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DTSTART;TZID=America/New_York:20260430T150000
DTEND;TZID=America/New_York:20260430T160000
DTSTAMP:20260505T020130
CREATED:20260428T125901Z
LAST-MODIFIED:20260428T125901Z
UID:10000057-1777561200-1777564800@csc.ncsu.edu
SUMMARY:Pushing the Frontier of (Small) Language Models
DESCRIPTION:Abstract:\nIn this talk\, I will explore key research contributions in efficient deep learning\, with a focus on training smaller yet highly capable language models. I will discuss approaches such as curating high-quality datasets and designing effective training curricula. The talk will cover different stages of training\, including pre-training\, mid-training\, and agentic reasoning and highlight techniques for pushing the boundary of performance via transfer from larger and/or more powerful union of models. I will conclude by outlining promising future research directions aligned with these ideas. \nBio:\nMojan Javaheripi leads the midtraining and synthetic data pillar at Reflection AI. Prior to joining Reflection\, she was a Principal researcher and technical advisor to the CTO at Microsoft\, as well as a resident researcher at OpenAI. Her research enhances open-source LLMs through new data sources\, training regimens\, and model architectures. She received her PhD from the University of California San Diego and her dissertation focused on efficient deep learning training and inference\, adversarial robustness\, and privacy-preserving deep learning. \nHost: Dongkuan Xu\nNote: This seminar is virtual. Zoom instructions ⤵️\nZoom URL: https://ncsu.zoom.us/j/95437928324?pwd=wzLmLrAfDsqW3dl13iD8i90Fa5biEV.1&jst=2\nZoom Meeting ID: 954 3792 8324\nZoom Passcode: 604198
URL:https://csc.ncsu.edu/event/pushing-the-frontier-of-small-language-models/
CATEGORIES:CS AI Seminar Series,Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260429T183000
DTEND;TZID=America/New_York:20260429T203000
DTSTAMP:20260505T020130
CREATED:20260403T162433Z
LAST-MODIFIED:20260414T175434Z
UID:10000054-1777487400-1777494600@csc.ncsu.edu
SUMMARY:Spring 2026 Senior Design Center's 'Posters and Pies'
DESCRIPTION:Join the Department of Computer Science for the Senior Design Center’s ‘Posters and Pies’ celebration on Wednesday\, April 29\, 2026 from 6:30 – 8:30 p.m. at the McKimmon Center in Raleigh. Each of our Spring 2026 Senior Design teams will proudly show off project demonstrations and poster displays! Dessert pies\, snacks and drinks will be served. \nAbout the Senior Design Center (SDC)\nFor three decades\, the SDC has brought student teams and corporate sponsors together to work on real-world problems\, with a goal of creating viable products\, services and solutions. This is one of the department’s strongest programs\, helping develop and differentiate graduates as they enter the industry in a time of change\, collaboration and growth. \nSee projects and sponsor information here. \nThank you to our program partners for their support of the Senior Design Center’s ‘Posters and Pies’: Blue Cross NC\, LexisNexis\, SAS and the Computer Science ePartners Program for their support of this event! \nMedia is welcome at this event. Contact Leslie Rand-Pickett for media and corporate partnership inquiries. \nMcKimmon Center visitor information.
URL:https://csc.ncsu.edu/event/spring-2026-senior-design-centers-posters-and-pies/
LOCATION:The McKimmon Center\, 1101 Gorman St\, Raleigh\, NC\, 27606\, United States
CATEGORIES:Senior Design Center
ATTACH;FMTTYPE=image/jpeg:https://csc.ncsu.edu/wp-content/uploads/sites/318/2025/11/R4_05774-copy-scaled.jpg
ORGANIZER;CN="Computer Science":MAILTO:lcrandpi@ncsu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260410T100000
DTEND;TZID=America/New_York:20260410T110000
DTSTAMP:20260505T020130
CREATED:20260409T155624Z
LAST-MODIFIED:20260410T021842Z
UID:10000055-1775815200-1775818800@csc.ncsu.edu
SUMMARY:Learning foundation operators and diffusion models over function spaces
DESCRIPTION:Speaker:\nLu Lu\, Yale University \nAbstract:\nAs an emerging paradigm in scientific machine learning (SciML)\, deep neural operators pioneered by us can learn nonlinear operators of complex dynamic systems via neural networks. In this talk\, I will present the vanilla deep operator network (DeepONet) and several extensions of DeepONet\, such as DeepONet with Fourier decoder layers and geometry-dependent/manifold operator learning. I will demonstrate their effectiveness on diverse multiphysics and multiscale 3D problems\, such as geological carbon sequestration\, full waveform inversion\, and topology optimization. I will present the first operator learning method that requires only one PDE solution\, i.e.\, one-shot learning\, by introducing a new concept of local solution operator based on the principle of locality of PDEs. I will also present the first systematic study of federated SciML for approximating functions and solving PDEs with data heterogeneity. Moreover\, I will present our recent work on diffusion models\, including FunDiff as a novel framework of diffusion models over function spaces for physics-informed generative modeling and solving forward and inverse PDE problems\, and RED-DiffEq as regularization by denoising diffusion models for solving inverse PDE problems. \nBio:\nLu Lu is an Assistant Professor in the Department of Statistics and Data Science at Yale University. Prior to joining Yale\, he was an Assistant Professor in the Department of Chemical and Biomolecular Engineering at University of Pennsylvania from 2021 to 2023\, and an Applied Mathematics Instructor in the Department of Mathematics at Massachusetts Institute of Technology from 2020 to 2021. He obtained his Ph.D. degree in Applied Mathematics at Brown University in 2020\, master's degrees in Engineering\, Applied Mathematics\, and Computer Science at Brown University\, and bachelor's degrees in Mechanical Engineering\, Economics\, and Computer Science at Tsinghua University in 2013. His current research interest lies in scientific machine learning and artificial intelligence for science\, including theory\, algorithms\, software\, and its applications to engineering\, physical\, and biological problems. His broad research interests focus on multiscale modeling and high performance computing for physical and biological systems. He has received the Department of Energy Early Career Award\, MIT Technology Review Innovators under 35 Asia Pacific\, Mathematics Young Investigator Award from MDPI\, and Joukowsky Family Foundation Outstanding Dissertation Award of Brown University. \nHost:\nDongkuan (DK) Xu\, CSC \nZoom:\nContact CS Grad Office for access questions.
