Jewon Jung

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On November 15, 2018, Jewon Jung commented on 3D Printing Better Surgery at the Mayo Clinic :

3D bioprinting is so exciting, and I loved seeing it being applied to make invasive surgery less harmful to the patient. I don’t think people are as aware of how poor surgery can leave a person cancer-free, but still feeling pretty debilitated. Sometimes entire organs and lymph nodes are removed. I think there should be another surgery using 3D bioprinting to reconstruct blood vessels, lymph nodes, organs.. Just because someone is no longer ill doesn’t mean all is well. There should be full restoration, and 3D printing as you describe it seems to present new options in the future.

On November 15, 2018, Jewon Jung commented on It’s Only A Matter Of Time: ML Sets Its Sights On Your Calendar :

I really like Apple’s approach, introducing Screen Time. I hope Marlo does something similar as well, to address the general feeling of being overwhelmed. Perhaps there can be a user setting such as “Crunch time” or “Overwhelmed” or “No Work, Just Play” so the app can at times give lots of suggestions, and at times give very very few suggestions, and only for specific event types (work vs. play vs. learning vs. random wild card event). Also, not just for individuals, but for duos trios and teams as well. For example, I really like 305fitness these days not only because it is such a fun workout, but also because it allows me to go with a group of friends for a group exercise. Some events are good when paired with the right people, so taking that into account would help me derive even more value from a suggested event.

On November 15, 2018, Jewon Jung commented on Machine learning as a tool to predict future earning power :

Because the majority of graduate students undertake huge upfront costs in the hopes of increased future earning potential, income sharing with a sponsor (such as the investor) makes a lot of sense! It reminds me of indentured servitude (in a good way(?), if that makes any sense). I’m assuming you have done segmentation to see if the high-earner is Corporate_Type (short run high earners, with a high starting salary in a corporate setting) or Non_Corporate_Type (long run eventual high earners, in successful startups). Both types would require different algorithms, after being categorized into a segment or persona.

Then, to make the algorithm more accurate for Corporate_Type, I would feed it indicators that help us see whether the candidate is hunting for high salary jobs. If we can get data to see if the candidate has registered for PE, investment banking, managerial consulting recruitment events, then this may serve as a good predictor. Obviously, a better predictor would be summer internships at such firms and satisfaction with said summer internship, but I’m assuming this is too late for your team.

So an early indicator might be if the good friends of this candidate are recruiting for high salary jobs. Perhaps you can mine LinkedIn or Facebook data to see whether the candidate’s social circle is comprised of high earners. My hypothesis is that this is likely to impact the income of the candidate, by making it more likely for this person to want to keep up with the Jones.

On November 15, 2018, Jewon Jung commented on Slack’s Battle Against Information Overload :

This was a great read. Your recommendations indeed seem to better inform Slack on what it should prioritize for its users. In particular, your suggestion of seeing how users interact on other platforms such as Dropbox and applying that information to prioritizing messages was compelling. I can see how interacting heavily with one user (exchanging files, etc) would make it so that I want to hear from that user. I would even consider letting your users/org be super intentional about how to limit the flood of info, and give them a feature where people can limit themselves to only 10 messages a day. If they do that, perhaps people will be much more mindful about what they share. Perhaps different channels can have different parameters — like #companyannouncements will only have 2~3 admins who can post twice a day on it, while #randomblabberings will be a steady stream of free flowing online chat like convos. Overall, if Slack truly wants orgs to fight info overload, then they should limit the traffic in the first place, whether through strict time limits (30 minutes a day) or message frequence limits (10 messages a day).

I can’t wait for the first 3D printed building. I wonder what the construction industry is doing to prepare for a potential disruption. I wonder what TU Eindhoven thinks of as their competitors. As they onboard more developers as partners, and as they solicit projects, I think a major roadblock will be convincing developers and clients that for XYZ situations, 3D printed buildings and infrastructure is a very good option. I would like to know more about what TU Eindhoven thinks their relative advantage is (what are the circumstances under which their technology is a good option for developers and clients)?

Overall, the idea of opening up data to the public is interesting. I don’t understand what opening up NYC’s 311 service requests to the public achieved however. I can see the data could serve as feedback — now, everyone can see what services’ numbers are in high demand. I’m guessing this would mean certain organizations will now know that they are more sought after than others, and/or that these organizations’ numbers are difficult for the public to access. However, this article seems to suggest that the possible changes are much more ambitious than that. I’d like to see exactly what those ambitious use cases are.

Although I see the point of harnessing the power of crowds to think of solutions more difficult to think of in house, I also see that it would be wasteful to host one-off competitions, having individual teams reinvent the wheel over and over again. Perhaps instead of hosting one-off competitions, Pfizer would be better off creating collaborative communities where teams can build off each others’ ideas.