Tag: Technology

Looking for a Job? The Three Online Profiles to Build that Can Help Open Doors

(My entire series on how to find a job is here. )

I have spoken with many people who are unemployed looking for a job or want to make a jump from whatever they are doing now to something better. I have already written about how to network with others to find a job, how to strategically prioritize the job application process, and how to nail an interview.

This piece will focus on how to create an online profile that can help you find a job. This two- or three-pronged social media strategy is incredibly helpful in presenting you as an expert professional in whatever field you are working in.

Step 1: Create a LinkedIn profile (and any industry specific online profiles).

Everyone needs a LinkedIn profile, especially when finding a job, so if you don’t have a profile create one. It’s a major hub for job networking in pretty much every industry.

But how to create a LinkedIn profile? For someone who is making a transition to a new field, the best strategy is to position oneself as if one has already done similar work. If you want to continue working in the type of job you’ve always been working in, that’s great; you already demonstrated your experience, but for those who want to make a transition from one field to the other, it can be best to highlight anything you have done that can seem like you have been doing that work or at least something similar.

If you have done something similar in a previous job, even if it’s just one project, blow it up on your LinkedIn description of that job. If you have a job where you can work on a side project that is related to what you want to do, then give it a try. For example, I have advised many who want to become data scientists to incorporate a data science component into whatever they are doing in their current jobs or to create a project at work they do on the side that involves data science. Thus, they can discuss their current work as involving data science work and use that to present themselves as a fledgling data scientist.

People often will describe themselves on LinkedIn as “aspiring data scientist,” “aspiring designer,” etc. Saying you are “aspiring” is the next best alternative if you absolutely cannot describe yourself as already doing it. If you are able to describe yourself as doing it, even if a minor capacity alongside other work, then do it. (And if you don’t have a job at the moment to incorporate a project into, see Step 3.) You will be treated as if your foot is in the door, not as someone knocking and waiting to be let in.

Step 2: Create any industry specific online profiles:

Also, important: if there are specific social medias for the industry you want to work in, create those as well. I’m a data scientist, and for tech, that would be GitHub. In my experience, artists often have an Instagram to showcase your art (frequently a different profile than their personal profile). Video editors often use Fiver. Some industries have specific social media where it all happens. How do you know what profile to create for the industry you want to work in?

Well, if you don’t already know, talk to people in the industry and just ask them (here’s how to network and talk to people in the industry again). They’ll tell you pretty quickly which ones.

Step 3: Work on projects in your new field

This is especially useful for those who are unemployed. Do something in the field. If push comes to shove, work on a personal project in the field, but if you can, do some part-time work helping someone else (networking helps you find these, so see that article; I keep mentioning it because it is important). You can thus list this as work experience. Never mind if it’s only a few hours a week: it’s still work experience. This helps present you as someone already in the field, circumventing the vicious cycle of entry level jobs in a field already requiring having work experience doing it.

For example, ten years ago when I took a data science boot camp during a transition period in between jobs considering becoming a data scientist, I networked extensively to learn about different professions and what people did in the industry. As such, I spoke with someone in charge of a boutique consulting firm who needed a data scientist for a project. I agreed and worked for an hourly wage maybe 4 hours a week on this project. It was actually helpful in itself because it was the first time I did data science in the “real world” outside the simulated environment of my lessons. But I also put it on my LinkedIn as my current experience (the profile didn’t make a distinction between full-time and part-time work experience anyways). My job title on LinkedIn was now officially “data scientist,” and after finishing the bootcamp in several months, I officially had six months experience working as a data scientist. This made me a beginner data scientist in interviews, not an aspiring data scientist, allowing me to circumvent the process of not having had work experience.

The project you work on can be informal. Friends or family may have a project they want you to work on that could fit this. If they officially give you money for your trouble, even if it’s only a few dollars or an ice cream cone, that is even better: on LinkedIn and your resume, it’s now a job. Even a company you started could in theory work. I don’t like the so-called “unpaid internships” because I find them exploitative, but even these could technically work. You don’t have to spend too much time working on them either. Just a few hours a week, and you’re still officially working in the field. People can often fit only a few hours a week into even the busiest schedule.

