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Suresh Venkatasubramanian
Works at U. Utah
Attended Stanford
Lives in Salt Lake City
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Do you want to know when your child is born whether they will commit a crime at age 18? This person thinks he can do it.
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Aaron Roth's profile photoNeal Patwari's profile photo
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Just so the internets know: that was tongue firmly in cheek.
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This is a short and sweet illustration of the challenges of mathematical modeling
 
If you're trying to solve a real world problem I think you have to tread very carefully, at the early stages, in the way your formulate it mathematically. Once you've formulated it, it's easy to imagine that this is now the problem you need to solve and forget about the original problem. You can then spend a long time making progress on solving the formulation, but fail to realise that you're actually stuck in a rut and that there's another formulation of the original problem that works much better.

I had the problem of trying to find optimal (in some sense) paths for moving a large number of vehicles from various A's to various B's. It seemed natural to pick one vehicle and then optimise for that. The problem of finding the optimal path could then be written as something like a calculus of variations problem. At this point I was now trapped in a particular formulation. Trying to solve the calculus of variations problem led to an ODE, but instead of initial conditions it had both initial and terminal conditions. You can discretise time and use a standard ODE solving method, but you can't simply step forward through time from the start as you don't know, for any choice of starting path, whether it will end the right way. It turns into a messy search problem. And then I had the prospect of solving this problem reliably for a large number of paths.

Eventually I realised I had to break out of my original formulation. Instead of solving for one entire path at a time it works better to solve for many paths incrementally and simultaneously. If you can solve and tabulate the solutions to the problem of getting from each A to each B over the time period s to t, and you can solve from the short time period from s-ds to s, then you can combine these to solve for the entire table of solutions from s-ds to t. Now it's just recursion. This is classic dynamic programming, which was in fact originally invented to solve this very problem in the case where the vehicles were missiles. It can be incredibly easy compared to solving millions of ODEs with initial and terminal conditions.

I think I basically recapitulated the history of control theory, which is unsurprising as I was getting my problem solving clues from web pages on control theory and colleagues who had some experience with related problems. The former approach is the Pontryagin principle approach [1] and the latter is the Hamilton-Jacobi-Bellman approach [2] (which is similar to reinforcement learning). The former approach is probably a good choice if there's a chance you can solve the equations exactly and get a simple formula. The latter approach seems to work better when the quantity you're optimising is determined by big tables of numbers.

[1] https://en.wikipedia.org/wiki/Pontryagin%27s_maximum_principle
[2] https://en.wikipedia.org/wiki/Hamilton%E2%80%93Jacobi%E2%80%93Bellman_equation
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Shriram Krishnamurthi's profile photo
 
My colleague John Hughes has a bunch of great examples of mistaking the model for the domain. One needs a certain level of maturity to appreciate the Modelers' Hippocratic Oath:
https://www.wilmott.com/archives/707 [CC +Dan Piponi]
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This explains why my students' drafts have so much process writing in them.
Link to Piled Higher and Deeper
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Algorithmic decision-making gets its day in court.
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On Thursday, the Supreme Court affirmed the constitutionality of an affirmative action program used by the University of Texas to increase diversity am ...
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The first ruling on algorithmic decision-making!
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I was invited last week to an event co-sponsored by the White House,Microsoft, and NYU called AI Now: The social and economic implications of artificial intelligence technologies in the near term. …
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The dangers of algorithms for tattoo identification. In which I make a brief tattoo-free cameo.

http://www.slate.com/articles/technology/future_tense/2016/07/the_potential_consequences_of_algorithmic_tattoo_identification.html
The hammer of Thor, or Mjölnir, is an ancient Norse symbol that has been appropriated by white supremacists since at least World War II. Hans Schweitze ...
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The propublica article does more than provide evidence of bias. It provides concrete examples of harm to individuals.
By now, you are likely to have heard of the fascinating report (and white paper) released by ProPublica describing the way that risk assessment algorithms in the criminal justice system appear to a…
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My blog post on a workshop I (virtually) attended at the London School of Economics.
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No more up/down votes in my review.
This year, NIPS received over 2400 submissions. That's -- well --- a lot! As a reviewer, I have been assigned 7 papers (note that this number will be utterly incomprehensible to theoryCS PC members who think that 30 papers is a refreshingly low load). But none of that is as interesting as what ...
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Sariel Har-Peled's profile photoSuresh Venkatasubramanian's profile photo
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"homework in 5th grade" might as well be a description of a paper review.
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Suresh's Collections
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I like algorithms. And I hope they're fair.
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CS prof, interested in algorithms, geometry, data mining, clustering
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  • Stanford
  • IIT Kanpur
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geomblog
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  • U. Utah
    Associate professor, present
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Salt Lake City
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Aarhus, DK - Stanford, CA - Philadelphia, PA - Morristown, NJ - New Delhi, India - Berkeley, CA
That this restaurant has an overall rating less than 4 is a travesty. Skip Finca's overrated food and preserve your ear drums: Cafe Madrid is a much more intimate (read: quiet and charming) Spanish fine dining experience, with possibly the best service I've ever had in Salt Lake City. Call ahead if you want the paella: it's worth it.
Public - a year ago
reviewed a year ago
3 reviews
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