{"id":42051,"date":"2025-03-19T11:02:09","date_gmt":"2025-03-19T15:02:09","guid":{"rendered":"https:\/\/www.bu.edu\/cise\/?p=42051"},"modified":"2025-03-19T11:07:10","modified_gmt":"2025-03-19T15:07:10","slug":"cise-faculty-recognized-for-groundbreaking-ai-research-with-airr-awards","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/cise\/cise-faculty-recognized-for-groundbreaking-ai-research-with-airr-awards\/","title":{"rendered":"CISE Faculty Recognized for Groundbreaking AI Research with AIRR Awards"},"content":{"rendered":"

Recognized for their cutting-edge research in artificial intelligence, 11 CISE faculty affiliates have been honored with AI Research Resource (AIRR) awards. <\/span>This <\/span>Hariri Institute<\/span><\/a> program supports AI research at Boston University by giving researchers access to the <\/span>New England Research Cloud (NERC)<\/span><\/a>, a regional computing infrastructure that provides cloud-based resources tailored to academic research. A collaboration between Harvard University and Boston University, NERC offers scalable computing power, data storage, and AI tools to support various research fields, from biomedical sciences to social analytics.<\/span><\/p>\n

These projects span a wide array of disciplines, from cloud computing and cybersecurity to medical research, demonstrating the advancements of CISE faculty in AI-driven research.<\/span><\/p>\n

Learn more about the projects:<\/span><\/p>\n

Advancing Cloud Operations Through AI<\/b><\/p>\n

The project \u201cAI for Automating Cloud Operations,\u201d led by <\/span>CISE Director and professor Ayse Coskun (ECE, SE)<\/b><\/a>, tackles key challenges in cloud operations through advanced AI techniques. By focusing on vulnerability detection and anomaly management, the research team, which also includes CISE faculty affiliates <\/span>associate professors Gianluca Stringhini (ECE)<\/b><\/a>, <\/span>Manuel Egele (ECE)<\/b><\/a>, and <\/span>Brian Kulis (ECE, CS, SE)<\/b><\/a>, aims to enhance the efficiency and security of cloud-based systems. Their work holds immense potential for optimizing cloud infrastructure and improving data security.<\/span><\/p>\n

Enhancing Sustainability and Efficiency in Computing<\/b><\/p>\n

Another project led by Coskun, \u201cAI for Improving Efficiency and Sustainability of Computing,\u201d focuses on increasing the energy efficiency of high-performance computing (HPC) systems and data centers. Collaborating again with Stringhini, Egele, and Kulis, as well as with CISE faculty affiliates <\/span>professor Ajay Joshi (ECE)<\/b><\/a> and <\/span>associate professor Emiliano Dall\u2019Anese (ECE, SE)<\/b><\/a>, the team is working on making large-scale computing systems, like data centers, more energy-efficient and environmentally friendly. They\u2019re using AI to manage computing resources better, automatically detect performance issues, and adjust power usage as needed.<\/span><\/p>\n

\u201cData centers require a lot of electricity to process the data, and they have these agreements with power supply companies like Eversource and National Grid, the companies and the data centers have their own arrangements,\u201d Joshi said. \u201cData centers don’t require the same amount of electricity or energy all the time, so if there is some way for data centers to talk to each other and then track the demand of these data centers, then it can lead to a better solution, both from the data center perspective as well as the power provider perspective.\u201d\u00a0<\/span><\/p>\n

Data centers, which power everything from streaming services to cloud storage, use a massive amount of electricity. The team is developing innovative ways for these centers to share their power needs without exposing sensitive information. By applying AI-driven strategies, they\u2019re trying to cut energy waste and lower carbon emissions while keeping computers running at peak performance. Testing these solutions on real systems will help bring them into widespread use.<\/span><\/p>\n

\u201cThere’s AI for sustainability, where we can come up with sophisticated AI algorithms to predict broadly how we are going to use our resources and how we can get the most bang for our buck,\u201d Joshi said. \u201cThe other way around is sustainable AI. Now they’re investing a lot of money in setting up more data centers, and more data centers means more carbon monoxide being emitted to the environment.\u201d<\/span><\/p>\n

Democratizing Multimodal Models<\/b><\/p>\n

Professor Venkatesh Saligrama (ECE, SE)<\/b><\/a> is leading the project \u201cDemocratizing Research on Multimodal Foundation Models,\u201d which aims to expand access to cutting-edge AI research. By addressing key issues such as fairness and efficiency in foundation models, the project ensures that researchers with limited computational resources can contribute meaningfully to this rapidly evolving field. This initiative fosters inclusivity and broadens the scope of AI advancements beyond well-funded institutions.\u00a0<\/span><\/p>\n

Understanding AI Through Human Development<\/b><\/p>\n

Saligrama is also leading \u201cBabyGPT: Developmentally Plausible Learning of Multimodal Foundation Models,\u201d an innovative project that uses ego-centric video data from young children to explore humanlike learning mechanisms. By investigating how language facilitates vision-related tasks, this research contributes to cognitive science and artificial intelligence, providing insights into efficient and developmentally plausible AI systems.<\/span><\/p>\n

