by Longitude fellows Alishahal Macknojia, University of Houston, and Elijah Sales, Rice University.

In the professional world, many companies and organizations across a wide array of industries depend on expertise to help them adapt to unexpected changes or to emerging technologies so that they can efficiently explore new approaches for their businesses. They often need assistance from consultants, who provide them research and analysis tools as well as advice or plans that could propel them towards their goals. Consultants bridge the gap between problems and solutions by offering their services to clients using their specialized knowledge. This is beneficial to both the company and the consultant as they can collaborate, when needed, to review and solve problems together. Companies can bring on the expertise they need from outside for specific projects, mitigate risks, or help implement a particular strategy. They pay a premium to have the convenience of solving problems efficiently with outside expertise rather than employ and train additional full-time staff.

Consultants need to have a robust set of skills, in some cases including both technical and analytical skills to pull off a project in a specified timeline. Consequently, it is becoming apparent that students with liberal arts backgrounds, whobring excellent communication and project management related skills among numerous other talents, excel at providingvaluable contributions. Their ability to translate technical aspects of a project into business terminology and, eventually, a solution, brings a comprehensive perspective to the consulting process. Based on the conversations with six interviewees from the consulting field through the Longitude.site program, we have observed that liberal arts backgrounds and expertise allow them to ensure efficient negotiation and implementation of ideas, leading to a more streamlined consulting process from start to finish.

Soft skills are repeatedly mentioned as essential by those in the professional world, and for consulting, it is no different. Emotional intelligence and self-awareness were two characteristics that a consultant who studied political science in college mentioned as valuable during managing projects with challenging clients. A senior consultant who studied cognitive sciences mentioned that learning the decision-making process was most beneficial to him in managing tough challenges.

Effective communication and project management are two skills that are deemed essential during the life cycle of consulting projects. Doing the due diligence of conducting research and working with data along with organizingtakeaways and preparing visual presentations is what makes up a large part of consulting. In the absence of preliminary material, asking as many questions as possible upfront can be instrumental in reducing the downtime later in theseprojects.

Another crucial skill in consulting that many Longitude interviewees mentioned is the ability to connect with others. Since many consultants often travel far and wide to meet their clients and suppliers, they would also have to be adept at interacting with others while keeping cultural traditions and customs in mind. A senior product consultant who majored in sociology explained that consultants would also have to be well-adjusted to whatever setting they are required to be in so as to perform their work to the best of their ability and not feel overwhelmed in a foreign city or country. History, linguistics, and cultural studies majors would most likely succeed in this aspect because they work through curriculums that foster intercultural and interpersonal understanding. Through early exposure to different cultures before working in consulting, many students with a liberal arts background would be able to form strong relationships with whomever they interact with, allowing them to ultimately succeed in getting their ideas and solutions across.

Consulting as a career can seem quite daunting at first to those who have a liberal arts background. However, given that consulting emphasizes creativity, communication, and connection, it is an industry in which liberal arts majors can certainly find their niche. Liberal arts students will always be seen as an asset as they can provide another approach and way of thinking that shouldn’t be discounted especially considering the concerns for reliance on artificial intelligence and automation in the future. Although automating certain aspects of consulting can be efficient, the unique connection between humans is irreplaceable and could prevent a complete shift to automation. Consultants with a liberal arts background, as mentioned earlier, tend to be well skilled at forming that sort of connection, which could give them an advantage no matter what path consulting takes in the future. Consulting may also shift towards specialization, potentially causing an increased dependence on a diverse skillset brought upon by liberal arts majors. As skilled learners, researchers, and presenters, liberal arts students have an immense potential to shine as consultants as long as they put their diverse, unique, and invaluable skills to good use. In that regard, the consulting industry is brimming with opportunities for liberal arts students who have a passion for learning about, connecting with, and implementing potentially life-changing solutions for others.

