Dr. Sara Algeri, statistics in astrophysics and other sciences
Dr. Jie Ding, statistics of streaming and decentralized data
Dr. Charles Doss, foundations of statistics and data science
Dr. Qian Qin, convergence theory of Markov chain Monte Carlo (MCMC)
Dr. Xiaotong T. Shen, machine learning, biomedical sciences, and engineering
Dr. Sisi Ma, statistics and machine learning for biology and medicine
Dr. Stefano Martiniani, statistical physics, dynamical systems, and machine learning
Dr. Karen Monsen, nursing informatics
Dr. Rui Zhang, natural language processing for biology and medicine
The datasets generated from large astronomical surveys and ambitious experiments in physics have recently revealed the fundamental importance of statistics to:
- Conduct reliable and reproducible analyses.
- Handle a large amount of data “with care”, i.e., minimizing the risk of false discoveries while maximizing the power of the detection tools adopted.
As a result, astrostatistics and, more broadly, astrophysical data science, plays a fundamental role in the discovery of new phenomena. As an astrostatistician with a strong interest in statistical methodology, Dr. Sara Algeri’s work aims to develop generalizable statistical solutions that directly address fundamental questions in the physical sciences, and can at the same time be easily applied to any other scientific problem following a similar statistical paradigm. In line with this, motivated by problems arising in high energy physics and astronomy, her current research focuses on statistical inference for signal detection, background estimation, distributed learning, and uncertainty quantification.
Streaming data of a massive scale and heterogeneous nature is emerging in statistical and artificial intelligence practices, e.g., recordings from distributed sensor networks, transactions from e-commerce platforms, and media from mobile devices. These data often need to be analyzed in real-time due to limitations in decision time, hardware capacity, and communication bandwidth. Dr. Jie Ding’s recent research aims to address the following challenges. Application domains include real-time Cardiac Organoid Maturation, Human-Robot Teaming, Threat Detection, etc.
- The underlying data patterns often dynamically vary with time so that model-based time series analysis may require frequent re-modelings and back-testing. How to efficiently strike the most sophisticated tradeoff between overfitting and underfitting?
- Real-world data are often heterogeneous in its quality, modality, and even format, requiring appropriate Information Fusion. How to develop novel frameworks of collaborative learning to scale and robustify single-agent learning capabilities?
- In the context of streaming data and collaborative learning, privacy is an inevitable concern from both data providers and service providers’ perspectives. How to evaluate and optimize the privacy-utility tradeoffs?
Dr. Ding’s research in Assisted Learning aims to significantly enhance the learning ability of decentralized organizations by developing communication protocols, without sharing data, algorithms, or tasks, to secure proprietary information.
Dr. Doss’ research focuses on foundations of statistics and data science.
Nonparametric regression and density estimation. In many contexts, especially with complex datasets, it is inappropriate or difficult to specify an overly simplistic parametric model. It is preferred to use so-called nonparametric techniques that are extremely flexible, can learn many and varied function shapes, and “let the data speak for themselves”. Dr. Doss works on studying such flexible nonparametric procedures and their properties.
Statistical inference, not just estimation. One of the fundamental requirements in science is to be able not just to provide estimates, but to provide uncertainty quantification, which we can do in terms of hypothesis tests or confidence intervals. Dr. Doss has developed confidence intervals/tests in nonparametric problems in which it is often challenging to conduct such tests.
Causal inference. “Correlation is not causation” is a commonly used phrase, but what is causation and how do we measure it? Causal inference combines statistical tools with a philosophical framework for what could have happened in an experiment under different possible “treatments” or interventions that did not actually happen. This is crucially important for observational studies, where correlations can be misleading and suggest incorrect relationships. Dr. Doss has worked on problems in causal inference, especially when the treatment/intervention is continuous.
Dr. Sisi Ma’s primary research interest is the application of statistical modeling, machine learning, and causal analysis methods in the field of biology and medicine. The questions she seeks answers to include how to leverage big data and analytical approaches to:
- Diagnose and prognose disease and disorders earlier and more accurately.
- Systematically and efficiently identify potential treatment targets for a given disease.
- Identify the best treatment for a particular patient.
She also works on theoretical aspects of predictive modeling and causal modeling.
Research at Dr. Stefano Martiniani’s Computational Science laboratory focuses on the design of novel theoretical and computational frameworks to address open problems in science and engineering. The approach of the lab draws primarily from statistical physics, dynamical systems, and machine learning. The lab is a cognitively diverse group of scientists and engineers whose backgrounds span physics, chemical and biological engineering, materials science, statistics, scientific computing and data science. The problems the lab works on emerge from lab participants theoretical interests, as well as their close interactions with experimentalists, with whom they pursue quantitative descriptions of experimentally observed phenomena.
Currently, the lab’s active research projects span:
- Fundamental work on the energy landscapes of disordered systems, such as amorphous solids, spin glasses, and neural systems.
- The development of a unifying framework of neural activity, with an emphasis on modelling sensory-evoked activity in the visual cortex, and working memory representation and manipulation in the prefrontal cortex.
- The integration of machine learning and sampling methodologies for (bio)molecular design and simulation.
- The development of experimentally viable approaches to measure entropy production in active matter systems.
- The exploration of the relationship between information, order and correlations in complex systems.
