Events for May 29, 2026

Methane and methanol are inexpensive and sustainable carbon feedstocks that can be used by methanotrophic bacteria as sole sources of carbon and energy and can be converted into value-added products. On the other hand, isoprenoids are valuable compounds that can be used for high performance fuels, flavoring agents and pharmaceuticals or for precursors to such products; however, methanotrophs do not readily produce isoprenoids. Methylomicrobium album BG8 is a methanotroph with high methanol tolerance and has been grown to high density in fed-batch operation. In this work, M. album BG8 is being modified to convert methane and methanol into isoprenoids. M. album BG8 contains the doxp pathway, which produces isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), two isoprenoid precursors, but it lacks enzymes which are required to transform these compounds into isoprenoids. In addition, works in other bacterial systems suggest low expression of the native doxp pathway leading to a potential bottleneck in the generation of isoprenoids. In this work, conjugation was used to modify M. album BG8 with plasmids for high and low expression levels of the genes involved in isoprenoid production. A library of constitutive and inducible promoters was developed using green fluorescent protein (GFP) and nanoluc as markers. Co-transformation of high and low expression vectors are being performed to improve the flux through the doxp pathway and, eventually, isoprenoid synthesis. This work provides a basis for the production of isoprenoids from methane and methanol by bacteria, an approach with great economic and environmental <a href="http://potential.Co-sponsored" target="_blank" title="potential.Co-sponsored">potential.Co-sponsored by: Resilience and Clean Energy Systems (RCES)Speaker(s): Shibashis DasVirtual: https://events.vtools.ieee.org/m/561182

Level up your studies this Spring!Are you a term 2 Electrical, Electronics, or Computer Engineering Tech student? Come to our drop-in tutoring sessions to get support in; Electrical 2, Electronic Devices, Digital Systems, Math for Technologists 2, and Networking and Data Communications. Boost your grades while connecting with friends and mentors!Sessions are held Thursdays 2-4pm and Fridays 3-5pm every week in V113 (IEEE room).No registration required, drop in anytime during the session that works for you!For any questions, reach out to IEEE NC Student Branch : ieeencstudentbranch@<a href="http://gmail.comAgenda:" target="_blank" title="gmail.comAgenda:">gmail.comAgenda: - Arriving students will sign in- Small group support and one on one support- Wrap-up and feedback for future sessionsRoom: 113, Bldg: Voyageur , 100 Niagara College Blvd, Welland , Ontario, Canada, L3C 7L3

How can we transform artificial intelligence (AI) and machine learning capabilities into reliable, autonomous robotic systems? How can we engineer AI systems within budget constraints, certify them with respect to stakeholder requirements, and ensure that they meet the needs of the end user? Answering these questions necessitates new engineering methodologies for AI systems, as well as AI algorithms that leverage the unique characteristics of engineering problems. In this talk, I will begin by presenting methods that integrate foundation models such as large language models and vision-language-action models with frameworks and algorithms for verifiable sequential decision-making. I will then present compositional approaches to reinforcement learning, which enable independent development and testing of separate learning-enabled modules and facilitate the reliable deployment of their compositions in practice. Finally, I will present control-oriented learning algorithms that combine data with prior physics knowledge, yielding learning-enabled systems that effectively control hardware after mere minutes of data collection and training. Experiments on robotic hardware, ranging from manipulators to ground vehicles to hexacopters, demonstrate the important role that these algorithms play in the fast and reliable transfer of learning-driven algorithms to their target, real-world operating <a href="http://environments.Co-sponsored" target="_blank" title="environments.Co-sponsored">environments.Co-sponsored by: Ryozo Nagamune | [email protected] | Dejan Kihas | kihas@<a href="http://ieee.orgSpeaker(s):" target="_blank" title="ieee.orgSpeaker(s):">ieee.orgSpeaker(s): Cyrus Neary Agenda: Event Start: 3:30pmTalk and Q&A: 3:40pmEvent End: 5:00pmBldg: MacLeod Building , Room MCLD 3038, 2356 Main Mall, Vancouver , British Columbia, Canada, V6T 1Z4, Virtual: https://events.vtools.ieee.org/m/559041