Cong Shi is an associate professor in the Department of Industrial and Operations Engineering at the University of Michigan College of Engineering. His primary research interest lies in developing efficient and provably-good data-driven algorithms for operations management models, including supply chain management, revenue management, service operations, and human-robot interactions. He received his Ph.D. in Operations Research at MIT in 2012, and his B.S. in Mathematics from the National University of Singapore in 2007.
Dr. Kochunas’s research focus is on the next generation of numerical methods and parallel algorithms for high fidelity computational reactor physics and how to leverage these capabilities to develop digital twins. His group’s areas of expertise include neutron transport, nuclide transmutation, multi-physics, parallel programming, and HPC architectures. The Nuclear Reactor Analysis and Methods (NURAM) group is also developing techniques that integrate data-driven methods with conventional approaches in numerical analysis to produce “hybrid models” for accurate, real-time modeling applications. This is embodied by his recent efforts to combine high-fidelity simulation results simulation models in virtual reality through the Virtual Ford Nuclear Reactor.
My lab researches how the human brain processes social and affective information and how these processes are affected in psychiatric disorders, especially schizophrenia and bipolar disorder. We use behavioral, electrophysiological (EEG), neuroimaging (functional MRI), eye tracking, brain stimulation (TMS, tACS), and computational methods in our studies. One main focus of our work is building and validating computational models based on intensive, high-dimensional subject-level behavior and brain data to explain clinical phenomena, parse mechanisms, and predict patient outcome. The goal is to improve diagnostic and prognostic assessment, and to develop personalized treatments.
Dr. Hadjiyski research interests include computer-aided diagnosis, artificial intelligence (AI), machine learning, predictive models, image processing and analysis, medical imaging, and control systems. His current research involves design of decision support systems for detection and diagnosis of cancer in different organs and quantitative analysis of integrated multimodality radiomics, histopathology and molecular biomarkers for treatment response monitoring using AI and machine learning techniques. He also studies the effect of the decision support systems on the physicians’ clinical performance.
Uncertainty quantification and decision making are increasingly demanded with the development of future technology in engineering and transportation systems. Among the uncertainty quantification problems, Dr. Wenbo Sun is particularly interested in statistical modelling of engineering system responses with considering the high dimensionality and complicated correlation structure, as well as quantifying the uncertainty from a variety of sources simultaneously, such as the inexactness of large-scale computer experiments, process variations, and measurement noises. He is also interested in data-driven decision making that is robust to the uncertainty. Specifically, he delivers methodologies for anomaly detection and system design optimization, which can be applied to manufacturing process monitoring, distracted driving detection, out-of-distribution object identification, vehicle safety design optimization, etc.
Yixin Wang works in the fields of Bayesian statistics, machine learning, and causal inference, with applications to recommender systems, text data, and genetics. She also works on algorithmic fairness and reinforcement learning, often via connections to causality. Her research centers around developing practical and trustworthy machine learning algorithms for large datasets that can enhance scientific understandings and inform daily decision-making. Her research interests lie in the intersection of theory and applications.
My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.
Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency
The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.
My PhD research focused on identifying the size and mineralogical composition of interstellar dust through X-ray imaging of dust scattering halos to X-ray spectroscopy of bright objects to study absorption from intervening material. Over the course of my PhD I also developed an open source, object oriented approach to computing extinction properties of particles in Python that allows the user to change the scattering physics models and composition properties of dust grains very easily. In many cases, the signal I look for from interstellar dust requires evaluating the observational data on the 1-5% level. This has required me to develop a deep understanding of both the instrument and the counting statistics (because modern-day X-ray instruments are photon counting tools). My expertise led me to a postdoc at MIT, where I developed techniques to obtain high resolution X-ray spectra from low surface brightness (high background) sources imaged with the Chandra X-ray Observatory High Energy Transmission Grating Spectrometer. I pioneered these techniques in order to extract and analyze the high resolution spectrum of Sgr A*, our Galaxy’s central supermassive black hole (SMBH), producing a legacy dataset with a precision that will not be replaceable for decades. This dataset will be used to understand why Sgr A* is anomalously inactive, giving us clues to the connection between SMBH activity and galactic evolution. In order to publish the work, I developed an open source software package, pyXsis (github.com/eblur/pyxsis) in order to model the low signal-to-noise spectrum of Sgr A* simultaneously with a non-physical parameteric model of the background spectrum (Corrales et al., 2020). As a result of my vocal advocacy for Python compatible software tools and a modular approach to X-ray data analysis, I became Chair for HEACIT (which stands for “High Energy Astrophysics Codes, Interfaces, and Tools”), a new self-appointed working group of X-ray software engineers and early career scientists interested in developing tools for future X-ray observatories. We are working to identify science cases that high energy astronomers find difficult to support with the current software libraries, provide a central and publicly available online forum for tutorials and discussion of current software libraries, and develop a set of best practices for X-ray data analysis. My research focus is now turning to exoplanet atmospheres, where I hope to measure absorption from molecules and aerosols in the UV. Utilizing UM access to the Neil Gehrels Swift Observatory, I work to observe the dip in a star’s brightness caused by occultation (transit) from a foreground planet. Transit depths are typically <1%, and telescopes like Swift were not originally designed with transit measurements (i.e., this level of precision) in mind. As a result, this research strongly depends on robust methods of scientific inference from noisy datasets.
As a graduate student, I attended some of the early “Python in Astronomy” workshops. While there, I wrote Jupyter Notebook tutorials that helped launch the Astropy Tutorials project (github.com/astropy/astropy-tutorials), which expanded to Learn Astropy (learn.astropy.org), for which I am a lead developer. Since then, I have also become a leader within the larger Astropy collaboration. I have helped develop the Astropy Project governance structure, hired maintainers, organized workshops, and maintained an AAS presence for the Astropy Project and NumFocus (the non-profit umbrella organization that works to sustain open source software communities in scientific computing) for the last several years. As a woman of color in a STEM field, I work to clear a path by teaching the skills I have learned along the way to other underrepresented groups in STEM. This year I piloted WoCCode (Women of Color Code), an online network and webinar series for women from minoritized backgrounds to share expertise and support each other in contributing to open source software communities.
Dr. Lu brings expertise in machine learning, particularly integrating human knowledge into machine learning and explainable machine learning. He has applied machine learning in a range of domain applications, such as autonomous driving and machine learning for optimized design and control of energy storage systems.
As an environmental epidemiologist and in collaboration with government and community partners, I study how social, economic, health, and built environment characteristics and/or air quality affect vulnerability to extreme heat and extreme precipitation. This research will help cities understand how to adapt to heat, heat waves, higher pollen levels, and heavy rainfall in a changing climate.