Step 4: Create a professional website/blog

This can be the most time-consuming but super important step: create a professional website or blog in the subject matter you would like to work in. If it’s data science, make a data science blog that also showcases your data science projects; if your a designer, make a blog about design that also showcases your design work; etc. This will position you as a knowledgeable person contributing ideas in the field.

For example, when I was fresh out of graduate school, looking for a job in data science, I initially created this website (ethno-data.com). My purposes for this blog have evolved overtime, but one of my initial reasons for creating it was to find a job. From that perspective, this website reaped dividends: you’d be surprised at how many people will reach out to me because they have found some of my articles interesting and have a role that might be a good fit for what I do.

Try to pick a specific niche within your field that is interesting to you personally and compelling to at least some others. It doesn’t have to be the most popular idea in the field; more important that it is something you enjoy and feel comfortable nerding out on. You can combine multiple areas of expertise you have, focusing on how they overlap (for me, that was ethnography and data science/AI). Make it reflect you. That both helps sell you and your unique contribution to the field, but it also tends to make it more interesting to you. You won’t want to post on content again and again if it’s not you.

There are so many different platforms to use to build the website (from WordPress to Bluehost to Medium to Squarespace and so on), so do your research on which one works best for you. These platforms tend to walk through the process for non-techies anyways. Set yourself a posting schedule and have at it. Whether you write articles once a week, every other week, every month, matters less. What tends to matter is that you post according to a consistent schedule.

Last but arguably most importantly, mention your articles on LinkedIn and any other social media platform you are using to network. If you post an article in your blog that week, write a statement on LinkedIn (or any other social media you are using) summarizing what you talked about and link to it. This will not only encourage people to read your pieces, but most importantly of all, it will present you online as someone contributing to the field. You are helping to advance your field. As such, people will be more likely to hire you.

Conclusion

That’s it. If you do all four of these, you will have a kick-butt online profile that will help you find a job, even if it’s a profession you are transitioning into. They are all practical things you could set up anytime. With the exception of Step 3, which sometimes requires coordination with others, you can do each of these on your own. You can even get started right now if you want.

The Big Difference Between This AI Craze and the Dot Com Bubble in the 90s

Photo Credit: Robynne O

“Tech startups are rushing to build the next game-changing innovation that will ‘change the world;’ while financiers are also rushing to fund each one in the hopes that they will own a stake in whatever world-leading companies emerge.”

This could just as easily describe the AI boom over the last few years and the Dot Com Bubble in the late 1990s. Both have a few major features in common: new technology produced a craze. In the Dot Com Bubble that was the internet, and now it is all the new AI technology. In the 90s, the hype caused people to overvalue tech companies, funding companies that never had a chance in the first place. This created a stock market bubble that eventually burst causing many overinflated companies to go belly up. The same will likely say the same thing about the current explosion in AI investment.

At the same time, the internet did significantly change the world. Among other things, it created new companies, including the corporate giants like Google and Facebook. Thus, investment, even significant investment, did make sense. The overinvestment may have created a bubble with many losers, but there were still many financial winners as well. The same likely could be said about this AI craze.

There is one very important difference, however. A difference that could significantly worsen how new AI technology develops and becomes implemented in our society:

The internet was created by a decade-long series of government-corporate sponsorships with the overall goal to decentralize communications (this book https://www.penguinrandomhouse.com/books/534709/the-code-by-margaret-omara/ is a fascinating detailed discussion of that history), but the recent AI technology has been mostly created by a handful of highly centralized large corporations, most often with the goal of creating a centralized “super-AI” or suite of AI products. This difference may prove crucial to how these technologies develop.

The internet was created to be decentralized, and decentralization became ingrained into the ethos of the technology. No single government or corporation uniquely controls what gets put onto the internet, and even as major companies/websites like Google, Facebook, and Amazon now dominate the internet ecosystem, they do not actively control or prevent people from contributing to the vast world that we call the internet. There are a lot of details about how to best implement this decentralization, as anyone who has sat through net neutrality or internet content debates knows, but the internet overall became a sprawling web that anyone could help create.

