Computational Science and Engineering

Faculty to use AI for Protein Design and Discovery with Support of $1.8 Million NIH Grant

admin — Mon, 13 May 2024 14:38:41 +0000

Faculty to use AI for Protein Design and Discovery with Support of $1.8 Million NIH Grant admin Mon, 05/13/2024 - 10:38

The National Institute of Health (NIH) has awarded Yunan Luo a grant for more than $1.8 million to use artificial intelligence (AI) to advance protein research.

New AI models produced through the grant will lead to new methods for the design and discovery of functional proteins. This could yield novel drugs and vaccines, personalized treatments against diseases, and other advances in biomedicine.

“This project provides a new paradigm to analyze proteins’ sequence-structure-function relationships using machine learning approaches,” said Luo, an assistant professor in Georgia Tech’s School of Computational Science and Engineering (CSE).

“We will develop new, ready-to-use computational models for domain scientists, like biologists and chemists. They can use our machine learning tools to guide scientific discovery in their research.”

Luo’s proposal improves on datasets spearheaded by AlphaFold and other recent breakthroughs. His AI algorithms would integrate these datasets and craft new models for practical application.

One of Luo’s goals is to develop machine learning methods that learn statistical representations from the data. This reveals relationships between proteins’ sequence, structure, and function. Scientists then could characterize how sequence and structure determine the function of a protein.

Next, Luo wants to make accurate and interpretable predictions about protein functions. His plan is to create biology-informed deep learning frameworks. These frameworks could make predictions about a protein’s function from knowledge of its sequence and structure. It can also account for variables like mutations.

In the end, Luo would have the data and tools to assist in the discovery of functional proteins. He will use these to build a computational platform of AI models, algorithms, and frameworks that ‘invent’ proteins. The platform figures the sequence and structure necessary to achieve a designed proteins desired functions and characteristics.

“My students play a very important part in this research because they are the driving force behind various aspects of this project at the intersection of computational science and protein biology,” Luo said.

“I think this project provides a unique opportunity to train our students in CSE to learn the real-world challenges facing scientific and engineering problems, and how to integrate computational methods to solve those problems.”

The $1.8 million grant is funded through the Maximizing Investigators’ Research Award (MIRA). The National Institute of General Medical Sciences (NIGMS) manages the MIRA program. NIGMS is one of 27 institutes and centers under NIH.

MIRA is oriented toward launching the research endeavors of young career faculty. The grant provides researchers with more stability and flexibility through five years of funding. This enhances scientific productivity and improves the chances for important breakthroughs.

Luo becomes the second School of CSE faculty to receive the MIRA grant. NIH awarded the grant to Xiuwei Zhang in 2021. Zhang is the J.Z. Liang Early-Career Assistant Professor in the School of CSE.

“After NIH, of course, I first thanked my students because they laid the groundwork for what we seek to achieve in our grant proposal,” said Luo.

“I would like to thank my colleague, Xiuwei Zhang, for her mentorship in preparing the proposal. I also thank our school chair, Haesun Park, for her help and support while starting my career.”

Summary sentence

The National Institute of Health (NIH) has awarded Yunan Luo a grant for more than $1.8 million to use artificial intelligence (AI) to advance protein research.

Summary

The National Institute of Health (NIH) has awarded Yunan Luo a grant for more than $1.8 million to use artificial intelligence (AI) to advance protein research.

Dateline

Wed, 11/29/2023 - 12:00

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Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

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Source updated

Mon, 05/13/2024 - 10:37

Machine Learning Could be Key to Early Leakage Detection in Underground Carbon Storage Sites

admin — Mon, 13 May 2024 14:38:41 +0000

Machine Learning Could be Key to Early Leakage Detection in Underground Carbon Storage Sites admin Mon, 05/13/2024 - 10:38

A new machine learning method could help engineers detect leaks in underground reservoirs earlier, mitigating risks associated with geological carbon storage (GCS). Further study could advance machine learning capabilities while improving safety and efficiency of GCS.

