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.

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.

Dr. Jian Luo completed his undergraduate and M.S. studies at Tsinghua University, Beijing, where he received a B.Sc.(Eng.) and a M.S. degree in Environmental Engineering in 1998 and 2000, respectively. He completed his Ph.D. in 2006 in Department of Civil and Environmental Engineering at Stanford University, California. The research Dr. Luo is conducting involves field, theoretical, and computational investigations of flow and reactive transport in subsurface; development and application of geostatistical methods for the spatial and temporal analysis of hydrogeologic and biochemistry data; development of computational algorithms and programs to simulate subsurface flow and reactive transport, and to assess the associated uncertainty; inverse modeling to estimate flow and transport parameters under uncertainty; and use of such computational methods and models to assess subsurface contamination, and to aid the optimal design of groundwater remediation operations.

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.

Anton Leykin received the Ph.D. degree from the University of Minnesota, Twin Cities. He works in nonlinear algebra with a view towards algorithms and applications. A large part of his recent work concerns homotopy continuation methods, which includes both theory and implementation in Macaulay2 computer algebra system. He is a member of the ACM, AMS, and SIAM.

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.