Rate My Professor Emmanuel Tadjouddine

Tadjouddine's research interests centre on algorithmic differentiation and its applications to numerical methods such as sensitivity analysis, optimisation, financial computing, and machine learning algorithms. He also investigates electronic markets, with a focus on the design and verification of auction mechanisms, as well as computer science education. He holds Fellowship of the Higher Education Academy (HEA) and membership in ETAPS e.V. Key publications include "Dialogue Based Decision Making in Online Trading" by W. Bai et al. (Transactions on Machine Learning and Artificial Intelligence, 2017); "A Nonmonotone Inexact Newton Method for Unconstrained Optimization" by H. Gao et al. (Optimization Letters, 2017); "Discrete Element Modelling of agglomerate impact using autoadhesive elastic-plastic particles" by L. Liu et al. (Powder Technology, 2016); "Calibration based on entropy minimization for a class of asset pricing models" (Applied Soft Computing, 2016); "Modeling and Simulation of Sequential Auctions: Pricing and Calibration Algorithms" (International Journal of Modeling, Simulation, and Scientific Computing, 2012); "Vertex-Ordering Algorithms for Automatic Differentiation of Computer Codes" (The Computer Journal, 2008); "Fast AD Jacobians by Compact LU Factorization" by J.D. Pryce and E.M. Tadjouddine (SIAM Journal on Scientific Computing, 2008); and "Jacobian Code Generated by Source Transformation and Vertex Elimination can be as Efficient as Hand-coding" by S.A. Forth et al. (ACM Transactions on Mathematical Software, 2004).