Further Reading

• https://psychology.okstate.edu/component/content/article/2-uncategorised/134-undergraduate-skills-acquired(discusses crucial skills that are prevalent among psychology students)
• https://hbr.org/2017/07/liberal-arts-in-the-data-age (speaks about the role of liberal arts in data centric work) 

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by Steven Feng, economics and global health technologies sophomore at Rice University, Houston (29.7° N, 95.3° W)

The buzzword “data” has recently become commonplace in the academic, professional, and everyday world, and discussions on data and its applications are ongoing and striking. In the current age of technology, where everything is integrated with some sort of hardware or computer program, avoiding data generation and collection is impossible. Every time a student receives an email, a parent buys a toy for their child on Amazon, or a researcher narrows their search criteria on an academic database, data is being generated and collected.

Though this data is inherently meaningless, professionals and academics can use computational and statistical techniques to analyze and create meaningful and significant conclusions from data; the study and practice of these techniques is known as data science. In the words of Pierre Elias, a cardiology fellow at Columbia University who also conducts research in data science, data science is the combination of three disciplines—computer science, statistics, and content expertise (the particular subject matter a specific data scientist is interested or knowledgeable in). According to Elias, successful data scientists are not necessarily experts in all three fields; most have a unique balance between the three which allows them to collaborate successfully with other data scientists who have slightly different skill sets.

Currently, professionals in nearly every field are integrating data science into their practice to optimize or modify current methods. Two commonly cited examples of data science are within marketing and transportation. Companies like Facebook and Google are notorious for compiling and analyzing user data to personalize advertisements for individual users and oftentimes are under scrutiny for the sly ways they collect and access this data. On the other hand, transportation companies such as Uber and Tesla use spatial and geographical data to reroute drivers based on lower driving times or mileage or to power self-driving cars.

Many other fields have been using data science for their benefit for a long period of time yet do not receive as much media attention as the others. One such field is health informatics, the collection and study of patient and clinical data in healthcare. The healthcare world spans multiple subdisciplines, including pharmacy, private healthcare institutions, academic institutions, and insurance, and within these disciplines are great amounts of data. There are substantial problems that can be improved on by the analysis of said data using data science techniques. Integrating technology into the long-standing world of healthcare has been a work in progress, and recent developments in the field of health informatics prove how much untapped potential still lies in the exploitation of health data.

Professionals in health informatics are primarily concerned with optimizing or overhauling existing healthcare infrastructure because errors in healthcare practices and physical capital (that is, machines and hardware used in healthcare, ranging from fax machines to MRI scanners) that create inefficiencies can be addressed with or remedied by health informatic techniques. Both Elias and Julian Yao, senior director of strategic initiatives at Covera Health, a startup that uses data science to improve patient care, expressed dissatisfaction toward the current state of physical infrastructure in healthcare. Yao likened the industry as a whole to an operation stuck in the 1970s, and Elias commented on this issue further by mentioning how he still receives large data sets through a fax machine and sometimes has to comb through the documents manually in order to extract relevant data. In this example, data science techniques can completely obsolete the existing capital by providing a streamlined method for practitioners to compile, organize, and send data electronically.

Another important source of inefficiency in healthcare is misdiagnosis, which may be avoided with more advanced data science practices. When doctors diagnose a patient’s symptoms, their reasoning can possibly be based on false or misleading data points; according to Elias, most patients do not describe their symptoms in enough detail, which can make a diagnosis less accurate. Even with adequate patient description, however, misdiagnosis can still happen: advanced images such as MRIs are often inherently difficult to interpret, and human error by doctors is always possible. Covera Health studied the differences in diagnoses from different practitioners by sending one patient with lower back pain to 10 professionals in the greater New York area. Shockingly, not one diagnosis appeared on all 10 reports, and according to Yao, “if you take the two most extreme reports and put them side-by-side, they don’t even look like the same patient.” A misdiagnosis can cost a patient valuable time and money, and it can lead to further medical complications if the patient undergoes treatment for a condition they do not have. It can also damage a doctor’s reputation and place them under considerable legal and financial pressure.

Though healthcare informatics is not the be-all-end-all, proper data science and machine learning techniques can significantly alleviate these problems. Computers, for example, can be trained to scrape, or extract, data from files. To do this, a data scientist would first design an algorithm telling a machine what sort of information to look for and then train the machine by feeding it data and, to put it simply, tell it what is right and wrong. For Elias, a machine that automatically compiles patient data is a considerable upgrade from his method of receiving faxes and then extracting data by hand.

