Tag Academia

Using Software to Discover Rare Diseases with Matt Might

http://traffic.libsyn.com/sedaily/Might_Edited_2.mp3Podcast: Play in new window | Download “In many ways, nature is still the fastest computer we have when it comes to studying disease.” Software engineering is a deterministic field. We write lines of code, and feed data into that code, expecting to get a certain answer. Computing is deterministic because humans developed it–we understand computers from top to bottom. The same cannot be said about biology. Matt Might is

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Distributed Systems with Leslie Lamport

http://traffic.libsyn.com/sedaily/lamport_distributed_systems.mp3Podcast: Play in new window | DownloadThis episode is a republication from my interview with Leslie Lamport on Software Engineering Radio. Leslie Lamport won a Turing Award in 2013 for his work in distributed and concurrent systems. He also designed the document preparation tool LaTex. Leslie is employed by Microsoft Research, and has recently been working with TLA+, a language that is useful for specifying concurrent systems from a high level.

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Computational Neuroscience with Jeremy Freeman

http://traffic.libsyn.com/sedaily/Neuroscience_Edited.mp3Podcast: Play in new window | Download “You want to take a scientist who knows a little bit of matlab programming and try to teach them mapreduce, and write a mapreduce program in java to do image processing? It’s a disaster!” Apache Spark is replacing MATLAB in the domain of computational neuroscience. The constraints of running MATLAB on a single machine can’t support the demands of neuroscience, which has huge

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PhD in Computer Science: Advantages and Disadvantages

From Tracy Chou’s answer via Quora: Advantages career opportunities A Computer Science Ph.D gives you a huge advantage for industry research labs, like Google Research. On occasion such labs accept people who only have master’s degrees, but this is extremely rare. A CS PhD also gives you an advantage for some quantitative finance positions, assuming your PhD is mathematical and algorithmic in nature. For some companies (typically larger ones), having a CS

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Applying Software Research to Industry with Andy Ko

http://traffic.libsyn.com/sedaily/answerdash_Edited_Final.mp3Podcast: Play in new window | Download “It’s not just about tools, it’s also a lot about how you structure a team, how you structure the communication between engineers, how you facilitate decision making – all of those things.” University research produces numerous papers about software engineering. Unfortunately, many of the problems explored by these software engineering researchers have no actual application. These papers fall into The Black Hole of

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PhD Student Emotions: The 4 Phases

From Matt Might’s answer via Quora: I got my Ph.D. in CS, dabbled in start-ups (once unsuccessfully, once with marginal success) and then returned to academia as a professor.  I’ll contrast these two lifestyles as best I can. The Ph.D. student experience follows a predictable path: Phase I: Excitement. Phase II: Depression. Phase III: Excitement. Phase IV: Extreme depression or extreme excitement. Phase I is when new Ph.D. students get excited

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Machine Learning for Businesses with Joshua Bloom

“You’ve got software engineers who are interested in machine learning, and think what they need to do is just bring in another module and then that will solve their problem. It’s particularly important for those people to understand that this is a different type of beast.”

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Learning Rails with Michael Hartl

“It really seemed like Rails just put everything together, so I thought well let’s give this thing a try, and I liked it.”

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Language Design with Brian Kernighan

“The best computer science is the kind where the theory is inspired by some practical problem, you develop a better theoretical understanding of what you want to do, and that feeds back into better practice.”

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Digital Nomadism with Michael Rosenthal

http://traffic.libsyn.com/sedaily/michael_rosenthal_final.m4aPodcast: Play in new window | Download “The main motivation for me was to gain more perspective on the world and my place in it.” Michael Rosenthal is a traveling programmer who gave up school and full-time job prospects in the U.S. to travel and freelance. This episode is a Christmas special because Michael is my younger brother, and his journey as a developer and a person has been inspiring

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Learning Machines with Richard Golden

“When I was a graduate student, I was sitting in the office of my advisor in electrical engineering and he said, ‘Look out that window – you see a Volkswagon, I see a realization of a random variable.’ ”

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Caml with Gérard Huet

“It was claimed that these [object-oriented] languages were better for modularity – and I don’t believe so.”

Caml is a functional programming language that is a dialect of the ML programming language family, developed at INRIA and formerly at ENS.

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Haskell with Lennart Augustsson

“We might be coming back to these languages that make it easier to program parallel machines.”

Haskell is a purely functional programming language that employs lazy evaluation.

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Racket with Matthew Flatt

“I think what functional programming does is codify what we’ve discovered are good ways to think about problems most of the time.”

Racket is a functional programming language similar to Lisp and Scheme.

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Automating the CS Classroom with Prah Veluvolu

Mimir is a platform that helps schools scale up their computer science program. It provides automated code grading, plagiarism checking, code quality analysis and additional course management tools.

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Robot-Human Interaction with Stefanie Tellex

Robots sometimes need help from humans when solving complex problems. Successfully solving a problem often demands a strong model for communication and well-trained robots.

Stefanie Tellex is an assistant professor at Brown University whose research focuses on constructing robots that use natural language to communicate with humans.

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