IEEE NL Section AGM 2025-2026
The IEEE NL Section warmly invites you to attend our 2025–2026 Annual General Meeting (AGM). This will be a hybrid event, held in person at CSF1203, Core Science Facility Building, Memorial University, and virtually via Webex.
The evening will feature Dr. Wanglong Lu, Senior Data Scientist in AI/Analytics at Nasdaq Verafin, Canada, who will deliver a technical talk titled “Generative Models for Semantic Image Editing: Multimodal Approaches.” We will also be joined virtually by a representative from IEEE Canada.
The event will begin at 6:00 PM with light refreshments, followed by the formal AGM proceedings at 6:30 PM. During the meeting, we will review the Section’s activities over the past year and outline plans for 2026. The technical talk by Dr. Lu will follow, and the event is expected to conclude by 8:00 PM.
Please register using the free registration link provided.
This is an excellent opportunity to connect with fellow IEEE members and colleagues in our local community. We look forward to seeing you there.
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- 45 Arctic Ave
- St. John's, Newfoundland and Labrador
- Canada A1C 5S7
- Building: Core Science Facility
- Room Number: CSF1203
- Contact Event Host
- Co-sponsored by IEEE NL Section, Memorial University of Newfoundland
- Starts 20 February 2026 03:30 AM UTC
- Ends 24 February 2026 08:30 PM UTC
- No Admission Charge
Speakers
Dr. Wanglong Lu
Generative Models for Semantic Image Editing: Multimodal Approaches
With the rapid advancement of digital imaging and machine learning, generative models have become powerful tools for image manipulation, transforming fields ranging from creative media to digital forensics. While recent progress has enabled the creation of highly realistic images, precise and controllable editing remains challenging. Key difficulties include generating fine details, preserving semantic meaning, and maintaining structural consistency in complex scenes.
This talk explores modern generative approaches to advanced image manipulation across four major paradigms: 1) Unconditional global image restoration, 2) Unconditional local inpainting, 3) Exemplar-guided conditional editing, 4)Multimodal image editing
I will present novel frameworks that improve restoration fidelity, data efficiency, and semantic coherence, enabling more realistic and practical image editing applications. In addition, I will introduce a tuning-free approach that overcomes the resolution limitations of existing diffusion models, enabling seamless high-fidelity image manipulation at up to 8K resolution using consumer-grade hardware.
Biography:
Wanglong Lu currently serves as a Senior Data Scientist in AI/Analytics at Nasdaq Verafin, Canada. He received his Ph.D. degree in computer science at Memorial University of Newfoundland, Canada, in 2025. His research interests include computer vision, pattern recognition, and generative models. Wanglong has published several high-impact papers in top-tier journals and conferences, including IEEE TVCG, TNSRE, Pattern Recognition, NeurIPS, and ECCV. He also holds four national invention patents. In addition to his research, Wanglong serves as a reviewer for international journals such as IEEE TIP, TMM, TCSVT, KBS, JVCI, and Displays.