The latest AI systems, in contrast, tend to be created by one large company seemingly with the goal of becoming the AI platform everyone uses. Large portions of current funding has gone into creating massive, super-AIs to be able to be the first company to create the supposedly massively intelligent AI so that they can own and thus profit from whatever it can do. This goal encourages consolidation.

Now, AI technologies like large-language models have a certain amount of centralization almost baked into their development. For example, it takes a lot of people and resources to scour the internet and create a large-language model, and more still to run and maintain such a large computational system everyday. Thus this industry may tend towards a certain amount of consolidation, but that doesn’t seem to explain all of the centralization.

The internet also takes a tremendous amount of resources to be able to allow billions of people to be able to communicate on multiple devices simultaneously. Other similar communication systems (such as the system of phone lines connecting everyone by phone) historically tended to create natural monopolies because of how much resources it took to build a phone network system, and inherently, the internet is not that different. Yet the usage of the internet became different. We don’t see a supply side monopoly in the same way. (Overtime, monopsony has arisen, where a few social media companies disproportionately leverage their widespread usage to their advantage, but that is different.) The communications themselves became something everyone had access to.

This centralization seems to have had a tremendous impact on how the current AI technology has developed and will continue to develop. AI seems focused on the interests of big (mostly tech) corporations. One of these companies’ main goals seems to be to automate routine human tasks. This can have an appeal with regular people, but this is really a priority of “business management” interested in saving money by increasing efficiency. Further, many companies are using the hype around this “AI revolution” as a face-saving way to reduce headcount anyways. Finally, the AI interfaces are built to suck users in, to make them want to use it more and more, in ways pretty common on most apps and major websites today. This too tends to be a business management interest.

A new major technology, like say this current generation of AI technologies, usually provides different people with varying amounts of opportunities, helping some and hurting others. Who it helps or hurts is often rooted in who designed it and what their interests/goals lead to it being designed in the first place.

For example, it’s not surprising that agentic coding platforms like Claude Code are really useful for software engineers. It seems to transform their space. Originally, most of the software engineer’s time was spent on busywork instead of the “cool” parts of programming, and Claude Code more or less automates their busywork, allowing them to focus on the cool stuff. Software engineers and other techies were the main people who created Claude Code, so of course, it’s useful for that space.

Many in other professions seem noticeably more cautious about the introduction of AI into their fields. For artists, for example, AI image generation tools seem to allow others to make their art without them. It takes away some of the most interesting parts of making art. Instead, the tools process their past art and allow others to make it without them. Here the process was designed for regular people in mind who do not have the ability to create their own art and noticed how it benefits them over artists.

Thus, we should think more about who is involved in building the AI technology, because that will influence what kinds of problems it gets built to solve. Right now, it seems to favor the interests of business elites and large corporations. I am not necessarily trying to morally condemn these people and corporations (maybe some are good people, maybe some are bad), but I want to point out how slanted their interests involved in building this technology have been and suggest we may need to broaden who “leads the charge” in the development of AI.

So what? What can we do about this? The interests with which a technology is first developed sets a course for how that technology is conceived and what kinds of things people imagine it doing, but nothing is ever completely set in stone. Technologies often grow and evolve over history, becoming used for purposes very different from their original conception. But obviously, the sooner one does this the better. Given that the technology is still young, this is the best time to incorporate other forms of thinking. How it develops now will have cascading impacts on what AI looks like in the future.

How can we build AI in different ways and move beyond the specific interests that hog the AI field now? Exactly how, I don’t know. It’s worth thinking about. I wrote this article to help you think about the problem and get your juices going. Maybe together we can develop a way to build AI in a way that serves the interests of regular people.

Could AI Be Different?

Photo Credit: Mohamed Nohassi

Many are concerned that AI will take away their jobs. This is not unreasonable, and one underlying reason why this is a fear is that the current AI technology has been partially built to automate. The corporate world has been trying to automate and mechanize human work for a long time, and in the last several decades, in particular, we have seen the steady routinization of white collar and thinking work. The recent AI technology has developed in this sociocultural context. I think that if this desire for automation wasn’t widespread in our society, recent AI technologies like large language models and other forms of generative AI either would have developed very differently or not developed in the first place.

