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 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. […]

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 […]

CAREER: Algorithms and Fundamental Limitations for Sparse Control

The proposal is to study the design of feedback control strategies which stabilize and steer systems by affecting them in only a few variables. The motivation comes from applications which are either large-scale or geographically distributed and therefore cannot be feasibly affected in many places. A primary motivating application is the control of metabolic chemical […]