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• Data Science Course Development Program Awards
• Postdoctoral Project Program Awards
• Data Science Resource Development Program Awards
• Graduate Travel Grant Presentations
• Software
• Talks
• Publications
• Past Seminars and Lectures

Data Science Course Development Program Awards

2022

• Ashrant Aryal (Lead), Department of Construction Science; Applied Data Science for the Built Environment.
• Scott A. Bruce (Lead), Department of Statistics; Lisa M. Perez, Department of High Performance Research Computing (HPRC); Computing Tools for Data Science.
• Heng Cai (Lead), Department of Geography; Lei Zou, Department of Geography; Spatial Data Science: Advances and Applications.
• Irfan Khan (Lead), Department of Marine Engineering Technology, Texas A&M University at Galveston; Data Science for Marine Cybersecurity.
• Hui Liu (Lead), Department of Marine Biology, Texas A&M University at Galveston’ Advanced Methods for Environmental Data.
• Jian Tao (Lead), Department of Visualization; Andre Thomas, Department of Visualization; Introduction to Digital Twins
• Data Science Course Development Submissions

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.

Career Initiation Fellow Recipients

2021

• Ashrant Aryal is an Assistant Professor in the Department of Construction Science, College of Architecture, where he leads the Human-centered Intelligent Built Environments lab and works at the intersection of construction science and data science. As his TAMIDS engagement, Dr. Aryal plans to lead workshop on building sensing and data acquisition using open-source hardware with a focus on smart building applications.
• Heath Blackmon is an Assistant Professor in the Department of Biology, College of Science, where he develops computational methods to accelerate the analysis of data within a quantitative genetic or phylogenetic framework, in order to address fundamental questions arising in evolutionary theory. Dr. Blackmon will lead an interdisciplinary hackathon on phylogenetic comparative statistical methods that explore the relations between genetics, evolution, and ecology.
• Dileep Kalathil is an Assistant Professor in the Department of Electrical & Computer Engineering, College of Engineering, where his research focuses on reinforcement learning and its applications in large-scale real-world engineering settings such as power systems. Dr. Kalathil will pursue outreach to PK-12 students Sciences in Data Science, leveraging ideas and examples from Reinforcement Learning and AI.
• David Lowe is an Assistant Professor and Digital Collections Management Librarian, Office of Scholarly Communications, University Libraries, where his work includes text mining and tools for document classification. He will develop a workshop on text mining and language processing with library collections.
• Manoranjan Majji is an Assistant Professor in the Department Aerospace Engineering, College of Engineering. where his research concerns data-driven modeling for control of autonomous system. Dr. Majji will organize a workshop on current trends and recent advances in data-driven dynamical systems in science and engineering.
• Arash Noshadravan is an Assistant Professor in the Department of Civil & Environmental Engineering, College of Engineering, where his research concerns the intersection of uncertainty quantification and predictive computational science with several applications in civil engineering and computational mechanics. Dr. Noshadravan will develop a research workshop on uncertainty quantification and machine learning.
• Daniel Tabor is an Assistant Professor in the Department of Chemistry, College of Science, where his research focuses on problems combining machine learning, theoretical chemistry, and materials science. He plans to host a series of computational molecular design hackathons that aim to develop quick solutions to materials problems through physics-based machine learning models.
• Na Zou is an Assistant Professor in the Department of Engineering Technology & Industrial Distribution, College of Engineering, where her research concerns interpretability and fairness in machine learning, network modeling and inference, and brain informatics. Dr. Zou will develop a short course on fairness in AI.

Postdoctoral Project Program Awards

2020

• 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.

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.

Data Science 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.

Graduate Travel Grant Presentations

• 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

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

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.

Past Programs

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

Past Seminars and Lectures

TRIPODS Distinguished Lectures (one lecture per semester)

• 1/23/2023; TRIPODS Distinguished Lecture: Multi-modal Artificial Intelligence and the Future of Universities, Dimitris Bertsimas, MIT, Presentation Slides, Video.
• 1/31/2022; TRIPODS Distinguished Lecture: Tensor Moment of Gaussian Mixture Models: Theory and Applications, Tamara Kolda, MathSci.ai & Northwestern University, Presentation Slides, Video.
• 10/18/2021; TRIPODS Distinguished Lecture: ScreeNOT: Exact MSE-Optimal Singular Value Thresholding in Correlated Noise, David Donoho, Stanford University, Video.
• 2/19/2021; TRIPODS Distinguished Lecture: Language, Brain, and Computation; Christos Papadimitriou; Columbia University, Presentation slides, Video.
• 9/9/2020; TRIPODS Distinguished Lecture: Conformal Prediction In 2020; Emmanuel Candès, Stanford University, Presentation Slides, Video.