Machines that can diagnose illnesses are a trending research interest amongst practitioners and data scientists alike. Following the same machine learning principles described above, data scientists can train machines to analyze MRIs and other images for symptoms by “feeding” the machine examples of MRIs where symptoms are present or absent. With enough data points, the machine can train itself to detect symptoms from new images. This automatic process can greatly improve the issue of misdiagnosis if the technology is trained properly and thus is able to detect conditions with any given MRI. There are constantly new developments with machines that can assist practitioners in diagnosing conditions. At Covera Health, for example, Yao and his team specifically tackle misdiagnosis in radiology by amassing clinical data and then analyzing it not only to improve diagnostic accuracy but also to ensure patients get the optimal care in order to improve outcomes. In addition, Elias mentioned developments in machine sensors to better interpret images from echocardiograms, and researchers at Stanford have developed an algorithm known as HeadXNet to detect brain aneurysms through MRIs. Groups of data scientists leverage that same core trifecta of computer science, statistics, and content expertise to effect life-saving changes in an industry long due for a technical overhaul.

New developments in healthcare informatics will take some time, given how arduous and time-consuming the process to gather data, develop algorithms, and train machines is. In the meantime, both Yao and Elias offered a common piece of advice to undergraduate students: learn data science. As more tasks are automated, data science is becoming more and more relevant and intertwined into every professional field, and the value of a data science background cannot be understated. Even still, data science will never be a one-stop-shop to solve all of the world’s problems but rather an important method in doing so. Elias stressed how data science is not a silver bullet destined to fix everything. Strong and reliable data science applications are on the way, however, and developments are only getting better and better.

Further Reading:

• The key word in “Data Science” is not Data, it is Science – In a quick yet informational blog post, Jeff Leek provides a quirky perspective on data science while describing parts of the overarching process of gathering and analyzing data. https://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/
• Stanford researchers develop artificial intelligence tool to help detect brain aneurysms – Taylor Kubota describes the development and use of the HeadXNet algorithm by medical professionals.
  https://news.stanford.edu/2019/06/07/ai-tool-helps-radiologists-detect-brain-aneurysms/
• Variability in diagnostic error rates of 10 MRI centers performing lumbar spine MRI examinations on the same patient within a 3-week period – The clinical study from which Yao draws his lower back pain anecdote. https://www.thespinejournalonline.com/article/S1529-9430(16)31093-2/pdf
• How Covera Health Is Redefining Quality Standards in Radiology – In this article, Kenny Kline, Founder of JAKK Solutions, describes Covera Health’s operations in more detail and describes their partnership with Walmart.
  https://www.inc.com/kenny-kline/how-covera-health-is-redefining-quality-standards-in-radiology.html
• Clinical research informatics: a conceptual perspective – This journal article defines and visualizes the process of clinical research informatics, referred to in this work as health informatics, starting with posing questions from “transitional discoveries” and then following through by collecting data, formulating hypotheses, and gathering and implementing evidence.
  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392857/
  

Longitude.site welcomes applications from students who are interested to explore other topics related to data science and healthcare. Apply here.

Joint article by:
Molly Turner, orchestral conducting master’s student at Juilliard, New York City (40.7° N, 74.0° W)   
Douglas Graham, computer science sophomore at Rice University, Houston (29.7° N, 95.3° W)

The caricature for a composer may go something like this: they’re sitting at a cluttered desk, staring at a blank piece of music in front of them. At the moment of inspiration, they furiously jot down notes as if dictating the sounds of the heavens. If only it were that easy! And perhaps our overdone stereotype of the computer scientist is a nerdy and antisocial individual who is smashing keys at a coffee shop, coding the next big thing. What computer scientist would go to a music party? And what composer would delight in C++ or JavaScript? We wouldn’t consider much overlap between either of these specialties. In fact, though, there are a multitude of areas where computer science intersects with our composition, production, and consumption of music—too many, even, for this article to cover in full. 