This raises a question: to what extent must AI technology reflect this automative impetus, or can we create other forms of AI that work very differently. I will reflect on that in this article, but I do not yet have a definitive answer.

From the Industrial Revolution to Ford’s assembly line, we have seen decades of technologies designed to automate blue collar work. That was the real idea behind factories. A shoe factory can build many more shoes much more quickly than a family of shoemakers. The technology and machinery is built to increase scale. Since the second half of the nineteenth century, we have seen a similar push in the white collar world. These jobs too became incorporated into a corporate, semi-mechanistic machine of reports and meetings that allowed corporations to churn out thinking content in a similar way to a factory. In a certain sense, computer algorithms themselves are an extreme form of this process: code are detailed instructions that a machine follows literally. Algorithms are then detailed instructions to complete various tasks or strategies efficiently, an ultimate form of mechanization.

Out this context, AI technology is just the next attempt to automate thinking. I think it seems like a qualitative jump that will increase the degree to which this is possible, more than simply a continuation in the trend, but it is still part of a longer historical trend. Nothing is really new under the sun. Many people and corporations who developed AI technology did so with the idea of automating certain kinds of thinking work in mind.

For example, many creative tasks like writing and drawing became seen as never fully automatable; sure, employers could influence the conditions in which these creative processes could happen, but on some level, a human had to sit down and actually create the art. Now that generating artistic products has become just another part of the automative process, where a program determines things randomly and the human creators may shift to a more editorial role, refining that output.

Historically, after the advent of pretty much any new technology, utopian optimists would say that this marks the end of work. No longer will humans have to work for the majority of the day; this new technology will do it for them. For example, in the 1950s, new house keeping technology like vacuums and dishwashers will allow housewives (seen as “women’s work” at that time) to complete all the housework in only a few minutes and spend the rest of their time relaxing. Similarly, utopians in the tech world have promised 4 hour work days as the latest piece of tech automates most busywork.

This never seems to happen, though. Modern home appliances did make cleaning quicker, but people increased their social expectations for how clean to expect a home to match, and suddenly, the housewives of the time spent the same amount of time cleaning as before.

Similarly, when new technology substantially automates aspects of professional work, employers end up expecting the same amount of work just with a bigger output. In our culture, we are obsessed with work, and without fixing that, new technology will not meaningfully decrease the amount of work; it will only shift the expected levels of that work and also shift what that work is.

All this relates to one of the biggest fears people have of the new AI technology: that it will steal our jobs. It has an element of truth. I don’t think it will remove all jobs forever, because our society will always invent new ways to make people work, but many specific jobs in this day and age are likely go by the wayside. Writing, for example, may shift to a type of editing, where one refines what ChatGPT does, something which may require less writers.

Within capitalism, there will always be more work to do, though: if the automative machine becomes more widespread (whether a factory, a regular computer program, or new AI technology), people will need to work to maintain that machine in complex ways. This may disenfranchise people as the skills they have cultivated no longer become useful, and these jobs could be more boring as the work becomes more and more routinized into a mechanized process.

But, we won’t see an end to work unless we see an end to our capitalist mindset that there ought to be work. To slow down, we need to remove the drive for more more more at all costs. This new AI technology or really any new technology for that matter won’t do that.

Where does all this leave us? I don’t fully know. One question for those concerned they’d loose their jobs due to AI would be, “Do you actually like your job?” Sure, you like your paycheck, but do you like your job? Some with amazing jobs they are passionate about stand to loose them, but many whose jobs are most threatened are precisely those who are already working mind-numbing drudge in the first place. They work jobs that they hate in fear that even their awful job will disappear on them.

What they really fear is an end to their livelihood. If they could have a livelihood without working their job, they’d prefer that in a heartbeat. For people in this situation, I’d suggest we rethink work in the first place. To do so, we may need to reimagine our relationship to work and profit as difficult as that conversation is.

