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.
CISE Students Win CSS TC 2024 Outstanding Student Paper Prize
The committee for the Outstanding Student Paper Prize 2024 announced this year’s prestigious award recipients. Among six national nominations, one paper written by two CISE students stood out for its innovative contributions to the field of smart cities. Ehsan Sabouni and H.M. Sabbir Ahmad and their collaborators are exemplars of academic excellence and collaborative spirit. […]
Collaborative Research: SaTC: CORE: Small: Research on Concurrent Inauthentic Account and Narrative Detection
Inauthentic accounts are commonly used by adversaries on online platforms to carry out fraudulent activities like false advertising, scams, and personal threats. These accounts appear to belong to real people, but actually portray fictitious personas and are controlled by miscreants through semi-automated means to deliver potentially harmful content. Promptly detecting inauthentic accounts and fraudulent content […]
The Future of Driving: Control Barrier Functions and the Internet of Vehicles
The National Highway Traffic and Safety Association reports that 94% of serious car crashes are due to human error. Christos Cassandras, Boston University Distinguished Professor of Electrical & Computer Engineering, Head of the Division of Systems Engineering, and a co-founder of the Center for Information & Systems Engineering (CISE), has made monumental contributions to the […]
SWIFT: Facilitating Spectrum Access by Noise Guessing
Wireless technologies play an essential role in enabling growth and prosperity in societies by supporting business, government, science and education, defense, and health sectors. The boom of connected Internet of Things (IoT) nodes and 5G wireless communications will lead to a many-fold increase in wireless data traffic. This data storm and connectivity-in-everything model will result […]
Wenchao Li Receives Prestigious NSF Career Award
Wenchao Li (ECE, SE, CS) was awarded a National Science Foundation Faculty Early Career Development (CAREER) award to further his research on specification-guided imitation learning (IL). Li uses a combination of formal methods and machine learning to build safe and trustworthy autonomous systems. The CAREER award is a five-year grant that will support Li’s research […]
A Coordinated Approach to Cyber-Situation Awarness Based on Traffic Anomaly Detection
This project aims at developing a suite of anomaly detection algorithms and tools monitoring network traffic and operating both at the local (resource) level and the wider (global) network level. It will leverage recent work by the PIs on statistical temporal anomaly detection using random and Markovian models and on detecting wider network spatial anomalies […]
Refinement Methods for Protein Docking based on Exploring Multi-Dimensional Energy Funnels
All successful state-of-the-art protein docking methods employ a so called multistage approach. At the first stage of such approaches a rough energy potential is used to score billions of conformations. At a second stage, thousands of conformations with the best scores are retained and clustered based on a certain similarity metric. Cluster centers correspond to putative predictions/models. Recent work […]
SCH: INT: Distributed Analytics for Enhancing Fertility in Families
The demands of modern life, education and career choices, as well as the availability of assisted reproductive technologies, are leading many individuals and couples to delay childbearing. This has contributed to infertility and sub-fertility emerging as significant public health problems in the U.S., affecting about 15% of couples, involving both men and women, and resulting […]
Collaborative Research: TRIPODS Institute for Optimization and Learning
This Phase I project forms an NSF TRIPODS Institute, based at Lehigh University and in collaboration with Stony Brook and Northwestern Universities, with a focus on new advances in tools for machine learning applications. A critical component for machine learning is mathematical optimization, where one uses historical data to train tools for making future predictions […]
AF: Small: Collaborative Research: New Representations for Learning Algorithms and Secure Computation
Recent success of machine learning is due in part to the availability of large datasets for training and testing purposes. However, the training process is computationally intensive and collected datasets are often privacy sensitive. This has led to providing Machine Learning as a Service (MLaaS), where data providers store their data in the cloud and […]