URL:https://csc.ncsu.edu/event/learning-foundation-operators-and-diffusion-models-over-function-spaces/
CATEGORIES:CS AI Seminar Series,Lecture/Seminar
ORGANIZER;CN="Computer Science":MAILTO:lcrandpi@ncsu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260402T183000
DTEND;TZID=America/New_York:20260402T193000
DTSTAMP:20260505T020130
CREATED:20260330T183117Z
LAST-MODIFIED:20260331T202314Z
UID:10000052-1775154600-1775158200@csc.ncsu.edu
SUMMARY:The Future of Software Development
DESCRIPTION:Title:\nThe Future of Software Development: Predictions from people who have seen it\, offered to those who will invent it \nAbstract:\nTogether\, Dr. Jennings and Mr. Joshua S. Allen have five CS degrees and over 50 years of industry experience in software development. Commercial software development is radically and rapidly changing\, and they have observations and predictions to share. Many changes have been coming for decades\, and AI is accelerating them. The role of higher education in the production of software will also change dramatically\, but with great hysteresis and significant missteps along the way. Come hear their perspectives and bring your own. And remember\, the best way to predict the future is to invent it\, as Alan Kay and others have pointed out. \nBio:\nJamie A. Jennings earned her Ph.D. in Computer Science at Cornell University in 1995\, then joined the faculty of Tulane University\, where she established a robotics lab to continue her work on cooperative navigation and manipulation with mobile robots. In collaboration with her graduate and undergraduate students\, this work included computational geometry\, algorithm design\, distributed systems and some applications of compiler design techniques. \nJennings left academia for a 19-year career in industry\, first as a Research Staff Member at IBM’ss T.J. Watson Research Lab\, then later as a Senior Technical Staff Member in IBM’ss Software Group (now called the Watson Cloud division). During this time\, she led the creation of several open technical standards as the chair of Expert Groups in the SyncML Initiative\, the Open Mobile Alliance and OSGi. She is the author of several software patents. \nIn August 2018\, Jennings joined the NC State Department of Computer Science as a teaching faculty member\, focused on undergraduate education. Her research interests are largely in applications of theoretical computer science. Working primarily with undergraduate researchers\, she and her students apply techniques from Programming Language Theory\, Compilers and the Theory of Computation (specifically\, automata and grammars). \nJennings is the creator and primary author of the Rosie Pattern Language\, a replacement for regular expressions that is designed to be used at industrial scale\, where there are \n\nMany expressions (patterns) in use\nHigh data volume\, velocity and variability\nMany software developers involved in a project.\n\nHost:\nKim Titus
URL:https://csc.ncsu.edu/event/the-future-of-software-development/
LOCATION:EB1 2015\, 915 Partners Way\, Raleigh\, 27606\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260328T090000
DTEND;TZID=America/New_York:20260328T140000
DTSTAMP:20260505T020130
CREATED:20260130T211511Z
LAST-MODIFIED:20260130T211623Z
UID:10000046-1774688400-1774706400@csc.ncsu.edu
SUMMARY:Spring 2026 Engineering Open House
DESCRIPTION:Join #NCStateCS on Saturday\, March 28 for University Open House! Chat with faculty and current students\, explore our undergraduate programs\, tour our labs and classrooms\, and experience all things Centennial Campus. \nDate: Saturday\, March 28\, 2026 \nTime: 9 a.m. – 2 p.m. \nEngineering Open House details
URL:https://csc.ncsu.edu/event/spring-2026-engineering-open-house/
LOCATION:Engineering Building 2\, 890 Oval Drive.\, Raleigh\, NC\, 27606\, United States
CATEGORIES:College,Department,University
ATTACH;FMTTYPE=image/jpeg:https://csc.ncsu.edu/wp-content/uploads/sites/318/2026/01/CS-Open-House-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260325
DTEND;VALUE=DATE:20260326
DTSTAMP:20260505T020130
CREATED:20260216T192717Z
LAST-MODIFIED:20260318T201641Z
UID:10000047-1774396800-1774483199@csc.ncsu.edu
SUMMARY:#GivingPack: NC State CS Day of Giving 2026
DESCRIPTION:Each year\, our incredible alumni\, faculty\, staff\, students\, ePartners and the broader computer science community come together to make an impact during Day of Giving. NC State Day of Giving is a 24-hour fundraising event celebrated university-wide. It’s also a chance to strengthen our department in critical areas. Last year’s Day of Giving supporters: \n🎤 sent students to present at high-profile conferences and events\n provided the money to attract and hire top-tier teaching and research talent\n supported numerous student groups\, including the CS Student Ambassadors and Women in Computer Science \nFor nearly 60 years\, NC State’s Department of Computer Science has worked to advance the citizens of North Carolina\, the nation and the world through excellence in computing education and research. \nOur department doesn’t just focus on the now; we’re interested in what’s next. We’re recruiting top-tier research and teaching talent. We’re helping our students enter the workforce in a time of change\, collaboration and growth. We’re engaging with key industry partners both at home and abroad. We produce the best of the best – from new tech and research to the talent of tomorrow: our CS grads! \nYour support is critical in driving us forward and advancing computer science education and research. Thank you.\nDonate to the Department of Computer Science Enhancement Fund \nAdditional Department of Computer Science Funds
URL:https://csc.ncsu.edu/event/givingpack-nc-state-cs-day-of-giving-2026/
CATEGORIES:College,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260305T150000
DTEND;TZID=America/New_York:20260305T160000
DTSTAMP:20260505T020130
CREATED:20260225T211012Z
LAST-MODIFIED:20260225T211024Z
UID:10000048-1772722800-1772726400@csc.ncsu.edu
SUMMARY:Deployable Robots that Learn
DESCRIPTION:Abstract:\nWhile many robots are currently deployable in factories\, warehouses and homes\, their autonomous deployment requires either the deployment environments to be highly controlled\, or the deployment to only entail executing one single preprogrammed task. These deployable robots do not learn to address changes and to improve performance. For uncontrolled environments and for novel tasks\, current robots must seek help from highly skilled robot operators for teleoperated (not autonomous) deployment. \nIn this talk\, I will present three approaches to removing these limitations by learning to enable autonomous deployment in the context of mobile robot navigation\, a common core capability for deployable robots: \n(1) Interactive Learning by Adaptive Planner Parameter Learning fine-tunes existing motion planners by learning from simple interactions with non-expert users before autonomous deployment and adapts to different deployment scenarios; (2) In-Situ Learning of vehicle kinodynamics allows robots to learn from vehicle-terrain interactions during deployment and accurately\, quickly and stably navigate robots on unstructured off-road terrain; (3) Reflective Learning by Learning from Hallucination enables agile navigation in highly constrained deployment environments by reflecting on previous deployment experiences and creating synthetic obstacle configurations to learn from. In addition\, I will also briefly introduce a few more recent work on robot night vision\, social robot navigation\, multi-robot coordination and human-robot interaction. \nBio:\nXuesu Xiao is an Assistant Professor in the Department of Computer Science at George Mason University. Xuesu (Prof. XX) directs the RobotiXX lab\, in which researchers (XX-Men and XX-Women) and robots (XX-Bots) work together at the intersection of motion planning and machine learning with a specific focus on developing highly capable and intelligent mobile robots that are robustly deployable in the real world with minimal human supervision. \nXuesu’s work has been deployed in real-world robot field missions\, including search and rescue effort in the Mexico City earthquake and the Greece refugee crisis\, decommissioning effort in the Fukushima nuclear disaster and multiple search and rescue exercises in the US. Xuesu’s research has been featured by The New York Times\, WIRED\, US Army and IEEE Spectrum. Xuesu has been awarded State Council of Higher Education for Virginia (SCHEV) Outstanding Faculty Award (OFA) Rising Star\, the Commonwealth’s highest honor for faculty at Virginia’s public and private colleges and universities. \nHost:\nPeng Gao
URL:https://csc.ncsu.edu/event/deployable-robots-that-learn/
LOCATION:EB2 3001\, 890 Oval Drive\, Raleigh\, NC\, 27606\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260304T090000
DTEND;TZID=America/New_York:20260304T150000
DTSTAMP:20260505T020131
CREATED:20260106T162133Z
LAST-MODIFIED:20260106T200241Z
UID:10000043-1772614800-1772636400@csc.ncsu.edu
SUMMARY:Computer Science Research Day Spring 2026
