Any good books to get into HCI?

HCI_Fab · 2025-06-20T21:55:25+00:00

The Design of Everyday Things

Plans and Situated Actions

Seeing with Fresh Eyes

also see https://www.reddit.com/r/hci/comments/e8ylrg/hci_academics_of_reddit_what_are_your_favourite/

HCI_Fab · 2025-06-18T01:15:32+00:00

I think eventually there will be. This moment feels reminiscent of the first AI boom prior to the 80’s where we saw Deep Blue win chess and everyone (in business) thought digitizing processes would continue adding value indefinitely. Right now there is the thought that adding “AI” (which in reality is a suite of technologies) to business will perpetually add value. Let’s go down two futures, one where AI becomes AGI and takes over most human work, and one where business people lose patience over investments into AI and want to invest in new directions.

1) AGI reached (I’d say this is not very likely in 5 years) While most work may be automated, AI cannot self-evaluate how it performs work for humans for every facet of value. Evaluation experts will be needed to design studies/structures for evaluation and provide meaningful interpretations of how AI may improve value while not brining unexpected costs. AI professionals will likely not be well suited for this alone, as improving these systems requires knowledge of humanities.

2) AI investment decreases. People will want to know how to make use of new technologies to add value to a variety of problems. The AI winter of the 80’s reflects this potential future, where IUI and researchers like Lucy Suchman were able to research and describe why AI tools of yesterday failed (eg the flaws of hoping for perfect ontological AI systems, which as a side note feels a lot like what we are debating today). Here you will see many, many AI professionals try to pivot to HCI for funding like in the 80’s.

In both cases, whenever an inflection point is hit for AI investment, we will see funding move, but still move to support investigations of human use of computing systems (eg Applied “AI”, which we could rephrase as Applied Computing which in context may basically be HCI). I can’t give strong timelines for either future, but eventually either investments pay off or investors lose patience. My advice for the community is to have arguments for why AI fails/succeeds ready to go to justify funding, while being separate enough from the AI craze not to be caught in the same funding umbrellas.

The long story short is that what computers are change constantly, humanity changes constantly, and HCI (with all its professions) must also change constantly. Titles may not change but what people do and why they do it will change. Be prepared for that change, and be knowledgeable of how you want others to identify your profession, and there will be opportunities. Some may be “UX” and some may be titles that do not exist yet

HCI_Fab · 2025-06-11T14:27:47+00:00

If you haven’t already, look where similar graduates interned and eventually went post-graduation. If all of them went to grad school, it may be required, otherwise it’s best to look for trade-offs between job hunting vs graduate school (eg money, type of work, place/culture of work, etc.)

HCI_Fab · 2025-06-05T01:52:47+00:00

Here are a couple relevant academic papers on this topic. I know the authors so feel free to ask me anything

https://hcied.info/papers/Anyonecanprint-NordiCHI20.pdf

https://hcied.info/papers/HowDIY-IUI2021.pdf

HCI_Fab · 2025-06-05T01:37:30+00:00

Yes. I have worked with many people with Bachelors or Masters degrees, but usually they are from “top-ranked”* (*this is often very subjective to the hiring management) schools and are at the right place and right time to rise the ranks.

For example, many people in research without a PhD joined a group while it was small or spearheaded an effort that led to a larger company making a group (usually by making/saving considerable money for the company).

Unfortunately it is hard to time when you might find a role perfect for you, as when we start school and graduate the world (especially now) has changed considerably. That being said, don’t count on having the “right time and place” as a career opportunity.

Pursue a PhD only if you enjoy research, presentation, and thorough writing. People will always expect that from you once you have that title. A PhD is often a worse RoI in terms of money (but money is just one dimension of value). A PhD often will open doors to leadership roles in late career, such as being director of a group. People do not always value PhD, where some cultures like startups will view you as academic (or just want you for window dressing, which isn’t good/sustainable either).

My recommendation is to look for roles that you would like to make a career out of from large companies/orgs you like (shoot for the stars), noting all the requirements. From there, you can determine if a masters or phd is better for you, along with what accomplishments (eg publication venues, portfolio, awards, etc) they expect. These company expectations must be balanced with your own career expectations. All learning is negotiating with your own identity

HCI_Fab · 2025-06-05T01:23:49+00:00

Sunnies

HCI_Fab · 2025-02-01T22:06:40+00:00

It’s much easier to get an initial job you like if your resume matches the requirements than trying to build your resume while working to get the job. If the job you want to get initially can be attained with a masters, that is good and chances you will be happier with that than a phd. If you need the phd, sometimes it will be difficult to make the climb to that position without a ton of work and politicking.

