ADAPT: Designing Activity-Informed Viral Diagnostic Assays

I wanted to give a pointer to a new preprint on bioRxiv on developing diagnostic assays for viruses, by (first author) Hayden Metsky (and others!) out of the Sabeti Lab at the Broad Institute (that I've been a bit involved with).  Hayden, who somehow is both a computer science PhD and an expert in virology, has devised a novel software pipeline for developing diagnostics that are designed from the start to deal with genomic diversity (a virus evolves to have many somewhat different variants) and the challenge of false matches (you don't want to get false positives from matching some other different virus) -- also known as sensitivity and specificity.  Algorithmically, he uses machine learning to determine scores for possible tests for matches to small pieces of the genome, or probes, and utilizes locality-sensitive hashing, combinatorial optimization algorithms for submodular maximization, and sharding pattern matching across tries as substages in the overall design.  

I am always excited to see algorithmic ideas being used to solve real-world problems, and this is a deep and difficult example of the "algorithmic lens"  at work.  I am optimistically hopeful that this type of technology will help drive the development of viral diagnostic and monitoring methods forward.     

Some additional pointers:  to code, to some array designs for SARS-CoV-2, and Hayden's twitter thread describing the work.  

TCS Connections Questionnaire

I wanted to link to a survey that is up entitled Committee on TCS Connections Questionnaire.  They are examining modifying approaches to publishing in the theoretical computer science community, and they are focusing on FOCS/STOC.

I personally approve of the idea of the committee, though I admit I am concerned that it's too little, too late.  For years, FOCS/STOC has been a culture concerned with some sense of "prestige" -- the number of accepted papers has to be kept low, because we want people outside of theory to take FOCS/STOC as an imprimatur for the top theory work.  Because of this, FOCS/STOC has stayed essentially the same size, while the field (whether you view the field as TCS or computer science writ large) has expanded.  This has led to a proliferation of additional conferences (ITCS, HALG, various theory workshops...) that reduce the importance of FOCS/STOC and their role in creating community cohesion.  It has also led to other areas (most notably AI) becoming the home to work that should be quite at home in major TCS conferences.  

I don't think FOCS/STOC is what is used to be (the central home for theory results, when theory was smaller) or what it has supposedly wanted to be (the home for the best theory results).  I think it makes a lot of sense to stop and think about what they should be for the future.  Hence the survey is important, and I encourage the theoretical computer science community to respond.  I'm not sure, though, that there are great answers -- external forces, and the community's general aversion to change, may mean that there is not much to be done.