DESCRIPTION:The Department of Computer Science will host a Research Day for industry leaders on Wednesday\, March 4 from 9 a.m. – 3 p.m. in EB2. This event will feature short research presentations from faculty members\, followed by a networking lunch and a graduate student poster session in the afternoon. \nResearch presentation topics include: \n\nSoftware modernization\nRobotics and cyber-physical systems\nOptimization for logistics and artificial intelligence/machine learning\nAnd more!\n\nThis is a great opportunity to learn more about our faculty’s research\, and to network with faculty and industry professionals. \nQuestions? Contact Leslie Rand-Pickett: lcrandpi@ncsu.edu
URL:https://csc.ncsu.edu/event/computer-science-research-day/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Department,Research Day
ATTACH;FMTTYPE=image/jpeg:https://csc.ncsu.edu/wp-content/uploads/sites/318/2025/05/IMG_0012.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260303T190000
DTEND;TZID=America/New_York:20260303T200000
DTSTAMP:20260505T020131
CREATED:20260225T205838Z
LAST-MODIFIED:20260225T205838Z
UID:10000049-1772564400-1772568000@csc.ncsu.edu
SUMMARY:Smarter Beats Bigger: A Heretic's Guide to AI
DESCRIPTION:Abstract:\nEveryone says AI needs more: more parameters\, more data\, more GPUs\, more money. I’m here to argue the opposite. \nThis talk begins with a cautionary tale from my student days—watching my research-professor uncle doubled over laughing in his basement lab because he and his grad student had just vaporized a large percent of their annual liquid helium budget. When your experiment explodes\, sometimes all you can do is laugh. \nThat moment taught me something: complexity has costs\, and they’re not always obvious until something goes boom. \nI’ll walk through thirty years of building AI systems that worked because they were simple\, not despite it. Like the expert system for raising pigs that outperformed the human expert who wrote its rules—built by a junior master’s student. Or modern ‘compact AI’ methods that match GPT-class results using 1/1000th of the data. \nHere’s the heresy: in most real software systems\, complexity collapses. Behavior funnels. The winning move isn’t throwing more compute at the problem—it’s asking a better question. \nIf you’ve ever suspected that the AI hype machine is missing something important\, come find out what. \nBio:\nTim Menzies (Ph.D. UNSW 1995\, ACM Fellow\, IEEE Fellow\, ASE Fellow) is a globally recognized leader in software engineering research\, best known for his pioneering work in data-driven\, explainable\, and minimal AI for software systems. Over the past two decades\, his contributions have redefined defect prediction\, effort estimation and multi-objective optimization\, emphasizing transparency and reproducibility. \nAs the co-creator of the PROMISE repository\, Tim helped establish modern empirical software engineering\, showing that small\, interpretable AI models can outperform larger\, more complex ones. Currently\, he works as a full Professor in computer science at NC State University. He is the director of the Irrational Research lab (mad scientists r’us). His research has earned over $19+ million in funding from agencies such as NSF\, DARPA and NASA\, as well as from private companies like Meta\, Microsoft and IBM. \nTim has published over 300 papers\, with more than 24\,000 citations\, and advised 24 Ph.D. students. He is the editor-in-chief of the Automated Software Engineering journal and an associate editor for IEEE TSE. His work continues to shape the future of software engineering\, focusing on creating AI tools that are not only intelligent but also fair\, transparent and trustworthy. For more information\, visit timm.fyi. \nHost:\nKim Titus
URL:https://csc.ncsu.edu/event/smarter-beats-bigger-a-heretics-guide-to-ai/
LOCATION:201 Park Shops\, 101 Current Dr.\, Raleigh\, NC\, 27607\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260212T180000
DTEND;TZID=America/New_York:20260212T190000
DTSTAMP:20260505T020131
CREATED:20251219T181851Z
LAST-MODIFIED:20260212T192304Z
UID:10000042-1770919200-1770922800@csc.ncsu.edu
SUMMARY:Labcorp ‘Leadership in Technology’ Speaker Series: Frank Price
DESCRIPTION:Frank Price\nSVP\, Chief Information Risk Officer\, Labcorp\nThe Cyber Threat Landscape for 2026\nCybersecurity has become one of the most critical challenges of the digital age. We will examine the rapidly evolving threat landscape\, including the impact of ransomware\, artificial intelligence’s growing role in attack and defense\, digital impersonation\, the growth of cybercrime as a business\, and readiness for post-quantum computing threats. Additionally\, we will address the concept of cyber resilience. \nBio\nA security practitioner since 1993\, Frank Price has built and refined cybersecurity and information risk management programs in multiple business sectors\, including international banking\, global telecommunications and high-end retail; however\, his passion and main focus have been in healthcare\, where he has led  programs for Medco Health Solutions\, CVS Health and Labcorp\, where he currently serves as the Chief Information Risk Officer. \nPrice has previously served on the Board of Directors for the Health Information Sharing and Analysis Center (H-ISAC)\, a global non-profit organization offering health care stakeholders a trusted community for sharing physical and cyber threat intelligence and best practices. He is a member of the Health Sector Coordinating Council’s Post-Quantum Cryptography Task Group and is a regular panelist and speaker at forums on cybersecurity and resilience. He has a bachelor’s degree in information systems management from New York University\, and has previously served as an adjunct professor at Rutgers University in New Jersey\, where he taught information security concepts to students as part of an experimental work/education program called SOAR. \n  \nNote to Computer Science Graduate Students:This lecture has been approved by the CS Graduate Oversight Committee and counts toward the required lectures for graduate students. \nWatch Live
URL:https://csc.ncsu.edu/event/labcorp-leadership-in-technology-speaker-series-frank-price/
LOCATION:EB2 1025\, 890 Oval Drive.\, Raleigh\, 27606\, United States
CATEGORIES:LabCorp Seminar,Lecture/Seminar
ATTACH;FMTTYPE=image/png:https://csc.ncsu.edu/wp-content/uploads/sites/318/2025/12/Labcorp-Frank-Price-Spring-26.png
ORGANIZER;CN="Computer Science":MAILTO:lcrandpi@ncsu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260202T120000
DTEND;TZID=America/New_York:20260202T150000
DTSTAMP:20260505T020131
CREATED:20260106T194545Z
LAST-MODIFIED:20260106T200145Z
UID:10000044-1770033600-1770044400@csc.ncsu.edu
SUMMARY:CS ePartners Career Connections – Spring 2026 Virtual Event
DESCRIPTION:Monday\, February 2\, 2026  |  12 – 3 p.m. \nVirtual via the Career Fair Plus platform. Students may register and sign up for meetings with companies via Career Fair Plus. \nEmployers: this event is open to members of the Department of Computer Science ePartners program. Contact Leslie Rand-Pickett\, lcrandpi@ncsu.edu for sign up and program information.
URL:https://csc.ncsu.edu/event/cs-epartners-career-connections-spring-2026-virtual-event/
CATEGORIES:Career Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260116T150000
DTEND;TZID=America/New_York:20260116T160000
DTSTAMP:20260505T020131
CREATED:20260109T190820Z
LAST-MODIFIED:20260109T190820Z
UID:10000045-1768575600-1768579200@csc.ncsu.edu
SUMMARY:Specializing Runtime Verification for Software Testing
DESCRIPTION:Abstract:  \nRuntime verification promises to make software more reliable by monitoring program executions against formal specifications of safety properties. This talk will present results from work that my group did over the last several years to realize this promise in the context of software testing. I will start by briefly summarizing our early results showing that runtime verification during testing finds hundreds of confirmed bugs that testing alone misses in large open-source software\, and that exploiting developers’ small but frequent code changes scales runtime verification better during continuous integration. Next\, I will discuss four challenges that we identified\, which must be addressed if runtime verification is to become more widely used among developers. Lastly\, I will present our recent results and works-in-progress that address these challenges\, and (if time permits) new types of software tests that we invented in the process. \nBio:  \nOwolabi Legunsen is an assistant professor in the Department of Computer Science at Cornell University. His research is on software engineering\, with a focus on software testing\, runtime verification\, and the unification of both approaches. Owolabi’s work has been recognized with four ACM SIGSOFT Distinguished Paper awards\, an NSF CAREER Award\, and an Intel Rising Star Faculty Award. \nHost: Marcelo d’Amorim
URL:https://csc.ncsu.edu/event/specializing-runtime-verification-for-software-testing/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251211T133000
DTEND;TZID=America/New_York:20251211T150000
DTSTAMP:20260505T020131
CREATED:20251104T161513Z
LAST-MODIFIED:20251205T161807Z
UID:10000037-1765459800-1765465200@csc.ncsu.edu
SUMMARY:Graduation Ceremony (Fall 2025)
DESCRIPTION:On December 11\, 2025 at 1:30 p.m. at Reynolds Coliseum\, the Department of Computer Science welcomes family and friends of our Fall 2025 graduates. We will be celebrating the dedication and commitment of our graduating doctoral\, master and bachelor degree students as they carry forward a tradition of excellence\, accomplishment and life-long learning. \nCan’t make the ceremony? View the livestream here.
