People

Research focus Scientific workflows, science gateways, distributed computing platforms

Eroma Abeysinghe develops and operates cyberinfrastructure platforms that enable researchers to run complex scientific applications and workflows on advanced computing systems. She supports research communities using SciGaP and CyberShuttle and coordinates ARTISAN research projects. She also serves as Project Manager for the Nexus Center supporting Georgia Tech's Nexus AI supercomputing initiative.

Research focus Computational neuroscience, AI for scientific discovery, interpretable machine learning

Giri Krishnan is a computational neuroscientist studying how intelligence emerges from neural computations in the brain and in artificial systems. His research combines computational neuroscience, machine learning, and AI to understand learning and memory while exploring how these principles can inspire new AI methods.

Research focus Computational chemistry, molecular modeling, science gateways

Sudhakar Pamidighantam is a computational chemist working at the intersection of molecular science and advanced computing. He leads the SEAGrid science gateway, which provides researchers access to HPC resources and scientific applications for chemistry and engineering.

Research focus AI for science, cyberinfrastructure, distributed systems, scientific workflows

Suresh Marru leads the ARTISAN Center at Georgia Tech, where his team builds AI-enabled cyberinfrastructure that accelerates scientific discovery across disciplines. His work focuses on distributed systems, scientific workflows, and open platforms that allow researchers to run large-scale simulations and AI models on advanced computing systems. He also leads the Nexus AI supercomputing initiative, helping develop next-generation infrastructure that makes powerful computing accessible to scientists nationwide.

Research focus Science gateways, distributed computing systems, cyberinfrastructure platforms

Dimuthu Wannipurage builds scalable systems that power science gateways and distributed computing platforms used by research communities around the world. His work focuses on reliable services for job orchestration, data movement, and secure computing environments that enable complex scientific workflows. At ARTISAN, he helps design and operate infrastructure connecting scientific applications, data, and advanced computing resources.

Research focus Cyberinfrastructure security, identity management, science gateway platforms

Lahiru Jayathilake develops secure cyberinfrastructure that enables researchers to access advanced computing systems and scientific applications. His work focuses on authentication, authorization, and identity management for distributed computing platforms and science gateways. As a lead contributor to Apache Custos, he builds security frameworks that allow research communities to securely access computing resources and services. At ARTISAN, he integrates secure identity and access management into platforms supporting large-scale scientific workflows and AI-driven research.

Research focus Scientific computing, neuroinformatics, research productivity tools

Yasith Jayawardana is a computer scientist building tools that bring high-performance computing directly into the researcher's IDE. At ARTISAN, he contributes to CyberShuttle and Apache Airavata and helps scientists scale their computational workloads across advanced computing platforms.

Research focus Plasma physics, magnetic reconnection, turbulence, dynamo theory, stellarator modeling

Amitava Bhattacharjee studies fundamental processes in laboratory, space, and astrophysical plasmas. His research spans magnetic reconnection, current-sheet formation, turbulence, dynamo theory, kinetic plasma theory, and the physics and computation of stellarators.

Research focus Biophysically detailed brain modeling, computational neuroscience, neural circuits

Anton Arkhipov leads the Bio-Realistic Modeling Team at the Allen Institute. His research integrates anatomical and physiological data to build detailed computational models of individual neurons and large-scale brain circuits for scientific discovery.

Research focus Scientific computing, parallel mesh infrastructure, multiphysics coupling, high-performance computing

Cameron Smith develops scalable scientific-computing methods and software for complex simulations on high-performance systems. His work includes parallel unstructured mesh infrastructure, adaptive modeling, multiphysics coupling, and computational workflows for applications such as fusion plasma and sea- and land-ice modeling.

Research focus Computational structural biology, molecular biophysics, membrane proteins, drug discovery

Emad Tajkhorshid develops and applies advanced computational techniques to characterize biological phenomena at molecular resolution. His research focuses on membranes and membrane proteins, including transport mechanisms, energy transduction, protein-lipid interactions, signaling, and structure-guided discovery.

Research focus AI-enabled scientific computing, high-performance computing, multiphysics, digital twins

Seid Koric leads interdisciplinary research combining artificial intelligence, high-performance computing, and advanced numerical methods. His work spans scientific machine learning, multiphysics simulation, materials processing, digital twins, and scalable engineering applications.

Research focus Cloud computing, research cyberinfrastructure, science gateways, distributed systems

Shava Smallen designs cloud computing systems and research cyberinfrastructure that make scalable resources easier for scientific communities to use. As a co-principal investigator and lead architect for the CloudBank user portal, she has helped develop services for cloud access, cost monitoring, governance, and international research collaboration.

Research focus Cardiovascular simulation, computational fluid dynamics, medical devices, virtual surgery

Alison Marsden develops numerical methods and patient-specific computational models for cardiovascular blood flow. Her research applies large-scale fluid mechanics, optimization, and uncertainty quantification to cardiovascular surgery, congenital heart disease, and medical-device design.

Research focus Biomedical simulation, high-performance computing, computational modeling, digital twins

Amanda Randles develops high-performance biomedical simulations and computational tools that reveal how human diseases localize and progress. Her research combines large-scale computing, patient-specific modeling, and fluid dynamics to study conditions ranging from cardiovascular disease to cancer.

Research focus Computational cardiovascular engineering, blood-flow simulation, biomechanics, science gateways

Justin Tran develops computational methods for studying cardiovascular blood flow and disease. His work combines engineering biomechanics, patient-specific simulation, and research cyberinfrastructure, including the SimVascular Gateway for education and large-scale cardiovascular research.

Research focus Microbial genomics, metagenomics, bioinformatics, microbiome engineering

Kostas T. Konstantinidis studies how microorganisms adapt to environmental change and cause disease. His research group develops genomics, metagenomics, metatranscriptomics, and bioinformatics methods to investigate microbial communities, water quality, environmental biotechnology, and the evolution of microbial species.

Research focus Machine learning, protein structure and function, biophysics, systems biology

Mohammed AlQuraishi works at the intersection of machine learning, biophysics, and systems biology. His laboratory develops computational models of protein structure, function, and interactions, then applies them at proteome scale to study biological signaling and disease.

Research focus Industrial engineering, operational excellence, student leadership

Boyang Gong is an industrial engineering student at Georgia Tech. He contributes to the Institute of Industrial and Systems Engineers as an Operational Excellence Board member and supports incoming students as a FASET Leader.

Research focus Continual machine learning, computer vision, streaming learning, efficient AI

Tyler Hayes develops machine-learning methods that enable neural networks to learn continually from evolving streams of data. A former Georgia Tech research scientist, his work spans continual and lifelong learning, computer vision, memory-efficient training, and AI for biological systems.