Data Science, AI & Machine Learning
Data Science, artificial intelligence (AI) and machine learning involve making accurate predictions, data mining, machine learning, and more to guide business decisions. Research areas include: bio inspired control using data from animals, computational biology, computational imaging, cyber security, medical informatics, simulation, and video analytics.
CPS: FRONTIER: COLLABORATIVE RESEARCH: BIOCPS FOR ENGINEERING LIVING CELLS
Recent developments in nanotechnology and synthetic biology have enabled a new direction in biological engineering: synthesis of collective behaviors and spatio-temporal patterns in multi-cellular bacterial and mammalian systems. This will have a dramatic impact in such areas as amorphous computing, nano-fabrication, and, in particular, tissue engineering, where patterns can be used to differentiate stem cells […]
CAREER: Rich and Scalable Optimization for Modern Bayesian Nonparametric Learning
Large-scale data analysis has become an indispensable tool throughout academia and industry. When the amount of data is very large, one often faces a tradeoff between the richness, flexibility, and potential predictive power of the models, and the computational requirements. While recent advances in statistics and machine learning provide us with a rich set of […]
III: Small: Structural Matrix Completion for Data Mining Applications
A common problem arising in science and engineering is that a dataset may only be partially measured. Often the complete dataset is naturally expressed as a matrix – for example, traffic flows in a city, gene expression across a set of treatments, or ratings of movies for users. Recently, a new solution strategy has emerged […]
IDBR: Type A: Collaborative research: High-speed AFM imaging of dynamics on biopolymers through non-raster scanning
This award by the Instrument Development for Biological Research (IDBR) program in the Division of Biological Infrastructure (BIO Directorate) is co-funded by the Particulate and Multiphase Processes (PMP) program in the Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET, Engineering Directorate). Non Technical Abstract The primary aim of this project is to create a […]
Interdisciplinary Team Sheds Light on How Proteins Bind
Finding Could Open Up New Drug Discovery Opportunities Over the past six years, an interdisciplinary team of College of Engineering faculty members—Professor Sandor Vajda (BME, SE), Research Assistant Professor Dima Kozakov (BME), Professor Yannis Paschalidis (ECE, SE) and Associate Professor Pirooz Vakili (ME, SE)—have been developing a set of powerful optimization algorithms for predicting the structures of complexes that form when […]
Sensor Research Wins $1M NSF Award
Enhancing the functionality of cyber-physical systems—those that integrate physical processes with networked computing—could significantly improve our quality of life, from reducing car collisions to upgrading robotic surgeries to mounting more effective search and rescue missions. Recognizing Boston University as a key contributor to this effort, the National Science Foundation has awarded Professors Venkatesh Saligrama (ECE, SE) […]
CIF: Small: Collaborative Research: Exploring Synergies of Multi-State Networks
The objective of this project is to undertake a comprehensive study of synergistic gains for multi-state communication networks and develop strategies that can exploit them. The project is organized into three complementary thrusts, focusing on 1) dynamic channels, 2) dynamic channel knowledge, and 3) mixed channel knowledge. The first thrust seeks ways to exploit channel […]
EAGER: Holistic Security for Cloud Computing: Verifiable Computation
A basic security concern inherent to outsourced computating services is guaranteeing the integrity of the information received from the cloud. The concern relates both to outsourced data storage and to the results of outsourced computations. There are several aspects to this problem like ensuring software correctness, protecting against intentional deviation and shortcuts, maintaining data provenance, […]
CIF: Small: Collaborative Research: A Unifying Approach for Identification of Sparse Interactions in Large Datasets
More than 2.5 quintillion bytes of data are created daily in the form of sensor measurements, web posts and clicks, surveillance videos, purchase transactions, and health-care records. However, not all data collected is informative and not all features are relevant to the outcomes of interest. While several researchers have focused attention on compressive sampling for […]
CIF: Medium: Collaborative Research: Interference-Aware Cooperation via Structured Codes: Creating an Empirical Cycle
The classical approach to wireless communication is to isolate communication links by maximizing signal strength and minimizing interference between users. This simple philosophy is supported by a rich theoretical foundation which has inspired powerful coding techniques and protocols that lie at the heart of modern wireless systems. However, these systems have recently become victims of […]