But all this brings us back to much older, longer conversations in our society. Why work in the first place? When people talk about AI, they see new innovation coming out of nowhere, not how this stuff is the next step in a wider trend towards automation in our society. And maybe it doesn’t have to be the way it is? Maybe if we can grapple with this, we could reimagine forms of AI not built implicitly to create an ever-spinning machine churning out more and more. Such AI could be much more interesting and beneficial to humanity.

Is data science still the sexiest job?

Photo Credit: Mahdis Mousavi

In 2012, this Harvard Business Review article argued that data science will be the sexiest job in the 21st century. At the time, data science was new and unheard of, with companies eager to use data scientists to revolutionize their practices. Is it still the sexiest job now? Well sort of, but not really. The field has gone through some significant transformations since these “wild west” early days. Now, data science as a discipline has become more streamlined and specialized.

Often key data scientists have slightly different titles like machine learning specialist, data engineers, etc. Machine learning and AI technology have changed the way data is processed and analyzed. This has automated parts of the tasks that data scientists have spent a long time working on, such as data cleaning (which still can take a long time) and initial data exploration, shifting the work necessary for humans to perform in the field to more specialized and fringe tasks. For example, many data scientists have become machine learning specialists focusing on fine-tuning these models or communication specialists focusing on how to use their business expertise to communicate complex findings with stakeholders and help decide what they should do about the results.

I think more than the technology, what has driven the specialization is the routinization of data science processes within an organization. Gone are the days of a lone data scientist at a company doing cutting edge work by themselves just figuring out what is possible. Data science as a field has fallen within the discipline and expectations of corporate bureaucracies. In its early stages, most data scientists worked alone or in small teams doing pioneering, experimental work figuring out how to apply the tools of the field to their organization in ways that people did not know were possible. That can still be the case. In every job I have had as a data scientist, for example, I have been the first data scientist in the entire organization or specific department I work in. But this is increasingly rare. Data science is now mostly one department at an organization, doing important but predictable routine work. All white collar professions get grafted in the “corporate machine” like this overtime.

Recent AI technology has contributed to this too by automating many of the low-level data processing and analyzing tasks so that non-specialists can perform them on their own. This is great, increasing the accessibility of tasks once considered obscure or even “magical” by regular people. Back in the day, to do much of any data modeling, you had to code it yourself, requiring a level of programming knowledge that was beyond a typical office worker or manager. That’s why they needed to hire a data scientist to analyze the data themselves. I hope in the long run using AI tools to tinker with data themselves and try out different theories will increase the data literacy and skillsets of regular professionals. It also means that data scientists are increasingly spending less time on these tasks and have moved to more complex, specialized work that still require quite a bit of technical human thinking.

Another factor that has driven this routinization is the increase in the number of people studying and doing data science. As demand for data science increase, more people have tried to become a data scientist, whether by receiving a degree in it or transitioning their careers into the field. This has led to more data scientists in the market. If this trend continues, eventually the field will become oversaturated, but the demand still seems to be higher than the supply, with more open jobs than people able to fill them.

This has still redefined what data science is. When many people join a field, it becomes difficult to maintain the same level of pioneering eclecticism. Instead, the types of tasks people do become routinized and standardized to provide consistency for a larger number of people, paralleling the transformation Max Weber describes religious movements undergoing from a charismatic leader to a routine social institution.

All of this leads to the current state of data science. This is not necessarily bad, but it is different. So, is data science still the sexiest job? Yes and no. Some of its specialist roles like machine learning specialist, I think, better maintain the excitement and cutting edge of that moniker. It’s still in high-demand, however, a fine field to work in.

The Question-Driven Data Scientist: Why Social Science is Key in the AI Era (Conversation with Eesha Iyer)

In my conversation, Eesha Iyer, an economist-data scientist, discusses how machine learning and artificial intelligence have changed what is possible. We are seeing a transition both from static inferential models common in economics for decades to dynamic, interactive systems that adjust in real-time.

