TAMIDS Program Outcomes

Research Workshop Grants Program

• Online Workshop: Scientific Machine Learning, October 2020
• Online Workshop: Artificial Intelligence Applications to Agriculture, July 2020
• Workshop on Astronomical Data Science, February 2020
• PhD Research Workshop on Computational / Artificial Intelligence, September 2019
• Workshop on FPGAs in the Era of AI and Big Data, September 2019
• Workshop on Data Science/AI/ML in Education, April 2019
• Workshop on Operational Data Science, February 2019

Instructional Workshop and Tutorial Program

• Bring Your Own Data Workshops, multiple events, ongoing
• NVIDIA Deep Learning Institute for Computer Vision, multiple events, ongoing
• Data Science Webinars, multiple events, ongoing
• Tutorial on Iterating Between the Data World and the Real World to Find Answers in Big Data, October 2020
• Tutorial on Functional and Shape Data Analysis, September 2020
• Tutorial on Conformal Prediction, September 2020
• Tutorial Workshop on Gaussian Processes and Bayesian Optimization, September 2020
• Tutorial Workshop on Introduction to Deep Learning, June 2020
• Tutorial Workshop on Business Analytics – Strategic Issues and Solutions, May 2020
• Tutorial Workshop on Reinforcement Learning – Algorithms and Applications, April 2020
• Tutorial Workshop on Interpretable Machine Learning – Concepts and Techniques, February 2020
• Data Science Bootcamp, August 2019

Education Support Programs

Data Science Course Development Program Awards, 2021

• Ulisses Braga-Neto (Lead),Department of Electrical and Computer Engineering; Scientific Machine Learning.
• Fernando A. Luco Echeverría (Lead), Department of Economics; Data Science for Future Decision-Makers.
• Simon Foucart (Lead), Department of Mathematics; Topics in Mathematical Data Science.
• Ann McNamara (Lead), Department of Visualization; Derya Akleman, Department of Statistics; John Keyser, Department of Computer Science & Engineering; Data Visualization and Analytics.
• Vincent VanBuren (Lead), Department of Medical Education; Machine Learning with Python for Medical Applications.
• Xinyue Ye (Lead), Department of Landscape Architecture & Urban Planning; Urban Data Science.
• Shuang Zhang (Lead) & Darren W. Henrichs, Department of Oceanography; Applied Data Science in Geosciences.

Research Support Programs

Data Resource Development Program Awards, 2020

• Raymundo Arroyave (PI), Department of Materials Science & Engineering, Development of a Software Platform For Accelerated Microstructure Design: From Data Generation to Curation and Analysis.
• Amir Behzadan (PI), Department of Construction Science, Theodora Chaspari (Co-PI),Department of Computer Science and Engineering, Preeti Zanwar (Collaborator): Department of Epidemiology & Biostatistics, CoFABS-Q: A dataset of face, body and speech cues in web-mined COVID-19 conversational vlogs.
• James Cai (PI), Department of Veterinary Integrative Biosciences, Enabling Single-Cell Data Resource to TAMU Investigators in Interdisciplinary Research.
• Xia Ben Hu(PI), Department of Computer Science and Engineering, AutoAD: Exploiting Automated Machine Learning for Anomaly Detection.
• Zhe Zhang (PI), Department of Geography, A Spatial Decision Support System for Cardiovascular Disease Risk Assessment in Response to the COVID-19 Crisis.
• Lei Zou (PI), Department of Geography, Jian Tao (Co-PI), Texas A&M Engineering Experiment Station, Ali Mostafavi (Co-PI), Departmental of Civil and Environmental Engineering, Tracking Social and Governmental Responses to Covid-19 Using Geospatial Big Data.

Postdoctoral Project Program Awards, 2019

• Youngjib Ham (PI), Department of Construction Science, Theodora Chaspari (Co-PI), Computer Science & Engineering, Boosting Workplace Productivity; Data-driven Framework for Enhancing Occupant Environmental Comfort.
• Matthias Katzfuss (PI), Department of Statistics, Sparse Approximation of Dense Kernel Matrices for Scalable Data Science.
• Hye-Chung Kum (PI), Health Policy & Management, School of Public Health, Mark Fossett (Co-PI), Department of Sociology, Alva Ferdinand (Co-PI), Health Policy & Management, School of Public Health, Population Informatics: Data Science using Big Data about People in Social, Behavior, Economic,and Health Sciences.
• Yang Ni (PI), Department of Statistics, Irina Gaynanova (Co-PI), Department of Statistics, Robert Chapkin (Co-PI), Department of Nutrition & Food Science, Raymond Carroll (Co-PI), Department of Statistics,  Studying Microbial Interactions and Host Heterogeneity via Data Integration.
• Shahin Shahrampour (PI), Industrial & Systems Engineering, Shallow Networks in Over-Parametrized Regimes: Rethinking Bias-Variance Tradeoff.
• Xiaoning Qian (PI), Electrical and Computer Engineering, Raymundo Arroyave (Co-PI), Materials Science & Engineering, Physics-Constrained Machine Learning for Intelligence Augmented Materials Discovery.

