Avoiding Implicit Biases in Faculty Recruitment: Part 2

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Explores gender disparities in engineering education, including implicit bias and stereotype threat.

Delves into the impact of gender on engineering education, discussing recruitment challenges, token representation, and implicit biases.

Explores strategies to overcome implicit bias through awareness, motivation, and effort, using practical techniques and examples.

Explores implicit bias in science education and gender stereotypes in academic performance.

Explores implicit bias, gradient descent, stability in optimization algorithms, and generalization bounds in machine learning.

Discusses key moments in faculty recruitment and strategies to overcome biases, including committee diversity and the 'He for She' initiative.

Introduces the importance of becoming aware of cultural assumptions and biases.

Covers strategies to avoid implicit bias in faculty hiring.

Covers modeling and numerical schemes for diffusion-convection problems.

By Prof. Volkan Cevher delves into the mathematics of deep learning, exploring model complexity, risk trade-offs, and the generalization mystery.