When considering which role I could fill best in the climate space, my THREE main considerations are:
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1) Do I enjoy the day-to-day work, and can I utilize skills that I’ve already mastered? From my years of astrophysics work, I know I enjoy working with software and data. Ideally, my day-to-day work would be using the machine learning, optimization, and scientific data analysis skills that I’ve been developing since 2015 to solve climate problems. Also, see my "Software Product" page to learn more details about the software products that I’ve developed.
2) Does the work I’m doing have a real impact on people’s lives? I understand this is not the easiest to quantify, but the main reason I’m leaving astrophysics is because I want to positively affect my community and the world. While understanding our universe and our place in it is important for humanity, it doesn’t help with our most dire issue. I want to work on direct climate solutions. While I understand there are other important and necessary indirect climate solutions positions, the tangibility of my positive contributions will give me the drive to continue working when things inevitably get tough with personal and global progress.
3) Is the culture and community of the people I work with a good fit for me? This is important because people are products of their environment and can only excel if their environment allows it. Thus, I’d want to be a part of a group that considers people’s opinions equally and with respect. Also, the community should understand that there are many factors affecting different groups and have a strong eye to counteract the disproportional mistreatment of underrepresented groups. This community must have a healthy work-life balance, and both cherish their work and know how to have fun outside of work.
With those criteria considered, studying deeply into solutions to reduce anthropogenic climate change, and thoroughly searching through job postings, I’ve come up with two ideal sectors for me: the energy transition and food and agriculture sectors. These are not the only positions that I would accept, as I know there are many positions that could fulfill my 3 main criteria. However, grid electrification and agricultural decarbonization resonate best with me.
Energy transition:
As stated on my "Home page", I think that decarbonizing the energy sector will have a drastic impact on our capability to reduce climate damage because this sector influences most other sectors. Transportation, building, mining, agriculture, industry, and all other sectors must draw power. Thus, electrifying our energy grid allows for a domino effect of decarbonization.
The main roles that I would be perfect to fill in this space would be system modeling and DER usage optimization. I have a strong background in modeling, during my PhD and postdoc. I used a method called retrieval analysis to simultaneously model the atmosphere of planets light years away from us and model the stellar atmosphere of their host stars. In astrophysics, retrieval analysis builds a slew of synthetic atmospheric observations that are dependent on a wide range of parameters and known atmospheric physics. The synthetic data are then continuously compared against the data to build a posterior distribution on the most likely features suggested by the data. I used a Bayesian approach (nested sampling) to build this posterior distribution (see McGruder et. al. 2023b, 2022, 2020, and Alderson et. al 2020). Though the application will be very different, my development and utilization of such packages demonstrates not only my strong statistical and modeling capabilities, but also my ability to bring in my understanding of the physics and science to better interrupt and utilize the energy system models I would develop. This effective combination of software and scientific understanding is what would set me apart in a position optimizing DERs. Additionally, I have been developing scientific optimization software since 2015, starting with simulating annealing to model observations of volcanism on Jupiter’s moon with an instrument on the James Webb Space Telescope (see Keszthelyi et. al. 2016). My most current optimization problems involve using mixed integer linear programming and reinforcement learning to model and improve electricity demands and cargo transportation. I am the sole advisor for two Indigo students on these projects.
Agricultural sector:
The agricultural sector has become a new passion for me, since my experience in the Climatebase Cohort 5 Fellowship. Obviously, food security is vital for any society to grow. However, even in the most developed nations, an alarming amount of food is wasted. Additionally, proven sustainable practices are not being adopted well. The waste and unsustainable practices have different beginnings and solutions, but I think attempting to address either would be invaluable.
The main roles that I could fill in the agriculture side are using data to model crop yield predictions, help develop adaptive techniques, monitor and quantify CO2 sequestration, and data-driven methods to improve and make more accessible regenerative techniques.
Such roles should include the collection of a variety of data, with a large component being remote sensing data. The ability to collect data from many observatories and scientifically interpret such data to direct the science is exactly what my PhD thesis was focused on. I collected data from many different instruments to build my atmospheric survey. This included data from giant (e.g. HST and JWST) and small (e.g. Gaia and TESS) space-based telescopes, large (e.g. Magellan Twin Observatory) and small (e.g. the Swiss 1.2-metre Leonhard Euler Telescope) ground-based observatories, and networks of observatories (e.g ASAS-SN). Often, I would also personally travel to the Atacama Chilean desert to collect data for my research and traveled to the Australian outback to be a part of fieldwork for the Harvard Origins of Life initiative (outside my PhD work). After collecting, assimilating, and analyzing the data from all of these sources, I then identified trends that have the potential to drastically change how we understand and observe exoplanet atmospheres (McGruder et. al. 2023a). This demonstrates my strength in combining and understanding diverse datasets.
On the food waste side, roles that I would excel in use predictive analytics to forecast demand, optimize food inventory, and streamline logistics – allowing for more targeted food production and efficient distribution. I would also fit in a team developing data-driven strategies for proper utilization, tracking, or management of food surplus and food waste. Software development (front and back end), making farm-to-table systems more accessible for the consumers and famers would also be an ideal position for me.
I have already discussed my strong data and optimization capabilities, which are essential for such positions. Equally as important are my diverse software development skills. The "Software Products" page provides details about the software packages in which I have participated. In summary, I have worked on over 20 software packages, which required many different skill sets. They required a strong understanding of Bayesian statistics, accurate error and uncertainties estimations, machine learning and artificial learning techniques, Monte Carlo methods, an intimate understanding of optical instrumentation, and other physics concepts. I’ve worked on frontend (with GUI and pipeline production) and backend software development. In about half of these projects, I was the lead developer, and I have collaborated with many different teams to complete the other projects. All of these experiences outline my capability to thrive in a wide variety of software-oriented positions.
While grid electrification and agricultural decarbonization are my ideal positions, there are many other positions that I could fit in, like emission monitoring, disaster response & mitigation, and research & development of new climate technologies and practices. For any position I fill, I will stand out with my strong scientific background, adaptability to many roles, strong collaborative skills, and drive to make the world a better place.
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