The GRILL team from the University of Glasgow consists of, left to right, Iain Mackie, Federico Rossetto (pictured on tablet), Paul Owoicho (standing), Carlos Gemmell, Jeff Dalton (standing), and Sophie Fischer.
The GRILL team from the University of Glasgow consists of, left to right, Iain Mackie, Federico Rossetto (pictured on tablet), Paul Owoicho (standing), Carlos Gemmell, Jeff Dalton (standing), and Sophie Fischer.

GRILL

The GRILL team consists of School of Computing Science graduate students Carlos Gemmell, Federico Rossetto, Iain Mackie, Sophie Fischer, and Paul Owoicho with Dr. Jeff Dalton as faculty advisor.

The GRILL team consists of School of Computing Science graduate students Carlos Gemmell, Federico Rossetto, Iain Mackie, Sophie Fischer, and Paul Owoicho with Dr. Jeff Dalton as faculty advisor. GRILL will be able to help people solve complex tasks in the kitchen, garage, and beyond. We will develop novel multi-modal algorithms that are grounded in the real world that can understand what you're doing, how you're doing it, and adapt to new challenges on the fly. This research will focus on reasoning over past interactions, modular structured task representations, and multi-modal information extraction.

Carlos G. - Team leader

Carlos is a second year NLP PhD student at the University of Glasgow supervised by Jeff Dalton. He is interested in developing neural language systems capable of externalising knowledge and computation through use of structured tools for question answering and conversational information seeking, as well as measuring the generalisation, robustness and reasoning capabilities of such systems. He has been published in SIGIR and led the GRILL Lab at TREC 2020 in the top five teams for conversational response ranking. Before his PhD, Carlos earned his B.S. degree in Computer Science from the University of Glasgow and interned as a Vision and Machine Learning engineer at HiBirdi.

Paul O.

Paul is a first-year PhD student studying the role of mixed-initiative in effective conversational search. He is particularly interested in how search systems can meet a user's complex information needs by expertly guiding them through the information space. Paul earned his MSc at the University of Glasgow where he developed novel Transformer-based summarization methods for the TREC Podcasts track. Paul also developed retrieval and query rewriting baselines for the TREC 2021 CAsT Track that involved cloud deployment on AWS with Docker, Kubernetes, and multi-GPU micro-services. In his free time, Paul enjoys weightlifting and cooking Nigerian recipes.

Sophie F.

Sophie is a 4th-year BSc student, starting a research Master’s (MSci) in the Fall. Her Honours thesis project “COP Bot: An agent for climate change education and advocacy” is a collaboration with the BBC Voice + AI group and received the best thesis award. She developed a multi-modal (voice + screen) Dialog-flow based agent deployed on Google cloud. It provides rich functionality with question answering, event recommendation, and social chit-chat elements based upon a specially crawled corpus around COP26 and climate change. She will assist in UX development, perform user lab studies, and develop novel approaches to enhance user engagement.

Federico R.

Federico is a second-year PhD student at the University of Glasgow working on Multimodal Learning for conversational systems, integrating audio, video and text. He graduated from the University of Pisa in Computer Science following an AI curriculum in 2019. Federico won the Fujitsu AI NLP Challenge 2018 with his team.

Iain M.

Iain previously worked as a Quantative Trader, founded an e-commerce start-up that supports one of the UK's fastest-growing brands, and is an Investor at Creator Fund (an early-stage European VC). He holds an MA in Applied Economics from the University of St Andrews and an MSc in Data Science from the University of Edinburgh. Iain is currently pursuing a PhD at the University of Glasgow under Jeff Dalton. His PhD focus is multi-task deep learning for information extraction and knowledge-centric ranking tasks. Iain develops new methods of retrieval for complex tasks using knowledge bases and text-based graph neural networks.

Jeff Dalton - Faculty advisor

Coming soon!

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GB, London
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JP, 13, Tokyo
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DE, BE, Berlin
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DE, BY, Munich
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IT, MI, Milan
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ES, M, Madrid
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US, TX, Austin
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US, CA, San Diego
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LU, Luxembourg
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX
GB, London
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis Basic Qualifications -Masters in Computer Science, Machine Learning, Robotics or equivalent with a focus on Computer Vision. -2+ years of experience of building machine learning models for business application -Broad knowledge of fundamentals and state of the art in computer vision and machine learning -Strong coding skills in two or more programming languages such as Python or C/C++ -Knowledge of fundamentals in optimization, supervised and reinforcement learning -Excellent problem-solving ability Preferred Qualifications -PhD and 4+ years of industry or academic applied research experience applying Computer Vision techniques and developing Computer vision algorithms -Depth and breadth in state-of-the-art computer vision and machine learning technologies and experience designing and building computer vision solutions -Industry experience in sensor systems and the development of production computer vision and machine learning applications built to use them -Experience developing software interfacing to AWS services -Excellent written and verbal communication skills with the ability to present complex technical information in a clear and concise manner to a variety of audiences -Ability to work on a diverse team or with a diverse range of coworkers -Experience in publishing at major Computer Vision, ML or Robotics conferences or Journals (CVPR, ICCV, ECCV, NeurIPS, ICML, IJCV, ICRA, IROS, RSS,...) We are open to hiring candidates to work out of one of the following locations: London, GBR