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Thomas Lancaster on Muck Rack

Thomas Lancaster

United Kingdom
As seen in: Nature, The Guardian, Educational Studies, The Conversation, Dialnet - Universidad de La Rioja, International Journal of Electronic Business
Covers:  education technology, higher education, plagiarism, academic integrity, computer science, startup companies, entrepreneurship
Experienced Computer Science academic and edtech enthusiast at @ICComputing. Researcher and speaker on contract cheating, plagiarism and academic integrity.

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Thomas Lancaster’s Biography

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Educationally connected, my passion is technology. I run several successful blogs, contribute to others, have articles in print and my own network of media connections. I have influence across higher education networks and regularly give invited presentations in-person and online. I have a keen interest in emerging ideas in journalism, particularly those engaging my computer programming background. As well as expanding my reach as a blogger, I am currently exploring the ways to best use my infl…

Articles

Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model - Molecular Psychiatry

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By Roberto Goya-Maldonado, Alyssa R. Amod, Zeynep Başgöze, Francesco Benedetti, Bianca Besteher, Katharina Brosch, Robin Bülow, Romain Colle, Baptiste Couvy-Duchesne, Kathryn Cullen, Udo Dannlowski, Christopher G Davey, Jan Ernsting, Jennifer Evans, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Nynke A. Groenewold, Tim Hahn Verified, Alec J. Jamieson, Andriana Karuk, Tilo Kircher, Bonnie Klimes-Dougan, Thomas Lancaster, Susanne Meinert, Bryon A. Mueller, Igor Nenadic, Amar Ojha, Sara Poletti, Maria J Portella, Joaquim Radua, Jair C. Soares, Aleix Solanes, Dan Stein, Frederike Stein, Aleks Stolicyn, Sophia I. Thomopoulos, Yara J. Toenders, Aslihan Uyar-Demir, Eduard Vieta, Martin Walter, Heather C. Whalley, Sarah Whittle, Nils Winter, Katharina Wittfeld, Margaret Wright, Mon-Ju Wu, Lianne Schmaal, Paul Thompson
| Nature Verified
Abstract Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD.
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StormBamboo Compromises ISP to Abuse Insecure Software Update Mechanisms

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By Ankur Saini, Paul Rascagneres, Steven Adair, Thomas Lancaster
| volexity.com
StormBamboo Compromises ISP to Abuse Insecure Software Update Mechanisms KEY TAKEAWAYS StormBamboo successfully compromised an internet service provider (ISP) in order to poison DNS responses for target organizations. Insecure software update mechanisms were targeted to surreptitiously install malware on victim machines running macOS and Windows. Malware deployed by StormBamboo includes new variants of the MACMA malware.
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures - Scientific Reports

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By Francesco Benedetti, Kathryn Cullen, Ian H. Gotlib, J Hamilton, Thomas Lancaster, Sara Poletti, Kang Hee Sim, Dan Stein, Katharina Wittfeld, Lianne Schmaal, Bryon A. Mueller
| Nature Verified
Abstract Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting.
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