deep learning | College of Engineering

NIH Awards $3.1M Grant to Kayhan Batmanghelich for Pulmonary Biomarker Research

in *NEWS, *Research, ECE - Bio-ECE and Digital Health, ECE - Data Science and Intelligent Systems, ECE Faculty, ECE Research, Electrical and Computer Engineering, Kayhan Batmanghelich

AI-driven approach integrates Multi-Modal Data and Biomechanics for Better Diagnosis and Tracking of Chronic Obstructive Pulmonary Disease (COPD)

Tagged: AI, Chronic Obstructive Pulmonary Disease, COPD, deep learning, Hariri Institute, Kayhan Batmanghelich, machine learning, NIH, self-supervised deep learning

Fall Conference Season Brings an Abundant Harvest for ECE Researchers

in *NEWS, *Research, Alumni, Ayse Coskun, ECE - Data Science and Intelligent Systems, ECE - Mobile/Cloud Computing and Cybersecurity, ECE - Photonics, Electronics, and Nanotechnology, ECE Faculty, ECE Graduate, ECE Research, ECE Students, Electrical and Computer Engineering, Emiliano Dall'Anese, Gianluca Stringhini, Prakash Ishwar

BU ECE researchers have had a strong start in the current conference season, earning three best paper awards in September/October 2024.

Tagged: awards, Ayse Coskun, Best Paper Award, cloud computing, deep learning, deep neural networks, Emiliano Dall'Anese, energy, Gianluca Stringhini, Mert Toslali, optimization, Prakash Ishwar, Syed Qasim

Analog Neural Networks: Nature Communications Publishes ECE Researchers’ Blueprint for Precision

in *NEWS, Ajay Joshi, Alumni, ECE - Data Science and Intelligent Systems, ECE Faculty, ECE Graduate, ECE Research, ECE Students, Electrical and Computer Engineering

First author Cansu Demirkiran (ECE PhD’24) and advisor Professor Ajay Joshi discuss “fault-tolerant analog neural networks.”

Tagged: AI, Ajay Joshi, analog, analog computing, Cansu Demirkiran, deep learning, deep neural networks, machine learning, Nature Communications

Unveiling the Hidden Signatures of Drug Resistance in Cancer Cells

in *NEWS, *Research, ECE - Bio-ECE and Digital Health, ECE - Data Science and Intelligent Systems, ECE - Imaging and Optical Science, ECE - Photonics, Electronics, and Nanotechnology, ECE Faculty, ECE Research, Electrical and Computer Engineering, Ji-Xin Cheng, Lei Tian

Supported by a $1.75M grant from the National Institute of Biomedical Imaging and Bioengineering, a multidisciplinary team of experts led by Professor Ji-Xin Cheng are developing a novel approach to establish high-speed, high-content and high-sensitivity mapping of cancer cell metabolism.

Tagged: cancer, cell metabolism, deep learning, Ji-Xin Cheng, Lei Tian, microscopy, stimulated Raman scattering