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| 1 | +- title: "Imageomics: Discovering Biological Traits from Images" |
| 2 | + key: "imageomics" |
| 3 | + hasimage: 1 |
| 4 | + width: 50% |
| 5 | + image: mechanobio.png |
| 6 | + description: "This work is supported by a [$15M NSF grant](https://news.vt.edu/articles/2022/03/three-virginia-tech-scientists-partner-on-multi-university-grant.html) to create a Harnessing Data Revolution (HDR) Institute on [Imageomics](https://imageomics.osu.edu/), a brand-new field in biology where images are used as the source of information about life, powered by novel advances in knowledge-guided ML (KGML). We are developing methods that make use of varying forms of structured biological knowledge (e.g., anatomy ontologies and phylogenies) to guide the training of ML models on images of organisms (e.g., fishes or butterflies) for a variety of downstream tasks such as species classification, image reconstruction, and trait discovery. This is in collaboration with computer scientists and biologists across 11 institutions, led by OSU." |
| 7 | + authors: M. Maruf, Mridul Khurana, Harishbabu Manogaran |
| 8 | + year: 2019 |
| 9 | + tag: 10001 |
| 10 | + highlight: 1 |
| 11 | + news1: |
| 12 | + news2: |
| 13 | + long: 0 |
| 14 | + |
| 15 | +- title: "Predicting the Mechanics of Living Cells" |
| 16 | + key: "mechanobio" |
| 17 | + hasimage: 1 |
| 18 | + width: 70% |
| 19 | + image: mechanobio.png |
| 20 | + description: "This is a collaborative project with researchers from Mechanical Engineering at VT to develop physics-guided ML methods for tracking, characterizing, and predicting the movement of cells and bacteria in fibrous environments using traction-force microscopy images collected in the field of mechanobiology. The physics knowledge that we are integrating in our ML methods includes phenomenological models of cell and bacteria migration and knowledge of the mechanical forces governing interactions between cells and fiber backgrounds. This work is supported by a [$1M NSF Medium grant](https://vtx.vt.edu/articles/2021/10/sanghani-center-researcher-receives--1-million-nsf-grant-to-pred.html) where I am the lead investigator." |
| 21 | + authors: Medha Sawhney |
| 22 | + year: 2023 |
| 23 | + tag: 10003 |
| 24 | + highlight: 1 |
| 25 | + news1: |
| 26 | + news2: |
| 27 | + long: 0 |
| 28 | + |
| 29 | + |
| 30 | +- title: "Modeling Quality of Water in Lakes" |
| 31 | + key: "ecokgml" |
| 32 | + hasimage: 1 |
| 33 | + width: 60% |
| 34 | + image: mechanobio.jpg |
| 35 | + description: "AThe goal of this project is to develop hybrid-ecology-ML models of lake water quality where some lake components are represented using ecology models while others are represented using KGML models. We aim to use KGML to improve the accuracy of current standards in lake modeling as well as to discover new knowledge of lake physics and system interactions. This is in collaboration with researchers from BIO at VT, and limnologists from Univ. of Wisconsin." |
| 36 | + authors: Abhilash Neog, Sepideh Fatemi, Aanish Pradhan |
| 37 | + year: 2019 |
| 38 | + tag: 10002 |
| 39 | + highlight: 1 |
| 40 | + news1: |
| 41 | + news2: |
| 42 | + long: 0 |
| 43 | + |
| 44 | +- title: "(Upcoming Project) Seafloor Characterization From Free Fall Penetrometers" |
| 45 | + key: "seafloor" |
| 46 | + hasimage: 1 |
| 47 | + width: 60% |
| 48 | + image: mechanobio.png |
| 49 | + description: "This work is supported by a recently funded grant from the Naval Engineering Education Consortium (NEEC) to characterize seafloor properties using data collected by free fall penetrometers. This project will involve development of physics-guided ML methods that integrate physics knowledge available as numerical model simulations in the design and training of neural network models." |
| 50 | + # authors: Torbjörn Wigren, Ruslan Seifullaev, André M. H. Teixeira |
| 51 | + year: 2022 |
| 52 | + tag: 10004 |
| 53 | + highlight: 1 |
| 54 | + news1: |
| 55 | + news2: |
| 56 | + long: 0 |
| 57 | + |
| 58 | +- title: "(Upcoming Project) Predicting the Evolution of Zoonotic Viral Diseases for Pandemic Prevention" |
| 59 | + key: "pandemic" |
| 60 | + hasimage: 1 |
| 61 | + width: 80% |
| 62 | + image: mechanobio.png |
| 63 | + description: "This is an upcoming project where we are aiming to forecast and control the next global pandemic by developing a new generation of predictive models of the evolution and human adaptation of animal viral sequences, powered by KGML. Our focus is to computationally predict zoonosis (transfer of virus from one animal species to another) and the events that occur when a virus enters and hijacks a host human cell. Specifically, we aim to predict which mutation(s) in a virus will permit it to jump species from an animal host, and infect and adapt to a human cell." |
| 64 | + # authors: Anh Tung Nguyen, Alain Govaert, André M. H. Teixeira, Sérgio Pequito |
| 65 | + year: 2021 |
| 66 | + tag: 10005 |
| 67 | + highlight: 1 |
| 68 | + news1: |
| 69 | + news2: |
| 70 | + long: 0 |
| 71 | + |
| 72 | +- title: "Solving Eigenvalue Equations and Partial Differential Equations (PDEs) in Physics" |
| 73 | + key: "pinn" |
| 74 | + hasimage: 1 |
| 75 | + width: 50% |
| 76 | + image: mechanobio.png |
| 77 | + description: "This is a collaborative project with computer scientists and physicists from Ohio State University, SUNY Bingamton, and University of Massachusetts Lowell. The goal of this project is to train neural network models for solving eigenvalue equations in physics problems (Schrodinger’s equation in quantum mechanics and Maxwell’s equations in optics) using physics-guided learning algorithms. We are also developing neural network architectures and learning algorithms for solving PDEs using limited number of ground-truth simulations. We are exploring ideas from several fields to improve the parameter efficiency, convergence speed, and generalization capabilities of neural networks, especially on out-of-distribution samples. This work has been supported by an NSF EAGER grant we received in 2020." |
| 78 | + authors: M. Maruf |
| 79 | + year: 2023 |
| 80 | + tag: 10006 |
| 81 | + highlight: 1 |
| 82 | + news1: |
| 83 | + news2: |
| 84 | + long: 0 |
| 85 | + |
| 86 | +- title: "Modeling Multi-phase Flow Dynamics" |
| 87 | + key: "flow" |
| 88 | + hasimage: 1 |
| 89 | + width: 50% |
| 90 | + image: mechanobio.png |
| 91 | + description: "The goal of this project is to develop ML models to predict forces experienced by particles suspended in moving fluids, using high fidelity simulations of particle-fluid systems and knowledge of essential physics underlying the interaction between particles and the flow fields (pressure and velocity). This is in collaboration with Naren Ramakrishnan from CS at VT and Danesh Tafti from ME at VT." |
| 92 | + # authors: M. Maruf |
| 93 | + year: 2023 |
| 94 | + tag: 10006 |
| 95 | + highlight: 1 |
| 96 | + news1: |
| 97 | + news2: |
| 98 | + long: 0 |
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