Mosharaf Chowdhury

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I am a computer scientist and an associate professor at CSE Michigan, where I lead the SymbioticLab (https://symbioticlab.org/). My research improves application performance and system efficiency of AI/ML and Big Data workloads with a recent focus on optimizing energy consumption and data privacy. I lead the ML Energy initiative (https://ml.energy/), a consortium of researchers focusing on understanding, controlling, and reducing AI/ML energy consumption. Over the course of my career, I have worked on a variety of networked and distributed systems. Recent major projects include Infiniswap, the first scalable memory disaggregation solution; Salus, the first software-only GPU sharing system for deep learning; FedScale, a scalable federated learning and analytics platform; and Zeus, the first GPU energy optimizer for AI. In the past, I invented the coflow abstraction for efficient distributed communication, and I am one of the original creators of Apache Spark. Thanks to my excellent collaborators, I have received many individual awards, fellowships, and paper awards from top venues like NSDI, OSDI, ATC, and MICRO.

Carol Menassa

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My group’s research focuses on understanding and modeling the interconnections between human experience and the built environment. We design autonomous systems that support wellbeing, safety and productivity of office and construction workers, and provides them opportunities for lifelong learning and upskilling. In all research projects, we work hard to ensure that the results are inclusive and benefit people of different abilities in their daily activities and empower them for nontraditional careers.

Cyrus Omar

Cyrus Omar

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I lead the Future of Programming Lab (FP Lab), where we design modern user interfaces for modern programming languages. Much of how we program today is rooted in tools designed 40+ years ago, e.g. how we enter code (using simple text editing, which leads to profligate parse errors), how we validate code (using tests or impoverished type systems), how we explore code (in a slow, batched, textual manner), how we communicate change (by throwing away the edits we performed and forcing diff algorithms to guess what we did), and so on. My lab develops new programming language and editor mechanisms, starting from theoretical foundations in mathematics and building up to human interfaces.

Integrating live GUIs into programs with holes

Integrating live GUIs into programs with holes

Christian Sandvig

Christian Sandvig

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I am a researcher specializing in discovering the consequences of computer systems that curate and organize culture. A major theme of my research investigates accountability mechanisms for machine learning and artificial intelligence. My research group coined the phrase “algorithmic auditing” in a 2014 paper; this was subsequently made suggested reading for submissions to the first ACM FAccT (Fairness, Accountability, and Transparency) Conferences. My work on algorithms and accountability was recommended by the White House Office of Science and Technology Policy in 2016 as one of five research strategies essential to the future of big data technologies in the US. I was the named plaintiff of a multi-year lawsuit against the federal government on behalf of computing researchers and journalists; this lawsuit changed the legal definition of “hacking” in the United States in 2022. I have also published research about social media, wireless systems, broadband Internet, online video, domain names, and Internet policy. My group blog about social media platforms was named one of the “Must-Follow Feeds” in science by Wired magazine.

A researcher tests a counterfeit, unauthorized copy of allegedly privacy-protecting fabric stolen from Adam Harvey's HyperFace design.

A researcher tests a counterfeit, unauthorized copy of allegedly privacy-protecting fabric stolen from Adam Harvey’s HyperFace design.


Accomplishments and Awards

Cheng Li

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My research focuses on developing advanced numerical models and computational tools to enhance our understanding and prediction capabilities for both terrestrial and extraterrestrial climate systems. By leveraging the power of data science, I aim to unravel the complexities of atmospheric dynamics and climate processes on Earth, as well as on other planets such as Mars, Venus, and Jupiter.

My approach involves the integration of large-scale datasets, including satellite observations and ground-based measurements, with statistical methods and sophisticated machine learning algorithms including vision-based large models. This enables me to extract meaningful insights and improve the accuracy of climate models, which are crucial for weather forecasting, climate change projections, and planetary exploration.

Dani Jones

Dani Jones

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Dani Jones’ research program drives CIGLR’s portfolio of research in data science, machine learning, and artificial intelligence, as applied to physical limnology, weather forecasting, water cycle predictions, ecology, and observing system design. This research program aims is to advance societal adaptations to the effects of climate change, including flooding of coasts, rivers, and cities. Dani’s background is in physical oceanography, with specific expertise in adjoint modeling for comprehensive sensitivity analysis and unsupervised classification for data analysis, mostly applied to the North Atlantic and Southern Ocean. In Dani’s current role, they are establishing CIGLR’s new Artificial Intelligence Laboratory, leveraging the institute’s extensive observing assets, datasets, modeling capacity, interdisciplinary expertise, and numerous regional and international partnerships.

Irina Gaynanova

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Dr. Gaynanova’s research focuses on the development of statistical methods for analysis of modern high-dimensional biomedical data. Her methodological interests are in data integration, machine learning and high-dimensional statistics, motivated by challenges arising in analyses of multi-omics data (e.g., RNASeq, metabolomics, micribiome) and data from wearable devices (continuous glucose monitors, ambulatory blood pressure monitors, activity trackers).Dr. Gaynanova’s research has been funded by the National Science Foundation, and recognized with a David P. Byar Young Investigator Award and an NSF CAREER Award. She currently serves as an Associate Editor for Journal of the American Statistical Association, Biometrika and Data Science in Science.

