group works on a wide variety of research and development projects related to: (1) human clinical & population genomics, (2) computational genomics for public health, and (3) computational approaches to functional genomics. He is particularly interested in the relationship between human genetic ancestry and health. His lab is also actively engaged in capacity building efforts in genomics and bioinformatics in Latin America. 

Ahmet Coskun is a systems biotechnologist and bioengineer, working at the nexus of multiplex imaging and quantitative cell biology.

Single Cell Biotechnology Lab is strategically positioned for imaging one cell at a time for spatial context. We are multi-disciplinary researchers interested in photons, ions, and electrons and their interactions with cells and tissues.  Using large-scale experiments and computational analysis, we address fundamental challenges in cancers, immunology, and pediatric diseases. Variability of single cell profiles can be used to understand differences in therapeutic response, as well as satisfy our curiosity on understanding how cells are spatially organized in nature.

Our lab aims to deliver biotechnologies for spatial multi-omics profiling vision at the single cell level.

1) Spatial genomics: Our lab was part of an early efforts to demonstrate spatially resolved RNA profiling in single cells using a sequential FISH method. We will continue leveraging seqFISH and correlation FISH (another computational RNA imaging method) for exploring spatial dynamics of cellular societies.

2) Spatial proteomics: Our lab develops expertise on antibody-oligonucleotide based barcoding for multiplex protein imaging using CODEX technology. We combine CODEX with super-resolution and 3D imaging to visualize and quantify subcellular epigenetic states of immune and cancer cells.

3) Spatial metabolomics: Our lab works on computational and isotope barcoding approaches for small molecule profiling using MIBI (Multiplexed ion beam imaging). 3D and subcellular metabolic state of individual cells are used to model functional modes of cellular decision making in health and disease.

We also develop machine learning and deep learning algorithms to make sense of imaging based single cell big data.

In a nutshell, we create image-based ‘omic technologies to reveal spatial nature of biological systems. We benefit from enabler tools:  Super-resolution bioimaging, barcoded biochemical reagents, advanced algorithms and automated microfludics. Topical interests include Spatial Biology, Liquid Biopsy, and Global Oncology.

Ahmet Coskun trained at Stanford (Postdoc/Instructor with Garry Nolan), Caltech (Postdoc with Long Cai) and UCLA (PhD with Aydogan Ozcan). His lab is currently funded by NIH K25, BWF CASI, Georgia Tech & Emory.