Christopher Chan

Data Scientist @ Effective Geoscientists
96 karmaJoined Jun 2022Working (0-5 years)



I am the community organiser for Effective Geoscientists, I used to work as a ESG Data Scientist.

My academic background lies in urban remote sensing, CNN application in remote sensing, and geostatistics. I aim to specialise in data pipeline engineering, management, architectures. When I'm distracted, I enjoy a board range of other topics from philosophy to anthropology to various languages. Outside my professional life, I like to climb, travel, cook, and secretly write more code.

I hope to marry Earth Observation, geospatial tech, and other alternative data with traditional professional services to tackle challenges in global development and catastrophic resilience.


Thank you for in-text citation and quality post, felt like quality posts backed by peer-review has been absence from EA Forum lately.

Where is catastrophic resilience from volcanic and nuclear risks, biosecurity and pandemic preparedeness?

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I would love to see more catastrophic resilience interview after a string of AI safety interviews. Perhaps volcanologists and nuclear security folks:

Mike Cassidy: Associate Professor in Volcanology, authored the best fallout post here.

Follow up with ALLFED and David Denkenberger.

Emad Kiyaei and Kolfinna Tómasdóttir: Setting up an AI risk in Nuclear Weapon civil consortium...

Thanks for reading Mike.

With an odd of 1 in 170, that will result in ~$8.964 billion USD (Note that this number and the above numbers are also yearly investment). Not exactly a trillion just yet. The report states that $428.51 trillion over 5-years loss should be considered, if the mean: $85.702 trillion USD GDP loss could be averted by 8.964 billion yearly, this will result in a cost-effectiveness ratio of roughly 9,600:1.

I.e. $1 USD in investment would save ~$9,600 USD in potential economic loss yearly. Although, given my modest understanding of insurance products, if a supereruption does occur,  I suspect that payout to loss will have a P<1 chance of actually materialising. I will need to do more research on this to provide a better answer...

As an overarching review of geospatial data and geoscientists role in EA, I wrote a post and started a channel role-geoscientists on EA anywhere. I would love to see people interested in contributing towards geospatial X-risk here:

Hi Nathan, thanks for reading. The point of this post was really to address 2 problems:

1. It is generally really difficult to find geospatial scientists (Earth Observation, GIS...) and Earth System Scientists (Geophysicist, Volcanologist...) people in EA. We are a minority here. We get really excited when we meet each other. We want to gather a community and say we can contribute.

 2. Geospatial data is very useful in many aspect of our cause areas, hence the breadth of case studies listed above, from neartermist to semi-longtermist (particularly supervolcano impact). While EA as a community don't seem to attract a lot of geoscientists in general (perhaps it should). Current career advice seem to always err on the side of take your quantitative skills and go work in consulting or finance (which was what I did too). We know that geospatial data and our niche skillsets can contribute, therefore, this post is also a chance to inform the rest of EAs the current geospatial research happening. Think about geospatial data and spatial analysis as a "common language" between an epidemiologist and a volcanologist. Perhaps direct cross-pollination might not happen, but a platform for skill-sharing would be useful.

So yes, geospatial research is underrated. I am not arguing more people should become a satellite image analyst, more advocating for if you work in global health or poverty alleviation already, perhaps also pay more attention to the geospatial aspect of it. Additionally, we could be a platform that perhaps provide geospatial research support if this community gain traction.

Hope this clarifies,

Shouldn't the EMA calculation for 3 years be:

EMA = Current_year*2/(3+1) + Last_yearEMA*(1 - 2/(3+1))

EMA = Current_year*0.5 + Last_yearEMA*0.5

And EMA calculation for 2 years be (your google sheet formula):

EMA = Current_year*2/(2+1) + Last_yearEMA*(1 - 2/(2+1))

EMA = Current_year*0.66 + Last_yearEMA*0.33

However, in the google formula you weighted the previous year by 0.66 and the current year by 0.33, meaning that you gave the most recent data less weightings and the EMA is actually 2 years instead of 3?
A slight change to use a 3 year-smoothing of a 3 year EMA

EMA(3 years) = Fund(t)*0.571+EMA(t-1)*0.286 + EMA(t-2)*0.143

*0.571+0.286+0.143 = ~ 1

I.e. 3 years EMA should actually be:



I feel like I am a neartermist mostly because of my studies and my comparative advantages, neartermist seems more likely to be higher in empathetic leaning person (not sure how to phrase this). However, my tech and interaction with applied AI and geoscience has also allow me to recognise the danger for longtermist risks which with let me approach the research and discussion with open-mindedness despite my comparative advantage in neartermist causes. One of the main attractiveness of EA to me originally was because the movement address both of my concerns.

Thanks for reading Larry, 

Your assessment is generally correct, however just one more thing. In ESG ratings, we also take into consideration industry clusters so that companies are ranked against their peers as much as possible. Additionally, in order to have more dimensions, we not only use company self disclosure data but also watch for controversial news, lawsuits.

This means that which data to use and how to normalise or "curve" they grade it on is in the discretion of the industry experts within each ESG rating company.

Of course, at the end of the day, funds hold multiple industries. Which means that they eventually get aggregated so lose some of that granularity. 

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