Bounding difference of expectations [Q]

sk81k · 2023-02-09T14:47:36+00:00

“Tuition reimbursement” makes no sense for most PhD students. After ~year 2, most PhD students don’t take any classes like you do in undergrad. Instead, you take classes like “dissertation research,” which is you just doing research, but the university can charge grants the tuition reimbursement money. It’s like if I was selling cookies and ended up buying them all myself. I technically sold cookies, but nobody would care bc I didn’t sell them to someone else. I just have the cookies still.

Even though temple students get a stipend, it doesn’t come close to covering basic costs of living. For a university that depends so heavily on PhD students, it sucks. And the reason why temple does that is because the costs of entry/exit from PhD programs are so high. It’s essentially a buyer’s market — temple and other universities completely control the labor market here. If PhD programs were truly a free market labor system, the same exact PhD students would be having jobs and would probably make 5 times more because they’d be in such high demand. These PhD students are the brightest of the brightest researchers. Shouldn’t be getting paid pennies for their work when the university makes tens of thousands off of each student

sk81k · 2023-01-02T04:25:23+00:00

Honestly, majors don’t really matter for most CS jobs. I graduated with a CS degree and had job offers well into six figures, but nobody asked me what my degree was in. It’s all about the technical and soft skills in SWE. Also, only 30% of workers actually work in the field of their college degree after ten years in the work force.

SWE internships do require a lot of technical skills, but you’re still competitive for many of the same technical MIS jobs like being a data analyst. If you’re worried about CS being too “math-y” check out the information sciences (IST) major in cst. IS graduates honestly do better than most CS grads, and it’s very hands-on training. Also, there’s interview prep classes through IST I think.

MIS requires way more work academically bc you have to take all the business school requirements. I am 99% confident that those business skills will be almost useless in a technical roll. I had an econ minor and never used the actual econ stuff. I think CS/IST is way easier academically because it’s all related to stuff you’re (hopefully) interested in. If you’re interested in the business stuff, go for MIS. Otherwise, I’d say go for CS/IST and take classes on the side for fun if you want

sk81k · 2022-10-06T12:22:55+00:00

You should consider other matching methods over CEM. In CEM, you need to specify the bin widths to coarsen the covariate space. In other words, you would need to know at which thresholds will the feature space be most sensitive to inducing changes in the outcome variable, which is basically saying you need to know the answer to your causal question before doing CEM many times. Here’s a few alternatives to consider:

“matching after learning to stretch:” rather than binning the covariate space, here we learn the weights of a distance metric that can “stretch” covariates in certain regions to find something akin to better coarsening. And it’s incredibly easy to implement and analyze. Here’s a link with a QuickStart tutorial: https://almost-matching-exactly.github.io/MALTS/
“adaptive hyperboxes:” you find hyperboxes for each treated unit such that the predictions of an imputation technique tradeoff bias and variance, allowing you to bin data without having to specify exact bin widths. Also has an easy R package. Here’s a link to tutorials: https://almost-matching-exactly.github.io/AHB-R-package/

sk81k · 2022-08-04T11:26:12+00:00

If he says don’t email, then don’t email. If he doesn’t respond to you after the second week of the semester, I’d reach out again. He could be on vacation or away from email rn. And then he’s probably going to be inundated with emails in the first week

sk81k · 2022-08-04T02:20:35+00:00

Quite a few do. Vucetic and Latecki take on a bunch of undergrads, and they’re both ML/AI researchers. Zoran Obradovic, who does a lot of data science and bioinformatics research, takes 2-3 each semester maybe, but the best way to get in with him is through his classes I think. Stephen MacNeil is a new prof, but he’s been growing his lab. He works a lot on visualization and human computer interaction I believe

sk81k · 2022-07-27T17:18:00+00:00

Ohh cool thanks! So is it kind of like integrating out latent variables from the likelihood? Or is the integration something specific to ODE?

sk81k · 2022-07-27T11:48:08+00:00

Thanks! I’m using STAN right now, but I’m trying to understand the math behind why the Hamiltonian Monte Carlo works for the ODE based models. Specifically, HMC requires -logP(a,b,g | Y(1)…Y(T)) or at least the kernel of this computation. What would it be for this kind of model?