URL:https://csc.ncsu.edu/event/graduation-ceremony-fall-2025/
LOCATION:Reynolds Coliseum\, 2411 Dunn Ave\, Raleigh\, NC\, 27607\, United States
CATEGORIES:Department
ORGANIZER;CN="Computer Science":MAILTO:lcrandpi@ncsu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251203T160000
DTEND;TZID=America/New_York:20251203T173000
DTSTAMP:20260505T020131
CREATED:20251117T173324Z
LAST-MODIFIED:20251118T163411Z
UID:10000041-1764777600-1764783000@csc.ncsu.edu
SUMMARY:Fall 2025 Senior Design Center's 'Posters and Pies'
DESCRIPTION:Join the Department of Computer Science for the Senior Design Center’s ‘Posters and Pies’ celebration on Wednesday\, December 3\, 2025 from 4 – 5:30 p.m. at the McKimmon Center in Raleigh. Each of our Fall 2025 Senior Design teams will proudly show off project demonstrations and poster displays! Dessert pies\, snacks and drinks will be served. \nRegister here. \nAbout the Senior Design Center (SDC)\nFor three decades\, the SDC has brought student teams and corporate sponsors together to work on real-world problems\, with a goal of creating viable products\, services and solutions. This is one of the department’s strongest programs\, helping develop and differentiate graduates as they enter the industry in a time of change\, collaboration and growth. \nFall 2025 Sponsors and Projects\n\nAriso: SciSummary’s Literature Review Mode\nBandwidth: AI Answering Machine Detector\nBenjamin Franklin Scholars: Alumni Association Portal Version 2.0\nDeutsche Bank: CODIN (COde Duplication INspector)\nFidelity: FidVent\nHeckman & Battestilli: MyDigitalHand\nHitachi Energy 1: Automated Test Report Generation\nHitachi Energy 2: Virtual Transformer Simulator\nImpartial 1: Justice Well Played – The Defense & Sentencing\nImpartial 2: Justice Well Played – Jury Deliberation\nKatabasis: King Crash’s Code\nLaboratory for Analytic Sciences 1: TuneTank: Model Fine-Tuning\nLaboratory for Analytic Sciences 2: Prompto – A Prompt Engineering Assistant\nLexisNexis: Resource Mapper\nMann+Hummel: High Volume IoT Monitoring & Control\nNC Department of Health & Human Services 1: IT Financial Management Dashboard\nNC Department of Health & Human Services 2: ReqWizard\nNC Parks & Recreation 1: Digital Passport\nNC Parks & Recreation 2: Digital Passport\nNC State CCEE Earthquake Research Team: Rapid Post-Earthquake Assessment of Bridges in Alaska\nNC State Computer Science Generative Intelligent Computing Lab: OceanConnect\nNC State Computer Science Undergraduate Curriculum Committee: Syllabus Tool\nNC State College of Management: MicroSim Lab\nNC State College of Natural Resources Christmas Tree Genetics Program: ROOTS 4.0\nNC State Department of Health & Exercise Studies: The Nutrition Adventure\nNC State Environmental Education Lab: Data Dashboard for Classroom Teachers\nNC State Sustainability Office: Waste Management Ticket Auto-Processor\nOpenDI: OpenDI Model Hub\nResumeFab: Adaptive Resume Generation\nSAS: Instant Insights – Data Cards for Your Data Assets\nSrougi: VR Biotech Education Minigames\nStallmann: Graph Algorithm Animation Tool (GALANT) Tree Extension\n\nSenior Design Center Teaching Team\, Fall 2025\n\nMargaret Scaturro Heil\, Director\nIgnacio Domínguez\, Assistant Director and Technical Advisor\nJason King\, Technical Advisor\nDavid Sturgill\, Technical Advisor\nCaio Batista\, Technical Advisor\nAlexander Card\, Technical Advisor\nAimee Allard\, Communications Coordinator\nKelly Markham\, Technical Communications Advisor\nMingshuo Wang\, Teaching Assistant\nJawad Saeed\, Teaching Assistant\nAshritha Bugada\, Teaching Assistant\nStephen Liu\, Teaching Assistant\nJacob Choi\, Teaching Assistant\n\nThank you to our program partners for their support of the Senior Design Center’s ‘Posters and Pies’: Blue Cross NC\, LexisNexis\, SAS and the Computer Science ePartners Program for their support of this event! \nMedia is welcome at this event. Contact Leslie Rand-Pickett for media and corporate partnership inquiries. \nMcKimmon Center visitor information.
URL:https://csc.ncsu.edu/event/fall-2025-senior-design-centers-posters-and-pies/
CATEGORIES:Senior Design Center
ATTACH;FMTTYPE=image/jpeg:https://csc.ncsu.edu/wp-content/uploads/sites/318/2025/11/R4_05774-copy-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251124T130000
DTEND;TZID=America/New_York:20251124T140000
DTSTAMP:20260505T020131
CREATED:20251113T192046Z
LAST-MODIFIED:20251118T201740Z
UID:10000040-1763989200-1763992800@csc.ncsu.edu
SUMMARY:AI and Accelerators: Boom or Bubble?
DESCRIPTION:Abstract:  \nGenerative AI (Artificial Intelligence) has reshaped both computer software and hardware. On the software side\, service providers invested in developing larger and more effective machine learning models aiming at providing better user experiences. On the hardware side\, researchers\, large companies and tons of startups innovate on hardware accelerators to support the incredibly fast growing software demand. \nHowever\, if we keep following the current trend in focusing on developing new service features\, generative AI or in general\, AI\, is likely to replay the story of the Internet Bubble story. None of the current generative AI service providings demonstrate a profitable business model that is self-sustainable from the perspective of operational costs\, carbon dioxide (CO2) emissions\, and capital expenditure. \nIn this talk\, Hung-Wei will share research experiences that make the dimming future of generative AI bright. Hung-Wei identified the key to successful and self-sustainable generative AI services must (1) revisit the algorithms in the complete AI/ML pipeline to fully exploit the use of underlying hardware\, (2) reuse existing architectural components before designing a new accelerator\, and (3) maximize the potential of each hardware accelerator through opportunities that emerging programming models reveal. \nBio:  \nHung-Wei is currently an associate professor in the Department of Electrical and Computer Engineering at the University of California\, Riverside. He is now leading the Extreme Storage & Computer Architecture Laboratory and focusing on accelerating applications through generalized computing on tensor processors\, AI/ML accelerators as well as intelligent data storage systems. He is recognized by facebook faculty research award and IEEE Micro ‘Top Picks from Computer Architecture’ in 2024 and 2020 for his research in designing efficient AI/ML systems. He was previously a visiting researcher at Google’s Tensorflow Lite team and Intel. He got his PhD from the Department of Computer Science and Engineering at the University of California\, San Diego. \nHost: Xipeng Shen
URL:https://csc.ncsu.edu/event/ai-and-accelerators-boom-or-bubble/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251120T173000
DTEND;TZID=America/New_York:20251120T183000
DTSTAMP:20260505T020131
CREATED:20251113T184329Z
LAST-MODIFIED:20251113T190718Z
UID:10000038-1763659800-1763663400@csc.ncsu.edu
SUMMARY:Responsible Intelligence: Ethics\, Safety\, and the Future of AI in Society
DESCRIPTION:Title: Responsible Intelligence: Ethics\, Safety\, and the Future of AI in Society \nAbstract:  \nArtificial Intelligence is transforming society\, from health care and education to public safety and environmental protection. In this Distinguished Series talk\, Udo Sglavo\, Vice President of Applied AI and Modeling at SAS\, explores how ethical principles and safety frameworks guide responsible AI innovation. \nDrawing from SAS’s work with READDI on pandemic preparedness and Medical Adherence Models that promote equitable care\, the session highlights how AI can serve humanity when designed with transparency and accountability. A Data for Good initiative using synthetic data and computer vision to protect endangered sea turtles adds a feel-good dimension to the story. \nThe talk also introduces agentic AI\, autonomous systems that act with purpose\, and synthetic data as key enablers of privacy-preserving\, inclusive modeling. These examples show how SAS is advancing AI responsibly\, without compromising safety or ethics. Let’s work together to build AI that earns trust\, respects human dignity\, and delivers societal value. \nBio:  \nUdo Sglavo leads Applied Artificial Intelligence and Modeling Research and Development at SAS. With a 25 year track record of fostering technology innovation and excellence\, Udo heads a team of expert developers and data& scientists dedicated to pioneering cutting-edge software and leveraging advanced models to transform the way the world works. \nBy imagining and building the next generations of AI-driven models and software solutions\, Udo helps organizations harness the power of data and analytics to solve their toughest business challenges and outpace the world around them. \nUdo’s commitment to pushing the boundaries of what AI can achieve has earned him recognition as a global thought leader shaping the future of technology. In addition\, Udo has written three publications and holds four patents in advanced analytics \nHost: Munidar Singh
URL:https://csc.ncsu.edu/event/responsible-intelligence-ethics-safety-and-the-future-of-ai-in-society/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251114T110000
DTEND;TZID=America/New_York:20251114T120000
DTSTAMP:20260505T020131
CREATED:20251113T185704Z
LAST-MODIFIED:20251113T191014Z
UID:10000039-1763118000-1763121600@csc.ncsu.edu
