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.
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 […]
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 […]
CAREER: Specification-Guided Imitation Learning
Imitation learning (IL) is a powerful learning paradigm that enables machines, such as robots or artificial intelligence (AI) systems, to learn from demonstrations provided by human experts or expert agents. However, in practice, human demonstrations can be inadequate, partial, imperfect, environment-specific, or suboptimal. To address these challenges, this project introduces a novel framework that allows […]
CISE Hosts Pivotal ‘AI for Understanding Earthquakes’ Workshop
On Friday, November 10, 2023, CISE organized a first of its kind workshop, drawing in a diverse gathering of experts hailing from all over the US and Italy to explore the intersection of artificial intelligence and earthquake sciences. The workshop was presented alongside an AI Challenge, where participants were challenged to predict earthquake timing and […]