Materials from Past Events

• 4/15/2024; TAMIDS Seminar; Cultivating Opportunity: Data Science in the USDA Research, Education, and Economics Mission Area; Cynthia Parr, USDA, Video.
• 4/1/2024; TAMIDS Seminar; Interdisciplinary AI Research – Is There a Recipe for Success?; Milan Sonka, University of Iowa, Video.
• 3/25/2024; TAMIDS Seminar; Merging Agents, Social Science, and Big Data into Artificial Societies: Providing Socio-technical Complex Adaptive Systems for Better Policy Evaluation; Andreas Tolk, The MITRE Corporation, Video.
• 3/18/2024; TAMIDS Seminar; Mathematical Modeling Backed Decision-making for Sustainable Livestock Systems: Perspectives from Animal Systems Modeling World; Karun Kaniyamattam, Texas A&M University, Video.
• 3/4/2024; TAMIDS Seminar; Modeling Discourse as Questions and Answers; Jessi Li, The University of Texas at Austin, Video.
• 2/19/2024; TAMIDS Seminar; Towards Commercially Viable Fusion Energy: Innovation, Challenges, and the Growing Contributions from AI/ML; David Hatch, Texas A&M University, Video.
• 1/22/2024; TAMIDS Seminar; Health Insurance Claims Data and Quasi-experimental Methods to Evaluate Outcomes of Health Policies: The Case of State-level Caps on Patients’ Out-of-pocket Spending for Insulin; Theodoros Giannouchos, The University of Alabama at Birmingham, Video.

• 11/13/2023; TAMIDS Seminar; Reimagining Spectral Graph Theory; Stephen Young, Pacific Northwest National Laboratory, Video.
• 11/6/2023; TAMIDS Seminar; Deeper Understanding of Deep Learning: Functional Analysis for Neural Networks; Robert Nowak, University of Wisconsin-Madison, Presentation Slides, Video.
• 10/30/2023; TAMIDS Seminar; Digital Twins: Driving 21st Century Transformation; Michael Grieves, Digital Twin Institute, Video.
• 10/23/2023; TAMIDS Seminar; Using Computational Methods and Technologies to Examine and Enhance Equity in Science Communication; Kaiping Chen, University of Wisconsin-Madison, Video.
• 10/2/2023; TAMIDS Seminar; Geography, Mapping, and the Nation’s Energy Mission: An Oak Ridge Experience; Budhendra Bhaduri, Oak Ridge National Laboritory, Video.
• 9/25/2033; TAMIDS Seminar; From Points of Light to Visions of Other Worlds: Communicating Exoplanet Exploration and Science at NASA Through Visualization and Storytelling, NASA Jet Propulsion Laboratory, Video.
• 9/18/2023; TAMIDS Seminar; Smart Technologies and Approaches to Ensure Food Security and Resilient Environment, Ali Fares, Prairie View A&M, Video.
• 9/11/2023; TAMIDS Seminar; AI Based Multi-Satellite Earth Remote Sensing and Causal Understanding of Earth Processes, Jianwu Wang, UMBC, Presentation Slides, Video.
• 8/28/2023; TAMIDS Seminar; Stacking Designs: Designing Multi-fidelity Computer Experiment with Target Predictive Accuracy, Chih-Li Sung, Michigan State University, Presentation Slides, Video.

• 4/19/2023; TAMIDS Tech Talk; We Are Losing our Scholarly Record – and What We Can Do About It, Martin Klein, Los Alamos National Laboratory, Video.
• 4/17/2023; TAMIDS Seminar; Vehicle Computing: Vision and Challenges, Weisong Shi, University of Delaware, Presentation Slides, Video.
• 4/10/2023; TAMIDS Tutorial; Frontiers of Graph Neural Networks with the Dive-Into-Graphs (DIG) Library, Shuiwang Ji, CSCE, TAMU, Presentation Slides, Video.
• 4/3/2023; TAMIDS Seminar; Cyber Cowboys: Wrangling Big Data on the Frontier of Open Science, Tyson Swetnam, University of Arizona, Presentation Slides, Video.
• 3/27/2023; TAMIDS Seminar; No Equations, No Variables, No Space, No Time:Data and the Modeling of Complex Systems, Yannis Kevrekidis, Johns Hopkins University, Video.
• 3/6/2023; TAMIDS Seminar; Achieving Reliable Causal Inference with Data-Mined Variables, Edward McFowland III, Harvard Business School, Presentation Slides, Video.
• 2/27/2023; TAMIDS Seminar; GIScience in a Hybrid Physical-Virtual World, Shih-Lung Shaw, University of Tennessee, Presentation Slides, Video.
• 2/13/2023; TAMIDS Seminar; The Sound of Science; Paul Miller, Artist, Writer, Musician, Video.
• 2/6/2023; TAMIDS Seminar; Deep Gaussian Process Surrogates for Computer Experiments; Bobby Gramacy, Virginia Tech, Presentation Slides, Video.
• 1/30/2023; TAMIDS Seminar; Bridging Ethical Algorithms, Law, and Practice Hiring and Beyond, Swati Gupta, Georgia Tech, Presentation Slides, Video.