SUMMARY:Accelerating Biomolecular Design with Generative AI
DESCRIPTION:Title: Accelerating Biomolecular Design with Generative AI \nAbstract:  \nThe discovery of biomolecules with desired properties is critical to advances in drug discovery and synthetic biology. This problem is challenging due to the combinatorial search space of biomolecules. In this talk\, I will present how generative AI can be used to accelerate the discovery process across small molecules\, proteins\, and RNAs. First\, I will present a generative deep learning approach for de novo antibiotic design\, where AI methods successfully discovered two lead compounds with in vivo bactericidal efficacy against multidrug-resistant bacteria in mice models. Second\, I will present an energy-based modeling approach for protein design named BindEnergyCraft\, which provides a principled way for calculating the likelihood of 3D structures and substantially improves the in silico binder success rate of current state-of-the-art binder design methods. Lastly\, I will present a diffusion model-based approach for designing RNA translational control elements\, using internal ribosome entry sites (IRESs) as a model system. Validated in human cells\, we find that AI-generated IRESs circumvent natural sequence constraints and improve IRES activity by nearly 10 fold. In summary\, our in silico and experimental results highlight the potential of generative AI for accelerating biomolecular design. \nBio:  \nWengong Jin is an assistant professor at Khoury College of Computer Sciences at Northeastern University and a visiting research scientist in the Eric and Wendy Schmidt Center at Broad Institute. His research focuses on geometric and generative AI models for drug discovery and synthetic biology. His work has been published in journals including ICML\, NeurIPS\, ICLR\, Nature\, Science\, Cell\, and PNAS\, and covered by such outlets as the Guardian\, BBC News\, CBS Boston\, and the Financial Times. He is the recipient of the Google Research Scholar Award\, BroadIgnite Award\, Dimitris N. Chorafas Prize\, and MIT EECS Outstanding Thesis Award. \nStream the seminar here. \nHost: Xiaorui Liu
URL:https://csc.ncsu.edu/event/accelerating-biomolecular-design-with-generative-ai/
CATEGORIES:CS AI Seminar Series,Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251113T131500
DTEND;TZID=America/New_York:20251113T140000
DTSTAMP:20260505T020131
CREATED:20251031T184015Z
LAST-MODIFIED:20251031T184015Z
UID:10000036-1763039700-1763042400@csc.ncsu.edu
SUMMARY:Exams with More Learning and Less Stress with a Computer-based Testing Facility
DESCRIPTION:Title: Exams with More Learning and Less Stress with a Computer-based Testing Facility \nAbstract:  \nExams are an important tool for summative assessment\, whose utility has only grown with the advent of large language models (LLMs) like ChatGPT\, because they can be implemented in a trustworthy manner. But exams are generally not well liked by either students or faculty. Students find them stressful. For faculty (and their course staff)\, they represent a large adminstrative burden to write\, proctor\, and grade. This large burden means they are done infrequently in many classes\, but this infrequent testing encourages cramming and leads to high test anxiety. \nIn this talk\, I’ll share (1) research on the benefits of frequent testing and ‘second-chance testing’ (optional exam re-takes) on increased student learning and decreased test anxiety\, (2) research on patterns of cheating on unproctored online assessments\, and (3) how we’ve reduced the instructor workload at Illinois to implement frequent testing through our Computer-based Testing Facility (CBTF). The CBTF is a collection of proctored computer labs that\, in conjunction with the PrairieLearn open-source question-asking platform\, enable our faculty to run sophisticated exams with almost no recurring effort even in the largest classrooms. For example\, our CS 1 course for majors (run by a single faculty member) ran weekly exams for 1\,150 students. Key enabling ideas for the CBTF include: (1) sophisticated auto-grading questions\, (2) question generators\, (3) asynchronous exams\, and (4) dedicated testing space and proctors. The CBTF has been running for over 10 years and proctored over 100\,000 exams last semester. \nBio:  \nCraig Zilles is a Professor and Severns Faculty Scholar in the Siebel School of Computer and Data Science at the University of Illinois at Urbana-Champaign. His current research focuses on applying computing and data analytics to education\, including the development of the Computer-based Testing Facility (CBTF). Previously\, his research focused on the interaction between compilers and computer architecture\, and he developed the first algorithm that allowed rendering arbitrary three-dimensional polygonal shapes for haptic interfaces (force-feedback human-computer interfaces). He received the IEEE Education Society’s 2010 Mac Van Valkenburg Early Career Teaching Award and an NSF CAREER award. At Illinois\, he has received a wide range of teaching awards\, including a 2018 Campus Award for Excellence in Undergraduate Teaching\, a 2013 Illinois Student Senate Teaching Excellence Award\, and the College of Engineering’s Rose Award (2007) and Everitt Award (2008) for Teaching Excellence. He holds 5 patents and his research has been recognized by a best paper awards from ASPLOS in 2010 and 2013 and by selection for inclusion in the IEEE Micro Top Picks from the 2008 Computer Architecture Conferences. \n 
URL:https://csc.ncsu.edu/event/exams-with-more-learning-and-less-stress-with-a-computer-based-testing-facility/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251113T110000
DTEND;TZID=America/New_York:20251113T120000
DTSTAMP:20260505T020131
CREATED:20251027T165502Z
LAST-MODIFIED:20251027T165750Z
UID:10000035-1763031600-1763035200@csc.ncsu.edu
SUMMARY:From Body Signals to Smart Environments: Embedded AI and Sensing for Wellness and Health
DESCRIPTION:Title: From Body Signals to Smart Environments: Embedded AI and Sensing for Wellness and Health \nAbstract:  \nRecent advances in smart devices and artificial intelligence are transforming personal fitness\, health awareness\, and self-care. Wearable mobile computing and Artificial Intelligence of Things (AIoT) systems now provide real-time insights into body signals and health metrics\, while increasingly informing medical decision-making. Yet\, challenges remain in achieving accessibility\, affordability\, comfort\, usability\, responsiveness\, and scalability. This talk explores how embedding intelligent systems into daily life redefines wellness\, fitness\, and healthcare—shifting toward pervasive\, preventive\, and proactive management and personalized care. \nI will present my research on developing accessible wearable sensing platforms that enable continuous monitoring and interaction across emotion\, fitness\, and wellness in diverse communities. This work includes novel form factors leveraging commercial off-the-shelf sensors\, computational fabrics\, and emerging soft electronics to capture multimodal signals\, paired with efficient and privacy-aware systems and algorithms. I will then highlight model-driven analysis approaches—spanning digital signal processing\, machine learning\, and foundation models—for biosignal analysis and emotion detection from physiological signals and speech. Finally\, I will discuss how everyday smart home devices can be repurposed to screen daily functioning\, assess mental health\, monitor physical well-being\, and deliver AI-enabled psychotherapeutic care in collaboration with licensed clinicians. \nBio:  \nShort Bio: Jingping Nie is an Assistant Professor in the School of Data Science and Society at the University of North Carolina at Chapel Hill. She received her Ph.D. in Electrical Engineering from Columbia University in 2025\, under the supervision of Prof. Fred Jiang and Prof. Matthias Preindl. She received her Master of Science degree (Honor Student) in Electrical Engineering from Columbia University in 2019 and her Bachelor of Science degree (magna cum laude with high honors) in Engineering Science from Smith College in 2017. Her research transforms everyday devices into intelligent healthcare and smart city solutions by co-designing hardware form factors and software for human-centric smart devices in Artificial Intelligence of Things (AIoT) systems. She aims to enhance personal wellness\, fitness\, and mental health by sensing the physical world in its many forms — and reimagining how AI and machine learning can transform the future of pervasive healthcare across the entire life cycle of data. Her work has been published in various top-tier journals and conferences and received multiple distinctions\, including the Best Paper and People’s Choice Demo at ACM MobiSys’24\, Best Paper at IEEE ITEC’21\, Best Demo at ACM/IEEE IPSN’20\, and Best Demo Runner-up at ACM SenSys ’22. She is a recipient of the 2023 Apple Scholars in AI/ML PhD Fellowship\, 2023 EECS Rising Stars\, 2025 CPS Rising Stars\, and the Columbia University Morton B. Friedman Memorial Prize for Excellence. She has served on technical and organizing committees of leading conferences and workshops in the field and is the associate editor for ACM HEALTH and Elsevier Smart Health journals. \nClick here to stream the seminar.
