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Research Interests
Our research theme is artificial intelligence (AI) for modeling biological intelligence (BI). Specifically, we develop optimization and machine learning, especially multimodal learning and generation across texts, images, graphs, and geometries, for structural bioinformatics, structural systems biology, and biomedical and health informatics. Some applications include
- Protein or ligand design
for binding affinity and specificity desired
- Structural
prediction of protein interactions
- Systems
biology, in particular, systems pharmacology
- Synthetic
biology, with applications in energy and therapeutics
- Bioinformatics and big data
I am actively seeking experimental and
computational collaborations. My main motivation is to
unravel molecular mechanisms and to modulate emergent behavior of
biomolecular networks with the development and application of
computational tools (including molecular modeling, network simulation,
optimization, machine learning, graph theory, and systems and control theory). To
that end, I aim at an iterative process that models and experiments can
feed each other.
Check out a video about our research.
Group Members
Current members:
- Dr. Jiwoong Park (Postdoctoral Research Associate, 2022.10-)
- Shaowen Zhu (Ph.D. 2019.9-) Google Scholar
- Qiang Zhang (Ph.D. 2019-; co-advising with Prof. Tie Liu)
- Rujie Yin (Ph.D. 2020.9-)
- Yuxuan Liu (Ph.D. 2023.9-)
Alumni:
Postdoc
- Dr. Su-Ping Deng (Postdoc 2018-19; Employment as of 2022: Data Scientist at the Hanover Insurance Group)
- Dr. Tomasz Oliwa (Postdoc 2013-14; First Employer: Univ. Chicago)
Ph.D.
- Yuning You (defended June 2024)
- Thesis: Generalizable Graph AI for Biomedicine: Data-Driven Self-Supervision and Principled Regularization
- Google Scholar
- Recipient of Quality Graduate Student Award (2023) and Chevron Scholarship (2021) from ECEN at TAMU
- Internships: Genentech (2023), Insitro (2022), Amazon (2021)
- Next: Postdoc at Caltech, Pasadena, California (Also with faculty job offer)
- Yuanfei Sun (defended Oct. 2023)
- Thesis: Protein Multimodal Learning for Variant Effect Prediction and Protein Engineering
- Google Scholar
- Internships: Arbin Instruments, Inc (2022)
- Next: Visiting Assistant Professor at Utah Tech, St. George, UT
- Wuwei Tan (defended June 2023)
- Thesis: Genetic Mutation Effect Prediction: Incorporating 1D Sequence and 3D Structure
- Google Scholar
- Internships: Cincinnati Insurance (2023)
- Next: Scientist at Molecule Mind Technology, Beijing, China
- Yue Cao (defended Oct. 2021)
- Thesis: Optimization, Learning and Generation for Proteins: Docking Structures and Mapping Sequence-Function Relationships
- Google Scholar
- Internships: Facebook (2021), IBM Research (2020)
- Next: Research Scientist at Meta (formerly known as Facebook), Seattle, WA
- Mostafa Karimi (defended Aug. 2020)
- Thesis: Circumventing Drug Resistance: Exact Combinatorial Optimization and Deep Generative Models
- Google Scholar
- Recipient of 2021 TAMU Association of Former Students Distinguished Graduate Student Award for Excellence in Research (Doctoral)
- Internships: Ancestry.com (2019), Anadarko Petroleum (2018)
- Next: Data and Applied Scientist at Microsoft, Redmond, WA. As of 2023: Applied Scientist at Amazon.
M.S.
- Shuying Zhu (Graduate Assistant, 2022.6-2024.1)
- Haotian Xu (M.S., May 2023)
- Next: Ph.D. Program in Data Science at Stony Brook University
- Yuhang Xie (M.S., May 2022)
- Next: Ph.D. Program in Geography at TAMU
- Yuting Gao (M.S., May 2022)
- Next: Software Engineer at Fujitsu, Dallas, TX
- Hao Yu Miao (Graduate Research Assistant 2021.10-2021.12 and Post-MS Researcher 2022.1-2022.3)
- Next: Software Engineer at Meta
- Rahul Sridhar (Graduate Research Assistant 2021.1-2021.6)
- Next: Machine Learning Engineer at ByteDance
- Fangtong Zhou (M.S., May 2020)
- Thesis: Identifying Nuclear Receptor Ligands through Sequence-Based Deep Learning
- Next: Ph.D. Program in CS at NCSU
- Shaowen Zhu (M.S., Aug. 2019)
- Thesis: De Novo Protein Design of Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN)
- Next: Ph.D. Program in EE at TAMU
- Di Wu (M.S., Dec. 2018)
- Thesis: Structured Sparsity Learning for Coevolution-Based Protein Contact Prediction
- Next: Research Associate at TAMU (2019-2020) then Ph.D. Program in CIS at Penn
- Haoran Chen (M.S., Aug. 2018)
- Thesis: Improving molecular-level protein docking and interpreting system-level cancer mechanism through machine learning
- Next: Ph.D. Program in Comp Biol at CMU
- Yi Yan (M.S., May 2018)
- Thesis: Efficiency prediction and mechanism discovery for the CRISPR-Cas9 system
M.E.
- Tianliang Li (Member 2016-18; M.Eng. 2018)
- Yuanfei Sun (Grad Summer Intern, 2016; M. Eng. 2017; Next: Ph.D. in Electrical Engineering at TAMU)
- Kuei-Fu Chen (Grad Summer Intern, 2016; M.Eng. 2017; First Employer: Bloomberg L.P.)
Undergraduate
- Shreeman Jayaram (Undergrad Honors Research Scholar 2023.8-2024.5)
- Thesis: Molecular Discovery Chatbot
- Andrew Shang (Undergrad Summer Research Grant (USRG), Summer 2021)
- Nithin Goriparthi (Undergrad Summer Research Grant (USRG), Summer 2021)
- Maxwell Huffman (Undergrad Honors Research Scholar 2020.6-2021.5)
- Thesis: Interconnected Financial Prediction using Time-Series and Network Data
- First Prize for Capstone at the 2021 Engineering Project Showcase
- Next: Microsoft
- Arghamitra Talukder (Undergrad Honors Research Scholar 2020.7-2021.5)
- Thesis: Multimodal Data Fusion and Machine Learning for Deciphering Protein-Protein Interactions
- Remote Collaborator 2021-2022. NSF CSGrad4US Fellowship 2022.
- Next: Texas Instruments. 2023: Ph.D. program in Computer Science at Columbia University
- Muhammad Ahmad (ECEN 491 Research Mentee, Fall 2019)
- S. S. N. Vishnu Siva Sai (Research Exchange Undergrad (REU) from IIT-Hyderabad, Summer 2019)
- Annie Rizvi (Research Intern, Summer 2018)
- Oluwaseyi Moronfoye (Research Intern, Spring 2018; Undergrad Summer Research Grant (USRG), Summer 2018; Next: M.S. in Finance at Mays Business School at TAMU)
- Elsherif Mahmoud (Research Exchange Undergrad (REU) from TAMU-Qatar, Summer 2017; Next: M.S. in Electrical Engineering at Columbia University)
- Clarissa Tovias (Research Intern, Summer 2016)
Funded Projects
Physics-Constrained Modeling of Molecular Texts, Graphs, and Images for Deciphering Protein-Protein Interactions (NSF CCF-1943008 (2020-25))
Unraveling Molecular and System-level Mechanisms of Human Disease-Associated Protein Mutations (NIH R35GM124952 (2017-22))
Dimension Reduction and Optimization Methods for Flexible Refinement of Protein Docking (NSF CCF-1546278 (2013-17))
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