We are also seeing a revamping of the workflow with AI systems clearing up time to do rudimentary programming tasks. Trivial programming tasks that once took quite a bit of a data scientist’s time are easier than ever, so now the key issue is becoming, What kinds of questions should we ask of the data? Qualitative and social science thinking are crucial for this new space. For Eesha, gone are the days when data scientists were technical workers spending hours writing code. In the current era, the question becomes how to formulate relevant research avenues to explore. For this, social scientists are more useful than ever.

In our conversation, we explore the implications all this has on the field of data science. She also advises how to learn data science in this shifting landscape. I hope you enjoy.

From Breadth to Depth: How to Create Opportunities in a Dynamic World (Part Two of My Conversation with Quynh Xuan Nguyen)

The world has been changing rapidly, so how can you develop your skills to work in such an environment? In this second part of our conversation, Quynh describes how she strategizes between depth and breadth in learning new skills in order to adapt to the changes in our world, whether those be limited job prospects or new AI technologies like ChatGPT changing the nature of work. Also, how do you find your way while still remaining true to yourself?

Her strategy has been to use breadth by developing skills across a wide variety of contexts to decide what she most likes to do in life and to adapt to the ways new technologies change work itself and the skills necessary for such work. As she gets older and more established, she then uses this to decide what areas she would like to explore in depth of the what she discovers that she enjoys most in life and also seems to pay well enough in the current economy. This is a resilient strategy in today’s changing world.

Here is more information about her life coaching, yoga, and self-improvement initiatives: https://songthanhthoi.me.

The Hustle of Finding Your Way in Life: Part One of My Conversation with Quynh Xuan Nguyen

How can you build a career for yourself when you have many interests in life? Quynh Xuan Nguyen has had many, many passions and is not the type of person who easily focuses on only one activity or job all day, everyday. In the first part of our interview, she describes how she developed multiple interests overtime to build several side hustles and careers ranging from becoming a yoga instructor to a banker to a data analyst, worked for different companies around the world, and what she learned from her adventures.

Multiple side hustles, I have found, are particularly common in Southeast Asia, like in Vietnam where Quynh lives. There many young adults such as her often must develop multiple careers and income streams to withstand unreasonable jobs, limited opportunities for advancement, changing economic conditions, and other societal trends she discusses in our conversation. These problems definitely occur in other parts of the world as well and may well be something you have faced. Not everyone enjoys doing one thing, or has the ability to do so in the contemporary economy, and her life provides helpful inspiration for how exploring multiple paths at once can build self-satisfaction and resilience.

Here is more information about her life coaching, yoga, and self-improvement initiatives: https://songthanhthoi.me.

Data-Driven Diversity: How Evidence-Based HR Can Create Equitable Organizations (with Élide Souza)

What is it like to use data science to understand employees in an organization to help improve people’s experiences at the firm? In this next podcast interview, I spoke with Élide Souza, a people’s data science at the Brazilian bank, Banco BV. She manages a data science team that researches how to improve employee’s experience and increase diversity.

This is part of a new trend called “People Analytics” where organizations hire data scientists within their HR (Human Resources) departments to conduct social science research in order to help improve organizational culture. In our conversation, she describes how she approaches such social research, including how she addresses potential bias, approaches intervention, and navigates the ethical implications of such work.

As a fellow social science-focused data scientist, I find this work fascinating.

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 3 of 3)

In the final part of our conversation, Chelsea Wang explains how her background in psychology has influenced her work in artificial intelligence. In particular, she describes how her social science background helped her develop and deploy her own version of the Mutual Theory of Mind as a psychologist within the field of artificial intelligence. When socializing, humans employ a recursive feedback loop of conceptualization of each other, and she explores the application of similar concepts to conversational AI systems.

She concludes by discussing her journey as a PhD student: what led her to seek her dissertation and her plans afterwards to use what she is learning now to conduct innovative and impactful work in the business world.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 2 of 3)

Chelsea Wang has spent many years trying to improve the cognitive process of artificial intelligence systems to better interact with humans. In this second part of our conversation, she explains her theories about metacognition, intelligence, and potential anthropomorphization of AI “thought” processes. Through this, she explicates her vision and approach to the potential social life of AI.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation: