About This Awards
Presenters
Alicja Kwasniewska, PhD
AI Lead, North America, Advertisement and Marketing, Amazon AWS
Software Architect and Data Scientist with PhD in AI, focused on image, text, voice, and other data analytics. Main interests cover biomedical applications of AI, computer vision and signal processing for resource-constrained devices. Author of 30+ whitepapers, presenter during International events and conferences.
Maciej Szankin
Principal Solutions Architect, GenAI, SiMa.ai
Maciej Szankin is a Principal Solutions Architect for Generative AI at SiMa.ai, where he leads the development of LLM and VLM-based solutions for edge applications in robotics, automotive, and drones. Since joining SiMa in January 2025, his work has focused on enabling low-latency, high-efficiency GenAI on custom hardware.
Prior to SiMa, Maciej conducted research at Intel Labs, specializing in large language models and hardware-aware AutoML. He is also a PhD candidate, working on thermal image processing and neural network architecture optimization for real-time inference on resource-constrained devices. With over 30 peer-reviewed publications, his expertise spans generative AI, multi-objective optimization, distributed ML, model compression, and neural architecture search.
Mateusz Ruminski
Vice President of Product, PrimeAudience
Advertising technology product leader, former management consultant, driving outcomes using state-of-the-art innovations.
Subarna Tripathi
AI Research Scientist, Intel Labs
As a technologist and a manager, I lead visual algorithms research at Intel Labs. We are looking into research problems such as video analysis and generation; efficient long-form structured and multimodal learning without hitting the memory / compute bottleneck.
My additional responsibilities include overseeing Intel’s global AI academic investment portfolio. We do collaborate with like-minded researchers from academia and industry!
Tz-Ying (Gina) Wu
AI Research Scientist, Intel Labs
I am a research scientist at Intel Labs, working on Computer Vision and Deep Learning, especially on video and multi-modal contextual learning. I received my Ph.D. from University of California San Diego (UCSD), advised by Prof. Nuno Vasconcelos in the Statistical Visual Computing Lab. My research focused on long-tail recognition with realistic and evolving models, aiming to generate reliable predictions and adapt to continually changing distributions. Before coming to UCSD, I received my B.S. and M.S. from National Tsing Hua University (NTHU), where I worked with Prof. Min Sun on multi-modal video-sensor fusion in the Vision Science Lab. Please refer to my CV for more details.