To start, a great deal of music composition actually involves a logic-based thinking that also underlies computer science. Sound waves passing through the air and into our ears are like information passing between systems. Logic-based thinking and music composition overlap in the work of Arnold Schoenberg (1874-1951), a composer who chose to organize sounds in a very systematic way with his development of the twelve-tone method. The twelve-tone method assigns each of the twelve chromatic notes in the octave a number instead of the traditional letter name. Then those numbers are put into a matrix with various conditions developed by Schoenberg. This matrix is the reference for which pitch is assigned in music. Essentially, traditional western harmony was abandoned for numerical outputs from a matrix. Milton Babbitt, who took inspiration from Schoenberg, was both a mathematician and composer and used these ostensibly diverse specialties to create music that furthered the efforts of Schoenberg and serialization of music. Babbitt and others would go on to serialize not only pitch, but also dynamics, articulation, rhythm, and other musical parameters. Babbitt and Schoenberg’s music haven’t drawn wide audiences, and some of their music is considered unlistenable. Their efforts, however, showed us the outer limits of logic-based thinking as a way to outsource one’s compositional agency.

Beyond the logic-based thinking that can be involved in composition, another intersection between music and computer science happens at the level of production. If we look broadly at the history of music, we start with the human voice and basic acoustic instruments. During the Renaissance, advances in physical models helped develop more powerful instruments that were louder and more versatile. Most recently, with the invention of recording and synthesizing technology, the spectrum of sound possibilities are now endless. Digital audio workstations (called DAWs), made possible by computer scientists, now provide anyone who wants to create music the platform to do so. Mozart used highly esoteric notation to represent his musical compositions. He also needed live and paid musicians to bring his music to life. But today, anyone with a laptop can seek out their creative potential, thus creating a much-needed mechanism for diverse music creators to produce their music. 

While logic-based thinking and technology certainly aid in the composition and production of music by humans, the emergence of music created by artificial intelligence (AI) brings the connection to a new level. This was brought to the public’s attention with a recent Google Doodle, which allowed users to compose a small melody and have it harmonized in the style of J. S. Bach. The algorithm works using machine learning, a type of AI where a computer is fed a large amount of example data (in this case, over 300 of Bach’s famous chorales) and uses those examples to generate its own example or prediction. The Google Doodle, along with similar attempts to write music through computer programs, has polarized the music community. Some, such as those working on the Magenta research project, a research collaborative where programmers publish AI models to augment musicians’ toolbox, saw the Doodle as a creative opportunity to extend musicians’ existing skills. Others, like Bach scholar Christopher Brody, expressed disappointment at the Doodle for failing to capture any important elements of Bach’s style and instead attempting to emulate Bach by finding compromises in his music. Similar disagreements are visible in the music community surrounding new AI algorithms that emulate certain composers or produce original music. Regardless of any argument, one fact remains concrete: no one has been able to generate music entirely automatically. That is, every piece of music “created” by AI so far has been based directly off of pieces written by a real, human composer. 

Although much of the conversation about technology in music centers around whether AI should play a role in composition, AI already has a prominent function in our consumption of music. It is heavily present in video-sharing platforms like YouTube and streaming services like Spotify, learning what listeners enjoy best and recommending them new music based on their preferences. This can be beneficial to listeners, who are given accurate recommendations by the AI algorithms, and it can benefit artists by promoting their work to listeners who may not have discovered them otherwise. On the other hand, as much as streaming companies tout their ability to unearth hidden music, their profit is ultimately determined by their quantity of listens. Their AI algorithms sometimes reflect this by recommending music they know viewers will listen to rather than suggesting the music they would enjoy most. Though this drawback may limit AI’s potential to give listeners exciting new recommendations consistently, it doesn’t cancel out the improvement in music accessibility that AI creates. 

Computer science majors and music majors may rarely interact on a college campus. In fact, on the Rice University campus, the Shepherd School of Music is about as far as you can get from most computer science classes. As we have explored, however, the interaction between these specialties has yielded many innovations that are transforming the way we create and listen to music. With these advances, accessibility to the composition, production, and consumption of music continues to increase but so do the questions about how much human agency is a necessary component to art.  Exploring this intersection has taught us that the push and pull between creativity and logic is the motor for progress, perhaps in any field. Out-of-the-box musicians and coders will be the initiators for breakthroughs in the future of technology and music. 