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• 11/28/2022; TAMIDS Seminar; The Trade-off Between the Value of Granular Data and Consumers’ Privacy, Fernando Luco, ECON, TAMU, Presentation Slides, Video.
• 11/21/2022; TAMIDS Seminar; The Deep Bootstrap Framework; Preetum Nakkiran, Apple, Presentation Slides, Video.
• 11/14/2022; TAMIDS Seminar; Probabilistic Solvers For ODEs And PDEs, Simo Särkkä, Aalto University, Presentation Slides, Video.
• 11/3/2022; TAMIDS Tech Talk; Research Partnerships Between Pacific Northwest National Laboratory and TAMIDS, Michael Henry, PNNL, Video.
• 10/31/2022; TAMIDS Seminar; Federated Data Analytics for the Internet of Federated Things, Raed Al Kontar, University of Michigan, Presentation Slides, Video.
• 10/24/2022; TAMIDS Seminar; Stochastic Gradients with Adaptive Stepsizes, Rachel Ward, University of Texas at Austin, Presentation Slides, Video.
• 10/17/2022; TAMIDS Seminar; The Ethics of Integrating Medical AI into Research, Practice, and Education, Sophia Fantus, University of Texas at Arlington, Presentation Slides, Video.
• 9/26/2022; TAMIDS Seminar; Operations Optimization Applications at General Electric, Srinivas Bollapragada, GE Research, Presentation Slides.
• 9/19/2022; TAMIDS Tutorial; Frontiers of Graph Neural Networks with the Dive-Into-Graphics (DIG) Library, Shuiwang Ji, CSCE, TAMU, Presentation Slides, Video.
• 9/12/2022; TAMIDS Seminar; The Role of Functional Aesthetics in Visual Analysis, Vidya Setlur, Tableau, Presentation Slides, Video.

• 4/25/2022; TAMIDS Seminar; Online Monitoring of Big Data Streams–Roadmap and Recent Advances, Kaibo Liu, University of Wisconsin–Madison, Presentation Slides, Video.
• 4/18/2022; TAMIDS Seminar; Online Nonnegative Matrix Factorization and Applications, Deanna Needell, UCLA, Presentation Slides, Video.
• 4/5/2022; TAMIDS Seminar; Modeling and Simulation of High Frequency Wind Vectors, Michael Stein, Rutgers University, Presentation Slides, Video.
• 3/28/2022; TAMIDS Seminar: Identifying Market Structure: A Deep Network Representation Learning of Social Engagement, Kunpeng Zhang, University of Maryland, Presentation Slides, Video.
• 3/22/2022; TAMIDS Tutorial: Introduction to Conversational AI, James Caverlee, CSCE, Presentation Slides, Video.
• 3/7/2022; TAMIDS Seminar: Data Science at Capital One, Fei Tong, Capital One.
• 2/21/2022; TAMIDS Seminar: The Art of Visualization, Nadieh Bremer and Shirley Wu, Authors of Data Sketches, Video.
• 2/14/2022; TAMIDS Seminar: Studying Risk and Crisis Communication during Emerging Infectious Disease Outbreaks from Social Media Big Data, Lu Tang, Department of Communication, Presentation Slides, Video.
• 2/7/2022; TAMIDS Seminar: Machine Learning / Natural Language Processing Experts Needed: How Do We Discover Emerging Gender Categories in 90 Volumes of Tagged Text? Laura Mandell (together with Bryan Tarpley and Kayley Hart), Center of Digital Humanities Research, Presentation Slides, Video.
• 1/24/2022; TAMIDS Seminar: A Unified Framework for Sequential Decisions under Uncertainty, Warren B. Powell, Princeton University & Optimal Dynamics, Presentation Slides, Video.

