Vineet Kamat

Vineet Kamat

By |

My group conducts research in automation and robotics to improve work processes in the construction, operation, and maintenance of civil infrastructure and the built environment. Our research has developed several licensable technologies that include visualization, perception, and modeling techniques to help on-site construction robots with autonomous decision making. We are particularly interested in exploring new methods for enabling collaborative work strategies for human-robot teams jointly performing field construction work. In addition, we are also interested in exploring methods to integrate data to support semi-autonomous mobility for people with physical disabilities in the urban built environment.

Data-Driven Co-Robotic Field Construction Work

Michael Cianfrocco

Michael Cianfrocco

By |

Dr. Michael Cianfrocco uses cryo-electron microscopy (cryo-EM) to determine protein structures to understand how nanometer-sized molecular machines work. While a powerful technique, cryo-EM data collection and subsequent image analysis remain bespoke, clunky, and heuristic. Dr. Cianfrocco is coupling his 16+ years of experience with artificial intelligence to automate data collection and processing by capturing human expertise into AI-based algorithms. Recently, his laboratory implemented reinforcement learning to guide cryo-electron microscopes for data collection [1, 2]. This work combined real-world datasets and Dr. Cianfrocco’s expertise with AI-driven optimization algorithms to find the ‘best’ areas of cryo-EM samples for data collection.

cryoRL Distributed Data Collection process diagram

Human users must curate and select areas for subsequent analysis after data collection. Subjective decisions guide how to process the single particles and determine what constitutes ‘good’ data. To automate subsequent preprocessing, Dr. Cianfrocco’s lab built the first AI-backed data preprocessing in cryo-EM by training CNNs to recognize ‘good’ and ‘bad’ cryo-EM data [3]. This work enabled fully-automated cryo-EM data preprocessing, the first step in the processing pipeline of cryo-EM data. In the future, Dr. Cianfrocco wants to continue improving cryo-EM workflows to make them robust and automated, eventually surpassing human experts in the ability of algorithms to collect and analyze cryo-EM data. 1. Fan Q*, Li Y*, et al. “CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection.” arXiv preprint arXiv:2204.07543 (2022). 2. Li Y*, Fan Q*, Optimized path planning surpasses human efficiency in cryo-EM imaging. bioRxiv 2022.06.17.496614 (2022). 3. Li Y, High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines. Structure. 2020 Jul 7;28(7):858-869.e3.

Sally Oey

Sally Oey

By |

Sally Oey’s group is studying massive star populations and the escape of ionizing radiation from starburst galaxies and super star clusters. The group is at the forefront of establishing a new paradigm for massive-star feedback, where superwinds from compact young star clusters fail to launch. Members have used numerical simulations and image processing techniques to investigate such conditions for allowing ionizing radiation to penetrate the dense gas in star-forming clouds and the interstellar medium in “green pea” galaxies and resolved nearby starbursts. The ionizing radiation may originate from massive binaries and their products, thus group members are carrying out data mining of observational surveys and binary population synthesis models to study how binarity manifests in stellar populations.

Leopoldo Pando Zayas

Leopoldo Pando Zayas

By |

My main research interest is in quantum gravity. Various aspects of quantum information and quantum chaotic systems have proven to be essential in recent developments.

Picture of Thomas Schwarz

Thomas A. Schwarz

By |

Professor Schwarz is an experimental particle physicist who has performed research in astro-particle physics, collider physics, as well as in accelerator physics and RF engineering. His current research focuses on discovering new physics in high-energy collisions with the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. His particular focus is in precision measurements of properties of the Higgs Boson and searching for new associated physics using advanced AI and machine learning techniques.

Picture of David Brang

David Brang

By |

My lab studies how information from one sensory system influences processing in other sensory systems, as well as how this information is integrated in the brain. Specifically, we investigate the mechanisms underlying basic auditory, visual, and tactile interactions, synesthesia, multisensory body image perception, and visual facilitation of speech perception. Our current research examines multisensory processes using a variety of techniques including psychophysical testing and illusions, fMRI and DTI, electrophysiological measures of neural activity (both EEG and iEEG), and lesion mapping in patients with brain tumors. Our intracranial electroencephalography (iEEG/ECoG/sEEG) recordings are a unique resource that allow us to record neural activity directly from the human brain from clinically implanted electrodes in patients. These recordings are collected while patients perform the same auditory, visual, and tactile tasks that we use in our other behavioral and neuroimaging studies, but iEEG measures have millisecond temporal resolution as well as millimeter spatial precision, providing unparalleled information about the flow of neural activity in the brain. We use signal processing techniques and machine learning methods to identify how information is encoded in the brain and how it is disrupted in clinical contexts (e.g., in patients with a brain tumor).

Krishna Garikipati

Krishna Garikipati

By |

My research is in computational science and scientific artificial intelligence, including machine learning and data-driven modelling. I have applied these approaches to physics discovery by model inference, scale bridging, partial differential equation solvers, representation of complexity and constructing reduced-order models of high-dimensional systems. My research is motivated by and applied to phenomena in bioengineering, biophysics, mathematical biology and materials physics. Of specific interest to me are patterning and morphogenesis in developmental biology, cellular biophysics, soft matter and mechano-chemical phase transformations in materials. More fundamentally, the foundations of my research lie in applied mathematics, numerical methods and scientific computing.

A schematic illustrating the range of ML methods comprising the mechanoChemML code framework for data-driven computational material physics.

Michael Craig

By |

Michael is an Assistant Professor of Energy Systems at the University of Michigan’s School for Environment and Sustainability and PI of the ASSET Lab. He researches how to equitably reduce global and local environmental impacts of energy systems while making those systems robust to future climate change. His research advances energy system models to address new challenges driven by decarbonization, climate adaptation, and equity objectives. He then applies these models to real-world systems to generate decision-relevant insights that account for engineering, economic, climatic, and policy features. His energy system models leverage optimization and simulation methods, depending on the problem at hand. Applying these models to climate mitigation or adaptation in real-world systems often runs into computational limits, which he overcomes through clustering, sampling, and other data reduction algorithms. His current interdisciplinary collaborations include climate scientists, hydrologists, economists, urban planners, epidemiologists, and diverse engineers.

Stefanus Jasin

By |

My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.