Computational Biology & Medicine
With the increasing availability of data and the improved understanding of system-level interactions in biological systems, computational methods have found many applications in biology and medicine. Research in CISE develops algorithms for long-standing problems in computational biology, such as predicting and characterizing protein interactions, optimizing metabolic networks, and developing computational neuroscience models. Another line of research develops computational methods to advance experimental observation techniques used in biology, from imaging methods to atomic force microscopy. A burgeoning area of research applies Artificial Intelligence (AI) methods to medical data, leading to disease/outcome prediction models and medical decision-making tools.
Collaborative Research: A Workshop on Pre-emergence and the Predictions of Rare Events in Multiscale, Complex, Dynamical Systems
Although pandemics have threatened human civilization since ancient times, how to predict and prevent them remains one of the most pressing challenges, calling out for innovative insights and practices. Pandemics emerge through incidental ‘perfect storms’: molecular changes in pathogens, gradual trends in climate, subtle shifts in ecological interactions among potential hosts, and even individual behavioral […]
Advancing COVID-19 Drug Development via Network Analysis
CISE Faculty Affiliate Mark Crovella (Prof., CS, Bioinformatics) has teamed up with Simon Kasif (Prof., BME, CS, Bioinformatics) and other CS researchers from across the U.S. to advance COVID-19 drug development via Network Analysis. The researchers are co-developing a machine learning methodology to analyze viral and human protein-protein interaction networks. Through this work, the researchers […]
PLOS Computational Biology: Learning from Animals: How to Navigate Complex Terrains
PLOS Computational Biology issued a press announcement on a paper that it published today authored by Boston University CISE Director Yannis Paschalidis (Professor ECE, SE, BME), PhD candidate Henghui Zhu (SE), former CISE post-doctoral associate Armin Ataei, former CISE visiting student scholar Hao Liu (Zhejiang University), along with University of Washington collaborators Professor Thomas Daniel (BIO, […]
Neuro-Autonomy: Neuroscience-inspired Perception, Navigation, and Spatial Awareness for Autonomous Robots
State-of-the-art Autonomous Vehicles (AVs) are trained for specific, well-structured environments and, in general, would fail to operate in unstructured or novel settings. This project aims at developing next-generation AVs, capable of learning and on-the-fly adaptation to environmental novelty. These systems need to be orders of magnitude more energy efficient than current systems and able to pursue complex goals in […]
How to Make Self-Driving Vehicles Smarter, Bolder
With $7.5M DOD grant, BU researchers head international team developing bioinspired control systems for self-navigated vehicles Autonomous vehicles that can maneuver themselves around any city are already out on our public roads, says Yannis Paschalidis, but operating off-road remains a challenge. “These vehicles are designed for very structured environments, within roads and lanes,” says Paschalidis, a […]
BU-led Research Team Wins Competitive $7.5 million MURI Grant to Create Neuro-Autonomous Robots
By Maureen Stanton, CISE Dream Team of Engineers, Computer Scientists, and Neuroscientists from BU, MIT, and Australia to develop neuro-inspired capabilities for Land, Sea, and Air-based Autonomous Robots A Boston University-led research team was selected to receive a $7.5 million Multidisciplinary University Research Initiative (MURI) grant from the U.S. Department of Defense (DoD). With this […]
A Computational Miniature Mesoscope for Large-Scale Brain Mapping in Behaving Mice
Scale is a fundamental obstacle in linking neural activity to behavior. While perception and cognition arise from interactions between diverse brain areas separated by long distances, neural codes and computations are implemented at the scale of individual neurons. An integrative understanding of brain dynamics thus requires cellular-resolution measurements across sensory, motor, and executive areas spanning […]
Convergence: RAISE Integrating machine learning and biological neural networks
The field of neuroscience is undergoing a rapid transformation, and within the next decade, it may become possible to capture data from millions of individual neurons at the same time. Such a technological advancement would allow scientists to record and analyze a significant fraction of the brain’s neural network at unprecedented spatial and time resolutions. […]
Stochastic Dynamic Modeling of Cellular Protein Interactions
Stochastic methods for modeling molecular-protein interactions form an entirely new set of ap-proaches to the important biological goal of simulating cellular biology in silico. Though great progress has been made in this direction by computational biologists over the past 15 years, the goal of “siliconizing” cellular molecular interactions still remains remote. More precisely, the current […]
CIF: Small: Collaborative Research: Signal Processing for Nonlinear Diffractive Imaging: Acquisition, Reconstruction, and Applications
There is a growing need in biomedical research to observe biological structure and processes on the length scales smaller than 100nm. Conventional optical systems cannot effectively provide such information, however, due to the infamous diffraction limit. The formulation of the diffraction limit fundamentally relies on the presumed linearity in the interaction between the illuminating light […]