DESCRIPTION
DATE: January 8th, 9th, 15th, 16th, 22nd, 23rd, 29th, & 30th, 2025
TIME: 5 pm - 7 pm
LOCATION: Virtually
This course is taught in EIGHT Wednesday/Thursday sessions (5:00 p.m. to 7:00 p.m.) from Wednesday, January 8th - Thursday, January 30th. You are siging up to attend ALL sessions.
Class size is limited to 40 people.
There will be breaks throughout the session.
STUDENTS WILL BE REQUIRED TO have a Windows or Mac computer. This must be a personal computer and not one issued by your workplace.
This training WILL NOT be recorded.
Explore the transformative power of machine learning in aerospace over eight immersive classes.
OBJECTIVES
Participants will learn:
- Discover how to enhance airport security with image data techniques and neural networks, identify aircraft using innovative model architectures and regularization methods, and optimize airport passenger flow through advanced traffic monitoring systems.
- Gain hands-on experience with managing real world datasets like LAX and CIFAR-10 for use in machine learning algorithms.
- This course will equip you with the ML skills to drive new innovations for efficiency and safety in the aviation industry.
course Instructor
Nya Domkam is a PhD candidate in the Applied Science and Technology program at UC Berkeley, specializing in computational biology. His research focuses on leveraging machine learning to design systems capable of modeling complex aspects of human biology, advancing the understanding and simulation of living systems.
Recognized as a QUAD Fellow, Nya is a leader in interdisciplinary innovation. Since 2022, he has been building AI applications that merge creativity with technology. One of his early projects involved developing an AI model that generates music based on textual prompts—an ambitious venture akin to a “ChatGPT for music.” His expertise also spans foundational machine learning applications like classification tasks, where he continues to refine his craft and explore new frontiers.
Afi Tagnedji is a highly skilled educator with over 2000 hours of graduate and professional level instructional experience specializing in evidence-based teaching methods tailored to diverse learning styles. With foundations in the mathematical principles underpinning AI, her expertise spans machine learning algorithms, neural network architectures, and optimization techniques. She has designed this course to blend rigorous technical training with hands on problem solving, ensuring participants develop both a deep theoretical understanding and the practical skills needed to innovate in the aviation industry.
AVELA offers underrepresented minority students an opportunity to gain hands-on experience developing and presenting STEM activities to K-14 students, which bolsters the technical and leadership skills of UW’s URM student body. Many K-14 schools and communities in the greater Seattle area lack access to STEM resources like physical hardware and technology, career mentorship from professional engineers, college application assistance, as well as awareness of free online opportunities that they can take advantage of. AVELA’s projects aim to build sustained partnerships with underserved K-14 schools and communities across Washington state to support a more equitable public education system. Undergraduate students in AVELA are mentored by graduate students, research professors, and industry allies, before they go on to mentor middle and high school students interested in STEM fields. Through this multi-tiered mentorship model, we aim to provide more K-14 students with opportunities to engage in hands-on STEM projects, and to discover new creative outlets that expose them to college and industry level tools and concepts. We currently have a network of 200 undergraduate and graduate students who receive our weekly text messages and emails, as well as an average of 50 students who show up to our weekly hybrid meetings.
During the 2022-2023 academic year (Fall/Winter/Spring), more than 90 different UW students from URM backgrounds participated in AVELA (37% male and 63% female/non-binary). AVELA members were able to teach, mentor, and tutor more than 1000 K-14 students in more than 35 classrooms and community centers (91% in-person and 9% virtual/hybrid) across Washington state. AVELA students have gone on to work at companies like Microsoft, NASA, Amazon, Boeing, IBM, Texas Instruments, Nvidia, and many others.
http://students.washington.edu/avelauw/index.html
