Professor David Thomas, University of Southampton
David Thomas studied Computer Science as an undergrad at the Dept. of Computing in Imperial, then he did his PhD in digital architectures in the same department. After 5 years as
a researcher associate and then research fellow, in 2010 he moved to the Dept. of Electrical and Electronic Engineer at Imperial as a Lecturer, then Senior Lecturer. In 2021 he joined the Electronics and Computer Science Dept. as a Professor. Both his research and teaching interests are at the intersection of software and hardware, particularly in the interaction and relationshops between programming languages, algorithms, computer achitecture and digital implementation. A lot of his research involves the use of FPGAs (Field Programmable Gate Arrays), as they provide a great playground for exploring and implementing new digital architectures, such as custom CPUs, application-specific accelerators, or new programming paradigms such as event-driven computing.
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Dr. Mirza Khalid Baig, National Institute of Technology, Rourkela, Odisha, India
Dr. Mirza Khalid Baig received his Ph.D. degree from the Centre for Bio-Inspired Technology (CBIT), Department of Electrical Electronics Engineering, Imperial College London, London, U.K., in 2018, under the supervision of Prof. Christofer Toumazou. After his PhD, he was a Postdoctoral Research Associate with Centre for Bioinspired Technology, Imperial College London. His work at Imperial was funded through the European Research Council (ERC) and the Engineering and Physical Sciences Research Council (EPSRC) Grants. Before working in academia, he worked in the Industry as an Electronics Engineer in the Product Design Team, to implement a novel authentication technology called Laser Surface Authentication (LSA).
He is currently an Assistant Professor at the Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela. His current research interests include developing AI-driven, wearable, biosensors and closed-loop medical devices for healthcare and precision medicine applications. His research at NIT Rourkela has been funded by Department of Science and Technology (DST), Indian Council of Medical Research (ICMR), Department of Biotechnology (DBT) and Aushandhan National Research Foundation (ANRF).
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Title: Tsetlinomics: Simplifying and interpreting multi-omics data using Tsetlin machines
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Multiomics data are rich in terms of the information they carry and convey. However, their information content is overshadowed by the inherent data complexity. This makes it difficult to derive inferences from the omics dataset without use of complex bioinformatics tools. In this talk, we will be looking at understanding how the simple interpretable logic rule approach of Tsetlin machines can be used to handle the complexity of omics datasets. The talk will cover the information theoretic aspect of multi-omics data and its link to Tsetlin machines. The talk will demonstrate the application of Tsetlin machines in developing single disease and cross disease risk estimations, discovering hidden patterns in multi-omics datasets, discovering genetic, epigenetic and metabolomic biomarkers. Finally, we will look toward the future of decentralized medicine, showcasing how the low-power, bitwise architecture of Tsetlin machines enables Edge AI deployment directly onto point-of-care diagnostic devices.
Dr. Gouthaman K V is a Researcher at Dolby Laboratories, working on foundation AI models for multi-modal understanding across audio, speech, music, video, and text. His research spans multi-modal AI, with a strong focus on audio-centric intelligence for next-generation media technologies. At Dolby, he also works on perceptual audio quality modeling, music and speech enhancement and restoration, as well as trustworthy, explainable, and responsible AI for multimedia applications. Prior to Dolby, he was a Senior Data Scientist at Myntra (Flipkart/Walmart), where he worked on multi-modal search and language-based retrieval systems for large-scale e-commerce applications. He received his PhD in multi-modal AI from IIT Madras, where his research focused on vision-language models, including visual question answering, image and video captioning, and related problems in multi-modal learning. Earlier, he worked on classical computer vision problems such as object tracking and facial expression recognition. He also actively contributes to the research community and serves as a reviewer for leading venues including CVPR, ICCV, ECCV, NeurIPS, ICASSP, and TPAMI etc.
Dr. Aveen Dayal is a Senior Multimodal AI Researcher at Dolby Advanced Technology Group India. He received his Ph.D. in Artificial Intelligence from the Indian Institute of Technology Hyderabad, where he was a Prime Minister’s Research Fellow and received the IIT Hyderabad Excellence in Research Award 2024. His research spans multimodal and generative AI, out-of-distribution robustness, efficient inference, content protection and attribution, and model evaluation. His work has been published at leading venues, including CVPR, WACV, ECCV, NeurIPS, and IEEE Transactions on Image Processing. Aveen has previously worked with Adobe Research and Microsoft Research India on generative AI, multimodal models, and efficient large-language-model inference. He has also been a visiting researcher at the University of Agder, Norway, where he contributed to interdisciplinary projects involving autonomous systems, biomedical imaging, sensor-based perception, and edge AI. He regularly contributes to the research community as a reviewer for major AI conferences and journals.
Dr. Gouthaman K V and Dr. Aveen Dayal will do a combined talk where abstract and title of talk will be presented shortly.
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Tutorials
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