Further Reading:

Topics covered: 

Milton Babbitt and System Based Composition (see page 7):
https://getd.libs.uga.edu/pdfs/sullivan_erin_l_200508_ma.pdf 

Music Composition, Schoenberg, and Matrices:
https://sites.math.washington.edu/~morrow/336_15/papers/rasika.pdf

Deep Learning and Music Composition:
https://cs224d.stanford.edu/reports/allenh.pdf

Bach Google Doodle:
https://www.google.com/doodles/celebrating-johann-sebastian-bach 

Debate on the Bach Doodle:
https://slate.com/technology/2019/03/google-doodle-bach-ai-music-generator.html

YouTube Algorithm:
https://www.youtube.com/watch?v=fHsa9DqmId8 

Spotify Algorithm:
https://medium.com/s/story/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe

Other topics not explicitly discussed:

MIDI (Musical Instrument Digital Interface) as Computer Instruments:
https://blog.landr.com/what-is-midi/

The Virtual Orchestra and MIDI sound banks that work within with DAWs:
http://motu.com/products/software/bmf-encore-soundbank

Virtual Reality and the Orchestra:
https://www.theguardian.com/music/2016/sep/29/virtual-reality-london-philharmonia-orchestra-esa-pekka-salonen-interview

A Brief History of Sampling, Music Production, and Technology:
https://www.musicradar.com/tuition/tech/a-brief-history-of-sampling-604868

Binaural Audio: Music, Physics, Stereo Sound, and “3D Sound” (has applications in gaming as well):
https://www.youtube.com/watch?v=Yd5i7TlpzCk 

Technology and Music Education:
http://solfeg.io/music-education-technology/

Longitude.site welcomes applications from students who are interested to explore other topics related to music and technology. Apply here.

Hallie Trial
Rice University
Houston (29.7° N, 95.3° W)

In the late 1800s, tissue-staining dyes began to captivate physician-scientist Paul Ehrlich. In particular, he noticed that some dyes stained only bacterial cells while leaving mammalian cells their natural color. Erhlich envisioned a toxic dye molecule that would bind to and kill bacteria but leave human cells perfectly healthy; such a molecule would cure bacterial illness. After testing more than 600 arsenic-based dye compounds, Ehrlich discovered the first modern antibiotic, arsphenamine. This compound allowed doctors to treat syphilis effectively for the first time. 

Just like Ehrlich’s dyes, all drugs operate by selective binding: they stick to some molecules but not others. Penicillin, for instance, attaches to and inactivates a vital protein that bacteria need to synthesize their cell walls, but the compound is harmless to human proteins. Aspirin helps control pain and swelling by binding to the enzyme that produces hormones called prostaglandins and thromboxanes, which serve roles in inflammation. 

With the ever-advancing modern understanding of biochemistry, scientists can pinpoint exact proteins involved in causing disease. Our current approach to drug development is to select a druggable target protein and then maximize the binding of a drug to that single protein while preventing other interactions. Ideally, minimizing effects on other proteins minimizes side effects. In the complex, intricately connected systems of living organisms, however, there are often multiple redundant pathways to achieve the same purpose. This means that when a drug alters the activity of just one biological molecule, other molecules can sometimes fill in, and the medication may not produce as strong an effect as expected. Furthermore, biological molecules influence one another’s activity in complicated ways, so unexpected side effects can emerge. 

A new approach to drug design called network pharmacology considers the interconnected living system as a whole rather than focusing on just one druggable protein. Dr. Anil Korkut from MD Anderson Bioinformatics and Computational Biology explained that network pharmacology “focuses on how pharmacological agents can alter the molecular network within a cell.” 

Molecules in a cell interact much like people within a large corporation. In order to determine which figures are the most important for the functioning of the corporation as a whole, one might consider who talks to whom, how their communication influences each person, and whether people could still get the information and orders they need without each communication exchange. Similarly, to investigate which molecules to target with drugs, molecular pharmacologists must use computational models to map the interactions between many different biological molecules and the pathways relevant to a disease. They might find one single, vital “CEO” molecule that they can target with a drug to alter the whole disease “corporation.” Other times, they might find a collection of a few important “employee” molecules that can alter the direction of the disease “corporation” when targeted together but not when working alone.

