Graduate Computer Science and Engineering

In a recent study, “Mutual Information Reduction Techniques and its Applications in Feature Engineering,” researchers in the Graduate Department of Computer Science and Engineering explore which mutual information "features" matter most in machine learning models.

Read the entire story in the Katz School blog.

Researchers in the Katz School’s Graduate Department of Computer Science and Engineering are turning to advanced tools like reinforcement learning—a type of machine learning that mimics decision-making in dynamic, uncertain environments—to probe consumer habits.

Read the story in the Katz School blog.

Imagine a future where streaming videos, video calls or surveillance footage look flawless, no matter the network conditions or quality variations. That’s the goal of a recent Katz School study, which will be presented at the 2025 IEEE Conference in January.

Read the story in the Katz School blog.

Dr. David Sweet, an industry professor in the Katz School’s Graduate Department of Computer Science and Engineering, has developed a method for improving how computers optimize complex tasks.

Read the story in the Katz School blog.

The Department of Graduate Computer Science and Engineering recently hosted a dynamic presentation of graduate student research, showcasing innovative capstone projects, independent studies and other research initiatives in Artificial Intelligence, Computer Science, Cybersecurity and Data Analytics and Visualization.

Read the story in the Katz School blog.

Designing a great video game is a balancing act, especially in strategy games where the thrill often comes from tackling the unexpected. Whether it’s an enemy ambush or a rare loot drop, the element of surprise keeps players on their toes. But too much randomness can feel unfair, while too little can make a game predictable and boring. Enter the world of probability-driven game design, a method that introduces structured uncertainty into gameplay. A new framework introduced by Katz Schoo researchers uses mathematical tools to strike this balance, creating levels that are not only engaging but varied and challenging.

Read the story in the Katz School blog.

With concentration becoming increasingly difficult to sustain in today’s fast-paced, distraction-filled environments, accurately monitoring people's focus is critical for boosting productivity, improving educational outcomes and supporting cognitive health.

Read the entire story in the Katz School blog.

Dr. David Li, program director of the M.S. in Data Analytics and Visualization in the Graduate Department of Computer Science and Engineering, have proposed a novel data-driven framework grounded in probability theory and statistical analysis to change how Olympic performance is measured. 

Read the story in the Katz School blog.

A team of researchers that includes Dr. Yucheng Xie, assistant professor in the Katz School’s Graduate Computer Science and Engineering, has developed a new technology called “mmPalm,” which uses millimeter wave signals to create an ubiquitous, low-effort authentication method through palm recognition.

Read the story in the Katz School blog.

Dr. Youshan Zhang, an assistant professor of artificial intelligence and computer science, has been awarded a $175,000 grant from the National Science Foundation for research aimed at improving cardiomegaly diagnosis in animals by the development of an innovative, AI-driven diagnostic tool.

Read the story in the Katz School blog.

Katz School researchers have designed a mobile app using AI technology that analyzes mammogram images and provides real-time diagnostic predictions, all from the convenience of a smartphone. They will present their project findings, outlined in the white paper, “Pink Guardian: A Gateway to Early Breast Cancer Detection,” at the 16th International Conference on Human System Interaction in Paris in July.

Read the full story.

Katz School and UMass Dartmouth researchers have introduced a novel approach to better understand the variables associated with weight loss by utilizing an advanced mathematical technique. Their findings, outlined in the white paper, “A Choquet-Integral Based Approach to Identify Weight Loss Component Subsets,” were presented in June at the IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

Read the full story.

Researchers have developed a series of algorithms using Siamese networks, a type of artificial intelligence, to better identify and track the body movements of stroke patients in order to assist in patient treatment and recovery. Their findings, outlined in the white paper, “Accurate Body Pose Matching for Individuals with Stroke Using Siamese Networks,” were presented in June at the IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

Read the full story.

Researchers have developed a method using a powerful technology to remove unwanted noise from the audio recordings of bird sounds. 

The method, called ViTVS, uses an image processing technology to divide audio signals into distinct parts, or segments, for isolating clean bird sounds from a noisy background. The approach, explained in the paper, “Vision Transformer Segmentation for Visual Bird Sound Denoising,” has been accepted for presentation at a conference on the science and technology of spoken language processing—InterSpeech 2024—by researchers from the Katz School’s Department of Computer Science and Engineering and Cornell University’s School of Public Policy.

Read the full story.

A Katz School student team's proposal to reform the New York City subway system is a finalist in a business case competition sponsored by the Zicklin School of Business at Baruch College. Their proposal, “A Data-Driven Approach to Urban Mobility,” leverages advancements in artificial intelligence, data analytics and Internet of Things (IoT) technologies to revitalize the Metropolitan Transit Authority (MTA).

Read the full story.

Dr. Honggang Wang, chair of the Katz School’s Department of Computer Science and Engineering, has received a $600,000 grant from the National Institutes of Health to create an artificial intelligence platform that would recognize patterns in longitudinal dietary data. The Innovative Pattern Analysis Tool, called iPAT, would employ a new machine-learning algorithm to facilitate comparisons between individual and population-level dietary patterns, and would generate evidence for dietary guidelines.

Read the full story.

Two Katz School AI students took first prize at the 2023 UC Berkeley AI Summit Generative AI Hackathon for solving a complex business problem using the latest in generative AI in under two hours. Niranjan Kumar Kishore and Tharun Prabhakar won in the category of “College Student, Data Scientists or Comp Sci,” with Prabhakar completing his generative AI application just two minutes behind Kishore in the contest. Both were awarded $1,000. 

Their winning submissions were an end-to-end automated generative AI application implemented in a secure Google Cloud that used large language models to answer a set of questions on the impact, opportunity and challenges of generative AI, what every CEO should know about generative AI, and generative AI in healthcare.

Read the full story.

Katz School mathematics researchers have developed a model that anticipates market crashes and financial bubbles in the Bitcoin industry. 

In the paper, “Why Topological Data Analysis Detects Financial Bubbles?,” Dr. Marian Gidea, associate dean for STEM education and research, and Samuel Akingbade, a Ph.D. student in mathematics, discuss how Topological Data Analysis (TDA), which identifies patterns and features in data not apparent through traditional statistical methods, can be used to detect early-warning signals of financial bubbles.

Read the full story.

With the help of AI, scientists can better model climate patterns, identify trends and make predictions, leading to a clearer understanding of climate change and its environmental impact. Katz School students in Artificial Intelligence and Data Analytics did their part for S&P Global Market Intelligence by sifting through large amounts of data, like satellite images that researchers use to monitor climate change, to create a set of geography-specific, super-resolution climate forecasts that will be valuable for climate research, policy planning and risk assessment.

Read the full story.