The feasibility study by Georgia Tech researchers explores using conditional normalizing flows (CNFs) to convert seismic data points into usable information and observable images. This potential ability could make monitoring underground storage sites more practical and studying the behavior of carbon dioxide plumes easier.

The 2023 Conference on Neural Information Processing Systems (NeurIPS 2023) accepted the group’s paper for presentation. They presented their study on Dec. 16 at the conference’s workshop on Tackling Climate Change with Machine Learning.

“One area where our group excels is that we care about realism in our simulations,” said Professor Felix Herrmann. “We worked on a real-sized setting with the complexities one would experience when working in real-life scenarios to understand the dynamics of carbon dioxide plumes.”

CNFs are generative models that use data to produce images. They can also fill in the blanks by making predictions to complete an image despite missing or noisy data. This functionality is ideal for this application because data streaming from GCS reservoirs are often noisy, meaning it’s incomplete, outdated, or unstructured data.

The group found in 36 test samples that CNFs could infer scenarios with and without leakage using seismic data. In simulations with leakage, the models generated images that were 96% similar to ground truths. CNFs further supported this by producing images 97% comparable to ground truths in cases with no leakage.

This CNF-based method also improves current techniques that struggle to provide accurate information on the spatial extent of leakage. Conditioning CNFs to samples that change over time allows it to describe and predict the behavior of carbon dioxide plumes.

This study is part of the group’s broader effort to produce digital twins for seismic monitoring of underground storage. A digital twin is a virtual model of a physical object. Digital twins are commonplace in manufacturing, healthcare, environmental monitoring, and other industries.

“There are very few digital twins in earth sciences, especially based on machine learning,” Herrmann explained. “This paper is just a prelude to building an uncertainty aware digital twin for geological carbon storage.”

Herrmann holds joint appointments in the Schools of Earth and Atmospheric Sciences (EAS), Electrical and Computer Engineering, and Computational Science and Engineering (CSE).

School of EAS Ph.D. student Abhinov Prakash Gahlot is the paper’s first author. Ting-Ying (Rosen) Yu (B.S. ECE 2023) started the research as an undergraduate group member. School of CSE Ph.D. students Huseyin Tuna Erdinc, Rafael Orozco, and Ziyi (Francis) Yin co-authored with Gahlot and Herrmann.

NeurIPS 2023 took place Dec. 10-16 in New Orleans. Occurring annually, it is one of the largest conferences in the world dedicated to machine learning.

Over 130 Georgia Tech researchers presented more than 60 papers and posters at NeurIPS 2023. One-third of CSE’s faculty represented the School at the conference. Along with Herrmann, these faculty included Ümit Çatalyürek, Polo Chau, Bo Dai, Srijan Kumar, Yunan Luo, Anqi Wu, and Chao Zhang.

“In the field of geophysics, inverse problems and statistical solutions of these problems are known, but no one has been able to characterize these statistics in a realistic way,” Herrmann said.

“That’s where these machine learning techniques come into play, and we can do things now that you could never do before.”

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Wed, 12/20/2023 - 12:00

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Bryant Wine, Communications Officer
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Source updated

Mon, 05/13/2024 - 10:37

Machine Learning Key to Proposed App that Could Help Flood-prone Communities

admin — Mon, 13 May 2024 14:36:41 +0000

Machine Learning Key to Proposed App that Could Help Flood-prone Communities admin Mon, 05/13/2024 - 10:36

A scientific machine learning (ML) expert at Georgia Tech is lending a hand in developing an app to identify and help Florida communities most at risk of flooding.

School of Computational Science and Engineering (CSE) Assistant Professor Peng Chen is co-principal investigator of a $1.5 million National Science Foundation grant to develop the CRIS-HAZARD system.

CRIS-HAZARD‘s strength derives from integrating geographic information and data mined from community input, like traffic camera videos and social media posts.

This ability helps policymakers identify areas most vulnerable to flooding and address community needs. The app also predicts and assesses flooding in real time to connect victims with first responders and emergency managers.