Scientists have investigated network pharmacology through experimental procedures like systematic screening and computational network analysis. In systematic screening, researchers study the relationship between only two drug targets at a time. For example, they might have one anticancer drug that they wish to combine with another drug—a co-drug—to improve its effectiveness. They could add the first anticancer agent to thousands of tiny cancer cell cultures, add a different second drug to each culture, and determine which two-drug combinations work most effectively. Alternatively, scientists can perform synthetic lethality chemical sensitization screening. This requires them to create many different cancer cell lines, each with one gene turned off. Then, they add their drug of interest to each cell line. Some cell lines will show increased sensitivity to the drug, and this tells investigators that the particular genes turned off in these sensitized cells might be useful targets for co-drugs. Screenings can often help uncover complicated, sometimes counterintuitive drug interactions and lead to improved medication combinations. 

Much of the future of network pharmacology lies in applying computational methods to biological networks and the chemicals that alter them. Advancements in computer models of the molecular networks within cells improve the efficiency of screenings by helping identify in advance which target combinations will likely effect the desired change. Once researchers decide which proteins or molecules to drug, cheminformatics databases correlating molecular structure with biological function and molecular binding can help chemists design compounds that affect those targets. 

An inherently interdisciplinary field like network pharmacology demands researchers from many different academic backgrounds. Projects need computationally-trained individuals with some knowledge of chemistry, biochemistry, or both, as well as experimental chemists and biologists with enough understanding of computational models to apply them. Most positions require a PhD in a related discipline, such as computational biology or chemistry, systems biology, statistics, biochemistry, chemistry, and many others. Undergraduate students can prepare for these diverse graduate programs with a wide array of majors, including bioengineering, biochemistry, chemistry, computational and applied mathematics, statistics, and even biological physics.

A few organizations currently specialize in network pharmacology. Aside from MD Anderson where Dr. Korkut runs his lab, he has highlighted groups at Harvard Medical School and Oregon Health & Science University. Harvard has a specialized Laboratory of Systems Pharmacology, and OHSU houses the Center for Spatial Systems Biomedicine, whose researchers conduct many projects in the related field of perturbation biology. As network pharmacology develops as a discipline, more opportunities may emerge both in industry and in academia. 

With further advancements in computational tools, network pharmacology may fundamentally alter how we think about chemical treatments for disease. It may also someday play a part in personalized medical care. If we develop the ability to create a distinct network model for each patient’s unique biology, Dr. Korkut stated, “we will be able to predict the immediate molecular changes in a given patient after therapy, and that will enable us to come up with better therapies, especially drug combinations… we are going to change people’s lives.”

Related Articles

This discusses the definition of network pharmacology, the goals of the field, relevant investigation platforms in the discipline, and future challenges. https://www.ncbi.nlm.nih.gov/pubmed/18936753.

This provides an introduction to networks in biology. https://www.ebi.ac.uk/training/online/course/network-analysis-protein-interaction-data-introduction/network-analysis-biology-0

The ACS page on cheminformatics provides information about the discipline cheminformatics, the questions it addresses, and careers available in the field. https://www.acs.org/content/acs/en/careers/college-to-career/chemistry-careers/cheminformatics.html

This research article authored by Anil Korkut and others provides an interesting example of network biology in action. The researchers constructed network models based on numerous experimental measurements to identify potential drug combinations for treating a drug-resistant melanoma cell line. https://elifesciences.org/articles/04640

Excerpts from a Longitude.site interview with Dr. Anil Korkut in student reflections.

Hallie Trial
Rice University
Houston (29.7° N, 95.3° W)

Humans were never meant to live in space. In microgravity, lack of mechanical stress on the body leads astronauts’ bones to dissolve, and smaller loads on astronauts’ muscles cause muscular atrophy. Radiation exposure outside the protective blanket of Earth’s atmosphere not only damages astronauts’ DNA, but it also suppresses their immune system, which makes ordinary illnesses more dangerous. Astronauts must not only face these extraordinary dangers but do so out of the reach of normal healthcare personnel and equipment. Space medicine is a growing field that addresses these health concerns.