URL:https://csc.ncsu.edu/event/from-body-signals-to-smart-environments-embedded-ai-and-sensing-for-wellness-and-health/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251027T110000
DTEND;TZID=America/New_York:20251027T120000
DTSTAMP:20260505T020131
CREATED:20251016T201245Z
LAST-MODIFIED:20251016T201245Z
UID:10000034-1761562800-1761566400@csc.ncsu.edu
SUMMARY:Toward Large-Scale Electronic Structure Theory Through Physics-Based Simulations\, Data-Driven Approaches\, and Quantum Computing
DESCRIPTION:Title: Toward Large-Scale Electronic Structure Theory Through Physics-Based Simulations\, Data-Driven Approaches\, and Quantum Computing \nAbstract:  \nSimulating the electronic structure of large molecular systems can greatly facilitate the design of molecules for solar to chemical energy conversion and chemical synthesis. However\, accurate first-principles simulations of the electronic structure of macromolecules are usually computationally expensive\, especially those that involve strong electron correlation. In this talk\, I will start from our motivating application on photoenzymes and discuss our computational strategies\, including physics-based simulations\, data-driven methods\, and quantum computing\, particularly quantum annealing\, to tackle this challenge. Most development for electronic structure theory on quantum computers to date are for gate-based quantum computing. Quantum annealing provides an alternative route to solving the electronic structure problem and can potentially be advantageous due to the large number of available qubits on the commercial quantum annealer and robustness to noise. I will discuss our strategy to utilize symmetry-adapted encoding and the variational principle to reduce the number of qubits needed for electronic structure theory on quantum annealers. \nBio:  \nDr. Sijia Dong is an assistant professor in the Department of Chemistry and Chemical Biology at Northeastern University\, with affiliations in the Department of Physics and the Department of Chemical Engineering. She received her PhD in Chemistry from California Institute of Technology in 2017\, advised by Prof. William A. Goddard III. She carried out her postdoctoral research at the University of Minnesota with Prof. Donald G. Truhlar and Prof. Laura Gagliardi\, and then at Argonne National Laboratory with Prof. Giulia Galli. Research in the Dong Lab focuses on developing and applying physics-based and data-driven computational methods on both classical and quantum computers to accelerate chemical discoveries. Sijia has been selected a Scialog Fellow for Automating Chemical Laboratories by Research Corporation for Science Advancement\, has won the American Chemical Society COMP OpenEye Cadence Molecular Sciences Outstanding Junior Faculty Award\, has won the Northeastern University College of Science Excellence in Mentorship Award\, has a Maximizing Investigators’ Research Award for Early Stage Investigators from the National Institutes of Health\, and is recognized as an Emerging Investigator by the Journal of Chemical Physics\, American Institute of Physics. Sijia also co-chairs the Early Career Board of the Journal of Chemical Theory and Computation. \nClick here to stream the seminar.
URL:https://csc.ncsu.edu/event/toward-large-scale-electronic-structure-theory-through-physics-based-simulations-data-driven-approaches-and-quantum-computing/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251023T183000
DTEND;TZID=America/New_York:20251023T193000
DTSTAMP:20260505T020131
CREATED:20250807T213249Z
LAST-MODIFIED:20251021T162558Z
UID:10000023-1761244200-1761247800@csc.ncsu.edu
SUMMARY:Labcorp ‘Leadership in Technology’ Speaker Series: Nate Johnson
DESCRIPTION:Nate Johnson\nVice President of Weather Content and Operations\, NBCUniversal Local\nArtificial Intelligence\, Real Impacts: Meteorology in the Era of AI\nAbstract\nFrom the earliest days of numerical weather prediction to the latest breakthroughs in artificial intelligence\, technology has propelled the science of meteorology. The emergence of AI is continuing that trend\, with applications now reaching across observation and modeling of the atmosphere to communication of forecasts and impacts. This talk traces the evolution of AI in meteorology\, from the first numerical models and early machine-learning-like methods to improve forecasts to modern approaches such as observation quality control\, AI-based atmospheric predictions\, and personalized weather services for different audiences. The discussion also highlights the opportunities and challenges AI creates for communicating forecasts in ways that strengthen decision-making\, resilience\, and public understanding.\n\nBio\nNate Johnson (B.S. Computer Science\, ’00) is Vice President of Weather Content and Operations for NBCUniversal Local\, where he guides strategy and innovation for weather coverage across 44 English- and Spanish-language TV stations across the country. He leads more than 120 weather professionals nationwide\, setting standards\, identifying best practices\, and providing tools and techniques they need to provide the most accurate forecasts on TV\, web\, apps\, and beyond. Johnson also coordinates the group’s climate reporting and collaborates with technology\, operations\, and digital teams on projects ranging from radar systems to app and website development.  Previously\, he was a meteorologist and executive producer at WRAL-TV in Raleigh.  His work has earned four regional Emmy Awards\, two national Emmy nominations\, a duPont-Columbia nomination\, and multiple Associated Press and Edward R. Murrow awards. A past president of the National Weather Association\, he is also a member of the NC State University Computer Science Hall of Fame and holder both the American Meteorological Society’s Certified Broadcast Meteorologist designation and the National Weather Association’s Seal of Approval.\n\n\nWatch the talk here.\nNote to Computer Science Graduate StudentsThis lecture has been approved by the CS Graduate Oversight Committee and counts toward the required lectures for graduate students.
URL:https://csc.ncsu.edu/event/nate-johnson/
LOCATION:EB2 1231\, 890 Oval Drive\, Raleigh\, NC\, 27606\, United States
CATEGORIES:LabCorp Seminar
ORGANIZER;CN="Computer Science":MAILTO:lcrandpi@ncsu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251023T100000
DTEND;TZID=America/New_York:20251023T110000
DTSTAMP:20260505T020131
CREATED:20251003T222034Z
LAST-MODIFIED:20251003T222502Z
UID:10000031-1761213600-1761217200@csc.ncsu.edu
SUMMARY:Resource Optimization for ML Inference Serving
DESCRIPTION:Title: Resource Optimization for ML Inference Serving \nAbstract:  \nMy research focuses on job scheduling and resource management in Machine Learning (ML) and Large Language Model (LLM) systems. With the growing popularity of deep learning models\, minimizing the monetary costs and maximizing the goodput of inference-serving systems have become critical challenges. Addressing these challenges requires efficient task scheduling within and across nodes\, as well as optimized resource management to ensure high performance\, resource utilization\, and adherence to Service Level Objectives (SLOs). However\, current approaches often fall short of fully addressing these challenges. In this talk\, I will present our novel methods designed to address these gaps and enable the efficient execution of ML/LLM workloads. I will also briefly discuss my ongoing and future research plans for advancing LLM systems. \nBio:  \nDr. Haiying Shen is currently an Associate Professor in the Department of Computer Science at the University of Virginia. She served as a Consulting Researcher at Microsoft in 2024\, focusing on LLM systems during her sabbatical. Her research area is distributed systems\, focusing on LLM/ML systems\, cloud computing\, big data and edge computing. Dr. Shen has made significant contributions to her field\, with an H-index of 51 and over 380 publications in top conferences and journals such as SIGCOMM\, OSDI\, EuroSys\, ASPLOS\, CoNext\, Infocom\, ICDCS\, IPDPS\, IEEE/ACM Transactions on Networking (TON)\, IEEE Transactions on Parallel and Distributed Systems (TPDS)\, and IEEE Transactions on Mobile Computing (TMC). Her papers have received the George N. Saridis Best Transactions Paper Award 2021\, best paper awards at CloudCom 2016 and NAS 2018\, a best paper runner-up award at ICCCN 2015\, best paper award nominations at ICPP 2021\, MASS 2011\, and CCGrid 2009\, and a best-in-session presentation award at INFOCOM 2017. She received the 2010 Microsoft Faculty Fellowship Award\, the 2015 IEEE Technical Committee on Scalable Computing (TCSC) Mid-career Award\, the 2015 IBM Faculty Award\, the 2013 NSF CAREER Award\, and the 2013 Sigma Xi Clemson Chapter Young Investigator Award. She currently advises ten Ph.D. students\, one MS student and two postdoctoral fellows. She is an Associate Editor for IEEE/ACM Transactions on Networking (TON)\, IEEE Transactions on Mobile Computing (TMC)\, and IEEE Networking Letters (NL). Dr. Shen has served on the program committees of numerous leading conferences and has been a program co-chair and general co-chair for several international conferences. \nClick here to stream the seminar. \nZoom Meeting ID: 931 3393 2255\nZoom Passcode: 269017
URL:https://csc.ncsu.edu/event/3575/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:CS AI Seminar Series,Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251018T090000
DTEND;TZID=America/New_York:20251018T140000
DTSTAMP:20260505T020131
CREATED:20251013T193443Z
LAST-MODIFIED:20251017T164150Z
UID:10000033-1760778000-1760796000@csc.ncsu.edu
SUMMARY:University Open House
DESCRIPTION:Come join #NCStateCS on Saturday\, October 18 for University Open House! Chat with faculty\, explore our spaces\, meet our awesome ambassadors and more. \nCollege of Engineering Open House details.
