RHUL CS seminars
Welcome to the RHUL CS seminars webpage. Our seminars run on Wednesdays during term time.
Each seminar is tagged with a general Topic and with Technical if technical content is to be expected or Departmental if it is suitable for a general audience. Joint seminars with Center for AI & Skills is denoted by + Center for AI & Skills. The department also runs a separate 🎂 PhD Cake Talks series for our PGR students.
Click on Show Details to see the venue, link to live-streaming, the abstract, and a link to the recording after the seminar (if available).
| Time | Speaker | Title | |
|---|---|---|---|
| Summer 2026 | |||
| 08 Sep 2026, 11:00 | Dr Fumihiro Maruyama (National Institute of Advanced Industrial Science and Technology (AIST), Japan) | Human-Machine Teaming and Personal AI Agents Machine Learning Technical + Centre for AI & Skills | Show Details |
VenueQUEENS-264 [MS Teams Link] Short BioDr. Maruyama is an invited senior researcher at Intelligent Platform Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Japan. He is also a special adviser on AI education at Chuo Computer & Communication College Group (CCG). He served as convenor of ISO/IEC JTC 1/SC 42/WG 4 (AI use cases and applications) until the end of 2024. Before joining AIST and CCG, he worked for Fujitsu Laboratories Ltd., Kawasaki, Japan, where he was engaged in research and development of CAD, AI, and CRM solutions and served for four years as managing director of Fujitsu Laboratories of Europe based in London, UK. Dr. Maruyama received his B.S. degree in Mathematical Engineering and Dr. of Engineering degree in Information Engineering from the University of Tokyo. He was awarded the IPSJ (Information Processing Society of Japan) 20th Anniversary Best Paper Award and the Prof. Motooka Commemorative Award. He is a life member of IEEE, IPSJ, the Institute of Electronics, Information and Communication Engineers of Japan, the Society for Serviceology, and the Japanese Society for Artificial Intelligence, where he served as executive vice president and auditor for four years and received its Distinguished Service Award. AbstractThis talk provides an overview of human-machine teaming (HMT) and personal AI agents. HMT expands the traditional view of AI systems by emphasizing the integration of human interaction and machine intelligence. Its framework, which is being standardized at ISO/IEC JTC 1/SC 42 (AI)/WG 4 (Use cases and applications), identifies five basic relationship types between humans and AI applications: human supervisor, human mentor, peer, machine mentor, and machine supervisor. Each type defines task division, information flows, and requirements to ensure safety, reliability, and effective collaboration. Requirements are specified for organizations, AI providers, human experts, and the human-machine team, ensuring that both human judgment and AI capabilities are respected and balanced. The talk presents personal AI agents as a special case of HMT, where the agent is dedicated to a single individual, combining the roles of expert and end user. These agents perform a wide range of tasks, from information retrieval and document drafting to personal coaching and communication. Unlike standard LLMs, personal AI agents continuously monitor user context, can proactively initiate interactions, and adapt to individual habits and preferences. The talk underscores the necessity of standardization for personal AI agents, focusing on user customization, situation awareness, privacy, and security. |
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| Autumn 2025 | |||
| 05 Nov 2025, 16:00 | Shalini Maiti (Meta AI & UCL) | Deep learning methods for 3D reconstruction and evaluation Machine Learning Technical + Centre for AI & Skills | Show Details |
VenueBedford 0-07 [MS Teams Link] Short BioShalini Maiti studied Information and Communication Technology at DA-IICT in India, did her masters at TU Graz, specializing in Computer Vision and Machine Learning. She is currently in the final year of her PhD at University College London and Meta AI. Shalini works with 3D vision, particularly with reconstruction, evaluation and generation of 3D objects. When she's is not huddled over academic deadlines, she spends time with good stories, shared experiences, pub quizzes and travel. AbstractReconstructing and evaluating 3D content poses challenges at both the modeling and assessment stages. For non-rigid objects, 3D recovery from 2D keypoints is ill-posed due to occlusions and entanglement between viewpoint and shape variation. Classical low-rank models impose global constraints but suffer from alignment difficulties and limited expressivity. By instead constraining localized subsets of shape within high-capacity unsupervised models, it is possible to preserve flexibility while ensuring geometric consistency, yielding over 70% error reduction on S-Up3D. In parallel, the rapid growth of text-to-3D generation has exposed limitations of current evaluation metrics, which either require ground-truth supervision (e.g., PSNR) or only measure prompt fidelity (e.g., CLIP). Gen3DEval addresses this by leveraging vision-language models fine-tuned for 3D object quality assessment, enabling reference-free evaluation across text fidelity, appearance, and surface geometry. Together, these approaches advance both the generative and evaluative foundations of 3D vision, highlighting pathways toward more accurate, scalable, and human-aligned 3D understanding. Seminar Recording |
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| 29 Oct 2025, 12:30 | Alessandro Pierro (Intel & LMU Munich) | Hardware-Algorithm Co-Design for ML Inference Machine Learning Technical + Centre for AI & Skills | Show Details |
VenueMoore Annex 034B [MS Teams Link] Short BioAlessandro Pierro is a researcher at Intel Labs and a doctoral candidate in computer science at the Ludwig-Maximilians-Universität München. His research focuses on accelerating machine learning and mathematical optimization workloads through hardware-algorithm co-design, using advances in parallel computing architectures. He is currently working on energy-efficient inference for linear recurrent networks to enable sequence modeling at the edge. AbstractThe growing demand for AI accelerators presents an opportunity to validate the technological readiness of emerging hardware architectures. This requires understanding how alternative computational paradigms perform on real applications and identifying which algorithmic trends align with their inherent strengths. This seminar will provide empirical results on current ML workloads running on the Intel Loihi 2 accelerator, a sparse, event-driven, spatially-mapped system. Results on State Space Models and recurrent LLMs demonstrate where Loihi 2 can excel, as well as which architecture-level enhancements could improve its performance on these workloads. We will also cover the implications for hardware architectures arising from recent trends in large-scale ML workloads, including sparse mixtures-of-experts, recurrent reasoning, speculative decoding, and hierarchical networks. Seminar Recording |
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