• 12/09/2021; TAMIDS Research Conference. Access slides and recording through the conference program here.
• 11/22/2021; TAMIDS Seminar: The Role of Lookahead and Approximate Policy Evaluation in Policy Iteration with Linear Value Function Approximation, R. Srikant, University of Illinois at Urbana-Champaign, Video.
• 11/15/2021; TAMIDS Seminar: Leveraging Online Labor Markets to Learn from Imperfect and Biased Humans, Maytal Saar-Tsechansky, University of Texas at Austin, Presentations, Video.
• 11/08/2021; TAMIDS Tutorial: Deep Learning for Symbolic Regression, Andrew Jiang, Texas A&M University, Presentation Slides, Video.
• 11/01/2021; TAMIDS Seminar: Some New Insights on the Fisher Randomization Test, Tirthankar Dasgupta, Rutgers University, Presentation Slides, Video.
• 10/25/2021; TAMIDS Seminar: Label Your Data With This One Weird Trick: Methods for Addressing the Digital Data Labeling Bottleneck, Alina Zare, University of Florida, Presentation Slides, Video.
• 10/11/2021; TAMIDS Seminar: Mathematical Methods for Privacy-Preserving Machine Learning, Thomas Strohmer, UC Davis, Presentation Slides, Video.
• 10/04/2021; TAMIDS Seminar: The Interplay between Online and Offline Learning, David Simchi-Levi, MIT, Presentation Slides, Video.
• 9/27/2021; TAMIDS Seminar: Learning Preferences with Irrelevant Alternatives, Johan Ugander, Stanford University, Presentation Slides, Video.
• 9/20/2021; TAMIDS Seminar: Translational Bioinformatics: Go Deep and Go Broad – Working Examples in Deciphering Molecular Heterogeneity of Ovarian Cancer; Chen Wang, Mayo Clinic College of Medicine, Presentation Slides, Video.
• 9/17/2021; ECEN Distinguished Speaker Seminar: Physics-Informed Learning for Diverse Applications in Science and Engineering, George Karniadakis, Brown University, Video.
• 9/13/2021; TAMIDS Seminar: Scalable Gaussian-Process Approximations for Big Data, Matthias Katzfuss, Texas A&M, Presentation Slides, Video.
• 9/8/2021; TAMIDS SciML Lab Seminar: Solving and Learning Phase Field Models Using Modified Physics Informed Neural Networks , Jia Zhao, Utah State University, Video.
• 9/6/2021; TAMIDS Seminar: Communication-Aware and Decentralized Strategic Learning in Networked Multiagent Systems, Ceyhun Eksin, Texas A&M, Presentation Slides, Video.

• 6/2/2021; TAMIDS SciML Lab Seminar: Hidden Physics Models, Maziar Raissi, University of Colorado Boulder, Presentation Slides, Video.
• 4/23/2021; TAMIDS Tutorial: Exploring Fairness and Socio-demographic Bias in Machine Learning, Theodora Chaspari, Texas A&M University, Presentation slides, Video.
• 4/21/2021; TAMIDS SciML Lab Seminar: NVIDIA SimNet: A Multi-Physics Neural Solver, Sanjay Choudhry, NVIDIA, Video.
• 4/14/2021; TAMIDS SciML Lab Seminar: Stiffness: Where Deep Learning Breaks and How Scientific Machine Learning Can Fix It, Chris Rackauckas, Massachusetts Institute of Technology, Video.
• 4/9/2021; TAMIDS Seminar: Sensing and Learning in Distributed Systems Operating under Resource Constraints, Haris Vikalo, University of Texas at Austin, Presentation slides, Video.
• 3/26/2021; TAMIDS Seminar: End to End Data Science, Pascal Van Hentenryck, Georgia Institute of Technology, Presentation slides, Video.
• 3/12/2021; TAMIDS Seminar: Image and Graph Deep Learning Methods for Cellular and Molecular Level Science Discoveries, Shuiwang Ji, Texas A&M University, Presentation slides, Video.
• 3/10/2021; TAMIDS SciML Lab Seminar: Bridging Physical Models and Observational Data with Physics-Informed Deep Learning, Paris Perdikaris, University of Pennsylvania, Video.
• 3/5/2021; TAMIDS Seminar: Predictive Modeling with Longitudinal Patient Clinical Records, Fei Wang, Weill Cornell Medicine, Presentation slides, Video.
• 2/26/2021; TAMIDS Seminar: Statistics, Topology and Data Analysis; Vasileios Maroulas; University of Tennessee, Presentation slides, Video.
• 2/12/2021; TAMIDS Tutorial: Managing Your Digital Footprints; Ravi Sen; Texas A&M University, Presentations slides, Video.
• 2/5/2021; TAMIDS Seminar: Sidewalk Extraction Using Aerial and Street View Images; Xinyue Ye; Texas A&M University, Presentation slides, Video.
• 1/29/2021; TAMIDS Seminar: Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead; Cynthia Rudin, Duke University, Presentation slides, Video.
• 1/22/2021; TAMIDS Seminar: Data Science for Organizational Modeling, Randy Garrett, DARPA, Presentation slides, Video (TAMU NetID login required per speaker’s request).