URL:https://csc.ncsu.edu/event/university-open-house/
LOCATION:Engineering Building 2\, 890 Oval Drive.\, Raleigh\, NC\, 27606\, United States
CATEGORIES:College,Department,University
ATTACH;FMTTYPE=image/jpeg:https://csc.ncsu.edu/wp-content/uploads/sites/318/2025/10/54460391140_60d94276f6_o-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251017T110000
DTEND;TZID=America/New_York:20251017T120000
DTSTAMP:20260505T020131
CREATED:20251013T143113Z
LAST-MODIFIED:20251013T143537Z
UID:10000032-1760698800-1760702400@csc.ncsu.edu
SUMMARY:Behavior-Aware Data Valuation for LLMs at Scale
DESCRIPTION:Title: Behavior-Aware Data Valuation for LLMs at Scale \nAbstract:  \nLarge Language Models (LLMs) depend on massive datasets whose quality and influence remain largely opaque. Data valuation offers principled methods to quantify how training data contributes to model performance and behavior. Yet\, scaling classical approaches such as influence functions to trillion-token corpora continues to be a major challenge. This talk introduces recent advances that address this gap\, including the linearized influence kernel\, a new and efficient metric that extends to LLMs with billion-scale parameters. We will also highlight system-level frameworks such as RapidIn and present empirical findings of LLM training\, including the slowly change phenomenon\, which enables forward-looking valuation of future training data. By combining principled algorithms\, system optimizations\, and case studies\, the talk aims to bridge the gap between theory and practice. \nBio:  \nZhaozhuo Xu is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology. He received his Ph.D. from Rice University and an M.S. from Stanford University. His research develops randomized algorithms to enhance the efficiency of AI systems on commodity hardware. Dr. Xu’s work has appeared in leading venues such as NeurIPS\, ICML\, ICLR\, OSDI\, and ACL\, as well as in journals including Nature NPJ AI. His innovations in scalable AI have been integrated into widely used libraries like Hugging Face. He serves as an Associate Editor for Neurocomputing and as an Area Chair for major conferences\, including NeurIPS\, ICLR\, ICML\, ACL\, EMNLP\, NAACL\, and COLING. He is a recipient of the AAAI New Faculty Highlights (2025)\, the NSF CRII Award (2025)\, and the Stevens Bridging Award. \nClick here to stream the seminar.
URL:https://csc.ncsu.edu/event/behavior-aware-data-valuation-for-llms-at-scale/
LOCATION:EB2 3001\, 890 Oval Drive\, Raleigh\, NC\, 27606\, United States
CATEGORIES:CS AI Seminar Series,Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251003T110000
DTEND;TZID=America/New_York:20251003T120000
DTSTAMP:20260505T020131
CREATED:20250930T181131Z
LAST-MODIFIED:20250930T181650Z
UID:10000030-1759489200-1759492800@csc.ncsu.edu
SUMMARY:Synergizing Sparse Sequence\, Experimental\, and AI-Predicted Structures for Protein-Nucleic Acid Interaction Predictions
DESCRIPTION:Title: Synergizing Sparse Sequence\, Experimental\, and AI-Predicted Structures for Protein-Nucleic Acid Interaction Predictions \nAbstract:  \nSequence-specific nucleic acid recognition underlies essential processes in gene regulation\, yet experiment-independent methods for simultaneous predictions of genomic DNA recognition sites and their binding affinity remain limited. Our group developed data-driven methods and simulation tools to predict and elucidate protein-nucleic acid interactions and their contributions in reshaping chromatin structures. Specifically\, we introduce the Interpretable protein-DNA Energy Associative (IDEA) model\, an interpretable residue-level biophysical model capable of predicting binding sites and affinities of DNA-binding proteins without relying on experimental binding data. By integrating the structures and sequences of known protein-DNA complexes into an optimized energy model\, IDEA enables a direct interpretation of the physicochemical interactions among individual amino acids and nucleotides. Using transcription factors as examples\, we demonstrate that IDEA accurately predicts genomic DNA recognition sites and their binding strengths. Additionally\, IDEA is incorporated into a coarse-grained simulation framework that quantitatively captures the absolute protein-DNA binding free energies. Collectively\, IDEA provides an integrated computational platform that alleviates experimental costs and biases in the assessment of DNA recognition and can be utilized for mechanistic studies of various DNA recognition processes. Finally\, I will present our recent progress in extending this framework to predict protein-single-stranded nucleic acid interactions and to design therapeutic aptamers. \nBio:  \nXingcheng Lin is an assistant professor in the Physics Department at North Carolina State University\, starting in August 2023. He is also affiliated with the Bioinformatics Cluster of the Chancellor’s Faculty Excellence Program. Dr. Lin earned his Ph.D. in Biological Physics from the Center for Theoretical Biological Physics and the Physics Department at Rice University. During his graduate studies\, he employed both atomistic and coarse-grained simulations to investigate the molecular mechanisms behind the invasion of influenza viruses. He also developed simulation-based tools to refine folded protein structures and to simulate intrinsically disordered proteins. Following his doctorate\, Dr. Lin conducted postdoctoral research in the Chemistry Department at the Massachusetts Institute of Technology (MIT)\, where he broadened his research focus to include the chromatin system. The Lin group focuses on integrating top-down data-driven approaches with bottom-up biophysical simulations to predict protein-nucleic acid interactions and understand their implications for genome regulation. \nClick here to stream the seminar.
URL:https://csc.ncsu.edu/event/synergizing-sparse-sequence-experimental-and-ai-predicted-structures-for-protein-nucleic-acid-interaction-predictions/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:CS AI Seminar Series,Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250922T100000
DTEND;TZID=America/New_York:20250922T110000
DTSTAMP:20260505T020131
CREATED:20250911T140114Z
LAST-MODIFIED:20250911T140331Z
UID:10000028-1758535200-1758538800@csc.ncsu.edu
SUMMARY:Requiem for a Drone: Stealthy Attacks on Autonomous Systems
DESCRIPTION:Speaker\nSibin Mohan\, George Washington University \nAbstract\nAutonomous Systems (AVs) require an accurate sensing and modeling of the real-world in order to carry out their missions. They use the observations from sensors to reason about the vehicle’s state and can correct for deviations\, even attacks. In practice\, even the most meticulously designed control systems always operate under a certain amount of noise because of the unavoidable errors involved in both sensor measurement as well as the modeling of complex vehicular dynamics. \nAll of this inherently creates a space that can be exploited by an adversary. In this talk\, I will present novel methods that can exploit this space\, using a software-only attack. Our system\, Requiem\, presents a blackbox attack -— i.e.\, there is no knowledge required about the internal details of the system —- the only requirement is that the state estimation function be “learnable” from observation of the inputs and outputs. Requiem constructs two sets of deep models (a ‘surrogate’ and a ‘spoofer’) to evaluate the system and generate the attack. The final result of a Requiem-based attack causes significant deviations in the physical system’s trajectory (sometimes by tens or hundreds of metres!). Meanwhile the system believes it is following the original mission parameters. Hence\, this is an attack that is hard to detect or defend against. \nIn this talk\, I will also present some initial ideas on how to detect these stealthy attacks using optical-flow based motion-verification. \nBio\nSibin Mohan is an Associate Professor in the Department of Computer Science at The George Washington University. He also holds an adjunct faculty appointment in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign. \nSibin completed his Ph.D. and M.S. in Computer Science from North Carolina State University. His undergraduate degree was in Computer Science and Engineering from Bangalore University\, India. In the past\, he worked at Hewlett Packard. His research has won multiple best paper awards and he is the recipient of the NSF CAREER award. \nSibin’s research interests are in the area of systems\, security\, networking and autonomous systems. Sibin has pioneered research to improve the resiliency and security of real-time\, cyber-physical and autonomous systems. Current research efforts include resiliency and security for autonomous and cyber-physical style systems\, securing operating systems via code debloating\, resiliency for safety-critical networks\, security for V2X systems and understanding the behavior of UAV swarms.