Specifically, within the data science domain, in relation to active projects, the lab is currently interested in:
- Neuroscience inspired artificial intelligence.
- The design of neural networks with built-in physical symmetries for molecular properties prediction and molecular simulation.
- The development of a computational infrastructure to enable researchers to query first principle molecular data, develop, deploy and share new models using a ML interoperable standard (ONNX).
- Understanding the relationship between neural architectures, optimization, data and the loss landscape.
- Representation learning and fitness landscape sampling for protein design and engineering.
Dr. Monsen directs the Center for Nursing Informatics and the practice-based research network the Omaha System Partnership. She leads numerous studies investigating health care quality and population outcomes using large datasets and diverse methods including data visualization.
In statistics and many fields of science, one often needs to sample from an intractable probability distribution, e.g., a posterior distribution from a Bayesian model. Markov chain Monte Carlo (MCMC) is an extremely popular class of algorithms for this type of job. An MCMC algorithm simulates a Markov chain that converges to the desired distribution. The elements of the Markov chain are then used as an approximate sample from the limiting distribution. To ensure that the algorithm yields reliable results, it is important to understand how fast the underlying Markov chain converges. Dr. Qian Qin’s research focuses on the theoretical convergence analysis of MCMC algorithms. He is particularly interested in the convergence properties of MCMC algorithms that arise in Bayesian models associated with large and/or high-dimensional datasets.
Billions of people around the globe use various applications of spatial computing daily—by using a ride-sharing app, GPS, the e911 system, social media check-ins, even Pokémon Go. Scientists and researchers use spatial computing to track diseases, map the bottom of the oceans, chart the behavior of endangered species, and create election maps in real time. Drones and driverless cars use a variety of spatial computing technologies.
Spatial computing works by understanding the physical world, knowing and communicating our relation to places in that world, and navigating through those places. It has changed our lives and infrastructures profoundly, marking a significant shift in how we make our way in the world. Even more compelling opportunities lie ahead. Dr. Shekhar’s research investigates the technologies and ideas behind current and future spatial computing technologies. Examples include GPS and location-based services, including the use of Wi-Fi, Bluetooth, and RFID for position determination out of satellite range; remote sensing and Geo-AI, which uses satellite and aerial platforms to monitor such varied phenomena as global food production, the effects of climate change, and subsurface natural resources on other planets; geographic information systems (GIS), which store, analyze, and visualize spatial data; spatial databases, which store multiple forms of spatial data; and spatial statistics and spatial data science, used to analyze location-related data.
 Spatial Computing, S. Shekhar and P. Cold, MIT Press Essential Knowledge Series, 2020.
 Spatial Computing, S. Shekhar, W. Are and S. Feiner, Communications of the ACM, 59(1):72-81, January 2016.
Dr. Shen’s primary research interest is machine learning and data science, with applications in biomedical sciences and engineering. Currently, his group’s active research projects include:
Causal discovery and inference. Causal relations, defined by the local Markov dependence, are fundamental to describe the consequences of actions beyond associations in science and medical research. For example, in gene network analysis, regulatory gene-to-gene relations are investigated to unravel the genetic underpinnings of disease, where latent confounders such as race and family relatedness could introduce spurious or missed associations in gene expression levels. The research question is how to identify and infer causal relations in the presence of confounders, nonlinearity, and interventions.
Numerical embeddings, language modeling, and generative models. Sentence generation creates representative examples to interpret a learning model as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. The research question focuses on the generation of a description of the underlying learning task to bridge the gap between structured and unstructured data.
Inference for a black-box learner. Explainable artificial intelligence demands interpretability and understanding of features of interest in addition to predictive accuracy. This is critical to a deep neural network. The research focus is on hypothesis testing for feature relevance to prediction.
Dr. Ju Sun’s group builds foundations and tools for making sense of data. The group’s recent efforts are focused on deep learning, which fuels the ongoing artificial intelligence revolution. They create robust deep learning techniques to enable reliable image recognition, develop faster numerical methods for performing learning with massive datasets, and revamp deep learning to tackle major unsolved scientific and engineering problems. The group applies these novel techniques and tools to unravel the mystery of high energy particles, depict the interior structures of physical and biological samples, and empower smart scooters that can travel safely with the assistance of a cheap onboard camera. Dr. Ju Sun’s group is especially fascinated by the prospect of transforming healthcare and medicine using artificial intelligence and data science. They have been working closely with medical researchers to tame brain tumors, fight COVID-19, and improve trauma and critical care.
Dr. Zhang’s research focuses on the development of novel natural language processing (NLP) methods to analyze biomedical big data, including published biomedical literature, electronic health records (EHRs), and patient-generated data from millions of patients. In particular:
- The secondly analysis of EHR data for patient care.
- Pharmacovigilance knowledge discovery through mining biomedical literature.
- Creation of knowledge base through database integration, terminology and ontology.
Current projects in Dr. Zhang’s lab include:
- Developing NLP methods and applications to extract information from clinical reports.
- Mining biomedical literature to discover novel drug-supplement interactions through genetic pathways.
- Repurposing existing drugs for COVID-19 treatment through link predictions and literature-based discovery.
- Developing computational methods to predict personalized cancer treatment caused cardiotoxicity in EHRs.
- Developing conversational agent for consumers with developed knowledge base.