Because of the enormous cost of sending people into space, astronauts cannot have a full medical team onboard to treat problems that arise. Therefore, before the mission begins, physicians called flight surgeons teach their assigned crew how to perform the basic medical procedures they might need. Flight surgeons also meticulously examine the health of each crewmember to ensure that they have no preexisting conditions that could endanger them during the mission. In this way, space medicine focuses much more heavily on prevention of illness than does ordinary medicine. As Emmanuel Urquieta Ordonez, a scientist at the Translational Research Institute for Space Health, pointed out in an interview for Longitude.site, “Aerospace medicine deals with very healthy people, and we prevent them from getting sick from the physiological changes that they experience during space flight… traditional medicine deals with sick patients, and you bring them back to health.”

Space medicine presents unique challenges in other ways, too. Unlike ordinary physicians, flight surgeons cannot directly talk to and touch their patients to come up with a diagnosis. In an interview published on Healthcare IT News, Shannan Moynihan, Deputy Chief of Space and Occupational Medicine at the Johnson Space Center, said that once the crew launches, “Our ability to communicate with and examine our patients is limited to audio and video communication via telemedicine.” During a mission, flight surgeons work in the Mission Control Center in case the astronauts need to contact them for a medical concern or emergency and hold weekly medical conference calls with the crew. Flight surgeons must also meet their space-going patients at the landing site, sometimes in far-flung locations, to help them readjust to Earth’s gravity.

In addition to flight surgeons, organizations like NASA need medically-trained administrators with an understanding of aerospace science to minimize the medical risk both astronauts and ground crews experience. J.D. Polk, NASA’s Chief Health and Medical Officer, explained that he oversees “the technical requirements that impact the astronauts’ health, such as how a vehicle will be built, whether radiation can be mitigated, how much exercise a crew can do on board, and their food and water needs during a mission,” in an interview for The DO.

Another path into space medicine is through medical research. Our understanding of how microgravity affects human physiology is still “in its infancy,” according to Shannan Moynihan at the 2019 Owls in Space Symposium. For longer space trips like the planned 2030 Mars mission to become a reality, we must advance our knowledge of how the environment of space impacts human health and develop new ways to mitigate those health risks. Emmanuel Urquieta and other scientists at the Translational Research Institute for Space Health contribute to this goal through a huge variety of projects. Some investigate which genes hardy microscopic organisms like tardigrades use to protect themselves from radiation in hopes of finding ways to make similar genes in humans work in such a way. Others are “trying to find out artificial intelligence and machine learning techniques to not just diagnose but to predict when a medical condition is going to happen,” said Urquieta. Such techniques will prove necessary for deep space explorations like the 2030 mission to Mars because traveling greater distances means greater communication delays with flight surgeons, making the crew’s ability to deal with medical emergencies using technology on hand even more vital.

Space medicine researchers can gain qualifications for their positions through PhD programs or combined MD/PhD programs. Aspiring flight surgeons must earn certification in aerospace medicine from the American Board of Preventive Medicine by completing an aerospace medicine residency and required graduate coursework. Besides certification, most successful flight surgeons have an extensive breadth of medical knowledge because they must diagnose and treat any health issue that emerges in orbit, from ordinary stomach complaints to emergencies like decompression sickness that could arise during a spacewalk.

Humans were never meant to live in space. And yet, we do. Despite the incredible technical, medical, and financial challenges in our way, we continue to reach out into the universe around us, ever driven to push back the frontier of the unknown. The limitations of our own bodies now pose some of the greatest barriers to advancement. As space medicine conquers these challenges, the deep space exploration humankind has dreamt of for generations may someday become reality.

Other suggested resources

An article about space medicine, including descriptions of individuals’ careers and information about opportunities for building a career in the field.
https://www.aamc.org/newsroom/newsreleases/469122/space_medicine_09072016.html

An interview with J.D. Polk, NASA’s Chief Health and Medical Officer, about his experiences in space medicine.
https://thedo.osteopathic.org/2016/12/nasas-top-doc-do-oversees-the-health-of-astronauts-preps-for-mars-mission/

Information about residency programs in aerospace medicine and the requirements for becoming an aerospace physician.
https://www.asma.org/about-asma/careers/aerospace-medicine/residency-programs-related-courses

An interview with Shannan Moynihan, Deputy Chief of Space and Occupational Medicine at the Johnson Space Center, about her career path and experiences in aerospace medicine.
https://www.healthcareit.com.au/article/out-world-medicine-meet-doctor-managing-astronaut-healthcare

A summary of the qualifications and duties of flight surgeons at NASA.
https://www.nasa.gov/content/flight-surgeons

Kimberly Tan
Business Analyst
McKinsey & Company
New York

Article originally published in Huffington Post in 2014 when Kimberly was a student at Stanford University. Republished here with permission.