AI-Driven Advancements in Biomedicine<\/b><\/p>\n

Professor Sandor Vajda\u2019s (BME, SE, CHEM)<\/b><\/a> project focuses on using large language models (LLMs) to improve the prediction of binding affinity and classification in biomedical applications. This project aims to enhance the accuracy of structural models for antibody-antigen and MHC-peptide complexes, providing critical advancements in drug discovery and immunotherapy research.<\/span><\/p>\n

Multi-Task AI\u00a0<\/b><\/p>\n

Associate Professor Wenchao Li (ECE, SE)<\/b><\/a> is leading two critical projects. The first, \u201cMulti-Task Decision Transformers,\u201d explores how reinforcement learning can help AI better generalize across multiple decision-making tasks.\u00a0<\/span><\/p>\n

AI Decision-Making<\/b><\/p>\n

Li\u2019s second project, \u201cUncertainty Quantification for Vision-Language Models,\u201d develops techniques to improve the reliability of AI-driven medical applications, particularly in generating automatic radiology reports. These projects highlight AI\u2019s growing role in decision-making and diagnostic applications.<\/span><\/p>\n

Strengthening Cybersecurity with AI-Driven Graph Models<\/b><\/p>\n

Professor David Starobinski\u2019s (ECE, SE, CS)<\/b><\/a> project, \u201cLarge Graph Models for Cybersecurity,\u201d uses advanced AI techniques to improve cybersecurity by automating key tasks like spotting vulnerabilities and fixing errors in threat databases.\u00a0<\/span><\/p>\n

Starobinski explained that \u201csoftware engineers or security analysts work on large-scale software projects, and they use software developed by third parties. The goal is to figure out whether this third-party software is secure or could be exploited for cyber security breaches, and even if it’s currently secure, whether in the future, someone could exploit it.\u201d\u00a0<\/span><\/p>\n

One of the main tools for this is a \u201cknowledge graph,\u201d which maps out connections between different software programs and known security risks, helping experts predict and prevent future threats. Starobinski\u2019s focus is on identifying weaknesses in third-party software.\u00a0<\/span><\/p>\n

AI for Social Media Content Moderation<\/b><\/p>\n

Gianluca Stringhini is also involved in a second project, \u201cDeveloping an AI-based Content Moderation Pipeline for Social Media.” This project researches how advanced AI techniques can be utilized to make social media platforms safer by detecting harmful content like hate speech, misinformation, and scams. The system improves content moderation online by combining language models, image analysis, and ranking algorithms.<\/span><\/p>\n

He explained, \u201cWe’ve been developing tools to automatically identify things like scams, hate speech, or false content, using a set of techniques including computer vision, information retrieval, traditional machine learning, and supervised learning. More recently, we started investigating whether AI models, large language models, or visual transformers can help.\u201d\u00a0\u00a0<\/span><\/p>\n

Stringhini is developing AI-powered tools beyond traditional moderation methods, which often lack context or struggle with complex content. His team\u2019s approach aims to be more accurate and adaptable, making moderation fairer and more effective.\u00a0<\/span><\/p>\n

Revolutionizing Neuropathology with AI<\/b><\/p>\n

Associate professor Lei Tian\u2019s (ECE, BME)<\/b><\/a> new research project, \u201c<\/span>Developing a Neuropathology Foundation Model for Advancing Analysis of Neurodegenerative Diseases\u201d,<\/span> utilizes the massive database of digital brain scans from BU\u2019s Alzheimer\u2019s Disease Research Center. The team on this project is building NeuroPath, the first AI model designed specifically for neuropathology. This advanced tool will help scientists better understand neurodegenerative diseases, leading to earlier diagnoses and improved treatments.<\/span><\/p>\n

Funded through the AIRR awards, these projects are making significant strides in AI innovation. From strengthening cloud security to expanding AI accessibility and improving medical diagnostics, CISE faculty members are pushing the limits of what AI can do. Their work reinforces BU\u2019s status as a leader in artificial intelligence and promises real-world benefits across multiple fields, shaping a safer and more efficient future.<\/span><\/p>\n

Read more about the Hariri Institute\u2019s BU-AIRR Award Recipients <\/span>here<\/span><\/a>.<\/span><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Recognized for their cutting-edge research in artificial intelligence, 11 CISE faculty affiliates have been honored with AI Research Resource (AIRR) awards. This Hariri Institute program supports AI research at Boston University by giving researchers access to the New England Research Cloud (NERC), a regional computing infrastructure that provides cloud-based resources tailored to academic research. A […]<\/p>\n","protected":false},"author":21479,"featured_media":42055,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[26,127,201],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/42051"}],"collection":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/users\/21479"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/comments?post=42051"}],"version-history":[{"count":3,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/42051\/revisions"}],"predecessor-version":[{"id":42054,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/42051\/revisions\/42054"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/media\/42055"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/media?parent=42051"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/categories?post=42051"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/tags?post=42051"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}