Many of the cryptocurrency transactions have involved fraudulent activities including ponzi schemes, ransomware as well money-laundering. The objective is to use Graph Machine Learning methods to identify the miscreants on Bitcoin and Etherium Networks. There are many challenges including the amount of data in 100s of Gigabytes, creation and scalability of algorithms.
Orienting to a novel event is a rapid shift in attention to a change in one’s surroundings that appears to be a fundamental biological mechanism for survival and essentially functions as a “what is it” detector. Orienting appears to play a central role in human learning and development, as it facilitates adaptation to an ever-changing environment. Thus, orienting can be viewed as an allocational mechanism in which attention sifts through the complex multi-sensory world and selects relevant stimuli for further processing. The selection of stimuli for further processing has implications for what will be encoded into memories and how strong those memory traces will be. The ability to differentiate between relevant and irrelevant input, to inhibit the processing of irrelevant stimuli, and to sustain attention requires control, and inhibitory processes that improve with age.
This project builds on a novel cellular model of human aging (Sturm et al. Epigenomics 2019) where we can investigate trajectories of multiple molecular features of aging over long time periods. The underlying multi-omic dataset includes epigenomic (DNA methylation), proteomic (protein abundance), bioenergetics (mitochondrial respiration), telomere length, and various secreted factors. A major challenge for the DSI Fellow will be to integrate the multi-omic dataset to capture dynamic signatures of mitochondrial dysfunction and cellular aging, working collaboratively with other scientists. The existing project is expected to result in one or more publications. Possibility to continue work for pay over the summer.
Advances in data collection technologies in neuroscience has resulted in a deluge of high-quality data that needs to be analyzed, and presented to the experimentalist in a meaningful way. Usually the “data analysis and visualization”-pipeline is built from scratch for each new experiment resulting in a significant amount of code duplication and wasted effort in rebuilding the analysis tools. There is a growing need for a unified system to automate much of the repetitive tasks and aid biologists in understanding their data more efficiently.
We aim to augment recovery in spinal cord (SC) injured patients. Electrical stimulation of the SC can facilitate recovery, but the mechanisms are not yet understood. One knowledge gap lies in the exact pathways that are recruited by stimulation. To close this gap, we have tested the effects of SC stimulation in people undergoing clinically indicated surgery. By testing the distribution and size of muscle responses to SC stimulation, we can infer which circuits are activated. We are also examining how SC injury changes those responses. We propose to use Bayesian methods to understand the interaction between muscle responses to stimulation and the MRI indicated pattern of damage. The project will involve construction of models linking multiple data modalities that predict muscle activity, followed by the modification of these models to account for patterns of damage. Construction of such models would enable a deeper understanding of SC stimulation leading to more effective stimulation paradigms.
Our goal is to use deep learning networks to understand which neurons in the brain encode fine motor movements in mice. We collected large datasets entailing calcium imaging data of active neurons and high-resolution videos when mice perform motor tasks. We want to use recent advances in deep learning to (1) estimate the poses of mouse body parts at a high spatiotemporal resolution (2) extract behaviorally-relevant information and (3) align them with neural activity data. Behavioral video analysis is made possible by transfer learning, the ability to take a network that was trained on a task with a large supervised dataset and utilize it on a small supervised dataset. This has been used e.g. in a human pose–estimation algorithm called DeeperCut. Recently, such algorithms were tailored for use in the laboratory in a Python-based toolbox known as DeepLabCut, providing a tool for high-throughput behavioral video analysis.
This position is associated with a new research project co-funded by the Earth Institute Frontiers seed funding and the UN Development Programme in Guinea. Its goal is to develop remote sensing technologies to track environmental impact from bauxite mining in rural communities and to help establish protocols for their use. We seek a research assistant who can (1) develop image classification to be applied to satellite imagery to determine extent and abundance of bauxite dust within specific communities in the Boké region (2) develop methods to make classified images available for download to smartphones in the field and (3) adapt existing application technologies to allow users in the field to upload field photographs and locations of potential impacts they identify. The resulting map would represent a composite of satellite imaged areas of impact verified in the field and documentation of other areas of impact not visible by satellite but already identified in the field.