I came to Stanford University as a freshman incredibly undecided about my future and overwhelmed by the opportunities I was presented with. As a result, I took classes in computer science, economics, math, philosophy, biology, psychology, and political science — all with the hope that I would stumble upon a field I wanted to pursue.

At times, I’ve admittedly felt behind other students. Behind those who came into college knowing exactly what they want to do. Those who have already completed half of their major requirements and who already have a specified goal to work toward. Those who seem to have their lives figured out.

But at other times, I feel like I’ve approached college in exactly the right way.

Surprisingly, it was Mark Zuckerberg (who visited my introductory Computer Science class last fall) who made me realize the importance of having a broad exposure to different disciplines. When asked what he would have done if he hadn’t pursued computer science, Zuckerberg laughed and said, “I actually know the answer to that. I would have pursued classics.”

That was unexpected. Why would a programming prodigy like Zuckerberg be interested in something as seemingly different as classics? But he explained his rationale, noting that the rigorous analysis needed to deconstruct Latin texts mirrored the thorough scrutiny needed to debug code, and that it was that kind of mindset that appealed to him the most.

From Zuckerberg’s visit, I realized something: that subjects aren’t distinct from one another, but are rather inextricably linked together, and that the skills developed through analyzing classic works can be applied to something as seemingly different as computer science. At their core, all academic subjects, from English to political science to math, are about applying logic and reasoning to tackle problems — whether through interpreting dense literature, studying state interactions, or solving abstract equations. In philosophy, I learned about the importance of tempering possibility with necessity and balancing a sense of infinitude with earthly responsibilities. In economics, I learned about opportunity costs and game theory, concepts I now use to make everyday decisions. And in computer science, I learned how to critically challenge my logical thinking and formulate more elegant ways of analytic thought. These subjects have all vastly altered my perception of the world, and having the chance to study them is what has been truly valuable about a college education.

Not everyone views college in this way. Many see it instead as a place to prepare for the workplace, a pre-professional incubator where we can hone specific skills to best market to employers. In his well-known article, “The Disadvantages of an Elite Education,” William Deresiewicz spoke out against this mindset, pointing out that students listen to a few speeches during college urging them to ask the big questions, yet “spend four years taking courses that train them to ask the little questions — specialized courses, taught by specialized professors, aimed at specialized students.”

This specialization is undoubtedly valuable in the workforce, where deep knowledge in one field drives breakthroughs within that field. But society needs more than just breakthroughs in specific fields. After all, the largest issues we face — from biological weapons to global warming to structural violence — are not the problem of just one discipline or group of people. They’re issues that all of humanity faces, and their solutions lay at the crossroads between scientific discoveries, political will, and moral determination.

How, then, can we expect to fix these issues — which intersect with so many different fields — when the people charged with solving them only understand one aspect of the problem?

The truth is, the solutions to our toughest problems will not be found through a narrow specialization, but rather through an interdisciplinary approach, where each field of study can provide a stepping-stone to the eventual solution. Developing well-sculpted, well-rounded individuals is thus critical, as these individuals will be the ones to integrate innovations from different fields and create smart fixes for the most pressing issues of the day.

Although some universities offer interdisciplinary majors, I don’t think that students necessarily need to major in an interdisciplinary field to broaden their perspective. I understand that some students do have a strong passion for one subject and want to devote their college time to studying that subject closely. And that’s fine. What I do think, though, is that students should not confine themselves to taking classes in their chosen fields, but should rather make an active effort to explore what different departments have to offer. Because ultimately, taking different kinds of classes is not only about learning the specific material, but also about opening up your mind to an entirely new way of thinking — stimulating a fresh set of minds that can competently challenge the enormous issues that society faces.