The u-in-STEM wall has been created to showcase and celebrate the diversity of academics in engineering mathematics-related disciplines who range in gender, ethnic background, sexual orientation, religion and area of expertise in an attempt to overturn the common mainstream misconception of a typical engineer. The wall aims to highlight not personal differences but what connects engineers together; the passion for mathematics and the desire to improve the world around us.
This is your About Page. It's a great opportunity to give a full background on who you are, what you do and what your website has to offer. Double click on the text box to start editing your content and make sure to add all the relevant details you want to share with site visitors.
Dr. Rediet Abebe
Dr. Rediet Abebe was born and raised in Addis Ababa, Ethiopa. She currently holds the position of Assistant Professor of Computer Science at the University of California Berkeley and is the co-founder of Blank in AI, and Mechanism Design for Social Good. Abebe’s work spans algorithms and AI, with a focus on equity and justice concerns. Her publications all showcase the interdisciplinary collaboration she has undertaken; they are particularly pertinent to students of Engineering Mathematics interested in both data science and mathematical modelling.
Rediet Abebe, Jon Kleinberg, and S. Matthew Weinberg. “Subsidy Allocations in the Presence of Income Shocks”. en. In: Proceedings of the AAAI Conference on Artificial Intelligence 34.05 (Apr. 2020), pp. 7032–7039. issn: 2374-3468, 2159-5399. doi: 10.1609/ aaai.v34i05.6188. url: https://aaai.org/ojs/index.php/AAAI/article/view/6188 (visited on 11/08/2020)
Rediet Abebe et al. “Roles for Computing in Social Change”. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Jan. 2020). arXiv: 1912.04883, pp. 252–260. doi: 10.1145/3351095.3372871. url: http://arxiv.org/abs/1912.04883 (visited on 02/02/2021)
Rediet Abebe et al. “Opinion Dynamics with Varying Susceptibility to Persuasion”. In: arXiv:1801.07863 [physics] (Jan. 2018). arXiv: 1801.07863. url: http://arxiv.org/ abs/1801.07863 (visited on 02/02/2021)
Prior to researching applied mathematics for his PhD at MIT, John Urschel played for the the Baltimore Ravens in the NFL for three seasons. His research encompasses machine learning algorithms, graph theory, and numerical linear algebra. Urschel’s interest in data visualisation and probabilistic models for machine learning are all very relevant areas of research for budding engineering mathematicians to further explore.
John C. Urschel and Joseph R. Galante. “Instabilities in the Sun–Jupiter–Asteroid three body problem”. en. In: Celestial Mechanics and Dynamical Astronomy 115.3 (Mar. 2013), pp. 233–259. issn: 0923-2958, 1572-9478. doi: 10 . 1007 / s10569 - 012 - 9461 - 8. url: http://link.springer.com/10.1007/s10569-012-9461-8 (visited on 02/02/2021) John C Urschel. “A Space-Time Multigrid Method for the Numerical Valuation of Barrier Options”. en. In: (), p. 20 John C Urschel and Jake Wellens. “TESTING k-PLANARITY IS NP-COMPLETE”. en. In: (), p. 8
Dr. Katie Bouman
Dr. Katie Bouman is an American engineer and computer scientist who led the development of Continous High-resoltion Image Reconstruction using Patch priors (CHIRP), which was used to capture the first image of a black hole in 2019. Bouman is an assistant professor at the California Institute of Technology (Caltech), where she researches new systems for computational imaging by integrating algorithm and sensor design. Her group at Caltech combines ideas from signal processing, computer vision, machine learning, and physics.
Kazunori Akiyama. “First M87 Event Horizon Telescope Results. IV. Imaging the Central Supermassive Black Hole”. en. In: The Astrophysical Journal Letters (2019), p. 52 Katherine L. Bouman et al. “A Low Complexity Sign Detection and Text Localization Method for Mobile Applications”. en. In: IEEE Transactions on Multimedia 13.5 (Oct. 2011), pp. 922–934. issn: 1520-9210, 1941-0077. doi: 10.1109/TMM.2011.2154317. url: http://ieeexplore.ieee.org/document/5766755/ (visited on 02/23/2021)
Prof. Peter Landin
Spending 15 years in academia and industry exploring theoretical computer science, Peter Landin was on the forefront of the computing revolution. His paper on The next 700 programming languages is well cited and his writings of the computational correspondence between programming languages are all characterised by his well-known dry sense of humour. Aside from being an outstanding computer scientist, Landin was also an avid activist and was once arrested as part of an anti-nuclear demonstration. In his latter life, Landin opened his house as a gay commune, which is where Aids: the Musical! was conceived.
P. J. Landin. “The next 700 programming languages”. en. In: Communications of the ACM 9.3 (Mar. 1966), pp. 157–166. issn: 0001-0782, 1557-7317. doi: 10.1145/365230.365257. url: https://dl.acm.org/doi/10.1145/365230.365257 (visited on 11/09/2020)
Dr. Fern Hunt
Born in 1948 to working-class parents, Dr Fern Hunt went on to become a probability theorist and mathematical biologist. She currently works at the USA’s National Institute of Standards and Technology where her work includes collaborations across academia and industry with various scientists and engineers. Hunt’s work is especially applicable for students of Engineering Mathematics, due to her speciality in dynamical systems and neat mathematical modelling.
Fern Y. Hunt, Michael A. Galler, and Jonathan W. Martin. “Microstructure of weathered paint and its relation to gloss loss: Computer simulation and modelling”. en. In: Journal of Coatings Technology 70.880 (May 1998), pp. 45–53. issn: 1935-3804. doi: 10.1007/ BF02697837. url: https://doi.org/10.1007/BF02697837 (visited on 02/02/2021)
A Mathematical Analysis of the Chitty Hypothesis — SpringerLink. url: https://linkspringer-com.bris.idm.oclc.org/chapter/10.1007/978-3-642-87893-0_5 (visited on 02/02/2021)
Efficient Algorithms for Computing Fractal Dimensions — SpringerLink. url: https : //link-springer-com.bris.idm.oclc.org/chapter/10.1007%2F978-3-642-71001- 8_10 (visited on 02/02/2021)
Fern Y. Hunt. “An Algorithm for Identifying Optimal Spreaders in a Random Walk Model of Network Communication”. en. In: (May 2016). Last Modified: 2018-11-10T10:11- 05:00. url: https://www.nist.gov/publications/algorithm-identifying-optimalspreaders-random-walk-model-network-communication (visited on 02/02/2021)
Prof. Andrew Ng
By co-founding Coursera and deeplearning.ai Andrew Ng has helped to ‘democratise deep learning’ teaching for over 2.5 million students across various online courses. Ng is one of the most influential computer scientists researching machine learning - in 2011 he co-founded Google Brain. Ng also showcases impressive entrepreneurship skills: in 2018 he started an investment fund and has since raised over $ 175 million for AI startups. Amongst various titles and awards, Ng earned his place as one of Time’s most influential people in 2013.
Andrew Y. Ng and Stuart J. Russell. “Algorithms for Inverse Reinforcement Learning”. In: Proceedings of the Seventeenth International Conference on Machine Learning. ICML ’00. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., June 2000, pp. 663–670. isbn: 978-1-55860-707-1. (Visited on 02/02/2021)
Rion Snow et al. “Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks”. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. Honolulu, Hawaii: Association for Computational Linguistics, Oct. 2008, pp. 254–263. url: https://www.aclweb.org/anthology/D08-1027 (visited on 02/02/2021)
Jeffrey Dean et al. “Large Scale Distributed Deep Networks”. en. In: (), p. 9