“Successfully deploying CRIS-HAZARD will harness community knowledge through direct and indirect engagement efforts to inform decision-making,” Chen said. “It will connect individuals to policymakers and serve as a roadmap at helping the most vulnerable communities.”

Chen’s role in CRIS-HAZARD will be to develop new ML models for the app’s prediction capability. These assimilation models integrate the mined data with predictions from current hydrodynamic models.

Along with making an immediate impact in flood-prone coastal communities, Chen said these models could have broader applications in the future. These include models for improved hurricane prediction and management of water resources.

The models Chen will build for CRIS-HAZARD derive from past applications aimed at helping communities.

Chen has crafted similar models for monitoring and mitigating disease spread, including Covid-19. He has also worked on materials science projects to accelerate the design of metamaterials and self-assembly materials.

“Scientific machine learning is very broad concept and can be applied to many different fields,” Chen said. “Our group looks at how to accelerate optimization, account for risk, and quantify uncertainty in these applications.”

Uncertainty in CRIS-HAZARD is what brings Chen to the project, headed by University of South Florida researchers. While the app’s novelty lies in its use of heterogenous data, inferring predictions can be challenging since the data comes from different sources in varying formats.

To overcome this, Chen intends to build new data assimilation models from scratch powered by deep neural networks (DNNs).

Along with their ability to find connections between heterogeneous data, DNNs are scalable and inexpensive. This beats the alternative of using supercomputers to make the same calculations.

DNNs are also fast and can significantly reduce computational time. According to Chen, the efficiency of DNNs can achieve acceleration hundreds of thousands of times greater than classical models.

Low cost and time make it possible to run DNN-based simulations multiple times. This improves reliability in prediction results in real-time once the DNNs are properly trained.

“The data may not be consistent or compatible since there are different models we’re trying to integrate, making prediction uncertain,” Chen said. “We can run these ML models many times to quantify the uncertainty and give a probability distribution or a range of predictions.”

CRIS-HAZARD also exemplifies the power of collaboration across disciplines and universities. In this case, machine learning techniques reach across state boundaries to help people that are vulnerable to flooding or other natural disasters.

USF Professor Barnali Dixon leads the project with Associate Professor Yi Qiang— both geocomputation researchers in the School of Geosciences, incorporating data science and artificial intelligence.

Subhro Guhathakurta collaborates with Chen from Georgia Tech. Along with being a professor in the School of City & Regional Planning, Guhathkurta is director of Tech’s Master of Science in Urban Analytics program and the Center for Spatial Planning and Analytics and Visualization.

Summary sentence

School of Computational Science and Engineering (CSE) Assistant Professor Peng Chen is co-principal investigator of a $1.5 million National Science Foundation grant to develop the CRIS-HAZARD system.

Summary

A scientific machine learning (ML) expert at Georgia Tech is lending a hand in developing an app to identify and help Florida communities most at risk of flooding.

CRIS-HAZARD‘s strength derives from integrating geographic information and data mined from community input, like traffic camera videos and social media posts.

Dateline

Thu, 09/28/2023 - 12:00

Contact

Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

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Source updated

Mon, 05/13/2024 - 10:35

Researchers to Study Connection Between Online Misinformation and Real-World Violence

admin — Tue, 07 Feb 2023 18:12:32 +0000

Researchers to Study Connection Between Online Misinformation and Real-World Violence admin Tue, 02/07/2023 - 13:12

It is well known that predators lurk on the internet seeking to harm others through misinformation, bullying, and hate speech. What is not understood, however, is how online abuse and harassment like this spread via misinformation can lead to real, physical violence in communities.

To understand and counter this phenomenon, faculty with Georgia Tech’s College of Computing is leading a two-year study on the impact of online violence-provoking misinformation and hate speech toward minority populations.

Realizing the implication on public health and safety, the Center of Disease Control and Prevention (CDC) is funding the study through a $678,000 grant. Georgia Tech’s Srijan Kumar is the principal investigator of the CDC grant while Munmun De Choudhury is co-PI.

