Skip to main content

HackNC & Carolina Data Challenge: Where code meets innovation

November 20, 2023

This fall, UNC Computer Science transformed into a hub of technological innovation and collaboration with two major student events: HackNC and the Carolina Data Challenge. These hackathons, led by student teams with department support, brought together more than 700 students from various colleges across North Carolina and beyond.

Bansal receives Indian Institute of Technology Kanpur Young Alumnus Award

November 14, 2023

Professor Mohit Bansal earned the IIT Kanpur Young Alumnus Award, conferred annually to two alumni under 40 years old who have contributed significantly to achieve exemplary recognition and distinction in their careers. IIT Kanpur is one of the most prestigious academic institutions in India, and thousands of its alumni are eligible for the award.

Researchers from Meta and UNC-Chapel Hill Introduce Branch-Solve-Merge: A Revolutionary Program Enhancing Large Language Models’ Performance in Complex Language Tasks

October 31, 2023

Branch-Solve-Merge (BSM) is a program for enhancing Large Language Models (LLMs) in complex natural language tasks. BSM includes branching, solving, and merging modules to plan, crack, and combine sub-tasks. Applied to LLM response evaluation and constrained text generation with several models, BSM boosts human-LLM agreement, reduces biases, and enables LLaMA-2-chat to match or surpass GPT-4 in most domains.

Oliva given NSF grant to enhance machine learning extrapolation

October 17, 2023

Assistant Professor Junier Oliva received a two-year NSF grant to improve the ability of machine learning models to extrapolate beyond the scope of their training dataset. The project will hopefully enhance scientific tasks across numerous disciplines, including chemical discovery and safety assessment.

Utilizing AI and machine learning for drug discovery

September 14, 2023

Junier Oliva and Alexander Tropsha received at two-year grant from the National Science Foundation for a new project, “Extrapolative Analyses for Reliable Machine Learning Driven Scientific Discovery”, the goal of which is to give machines the ability to introspectively assess training set limitations and either address them or alert human users. The project will directly impact the drug discovery process.