Category Archives: Algorithms

algorithms, combinatorics, probability and other aspects of theoretical computer science (such as lower bounds etc)

My Review of “Centralized Allocation of Human Resources: An Application to Public Schools”

My Review of “Centralized Allocation of Human Resources: An Application to Public Schools” for Computing Reviews is now online here (requires Computing Reviews membership).

It covers a very interesting work done by Laura López-Torres and Diego Prior of Universitat Autònoma de Barcelona related to workforce planning in the context of public schools in Catalonia, in northeast Spain.

Workforce allocation using artificial intelligence is one of the core strengths of BizMerlinHR, so this paper was a natural fit for me to review.

My Review of Worker skill estimation paper by Rahman et al

My review of “Worker skill estimation in team-based tasks” paper by Rahman, Thirumuruganathan, Roy, Amer-Yahia and Das, is now available on Computing Reviews here. (Requires ACM membership, and it is incidentally, my 11th review for ACM Computing Reviews – you can find the other ones here.)

Companies generally struggle with the problem of estimating the skills of individuals, as it is a hard thing to keep updated and has enormous applications in optimal team formation. Skills are sometimes subjective, can have many variations (Spoken French expertise, or Written Legal French expertise, for example) and change rapidly over time. Historically, skill management used to be only for large companies implementing million dollar SAP HR system implementations, but with the advent of cloud hosted SaaS applications, that situation is now changing. I am fortunate to work on the BizMerlin‘s exciting skill matrix product, so this paper was all the more interesting for me.

My Review of Data Characterization paper by Wang et al

Review of “An improved data characterization method and its application in classification algorithm recommendation” paper by GuangtaoWang, Qinbao Song and Xiaoyan Zhu is now available on Computing Reviews here.

Classification is an active research problem, and numerous classification algorithms have been proposed over the past few years. Some algorithms perform better than others, based on the dataset. “No Silver Bullet” or “No Free Lunch Theorem” is an informal theorem that states that no single classification algorithm outperforms other classification algorithms on all data sets. This informal theorem is essentially what keeps many data scientists in business – each data set has its own idiosyncrasies, and different classification algorithms need to be explored to find the one that best meets the needs of the problem at hand. Continue reading full review.

Latest version of Algorithms Brochure

Yet another 6212 class wraps up, and as usual I leave the 70 odd students with an algorithms brochure as a parting gift. It may not be technically possible to condense the material taught during the entire semester into two pages (especially considering the Levenshtein distance between Dynamic Programming and NP-completeness), but still it is a try.