• 11/20/2020; TAMIDS Tutorial: Introduction to Generative Adversarial Networks (GANs), Zhangyang “Atlas” Wang, The University of Texas at Austin, Presentations slides, Video.
• 11/13/2020; TAMIDS Seminar: Inverse Multiobjective Optimization: Models, Insights and Algorithms, Bo Zeng, University of Pittsburgh, Presentation Slides, Video.
• 11/10/2020; TAMIDS Tech Talk: Vinit Sehgal: Large-scale Geospatial Analysis with R, Vinit Sehgal, Texas A&M University, Video.
• 11/06/2020; TAMIDS Seminar: YouTube Video Analytics for Health Literacy and Chronic Care Management: An Augmented Intelligence Approach to Assess Content and Understandability, Rema Padman, Carnegie Mellon University, Presentation Slides.
• 10/30/2020; TAMIDS Seminar: Distributed Stochastic Approximation and Multi-Agent Reinforcement Learning, Justin Romberg, Georgia Institute of Technology, Presentation Slides, Video.
• 10/23/2020; TAMIDS Seminar: Computational Frameworks for Higher-Order Network Data Analysis, Austin Benson, Cornell University, Presentation Slides, Video.
• 10/16/2020; TAMIDS Tutorial: Iterating Between the Data World and the Real World to Find Answers in Big Data, Hye-Chung Kum, Texas A&M, Presentation Slides, Video.
• 10/09/2020; TAMIDS Seminar: Local Gaussian Process Regression for Spatial Data, Arash Pourhabib, Google, Presentation Slides (waiting for internal approval), Video.
• 10/06/2020; TAMIDS Tech Talk: ADCME: Machine Learning for Computational Engineering, Kailai Xu, Stanford University, Presentation Slides, Video.
• 10/02/2020; TAMIDS-CoDHR Seminar: Leveraging the Alignment between Machine Learning and Intersectionality: Using Word Embeddings to Visualize Intersectional Experiences of the Nineteenth-Century U.S. South, Laura Nelson, Northeastern University, Presentation Slides, Video.
• 9/25/2020; TAMIDS Seminar: Towards a Paradigm for Visual Modeling; Fahd Husain, Uncharted Software; Presentation Slides, Video.
• 9/18/2020; TRIPODS Distinguished Tutorial: Functional and Shape Data Analysis; Anuj Srivastava, Florida State University, Pre-Workshop Materials, together with a Note for Attendees; Presentation Slides, Video.
• 9/4/2020; TAMIDS Tutorial: Gaussian Processes and Bayesian Optimization; Rui Tuo, Texas A&M, Lecture Slides, Demo Slides, Demo Code, Video.
• 6/12/2020; TAMIDS Tutorial: Introduction to Deep Learning; Boris Hanin, Texas A&M, Presentation Slides, Video
• 5/15/2020; TAMIDS Tutorial: Business Analytics: Strategic Issues and Solutions; Venkatesh Shankar, Texas A&M, Pre-workshop Materials (TAMU NetID login required), Presentation Slides, Video (TAMU NetID login required per speaker’s request)
• 4/24/2020; Data Science Seminar; Pitfalls of Big Data; Vicki Bier, University of Wisconsin, Presentation Slides, Video
• 4/3/2020; TAMIDS Tutorial: Reinforcement Learning – Algorithms and Applications; Dileep Kalathil, Texas A&M; Presentation Slides, Video
• 2/21/2020; TAMIDS Tutorial: Interpretable Machine Learning: Concepts and Techniques; Xia “Ben” Hu, Texas A&M; Presentation Slides
• 1/31/2020; Data Science Seminar; Post-hoc Uncertainty Quantification for Remote Sensing Observing Systems; Amy Braverman, JPL Nasa / Caltech; Presentation Slides
• 1/17/2020; Data Science Seminar; Learning to Benchmark; Alfred O. Hero III, University of Michigan; Presentation Slides, Video