Qiong Yang

Qiong Yang

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My research program at the University of Michigan (UM) integrates the fields of biophysics, quantitative systems biology, and bottom-up synthetic biology to understand complex stochastic cellular and developmental processes in early embryos.
We have developed innovative computational and experimental techniques in microfluidics and imaging to allow high-throughput quantitative manipulation and single-cell lineage tracking of cellular spatiotemporal dynamical processes in various powerful in vitro and in vivo systems we established in my lab. These systems range from cell-free extracts, synthetic cells reconstituted in microemulsion droplets, presomitic mesoderm (PSM) and progenitor zone (PZ) cells dissociated from the zebrafish tail buds, their re-aggregated 2D and 3D cell-cell communications, ex vivo live tissue explants, and live embryos.
Our current research questions center around the understanding of the design-function relation of robust biological timing, growth, and patterning, how individual molecules and cells communicate to generate collective patterns, and how biochemical, biophysical, and biomechanical signals work together to shape morphogenesis during early embryo development.

Bing Ye

Bing Ye

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The focus of our research is to address (1) how neuronal development contributes to the assembly and function of the nervous system, and (2) how defects in this process lead to brain disorders. We take a multidisciplinary approach that include genetics, cell biology, developmental biology, biochemistry, advanced imaging (for neuronal structures and activity), electrophysiology, computation (including machine learning and computer vision) and behavioral studies.

We are currently studying the neural basis for decision accuracy. We established imaging and computational methods for analyzing neural activities in the entire central nervous system (CNS) of the Drosophila larva. Moreover, we are exploring the possibility of applying the biological neural algorithms to robotics for testing these algorithms and for improving robot performance.

A major goal of neuroscience is to understand the neural basis for behavior, which requires accurate and efficient quantifications of behavior. To this end, we recently developed a software tool—named LabGym—for automatic identification and quantification of user-defined behavior through artificial intelligence. This tool is not restricted to a specific species or a set of behaviors. The updated version (LabGym2) can analyze social behavior and behavior in dynamic backgrounds. We are further developing LabGym and other computational tools for behavioral analyses in wild animals and in medicine.

The behavior that this chipmunk performed was identified and quantified by LabGym, an AI-based software tool that the Ye lab developed for quantifying user-defined behaviors.

The behavior that this chipmunk performed was identified and quantified by LabGym, an AI-based software tool that the Ye lab developed for quantifying user-defined behaviors.

What are some of your most interesting projects?

1) Develop AI-based software tools for analyzing the behavior of wild animals and human.
2) Use biology-inspired robotics to test biological neural algorithms.

How did you end up where you are today?

Since my teenage years, I have been curious about how brains (human’s and animals’) work, enjoyed playing with electronics, and learning about computational sciences. My curiosity and opportunities led me to become a neuroscientist. When I had my own research team and the resources to explore my other interests, I started to build simple electronic devices for my neuroscience research and to collaborate with computational scientists who are experts in machine learning and computer vision. My lab now combines these approaches in our neuroscience research.

What makes you excited about your data science and AI research?

I am very excited about the interactions between neuroscience and data science/AI research. This is a new area and has great potential of changing the society.

Liyue Shen

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My research interest is in Biomedical AI, which lies in the interdisciplinary areas of machine learning, computer vision, signal and image processing, medical image analysis, biomedical imaging, and data science. I am particularly interested in developing efficient and reliable AI/ML-driven computational methods for biomedical imaging and informatics to tackle real-world biomedicine and healthcare problems, including but not limited to, personalized cancer treatment, and precision medicine.

In the field of AI/ML, we focus on developing reliable, generalizable, data-efficient machine learning and deep learning algorithms by exploiting prior knowledge from the physical world, such as: Prior-integrated learning for data-efficient ML Uncertainty awareness for trustworthy ML. In the field of Biomedicine, we focus on developing efficient computational methods for biomedical imaging and biomedical data analysis to advance precision medicine and personalized treatment, such as: Multi-modal data analysis for decision making Clinical trial translation for real-world deployment.

In the field of AI/ML, we focus on developing reliable, generalizable, data-efficient machine learning and deep learning algorithms by exploiting prior knowledge from the physical world, such as: Prior-integrated learning for data-efficient ML Uncertainty awareness for trustworthy ML. In the field of Biomedicine, we focus on developing efficient computational methods for biomedical imaging and biomedical data analysis to advance precision medicine and personalized treatment, such as: Multi-modal data analysis for decision making Clinical trial translation for real-world deployment.

What are some of your most interesting projects?

Our goal is to develop efficient and reliable AI/ML-driven computational methods for biomedical imaging and informatics to tackle real-world biomedicine and healthcare problems. We hope the technology advancement in AI and ML can help us to better understand human health in different levels. Specifically, we develop Biomedical AI in different parts, including:
– AI in Biomedical Imaging: develop novel machine learning algorithms to advance biomedical imaging techniques for obtaining computational images with improved quality. Specifically, relevant topics include but not limited to: Implicit neural representation learning; Diffusion model / Score-based generative model; Physics-aware / Geometry-informed deep learning.
– AI in Biomedical Image Processing and Bioinformatics: develop robust and efficient machine learning algorithms to extract useful information from multimodal biomedical data for assisting decision making and precision medicine. Specifically, relevant topics include but not limited to: Multimodal representation learning; Robust learning with missing data / noisy labeling; Data-efficient learning such as self- / un- / semi-supervised learning with limited data / labels.

How did you end up where you are today?

I am an assistant professor in the ECE Division of the Electrical Engineering and Computer Science department of the College of Engineering, University of Michigan – Ann Arbor. Before this, I received my Ph.D. degree from the Department of Electrical Engineering, Stanford University. I obtained her Bachelor’s degree in Electronic Engineering from Tsinghua University in 2016. I is the recipient of Stanford Bio-X Bowes Graduate Student Fellowship (2019-2022), and was selected as the Rising Star in EECS by MIT and the Rising Star in Data Science by The University of Chicago in 2021.


Accomplishments and Awards