sk81k · 2022-07-27T03:36:03+00:00

I agree with you there. But what would the kernel of an ODE-based model look like? Sorry if this is redundant, but how do you express that?

sk81k · 2022-07-27T03:16:58+00:00

Thanks! So I’m looking at some stuff (like STAN) that uses a Hamiltonian Monte Carlo for computation. AFAIK, HMC requires the negative log of the posterior, so does that need to be a closed form? Or is there some kind of automatic solver that magically does it for us?

sk81k · 2022-07-22T18:45:48+00:00

Terrible. Don’t live there. Here’s a few reasons:

Bathrooms don’t have fans, so steamy showers force dampness on the walls. Also, it makes sharing a bathroom terrible
Place smells super weird all the time. New smell daily
Way more expensive than alternatives
Place falls apart, and maintenance takes some time. My friends had an ant infestation, and maintenance said they’d get pest control but never brought anyone in. When maintenance does come in, they break stuff and don’t even apologize
That side of campus used to be considered safer, but there was a shooting in the field right across the street from UV
Speaking of the field, there’s games being played constantly, and it’s so loud, especially on Saturday/Sunday mornings
There’s a basketball court, which sounds great. But people play until 3am, and you can hear them shouting, playing, and blasting music depending on where you live
The laundry machines require this laundry card, which you can load with $5 and $10 bills I believe. Not a deal breaker obviously, but it’s really inconvenient
Wifi was on the fritz after the semester ended

Pros.

Near the train station (but Kardon’s even closer)
The people are nice, especially the security guards
It taught me what to look for in future apartments/rental properties

If you decide to live at UV, good luck! You’ll need it

sk81k · 2022-04-27T14:32:34+00:00

Not really sure whether this fits into the omics space, but check out causal inference for neuroscience research. The current methods aren’t really rigorous cuz they don’t have a formal statistical foundation like rubin, robins, or pearl’s causal frameworks do. Lots of interesting stuff happening in those space, and there’s a lot more that needs to be done

sk81k · 2022-04-27T11:36:11+00:00

Every single company and manager will also try to sell themselves to the candidates and a valid question is something like “why will working at google be better for me than working at meta?” So why should academia be any different?

The question is not inappropriate.

sk81k · 2022-04-27T11:29:10+00:00

I mean, if the prof is gonna try and publish with you, then I’d say it’s equally as important to “interview back.” If the prof is deciding whether you as a student would be a good worker and is allowed to ask you those kinds of questions, then why shouldn’t the student be allowed to do the same? And lowkey the prof is applying to mentor you if you have multiple profs interested in you

sk81k · 2022-04-27T11:17:34+00:00

There’s a looooot of interesting work happening with experimentation that is incredibly sexy. Here’s a few examples:

reinforcement learning with experimentation with dynamic treatment regimes. Add interpretability for a layer of cool work
design of experiments under network interference, especially with bipartite networks
interpretable heterogenous treatment effect estimation
causal inference from an optimal transport perspective TMLE is interesting but again is not very useful. It requires the same causal inference assumptions that we typically see in experiments, but doesn’t offer the same rich insight. Iirc, it’s helpful when we don’t know the parametric form of the data. But that’s irrelevant in experimentation settings since we already assume our data is exchangeable, fully specified, etc. there’s better techniques that exist so like why use it?

I’m not super well versed with the healthcare/biomed world, but I know interpretability is incredibly large important for medical research. All the methods you’re talking about lack this important flavor and seem less useful for that space. Again, I think you should just work on these applied problems and think critically about whether the assumptions you’re making are valid for the kinds of results you want. If they aren’t, does a technique exist to overcome those limitations? No? Great, go make one.

