Typeset’s interview series on Connecting Global Scholarly Publishers And Researchers. We had the opportunity to interview Carla P. Gomes, Professor and Director at Institute of Computational Sustainability, Cornell University.
A quick introduction of Gomes: Her research group has been supported by over $50M in basic research funds. She is currently the lead PI of a new NSF Expeditions-in-Computing that established CompSustNet, a large-scale national and international research network, to further expand the field and Computational Sustainability. She has (co-)authored over 150 publications that have appeared in various publications focused on and conjugated with Nature, Science, and a variety of conferences and journals in AI and Computer Science, including five best paper awards. Gomes is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the Association for Computing Machinery (ACM), and a Fellow of American Association for the Advancement of Science (AAAS).
Cutting the clutter, let’s jump to the section where she reveals the details about her early career, its challenges and how she scaled up despite all odds.
1. Could you share with our readers about your journey from a scholar to a prominent and well-respected researcher?
It has been an exciting journey, for sure. I have had incredible mentors, including my Ph.D. advisor, Prof. Austin Tate, who has been instrumental throughout my career. In general, the advice from senior researchers has been invaluable throughout my research career. I have also collaborated with many researchers, including my students and researchers in other areas and fields. Cross-disciplinary collaborations have played a crucial role in getting different viewpoints and learning about interesting computational research problems.
2. While working in cross-disciplinary collaborations and domains as you mentioned, you must have gone through various evolutions in yourself.
How well do you see, all that now?
In the early stages of my career, I focused on more abstract computer science questions, particularly concerning Artificial Intelligence (AI), my main research area. In the last 15 years, I have become deeply immersed in the establishment and nurturing of the new field of Computational Sustainability. Computational Sustainability aims to help address computational questions concerning societal and environmental challenges facing humanity for a sustainable future. Developing approaches to tackle computational sustainability applications has also helped me advance research in AI.
3. Apart from developing strategic approaches and an effective handling of issues, what is the key ingredient behind your success as an author/researcher?
I am very passionate about my research! Another critical ingredient has been the courage to ask hard questions and pursue big ideas and visions. Of course, solid work and steady commitment are crucial and get us surprisingly far. Perseverance and going deeper into questions to create substantial contributions are also vital. Impactful research requires dedication and hard work. But above all, I'm very passionate about my research and try to have fun.
4. Please, share the experience of your first journal article. What were the challenges you faced, then?
I wrote my first article at the end of my master’s degree. The article was, to some extent, a summary of the thesis. The main challenge was not so much due to the lack of technology but rather how to be concise and convert a master’s thesis into a much shorter and exciting paper. I was very fortunate. I had a great advisor, Prof. Teresa Almeida, who played a key mentorship role throughout my master’s degree and in the writing of my first article. I am very appreciative of her tremendous support and mentorship.
5. Could you share some details and insights into the works that you are involved in lately and how far do you see its real world applications?
As I mentioned earlier, for the last 15 years, I have pursued research in the new field of Computational Sustainability. My research in Computational Sustainability integrates various areas within computer science and AI, such as knowledge representation and reasoning, optimization, and machine learning, driven by applications in sustainability domains. Concrete examples of computational sustainability challenges that I have tackled are the design and optimization of wildlife corridors, species distribution modeling, multicriteria optimization for hydropower planning in the Amazon basin, and AI techniques to accelerate the discovery of clean energy materials.
In a recent perspective, entitled Computational sustainability meets materials science, which was published in Nature Reviews Materials in July of 2021, we discuss computational synergies, in particular, resulting from ecology and materials science applications. For example, models for predicting bird distributions inspired models for predicting materials properties and crystal structure phase mapping.
6. Since you are a person working on multiple domains concurrently, anything else other than computational sustainability has caught your fascination or spurred you to dig for it?
I am very much interested in developing AI approaches to accelerate scientific discovery, particularly for clean energy. Our recent work entitled Automating crystal-structure phase mapping by combining deep learning with constraint reasoning was the cover article of the September issue of Nature Machine Intelligence. In this work, we propose an approach called Deep Reasoning Networks (DRNets), which requires only modest amounts of (unlabeled) data, in sharp contrast to standard deep learning approaches. DRNets reach super-human performance for crystal-structure phase mapping, a core, long-standing challenge in materials science, enabling the discovery of solar-fuels materials. DRNets provide a general framework for integrating deep learning and reasoning for tackling challenging problems. For an intuitive demonstration of DRNets, using a simpler domain, we solve Sudoku problems. The article DRNets can solve Sudoku, speed scientific discovery, by Tom Fleischman, provides a perspective for a general audience about DRNets.
7. You have been deeply and passionately involved in this writing domain for a longtime now.
How do you see the difference between the time you started and now?
There are many more different publication venues. On the one hand, this is good, giving authors more opportunities to get published. On the other hand, the increasing number of publication opportunities makes it more challenging to bring attention to one's research because the potential target audience is "scattered" among different venues. In this Covid era, the dissemination of research has been further hampered by the lack of in-person meetings.
8. What are your views on Open Access Journals? Do you think Open Access Journals have a bright future or just a bubble?
I am absolutely in favor of Open Access, and it is meant to be the future of scientific publishing. Open Access journals are becoming a key driving force in the wide distribution of scientific research. However, there is still real value in having professional editing and process management. So, finding a suitable model is essential.
9. You’ve worked on several collaboration projects with your students and fellow researchers as well.
What are the mistakes that authors very often make?
Within my field, sometimes authors write in a rather cryptic mathematical-oriented style. While describing the technical contributions, precision is essential, making the contributions accessible and clear is also crucial. Clarity and well-chosen examples are critical to communicating research. It is also imperative to be familiar with state of the art in your field and relate your research to work done by other researchers in the area. And, of course, as I mentioned earlier, collaborations are important and a great way of expanding and enriching one's repertoire of ideas.
10. What advice would you give to early-career researchers to avoid common mistakes while writing research papers?
It is not enough to do excellent research. Everyone is very busy, so the communication of one’s work is essential for its dissemination. Write clearly and well so that people enjoy reading about your work and get excited about it, which will make it easier for other researchers to learn about what you are doing, how to replicate it, extend it, and adapt or modify it. Also, learn how to give formal and informal talks to disseminate your work further and share your experiences with others. Your web page should be regularly updated, and you should make your research papers and code easily accessible.