Thiago Serra
@thserra.bsky.social
3.1K followers 1.1K following 590 posts
Assistant professor at University of Iowa, formerly at Bucknell University, mathematical optimizer with an #orms PhD from Carnegie Mellon University, curious about scaling up constraint learning, proud father of two
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Iowa City folks:

Tomorrow(Friday, October 17) I will be talking at the University of Iowa Computer Science's Colloquium @uiowacs.bsky.social about optimization over trained neural networks.

The talk is at 3:30 PM in room 110 MLH.

For more details: cs.uiowa.edu/event/34670/0
A little tease:
🔍 Resume Matching – Are standardized resumes advantaged by AI tools?
📂 Resume Classification – How do we stop past biases from repeating?
🤖 Large Language Models – Can summaries distort or reward prompt tricks?
📜 Humanities Perspective – Do hiring tools embed cultural values & power?
AI is reshaping hiring—but how exactly?

In this IJHRM editorial with Melissa Intindola, Neil Boyd, and John Hunter, we explore why understanding AI’s impact on hiring requires both technical insight and HR expertise.

www.tandfonline.com/doi/full/10....
Interdisciplinary narratives on artificial intelligence & personnel selection systems
Published in The International Journal of Human Resource Management (Vol. 36, No. 14, 2025)
www.tandfonline.com
@jannisku.bsky.social is giving a very interesting talk on explainable (integer) optimization at the @euroorml.bsky.social seminar, highlighting counterfactual explanations before diving on new work with Coherent Local Explanations for Mathematical Optimization (CLEMO): arxiv.org/abs/2502.04840
It was great having Joe Paat visiting The University of Iowa Tippie College of Business on Friday — and having a chance to further understand this work!
Last, but not least, I also managed to see how fast my little niece is growing during my quick hop connecting in Sao Paulo! 😍

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This was a great opportunity for strengthening my ties with the Brazilian AI community and meet many talented colleagues. I am very grateful for the invitation by Rosiane de Freitas-Rodrigues to play such an important role at the conference, and for reconnecting me with my academic origins.

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Last week I had the immense privilege of giving the opening keynote talk of the BRACIS - Brazilian Conference on Intelligent Systems #bracis2025 and visiting the beautiful city of Fortaleza for the first time.

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Second, she has partnered with SAS Brasil to first train people who will produce a better and more inclusive melanoma dataset. With a sensitization of the human who diagnoses about what types of lesions are malign on different skin tones, she hopes that we will eventually train the right model!

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The recognition of her work led to new and unexpected references and collaborations. First, the cosmetic industry is considerably ahead in cataloging skin tones, and in fact celebrated her contributions.

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On the other hand, those models missed the mark in lesions at body extremities like the hand palm, foot sole, and under the nails; which have considerably higher incidence in darker-skinned individuals.

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By removing the skin lesions entirely from the images in one way or another, her team still obtained models that were still considerably accurate due to other characteristics present in those images.

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Closing #bracis2025 with mastery, Sandra Avila talked about her journey working on diagnosing melanomas (a type of skin cancer).

Interestingly, the story starts with winning a classification contest, and then evolves from her working backwards to figure out what the models actually predicted.

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Although such attacks can be successful without interfering directly with the node being attacked, they lead to high-rank tensors. Hence, low-rank tensor approximations end up being a way to filtering the attacker’s noise from the information encoded in the graph.

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Towards the end, Papalexakis talked about computationally efficient clustering methods, and how they may be bound to adversarial attacks in graph neural networks.

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Rank-one tensors are blocks obtained as the product of vectors, which approximate part of the information in the tensor.

That allows to investigate networks in a number of ways, such as in one of their studies on citations, coauthorship, and clustering of scholars in different areas.

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Papalexakis then uses the example of a graph in which multiple edges between the same vertices are possible, which leads to one adjacency matrix for each label and therefore to a “3-dimensional matrix”, or tensor, if we want all of those adjacency matrices as part of a same mathematical object.

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Preparing the ground for a future generalization from matrices to tensors, Papalexakis characterized rank-one matrices as those that can be obtained from the product of a column and a row. That entails a perspective of low-rank approximations as a sum of few such column-row products.

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Evangelos Papalexakis gave an interesting and accessible keynote about latent structure and tensor decomposition at #bracis2025

The identification of latent structure was motivated with the observation from Miller’s Law that we only keep “7 +/- 2” items in memory at any given time time.

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Today I learned that there is a dataset about puns in Portuguese called Puntuguese 😂

… and also that much better results on this dataset will be in the proceedings of #bracis2025

The talk was presented by Jhúlia de Souza Leal’s master’s advisor on behalf of the authors