Aqua Asberry


Aqua Asberry

Histology Lab Manager

As a student, Aqua Asberry, HT(ASCP)CM, studied chemistry and was determined to become a forensic scientist. An intuitive mentor steered her towards histology, and Ms. Asberry is currently Research Histology Manager at Parker H. Petit Institute for Bioengineering and Bioscience at Georgia Tech.

aqua16@gatech.edu

404-385-2611

Office Location:
Petit Biotechnology Building, Office 1124

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University, College, and School/Department

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Doby Rahnev

Rahnev

Doby Rahnev

Associate Professor

Dr. Rahnev received his Ph.D. in Psychology from Columbia University in 2012. After completing a 3-year post-doctoral fellowship at UC Berkeley, he joined Georgia Tech in 2015 where he is currently Blanchard Early Career professor. His research focuses on perceptual decision making – the process of internally representing the available sensory information and making decisions on it. Dr. Rahnev uses a wide variety of methods such as functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), psychophysics, computational modeling, and deep neural networks (DNNs). Dr. Rahnev’s work appears in high-impact journals such as Behavioral and Brain Sciences, PNAS, Nature Communications, and Nature Human Behavior. He has received over $3.5M in funding, including PI grants from NIH, NSF, and the Office of Naval Research.

rahnev@psych.gatech.edu

Office Location:
J.S. Coon 130

https://rahnevlab.gatech.edu/

University, College, and School/Department
Additional Research:

Big Data

Human Augmentation 


IRI Connections:

Emily Sanders

Emily Sanders

Emily Sanders

Assistant Professor

Dr. Emily D. Sanders is an Assistant Professor in the Woodruff School of Mechanical Engineering at Georgia Tech. She obtained her Ph.D. at Georgia Tech in 2021, where she developed new topology optimization methods for design of tension-only cable nets, elastostatic cloaking devices, and multiscale structures and components. Dr. Sanders hold a bachelor’s degree from Bucknell University and a master’s degree from Stanford University.

emily.sanders@me.gatech.edu

Research Focus Areas:
  • Additive manufacturing
  • Advanced Manufacturing
  • Algorithms & Optimizations
  • Architecture & Design

IRI Connections:

Jeff Skolnick

Jeff Skolnick

Jeff Skolnick

Regents’ Professor, School of Biological Sciences
Director, Center for the Study of Systems Biology
Mary and Maisie Gibson Chair & GRA Eminent Scholar in Computational Systems Biology

skolnick@gatech.edu

Website

University, College, and School/Department
Research Focus Areas:
  • Bioinformatics
Additional Research:
  • Computational Biology
  • Health & Life Sciences

IRI Connections:

Ahmet Coskun

Ahmet Coskun

Ahmet Coskun

Assistant Professor of Biomedical Engineering

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.

acoskun7@gatech.edu

404-894-3866

Office Location:
Petit Biotechnology Building, Office 1311

Website

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    University, College, and School/Department
    Research Focus Areas:
    • Bioinformatics
    • Cancer Biology
    • Cell Manufacturing
    • Chemical Biology
    • Computational Genomics
    • Public Health
    • Regenerative Medicine
    • Systems Biology
    Additional Research:
    The Single Cell Biotechnology Lab aims to study spatial biology in health and disease. Our research lies at the nexus of multiplex bioimaging, microfluidic biodynamics, and big data biocomputation. Using high-dimensional nanoscale imaging datasets, we address fundamental challenges in immuno-engineering, cancers, and pediatric diseases. Our lab pursues a transformative multi-omics technology to integrate spatially resolved epigenetics and spatial genomics, proteomics, and metabolomics, all in the same platform. We uniquely benefit from super-resolution microscopy, imaging mass spectrometry, combinatorial molecular barcoding, and machine learning to enhance the information capacity of our cellular data. Variability of single cell images can be used to understand differences in therapeutic responses, as well as satisfy our curiosity on understanding how cells are spatially organized in nature.

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