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.

jjpark

Jeong Joon Park

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3D reconstruction and generative models. I use neural and physical 3D representations to generate realistic 3D objects and scenes. The current focus is large-scale, dynamic, and interactable 3D scene generations. These generative models will be greatly useful for content creators, like games or movies, or for autonomous agent training in virtual environments. For my research, I frequently use and adopt generative modeling techniques such as auto-decoders, GANs, or Diffusion Models.

In my project “DeepSDF,” I suggested a new representation for a 3D generative model that made a breakthrough in the field. The question I answered is: “what should the 3D model be generating? Points, meshes, or voxels?” In DeepSDF paper, I proposed that we should generate a “function,” that takes input as a 3D coordinate and outputs a field value corresponding to that coordinate, where the “function” is represented as a neural network. This neural coordinate-based representation is memory-efficient, differentiable, and expressive, and is at the core of huge progress our community has made for 3D generative modeling and reconstruction.

3D faces with apperance and geometry generated by our AI model

Two contributions I would like to make. First, I would like to enable AI generation of large-scale, dynamic, and interactable 3D world, which will benefit entertainment, autonomous agent training (robotics and self-driving) and various other scientific fields such as 3D medical imaging. Second, I would like to devise a new and more efficient neural network architecture that mimics our brains better. The current AI models are highly inefficient in terms of how they learn from data (requires a huge number of labels), difficult to train continuously and with verbal/visual instructions. I would like to develop a new architecture and learning methods that address these current limitations.

Cam McLeman

Cam McLeman

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My research interests lie in the application of mathematical tools to machine learning models, e.g., using tools from graph theory and stochastic processes to study graphical neural networks, and conversely, the application of artificial intelligence to mathematical proofs, e.g., automated theorem-proving and theorem-generation. With IDEAS, I also work to use more standard applications of machine learning models to solve problems for groups who traditionally lack access to data science expertise.

My doctoral training was in algebraic number theory, and one of the boasts of number theory is that you are required to use tools from every discipline in mathematics to understand all of its facets. This brought me in contact with graph theory both in the abstract and in the applied setting of Markov chains and stochastic processes, and using these ideas to model evolutions of systems in natural settings. Most recently, the dynamic updating of stochastic processes on graphs is very similar in spirit to the training of many models of neural networks, and exploring the symbiosis between these two sets of ideas has been a driver of my recent research.

In IDEAS, we are excited about taking the reams of student and faculty expertise and research at the University of Michigan and using it “for the people” — finding ways of furthering the goals of small businesses or local community groups that do not have the resources to have a data scientist on staff. On the research front, I personally am very excited to see how the study of mathematics evolves as generative AI models meet formal theorem-proving systems.

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

jianghui

Hui Jiang

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My research focuses on statistical genomics and computational statistics. I am interested in developing statistical and computational methods for the analysis of large-scale biological datasets generated by modern high-throughput technologies such as next-generation sequencing. I implemented many of the methods that I developed as software tools and packages to be used by the research community. I am also interested in developing efficient algorithms and methods that deal with computational problems arising from statistics genomics. I have worked on and am working on problems including efficient algorithms for resampling-based hypothesis testing, penalized modeling and optimization algorithms for model fitting, as well as computational methods for density estimation and machine learning.

Nishil Talati

Nishil Talati

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I am a research faculty at the Computer Science and Engineering department at University of Michigan. I work with a group of talented PhD students on computer architecture, compiler techniques, and software engineering. My group focuses on developing novel software and hardware solutions to optimize large-scale data intensive worklods (e.g., graph traversals).

I earned my PhD degree in CSE from University of Michigan, Ann Arbor, USA, master’s degree in EE from Technion – Israel Institute of Technology, Haifa, Israel, and an undergraduate degree in EEE from BITS Pilani, Goa Campus, Goa, India.

Minji Kim

Minji Kim

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Communication mechanisms exist inside our cells to regulate gene expression and function. In cells, communication between a gene and sequences that regulate the gene occurs through physical contact. These physical contacts between regulatory element enhancers and the gene promoters may activate gene expression. Our goal is to understand how this communication occurs within the nucleus. In particular, we leverage 3D genome mapping technologies to capture the dynamic chromatin interactions and study gene regulation in the context of developmental biology. Algorithms on graphs allow us to interpret these data generated from high-throughput sequencing assays.

How did you end up where you are today?

I studied electrical engineering, hoping to advance the field of information and coding theory. Instead, I aim to understand the communication mechanisms governing gene transcription.

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

Interpreting biological data is often a creative process. I enjoy browsing through the data, connecting pieces, and finding patterns.

Siddhartha Srivastava

Siddhartha Srivastava

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My research broadly revolves around extending, specializing, and developing novel ML/AI methods for computational mechanics. My primary focus is data-driven physics-based modeling that utilizes approaches like Variational System Identification and PDE-constrained optimization. I apply these methods for inferring PDE models for complex physical phenomena, for instance, foldings during brain growth, deformation mechanics in soft matter (human tissue and ligaments), and migration and proliferation in biological cells. I also develop graph-based approaches for Machine Learning and NISQ (Noisy Intermediate Scale Quantum) computing. These methods are rooted in classical physics and mathematical analysis but simultaneously developed in concert with real-life experimental data.

Maria Masotti

Maria Masotti

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Dr. Masotti develops statistical methods for data derived from medical imaging processes. This data presents unique challenges such as spatial and temporal autocorrelation and high dimensionality.

Dr. Masotti developed a 3D boundary detection algorithm for discovering prostate cancer lesions non-invasively via multi-parametric MRI. This image displays the different theoretical tumor volumes in blue and the estimated boundary surface in red.

Image: Dr. Masotti developed a 3D boundary detection algorithm for discovering prostate cancer lesions non-invasively via multi-parametric MRI. This image displays the different theoretical tumor volumes in blue and the estimated boundary surface in red.

 


Accomplishments and Awards

Abdon Pena-Francesch

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The BIOINSPIRED MATERIALS LAB is an interdisciplinary research group working on biomaterials science, polymer chemistry, soft matter physics, and nanotechnology, with focus on exploring biological and bioinspired functional materials to develop solutions for healthcare, robotics, and sustainability. Particularly, we are interested in multiscale modeling of soft biomaterials (from molecular dynamics to continuum models) and learning/control methods in microrobotics.