URL:https://csc.ncsu.edu/event/requiem-for-a-drone-stealthy-attacks-on-autonomous-systems/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Lecture/Seminar
ORGANIZER;CN="Frank Mueller":MAILTO:fmuelle@ncsu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250919T110000
DTEND;TZID=America/New_York:20250919T120000
DTSTAMP:20260505T020131
CREATED:20250915T141215Z
LAST-MODIFIED:20250930T181812Z
UID:10000029-1758279600-1758283200@csc.ncsu.edu
SUMMARY:Breaking Barriers: Advancing Long Context LLMs
DESCRIPTION:Speaker:\nZirui Liu\, University of Minnesota \nAbstract:\nLLMs have demonstrated impressive conversational abilities. However\, scaling them to handle longer contexts\, such as extracting information from lengthy articles—a critical task in healthcare\, law\, and finance applications—presents significant challenges. The two main obstacles are: first\, LLMs struggle to process input lengths beyond what they encountered during pre-training; second\, even when information is accurately extracted from extended contexts\, deploying LLMs in real-world scenarios is limited by hardware capacity. I will discuss recent advances in serving long context LLMs at scale. To address the first challenge\, I’ll present our work on extending LLM context length 10X by coarsening the positional encoding. For the second challenge\, I will highlight our recent success in 2-bit KV Cache quantization. Lastly\, I will briefly discuss the reproducibility issue of reasoning evaluation. \nSpeaker Bio:\nZirui Ray Liu is an Assistant Professor of Computer Science at University of Minnesota. His interests lie in the broad area of Machine Learning and Data Mining. He regularly published papers in top venues such as\, NeurIPS\, ICML\, ICLR\, and MLSys. His work has been integrated into widely used NLP tools like Llama.cpp and Huggingface Transformers\, and was highlighted at Google I/O sessions. Website: https://zirui-ray-liu.github.io/ \nSpecial Instructions:\nThis seminar will be hosted online only. \nHost:\nXiaorui Liu\, CSC
URL:https://csc.ncsu.edu/event/breaking-barriers-advancing-long-context-llms/
CATEGORIES:CS AI Seminar Series,Lecture/Seminar
LOCATION:https://ncsu.zoom.us/j/91683735738
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250915T120000
DTEND;TZID=America/New_York:20250915T150000
DTSTAMP:20260505T020131
CREATED:20250820T150334Z
LAST-MODIFIED:20250820T150334Z
UID:10000025-1757937600-1757948400@csc.ncsu.edu
SUMMARY:ePartners Career Connections
DESCRIPTION:This event is offered exclusively for our ePartners & Super ePartners\, and provides a targeted environment for companies to dialogue with NC State Computer Science students. \nThe ePartners Program provides a framework for developing and nurturing meaningful collaborative relationships between the global business community and NC State’s Department of Computer Science. If you are not an ePartner and would like more info\, contact Leslie Rand-Pickett.
URL:https://csc.ncsu.edu/event/epartners-career-connections/
LOCATION:Hunt Library – Duke Energy Hall\, 1070 Partners Way\, Raleigh\, NC\, 27606\, United States
CATEGORIES:Career Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250910T114500
DTEND;TZID=America/New_York:20250910T124500
DTSTAMP:20260505T020131
CREATED:20250828T201916Z
LAST-MODIFIED:20250828T201916Z
UID:10000026-1757504700-1757508300@csc.ncsu.edu
SUMMARY:Seminar: Introduction to TU Dortmund University\, Germany
DESCRIPTION:Title: Introduction to TU Dortmund University\, Germany \nAbstract:  \nTU Dortmund University is located in the heart of the European Rhine-Ruhr metropolitan region\, which is home to 10 million people. This region is the logistical hub of Europe and TU Dortmund University has therefore played a decisive role in shaping logistics. Logistics is interpreted in a modern way at the Chair of Material Handling and Warehousing. In our Inno Lab\, the logistics of tomorrow is interpreted as a cyber physical system. Our lab is designed for many applications. From the development of modern robots that reach speeds of up to 10 m/s and swarms of drones\, our test area offers everything in terms of innovation. In their talk\, Dr. Jérôme Rutinowski and Sven Franke will present the research infrastructure\, current research projects and plans for the future of the Inno Lab. This includes the topics of computer vision\, mobile robotics\, human activity recognition and more. \nBio:  \nDr. Jérôme Rutinowski studied mechanical engineering at the Ruhr University Bochum. During his studies\, he spent a semester abroad at the Universidad Politécnica de Madrid. In 2020\, he started as a research associate at the Chair of Material Handling and Storage. In 2024 he defended his Ph.D. thesis entitled Reliable identification of logistic entities based on inherent visual features. Since then\, he has been working as Deputy Head of Research and Operations and is responsible for the strategic and scientific development of the institute. \nSven Franke studied industrial engineering at TU Dortmund University. During his studies and afterwards\, he worked at the Fraunhofer Institute for Material Flow and Logistics in the Warehousing and IT Planning department. He has been working as a research associate at the Chair of Material Handling and Warehousing since 2022. In his research\, he focuses on communication interfaces in mobile robotics to industrial peripherals. He is also interested in definitions and perceptions of terms in mobile robotics as part of his Ph.D.
URL:https://csc.ncsu.edu/event/seminar-introduction-to-tu-dortmund-university-germany/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Lecture/Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250905T110000
DTEND;TZID=America/New_York:20250905T120000
DTSTAMP:20260505T020131
CREATED:20250902T211846Z
LAST-MODIFIED:20250903T185028Z
UID:10000027-1757070000-1757073600@csc.ncsu.edu
SUMMARY:Seminar: Youzuo Lin
DESCRIPTION:Watch the Zoom stream of the seminar here. \nTitle: \nToward Real-Time Ultrasound Computed Tomography: Bridging Wave Physics and Data-Driven Learning \nAbstract:  \nUltrasound Computed Tomography (USCT)\, also known as Full Waveform Inversion (FWI)\, reconstructs the mechanical properties of biological tissues by modeling the full propagation of ultrasound waves. This modality shows great promise for advanced applications such as breast\, neuro\, and prostate imaging\, yet its clinical adoption has been limited by the trade-off between accuracy and computational efficiency. Physics-based reconstruction methods achieve high-resolution\, quantitative maps of tissue properties but are computationally demanding and sensitive to model uncertainties. Data-driven approaches\, particularly deep learning\, have recently offered accelerated solutions but often lack robustness and generalizability. In this work\, we present hybrid USCT strategies that bridge wave physics and machine learning. By embedding physical principles into self-supervised learning frameworks\, our methods substantially reduce computational cost while maintaining reconstruction fidelity. We demonstrate their efficacy in challenging prostate imaging scenarios\, highlighting their potential to advance USCT toward real-time clinical translation. \nBio:  \nYouzuo Lin is an Associate Professor in the School of Data Science and Society at the University of North Carolina at Chapel Hill. Previously\, he served as a Senior Scientist at Los Alamos National Laboratory. He earned his Ph.D. in Applied and Computational Mathematics from Arizona State University in 2010. Youzuo’s research focuses on scientific machine learning methods and their applications\, particularly in computational wave imaging\, ultrasound tomography\, geophysical inversion\, and UAV image analysis. He has published over 100 articles in leading journals and conference proceedings and is a co-inventor on several U.S. patents related to ultrasound imaging techniques.
URL:https://csc.ncsu.edu/event/seminar-toward-real-time-ultrasound-computed-tomography-bridging-wave-physics-and-data-driven-learning/
LOCATION:EB2 3211 Seminar Room\, 890 Oval Dr.\, Raleigh\, NC\, 27695\, United States
CATEGORIES:CS AI Seminar Series
END:VEVENT
END:VCALENDAR