Connected science: solve disconnected lab data with integrated workflows
Webcast supported by
Starting at 3pm GMT (11am EST)
Today, many laboratories have a data connection problem - with separate instruments, systems and spreadsheets that hinder progress.
Scientists input data manually, context is lost between experiments, and teams repeat work because data is siloed across departments or even partner organisations. Fragmented data is also harder to protect, which makes it difficult to maintain the security and traceability needed to safeguard personal data and intellectual property in the cloud.
Connecting the lab end-to-end can fix much of this, but few organisations can start from scratch. The challenge is joining up the infrastructure and workflows they already have.
In this panel, Colin Fyffe, Ángel Gil Nolskog, Tom Whitehead and Tariq Khoyratty will explain how labs are connecting existing systems to reduce manual work and reach decisions faster. Our panel of experts will then show connected science in practice, from hands-free experiment documentation to AI-guided formulation and executable SOPs. The session closes with a panel discussion and live Q&A with the audience.
Who should attend?
- IT and infrastructure leaders planning or running hybrid AI-HPC environments
- Data scientists responsible for model accuracy, reproducibility and governance
- Hands-on scientists who want their data platform to stop being the bottleneck in discovery work
- Researchers and academics deciding the next step in their data and compute strategy
What will you learn?
- IT infrastructure leaders looking for guidance on SaaS procurement and implementation
- Hands-on scientists seeking tips on optimising their task management with the aid of AI and machine learning
- Laboratory data stewards responsible for governance and model management
Speakers
Moderated by Robert Roe, Editor of Scientific Computing World Robert has been a journalist covering scientific computing for 15 years. He specialises in covering high-performance computing (HPC), laboratory informatics, computer-aided modeling and AI applications in scientific research. He spearheads SCW’s major publication initiatives, including the annual SCW75, which honours influential leaders shaping research computing infrastructure. He will moderate the discussion and take questions from attendees at the end. |  |
Colin Fyffe Colin joined Revvity in May of 2023 to expand our developer relationships and Signals AI. Prior to Revvity, Colin spent a decade as a software engineer for PayPal before moving into product management with an ISP. He brings expertise in creating customer-friendly documentation, defining best practices, and providing practical examples to customers. |  |
Ángel Gil Nolskog Ángel Gil Nolskog is Senior Key Account Manager at LabVoice, where he helps pharma, biotech and research organisations bring AI-guided, hands-free data capture into their labs. A molecular biologist by training, he has worked in voice-enabled lab digitisation since 2019. He brings in-depth perspectives on partnering with scientists, lab operations and IT teams, to connect bench work to systems like Revvity Signals. |  |
Tom Whitehead Tom is the Head of Machine Learning at Intellegens, leading the Science Team. He has a PhD in theoretical physics from the University of Cambridge. Tom's team works closely with R&D teams in the materials, chemicals, and manufacturing sectors to understand their product and process development problems and to apply the Intellegens technology to accelerate innovation. He is also active in developing new machine learning methodologies to address bottlenecks in high-value data analysis. He contributes extensive experience in overseeing practical applications of machine learning in industry. |  |
Tariq Khoyratty Tariq helps connect laboratory instruments and workflows with enterprise systems through the Scitara DLX (Digital Lab Exchange) platform. From earning a DPhil in Immunology from Oxford, Tariq transitioned from wet-lab research to computational biology, going on to build an AI-driven SaaS product for flow cytometry analysis, deliver enterprise pharma data engineering projects at TetraScience, and lead the NCI’s Cancer Genomics Cloud program at Velsera. He brings a unique blend of scientific and technical experience for bridging the gap between AI capabilities and the realities of scientific research. |  |