Products from work supported by TAMIDS Postdoc Project and Data Development Programs

Publications

• Attari, Vahid, Pejman Honarmandi, Thien Duong, Daniel J. Sauceda, Douglas Allaire, and Raymundo Arroyave. Uncertainty propagation in a multiscale CALPHAD-reinforced elasto-chemical phase-field model. Acta Materialia 183 (2020): 452-470.
• Feng K., Zanwar P., Behzadan A.H., Chaspari T (2020), Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs, Proceedings of the Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends (ATQAM/MAST’20), 28th ACM International Conference on Multimedia, Seattle, WA: https://dl.acm.org/doi/10.1145/3423268.3423584
• Osorio, D., Cai, J. (2020), Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA sequencing data quality control. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa751
• Osorio, D., Zhong, Y., Li, G., Huang, J., Cai, J. (2020) scTenifoldNet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data. Patterns. https://doi.org/10.1016/j.patter.2020.100139
• Kevin Feng, Preeti Zanwar, Amir H. Behzadan, Theodora Chaspari, Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs, ACM Multimedia workshop on Media Analytics for Societal Trends (MAST), 2020.

Software

• Osorio, D., Cai, J. (2020), scTenifoldNet: Construct and Compare scGRN from Single-Cell Transcriptomic Data https://cran.r-project.org/package=scTenifoldNet
• Osorio, D., Cai, J. (2020), scTenifoldKnk: In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks https://cran.r-project.org/package=scTenifoldKnk

Talks

• Hee Cheol Chung (2020), Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks, International conference CMStatistics, Dec 20, 2020 
• J. Cai (2020), 3rd Workshop on Computational Advances for Single-Cell Omics Data Analysis (CASCODA 2020), 12/10/2020
• J. Cai (2020), 7th NCHU GEAR UP forum, 12/3/2020
• J. Cai (2020), TAMU-NCHU research forum, 11/29/2020
• J. Cai (2020), 1st Annual Gulf Coast Consortia (GCC) Single Cell Omics Cluster Symposium,10/8/2020
• J. Cai (2020), EUSTM-2020 on COVID-19 – 7th Annual Congress of the European Society for Translational Medicine on Covid-19, 9/25/2020

Conference presentations supported by TAMIDS Graduate Student Travel Program

• Zhengyang Wang (Computer Science and Engineering, Advisor: Shuiwang Ji), SIAM International Conference on Data Mining (SDM 2019), “Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions“
• Megnan Du (Computer Science and Engineering, Advisor: Ben Hu), IEEE International Conference on Data Mining (ICDM 2019), “Learning Credible Deep Neural Networks with Rationale Regularization“
• Hongyong Gao (Computer Science and Engineering, Advisor: Shuiwang Ji), International Confernece on Knowledge Discovery and Data Mining (SIGKDD 2019), “Graph Representation Learning via Hard and Channel-Wise Attention Networks“
• Imtiaz Ahmed (Industrial and Systems Engineering, Advisor: Yu Ding), IISE Annual Conference & Expo 2019, “Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point Anomaly Detection”.
• Jeff Martin (Wildlife & Fisheries, Advisor: Perry Barboza), American Society of Mammologists (ASM) 2019, “Drought and temperature influence spatial and temporal variation in growth of North American Bison”
• Rachel Short (Ecosystem Science & Management, Advisor: Michelle Lawing), North American Paleontologist Convention (NAPC) 2019, “Community limb morphology of Artiodactyla as an environmental predictor”
• Peter Ferguson (Physics and Astronomy, Advisor: Louis Strigari), RRL/CEP 2019- Frontiers of Classical Pulsators: Theory & Observations Conference 2019, “Three-dimensional structure of the Sagittarius dwarf spheroidal core from RR Lyrae”
• Daniel Osario (Electrical & Computer Engineering, Advisor: James Cai), Algorithms and Models for Single Cell Genomics Workshop 2019, “Single-Cell Expression Variability Implies Cell Function“
• Jingjia Li (Ecosystem Science and Management, Advisor: Claudia Casola), The Society for Molecular Biology & Evolution Conference 2019, “Identifying promoter DNA regulatory motifs associated with drought related genes”
• Megha Yadav (Computer Science and Engineering, Advisor: Theodora Chaspari), IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, 2019, “Speak Up! Studying the Interplay of Physiological Indices with Individual and Contextual Factors for Quantifying Public Speaking Anxiety”
• Elham Nikbakht (Educational Psychology, Advisor: Fuhui Tong), TESOL International Conference, 2019, “Investigating the interplay of translanguaging on bilingual learners’ reading-to-write tasks
• Anne Salvador (Molecular and Cellular Medicine, Advisor: David Threadgill), Training in Dr. Gudrun Brockman’s Lab in Berlin Germany