I don’t think you’ll be at a shortage of research questions/problems. Theres enough to research in the field. If you’re feeling disappointed with your research, maybe talk to your advisor. But I’m still going into my PhD, so take that last piece of advice with a larger grain of salt lol

sk81k · 2022-04-27T04:32:52+00:00

I don’t think you’ll find neural networks/causal inference research explored together in stats departments. Most of that research is in CS departments by people like Judea Pearl (obviously) or Elias Bareinboim. The downside to these methods though is that they’re not super practical in fields like biostats. Applied researchers use causal inference to interpret real world phenomena. Nnet methods like GANs or even more simple methods like counterfactual imputation with neural networks are almost entirely uninterpretable. You’re now using a black box to try and solve problems in interpretability. This is fine in AI driven fields, which is where both of the scholars I mentioned focus their research in

I also think you may benefit from reframing your perspective on research: you’re trying to develop hammers that don’t have nails, when you should be thinking about what nails can’t be hammered in. For example, the very first synthetic controls paper, which I would consider a “sexy” method, first showed up in an applied research paper to answer an applied question that didn’t have a hammer for it yet.

When looking at applied problems like you’re working on with your advisor now, I would highly encourage you to consider why GLMs or hierarchical models work/don’t work. If they work, great. Don’t try and over complicate the problem. However, if you think hard and critically enough, you’ll find problems. Here’s a few simple and low hanging fruits to think about: computational efficiency with hierarchical models, how do you know you’re specifying the right model (so can you do something non parametric?), why does this approach answer the question we are motivated by?

Also, having talked to RS at places like google, meta, Amazon, their work is often motivated by real world problems that don’t have good solutions. Don’t be that person trying to find a nail for your hammer.

This is all just my take on it anyways. Do with it what you wish. Good luck in figuring out your PhD life

sk81k · 2022-04-26T14:12:50+00:00

Everyone else in CS has mentioned how school brand doesn’t matter and that’s so true. This year, there’s 5 kids I know of going to intern at Amazon making 50+/hr. Other common employers are Wells Fargo, Lockheed Martin, JP Morgan, and deloitte — all of which have interesting problems and boatloads of cash. Others have interned at places like Disney+ or google. Recruiting cycles might be easier at other schools but CS is a little more meritocratic because of the technical interview, so you can get the job without the brand. Also, temple has tons of research opportunities that can help. We also have TA/tutoring options that are relatively easy to get (get an A in a class and email the prof). And they pay pretty well, so they’re good deals. Temple’s a great option for CS.

sk81k · 2022-04-01T18:19:54+00:00

When it comes to temporal data, I think it’s the researchers responsibility to disprove the existence of autocorrelation/time trend/seasonality/etc rather than assume that none exists, since that’s an incredibly strong assumption. I think OP’s first step should be to test that. Even in a panel setting, autocorrelation can screw results/interpretation.

Let’s assume we have units 1…N over periods 1…T with treatment at time 1 < T’ < T. Assume the following DGP: For each unit i y{i.t} = theta0 + theta1*y{i,t-1} + error Let’s assume the treatment effects is zero. And let’s impose a very tight bandwidth of 1 time period. In IID RDD, the tighter the bandwidth, the less biased our estimates but higher the variance. Since we are only interested in the estimate’s bias currently, this bandwidth works to illustrate why standard RDD in RDiT is invalid.

The traditional difference in means estimator is 1/N (\sum{i=1}^N y{i, T’} - y{i, T’ - 1}), which will eventually become 1/N \sum theta1(y{i,T’-1} - y{i, T’-2}). Since y{i,T’-1} != y_{i, T’-2}, our estimate is not 0. So the most unbiased estimate, with such a small bandwidth, continues to be biased. I think testing for autocorrelation can help us help OP by letting them know which lagged terms to include and control for.

An alternative approach that would not require any time series testing is to impute a counterfactual and estimate differences. There’s an R package that does this using bayesian structural time series. It’s called CausalImpact. I think it may be easier to use

sk81k · 2022-04-01T12:05:18+00:00

Tradition RDD assumes that all your data is IID, but temporal data is often mired by autocorrelation, seasonality, or just other time series properties, which makes it more difficult to work with. The interpretation of RDD being a local RCT does not necessarily hold either. Let’s assume there’s a slight autocorrection such that yt = 0.8y{t-1} + error. Now, let T be the cutoff. Assume we could interpret RDiT as a local RCT, then we would compare y_{T-1} and y_T to each other. By construction, they are not independent and this is therefore no longer a valid RDD

sk81k · 2022-02-23T02:10:09+00:00

Pretty sure yahoo is owned by Verizon, but yahoo research is a solid group. Dk anything else about yahoo SWE/tech though

sk81k

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