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Recent News
CS majors Harshita Kalbhor and Grace Cai reflect on leadership, mentorship and building a more inclusive community in STEM.
On any given evening in the University of Maryland’s engineering labs, the hum of 3D printers and the glow of soldering irons signal another round of preparations for the next big robotics competition. But this...read more
In most situations, University of Maryland scientist Guangyang Fang would advise you not to walk into a lightning strike. But now, he’s encouraging everyone to dive right into a thunderstorm – in virtual reality, that...read more
Research and innovation combining wearable technologies and artificial intelligence (AI) could soon revolutionize how we engage with the world—creating smarter, more intuitive systems that not only understand their environment, but are able to respond to...read more
In the ever-evolving world of computational biology, one of the greatest challenges has always been deriving knowledge and insight from the massive amount of both DNA (double helix) and RNA (single helix) data through which...read more
Agarwal finds success in competitive programming in cybersecurity and blockchain tracks.
Vitthal Agarwal , a freshman double majoring in computer science and mathematics at the University of Maryland, has quickly established himself in the hackathon circuit. The international student has competed in several high-profile events, securing...read more
Emojis—those expressive yellow faces and diverse icons that many of us use in texts and emails—offer a fun and quick way for people to share sentiments, convey emotions, and establish aspects of their identity that...read more
In a Q&A, Chen shares insights from his startup journey with FiscalNote and Nitra at the Mokhtarzada Hatchery Speaker Series.
The path to entrepreneurship is rarely straightforward. For many, hands-on experience in building projects, learning from failures and adapting to challenges can be just as valuable as formal education. University of Maryland Department of Computer...read more
A University of Maryland expert in machine learning has been awarded funding from the National Science Foundation (NSF) to explore how autonomous systems can learn from each other and make decisions together in complex, real-world...read more
Upcoming Events
Event
Talk
03.31.2025 14:00 to 16:00
PhD Defense: Building Reliable AI under Distribution Shifts
Bang An
IRB IRB-4107 or https://umd.zoom.us/j/7345152267
PhD Defense: Building Reliable AI under Distribution Shifts
Bang An
Talk
03.31.2025 14:30 to 16:00
PhD Proposal: Modeling Human Factors and Traffic Dynamics for Generalization in Autonomous Driving
Laura Zheng
IRB-5105 OR https://umd.zoom.us/j/94667161518?pwd=qBhj3htYDRewRcwPVgVQW0Y4TrZy0d.1
PhD Proposal: Modeling Human Factors and Traffic Dynamics for Generalization in Autonomous Driving
Laura Zheng
Talk
04.01.2025 10:00 to 12:00
PhD Defense: Minimalist Sensing Toward Ubiquitous Perception
Yang Bai
IRB IRB-5105
PhD Defense: Minimalist Sensing Toward Ubiquitous Perception
Yang Bai
Talk
04.01.2025 11:00 to 12:00
From Code Generation Towards Software Engineering: Advancing Code Intelligence w/ Language Models
Yangruibo (Robin) Ding
IRB 4105 or https://umd.zoom.us/j/94340703410?pwd=rrXaGSXSpabcMTtDNmeCNf2Ih2fQYE.1
From Code Generation Towards Software Engineering: Advancing Code Intelligence w/ Language Models
Yangruibo (Robin) Ding
Talk
04.01.2025 12:30 to 15:00
PhD Defense: Advancing Visual Assets: Taming Deep Priors for Editing and Enhancement
Yiran Xu
IRB IRB-4107 or https://umd.zoom.us/j/9373888068
PhD Defense: Advancing Visual Assets: Taming Deep Priors for Editing and Enhancement
Yiran Xu
Talk
04.02.2025 11:00 to 12:00
Beyond Scaling: Frontiers of Retrieval-Augmented Language Models
Akari Asai
IRB 4105 or https://umd.zoom.us/j/94340703410?pwd=rrXaGSXSpabcMTtDNmeCNf2Ih2fQYE.1
Beyond Scaling: Frontiers of Retrieval-Augmented Language Models
Akari Asai
Talk
04.03.2025 14:00 to 16:00
PhD Defense: New Efficient Algorithms for Nested Machine Learning Problems
Junyi Li
IRB IRB-4109
PhD Defense: New Efficient Algorithms for Nested Machine Learning Problems
Junyi Li
Talk
04.04.2025 09:30 to 11:30
PhD Defense: Toward Learning Non-Spurious Functions: From Representation to Decision Through Data and Model Design
Xiaoyu Liu
IRB IRB-4105 or https://umd.zoom.us/j/93564298810
PhD Defense: Toward Learning Non-Spurious Functions: From Representation to Decision Through Data and Model Design
Xiaoyu Liu
Talk
04.04.2025 11:00 to 13:00
PhD Defense: Practical Multiparty Protocols from Lattice Assumptions: Signatures, Pseudorandom Functions, and More
Kamil Doruk Gur
IRB-5105 or https://umd.zoom.us/my/kdgur?pwd=S0ZEWHJWdC94M3RhNXZtK3NFcE41UT09&omn=92...
PhD Defense: Practical Multiparty Protocols from Lattice Assumptions: Signatures, Pseudorandom Functions, and More
Kamil Doruk Gur
03.31.2025 14:00 to 16:00
PhD Defense: Building Reliable AI under Distribution Shifts
IRB-4107 or https://umd.zoom.us/j/7345152267
PhD Defense: Building Reliable AI under Distribution Shifts
04.01.2025 12:30 to 15:00
PhD Defense: Advancing Visual Assets: Taming Deep Priors for Editing and Enhancement
IRB-4107 or https://umd.zoom.us/j/9373888068
PhD Defense: Advancing Visual Assets: Taming Deep Priors for Editing and Enhancement
04.02.2025 11:00 to 12:00
Beyond Scaling: Frontiers of Retrieval-Augmented Language Models
IRB 4105 or https://umd.zoom.us/j/94340703410?pwd=rrXaGSXSpabcMTtDNmeCNf2Ih2fQYE.1
Beyond Scaling: Frontiers of Retrieval-Augmented Language Models
04.02.2025 11:00 to 12:00
Quantum codes as robust phases of matter
ATL 3100A and Virtual Via Zoom: https://umd.zoom.us/j/2821742741?pwd=SWJSRm1DTGFoYUtVMllVSEo5bzdmdz09
Quantum codes as robust phases of matter
04.03.2025 14:00 to 16:00
PhD Defense: New Efficient Algorithms for Nested Machine Learning Problems
IRB-4109
PhD Defense: New Efficient Algorithms for Nested Machine Learning Problems
04.04.2025 09:30 to 11:30
PhD Defense: Toward Learning Non-Spurious Functions: From Representation to Decision Through Data and Model Design
IRB-4105 or https://umd.zoom.us/j/93564298810
PhD Defense: Toward Learning Non-Spurious Functions: From Representation to Decision Through Data and Model Design