“The conversations we have and the information we consume on the web really shape our behavior not just online, but in the real-world,” said Kumar, an assistant professor with the School of Computational Science and Engineering. “We need systematic evaluations and an all-hands-on-deck approach to study what sort of societal impact harmful online content has on health, equity, integrity, and safety.”

Inspiration for this study comes from the researchers’ observations of prevalent hate speech and calls for racially motivated violence on the internet and social media. This issue became more prominent during the Covid-19 pandemic.

In their grant proposal, the researchers say more than 11,000 incidents of physical violence and online aggression against Asians were reported during the pandemic. They also state that misinformation about Covid-19 led to destruction of property and over 800 deaths.

To understand the connection between virtual misinformation and physical violence, the study has four stated goals:

Identify health misinformation that promotes community violence
Map and measure prevalence of violence-provoking health misinformation across social media platforms
Establish the causal impact of such misinformation on consumers’ reactions and intention to engage in harm
Design mitigation and intervention strategies to reduce the prevalence of such misinformation

According to the group, social media is often “ground-zero” for health misinformation where it spreads at exceptional speed and scale. That is why the team intends to study hate speech and misinformation on social media platforms Twitter and Reddit.

“The very use of these platforms is impacting us in different ways. Sometimes these impacts are good, sometimes they are bad,” said De Choudhury, an associate professor with the School of Interactive Computing. “As we think about our wellbeing and the role of these online platforms, we cannot ignore the very fact that misinformation on those platforms is affecting our wellbeing in negative ways.”

The group selected Twitter and Reddit specifically due to their prominence in networking and wide range of demographically diverse users, a fact supported by Pew Internet Research statistics.

Diversity of the two social media platforms make them the ideal ecosystem for what the group intends to observe: anti-Asian and Anti-Black violence-provoking misinformation.

Given the correlation of Covid-19 misinformation spread and increase of discrimination and violence toward Asian and Black communities during the pandemic, the researchers believe their findings will make meaningful impact for people affected by this public health issue.

For example, the group points out that misinformation masquerading as medical racism further targeted Black communities. As a result, this both degraded trust in institutions and diminished vaccine efficacy during the Covid-19 pandemic.

Purdue University Assistant Professor Laura Schwab-Reese, an expert in community and behavioral health, joins Kumar and De Choudhury the research group. Together, they will collaborate with the non-profit Anti-Defamation League during the study. Backed with CDC funding, the team will develop tools to study and find solutions to violence-provoking health misinformation.

For example, the researchers will start by developing algorithms to detect health misinformation and violence-provoking hate speech targeting minorities. These algorithms will rely on the latest machine learning methodologies and social media data sets.

From there, the group can build an interactive dashboard that maps the spread of violence-provoking misinformation online and offers analytic capabilities on the visualized data for end users. This will provide quantitative insight about the causal relationship between misinformation exposure and violent attitudes toward targeted communities.

Finally, the team will provide an intervention design plan to mitigate the impact of the misinformation. This will include presenting exploratory evaluation of interventions, and even creating new social media-based intervention tools that can interrupt misinformation.

By leveraging computing methods and interdisciplinary collaboration, the group is poised to make online and physical communities safer places for all.

“The reason we are partnering with the Anti-Defamation League and public health researchers is because community engagement is paramount in this work,” De Choudhury said. “One of the unique things about this study is that the computational techniques and interventions we will develop will be informed by the communities who are targeted by these incidents.”

Summary sentence

Srijan Kumar and Munmun De Choudhury receive CDC grant

Dateline

Wed, 10/12/2022 - 12:00

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Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

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Atlanta, GA

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Source updated

Wed, 10/12/2022 - 09:47

Computational Science and Engineering

Faculty to use AI for Protein Design and Discovery with Support of $1.8 Million NIH Grant

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Machine Learning Could be Key to Early Leakage Detection in Underground Carbon Storage Sites

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Machine Learning Key to Proposed App that Could Help Flood-prone Communities

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Researchers to Study Connection Between Online Misinformation and Real-World Violence

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