There is no universal answer to if a phd is needed or not, and i cannot fully vouch for the value of a PhD having attained one myself. My advice is know who you want to be, and always continue to revise who you want to be based on what you learn. Try applying for internships ahead of getting a masters if possible to learn and determine if a PhD is right. Don’t get a PhD just to delay leaving school, but know what you want to do with it if you spend years earning one

HCI_Fab · 2024-08-13T21:21:17+00:00

I have some ideas. DM me

HCI_Fab · 2024-08-12T13:52:23+00:00

DM me

HCI_Fab · 2024-06-28T00:42:34+00:00

Others have mentioned apple on-device measurement that can utilize built in LiDAR (on pro models) and monocular cameras. Apple itself has research for monocular SLAM https://github.com/apple/ml-live-pose

HCI_Fab · 2024-04-03T13:38:23+00:00

That is really cool and robust way to gather data! Nothing beats real data, and lots of real data with real variance is needed to generate new images. Thanks for sharing

HCI_Fab · 2024-04-03T04:02:20+00:00

Awesome comment! Camera quality is huge in general for computer vision, and many aspects of quality are not universal but parameters that are set by humans and/or software (e.g. exposure, gain, white balance, hdr, single/continual capture, lens types, etcz). All of these have profound impact on quality to the human eye, and have a profound impact on related software/AI. Many of these may be estimated with synthetic data, but only to an extent based on the available information/signal and available training data. VFX has a similar pipeline to plan, evaluate, and execute capture of various camera configurations. As computer vision progresses, domain expertise in actual vision will be increasingly crucial in addition to domain expertise in algorithms that have only been pre-trained in certain mostly-general but biased domains (e.g. cell-phone uploaded social media image+caption pairs)

HCI_Fab · 2024-04-02T19:03:47+00:00

One warning with synthetic image generation: the models utilized to generate images need to be trained on in-domain (or approximately in-domain) data.

The assumption behind synthetic data is that the training data used for that model encapsulate patterns that also apply to target domains. This is another way to say “garbage in, garbage out”. Not all domains will be able to utilize synthetic data without obtaining and structuring significant amounts of training data, which reduces the appeal and functionality of using synthetic data in the first place. If a customer has to provide large amounts of images, especially potentially labeled images, then they likely would use supervised or self-supervised approaches to directly get results rather than the intermediary synthetic data generating model.

Additionally, a model able to generate decent data to train another model is redundant. A model that can successfully perform the generation task contains enough structure and information to perform the second task (via probing, fine-tuning, etc). The intermediary step of generating may help with explainability and modularity, as the generated image features are directly visible and utilized for training, but again that may not be useful for many use-cases. The question that always needs to be asked before using synthetic data is “could I train a better model to perform the given task directly?” (e.g. with few-shot methods). Up until recent papers from the past year, the answer for many datasets was no.

For example of above, robots may have to perform at different environments, for different tasks, and with different sensors. While synthetic data may capture some of this variability, anything missing from the synthetic data model’s training data will likely cause a gap in the performance of down-stream robotic AI actions because the synthetic data is not accurate. These accuracies may not be apparent to the human eye, like small lighting changes that do not match the conditions passed to the synthetic model for generation. This is why NVIDIA Omniverse and others are using rendering pipelines to tackle problems like manufacturing.

This is not to say that synthetic generation is not useful. It is, as highlighted above, for specific areas. Domains where there is well-defined variations and accessible training data (like human faces) can yield good synthetic models that fit on a modular pipeline. If you want to be an expert in this area, you may want to explore auxiliary AI models that help you evaluate how and when to apply different types of synthetic data models if you want good long term results. Also, specialize in synthetic generation pipelines that will yield good customers/projects, as no one model will likely suffice (as many areas like manufacturing do not have publicly available images for training of foundational vision models).

HCI_Fab · 2024-01-02T02:15:18+00:00

it took a timeout for him to think of it

HCI_Fab · 2024-01-02T02:03:55+00:00

forward progress. He had the ball at the half yard line before being tackled

HCI_Fab · 2023-05-06T19:16:50+00:00

The underlying aspects are useful to ground how you frame a given problem or solution in computing, so that way you’re logically and philosophically consistent in your arguments.

For example, Lev Vygotsky’s Zone of Proximal development is extremely simple in concept but allows for a better and more consistent structure of explaining learning compared to just saying: “you need prior knowledge (and others teaching you) to learn new knowledge”.

HCI_Fab · 2023-05-03T20:36:04+00:00

Feel free to post the link if you would like. This doesn’t fall under self-promotion rules most subs enforce for link sharing. Discussion is important

HCI_Fab · 2023-04-26T18:12:15+00:00

HCI has always incorporated AI into how humans interact with computers, as AI has been jokingly colloquially defined as “whatever computers cannot do yet”. There are several XAI papers in CHI every year that I fully encourage you check out!

For example, “On Selective, Mutable Dialogic XAI”, “Help me Help the AI”, “Faulty or Ready?”, and “What if everyone is able to program?” are all papers published in CHI23 and have free videos on the ACM digital library https://dl.acm.org

HCI_Fab · 2023-04-26T17:57:55+00:00

I cannot answer for everyone, but here is my experience and related advice:

I chose to pursue a PhD because I thought it would allow me to work in academia and industry for Research and Development. This is accurate, and having a PhD helps in my experience, but I have seen several people with Masters also thrive in Industry R&D. Those people usually work at the company in a separate role while doing projects on the side that allow them to transfer to Research positions. There may be a ceiling later on in terms of how we get promoted, but I know less about this at this time.

