Please complete the registration form below to participate in the Sandia National Laboratories Annual ML/DL Workshop. Attendee Name(Required) Are you a United States citizen?(Required) Yes No Are you external to Sandia National Laboratories?(Required) Yes No Are you a student?(Required) Yes No Student University Email(Required) Company/Organization(Required)Argonne National LaboratoryIdaho National LaboratoryLawrence Livermore National LaboratoryLos Alamos National LaboratoryNew Mexico TechNorth Carolina State UniversityPacific Northwest National LaboratoryThomas Jefferson National Accelerator FacilityThunderbolt Global AnalyticsUniversity of Nebraska – LincolnUniversity of California, IrvineUniversity of Colorado BoulderUniversity of New MexicoUniversity of Tennessee KnoxvilleUniversity of Texas at AustinOtherOther Company/Organization Name:(Required) Sandia Location(Required) New Mexico California Other Sandia Other LocationPlease indicate your location. HiddenSNL Organization SNL Department(Required)Please note: If you are an intern please provide the department your internship is with.Email(Required) Phone(Required)As an SNL employee, are you interested in participating in the following? Hackathon Tutorials Classified Sessions As an SNL employee, are you interested in participating in the following? Hackathon Tutorials As a student, are you interested in participating in the following? Hackathon Tutorials Classified Sessions (must have clearance) As a Sandian, are you interested in participating in the following speed networking sessions? Monday, August 11, 2025 (Lunch Session) Tuesday, August 12, 2025 (Virtual Session) As an SNL employee, are you interested in volunteering for this event? Yes No Maybe Which of the following topics are you interested in? Select all that apply.(Required) Applications (e.g., pathing, remote sensing, power systems) Deep Learning (e.g., architectures, generative models, optimization, foundation models, LLMs) Generative AI (e.g., LLMs, diffusion models, transformers) Infrastructure (e.g., libraries, scalability, distributed solutions) Probabilistic Methods (e.g., variation inference, casual inference, Gaussian processes) Reinforcement Learning (e.g., decision and control, planning, hierarchical RL, robotics) Scientific Machine Learning (e.g., physics-informed training, physics-based models, PDEs/ODEs) Theory (e.g., control theory, learning theory, algorithmic game theory) Trustworthy Machine Learning (e.g., accountability, causality, fairness, privacy, robustness) How did you hear about us?Are you interested in future workshops? Yes No What topics would you like to see presented at future workshops?HiddenNumberCAPTCHAPhoneThis field is for validation purposes and should be left unchanged.