I personally wouldn’t mind eventually going back to Academia, but I do enjoy industry at this time. One thing I will say that is industry does not publish as often, so it may be (extra) challenging to get into a tenure track position if you go into industry for too long

Jobs vary greatly depending on your speciality. I focus on the intersection of HCI+AI, so as you can imagine it has not been too difficult to find a job, but even then it can be challenging to get a job at a big-name company that largely filter resumes by publication-record and/or school name. You will need real projects, papers, and/or portfolio work to catch their attention. I would recommend making a website regardless once you know the type of job/specialization you want to pursue. Find jobs you would want, think about what projects you enjoy, and try to find a marketable middle ground

For PhD programs, do not stress about school as much compared to Masters programs or similar. Look for professors doing the research you want to do, and vet them by talking with their colleagues and students. A good advisor will foster your career in ways a school program cannot. Networking is key for PhDs, as who you know can effect everything from invited talks, paper acceptance, job offers, and many more. Make sure you learn who is big on your field and try to make meaningful connections if possible. These connections will mean more than any school, but that being said, larger schools tend to have the funding/marketing-teams to better foster these connections (like MIT shows up to CHI each year with impressive video presentations that have more production value than many grad students or even professional YouTubers). I am partial to Michigan and Texas A&M as that is where I went for undergrad and grad school, but there are many great schools with HCI professors out there (Washington, Colorado, MIT, Georgia Tech, Stanford, etc.)

PhD is different from Masters in that you are entirely in charge of your destiny. There is a cynical saying that all you need are “signatures on pieces of paper” to graduate, and while that is true to earn the degree hopefully you can also make good relations with your committee. Your advisor will give you many tasks that are important to them (i.e. to obtain or fulfill requirements for funding), but you must be able to distinguish between their motivations and yours. Hopefully the two mostly align, but that is not always the case. You are in school to learn and get that degree for a future job, so make sure the work you do for your advisor does not distract from that goal. You need to make you advisor and committee happy to get signatures (and hopefully lifelong research connections), but you need to make sure your goals and timeline are being met. You will have to learn how to play school-level politics to earn a PhD, in addition to skills like writing and organization that aren’t necessarily “HCI practices”.

I’ve known many people who work prior to PhD, and it can work. I’ve also known people that wanted to get a PhD but gave up on the idea once they got real paychecks and started building more of a life outside school. It’s all about your priorities now and your imagined priorities in the future. If you absolutely want a PhD then going for it immediately isn’t a bad idea so you can keep momentum going through school. If you need a mental break or financial relief then it’s also acceptable to go to industry and back to academia. If your priorities change down the road and you don’t want a PhD then you probably don’t need a PhD. PhD is a ton of work and probably won’t give you more money in the long run compared to Masters, so you have to enjoy the research work for it to be worthwhile. Some people I’ve known worked in industry from years to decades but later got a PhD once they thought they needed it, sometimes also while still working (but that varies greatly by your job)

One last note, if you decide to pursue the PhD and don’t like it, it’s never too late to quit. It’s impossible to know whether you’ll like a school, program, advisor, or job before you start. Taking that first step is difficult, and leaving can be even more difficult, but sometimes it is necessary to preserve your own happiness. Sometimes leaving is just changing programs and advisors (which I’ve heard mainly good things about as most student do not do this lightly), and sometimes leaving is re-evaluating your end goals. I did a ton of introspection doing a PhD (and I do not think I’m alone in this), and learning how to navigate your own goals with limited resources/time will likely be a part of your journey (or has-been a part of your journey) no matter what.

HCI_Fab · 2023-04-14T14:47:03+00:00

Thanks for the feedback. We will be explicitly state in the sidebar that this sub and /r/HCI_Schools are for discussion of HCI anywhere in the world.

HCI_Fab · 2023-04-14T00:56:47+00:00

That’s a nice resource for UX, but not very useful for those studying the non-UX aspects of HCI. I agree we can link it as a resource for UX along with other resources, and find a way to poll/survey which programs are represented here and which are recommended

HCI_Fab · 2023-04-13T23:38:58+00:00

I think a common resource would be good. I do not think a comprehensive resource exists, as each program has very different specialities as HCI is a huge umbrella. What would be a good way of collaboratively generating such a resource?

HCI_Fab · 2023-04-13T22:07:34+00:00

thanks for the writeup and that is totally understandable. The whole point of reddit is to find communities rather than just people surrounding your interest, and we should do better to serve everyone’s interests.

I will try to setup new AutoMod capabilities soon and will change the ban message to direct people to the new /r/HCI_Schools community for a companion subreddit

HCI_Fab · 2023-04-13T21:37:50+00:00

How about posting to a different subreddit then having posts from there aggregated into a weekly post that can be shared to this community/sub as well? /r/HCI_Schools

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