5.28.24
Anna Janka
Leonid Leiva Ariosa
Joachim Buhmann viewed everything he did during his academic career as a service to society. (Photograph: ETH Zurich / Leonid Leiva Ariosa)
Since 2003, when Joachim Buhmann became an ETH professor, he has helped shape the explosive development of machine learning. It is not technical progress that worries him, but how society deals with it. Shortly before his retirement, he looks back on his academic career.
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About
Joachim Buhmann was a Professor of Practical Computer Science at the University of Bonn from 1992 to 2003, before he accepted a position at ETH Zurich and became a Full Professor of Computer Science. In his teaching and research, he focused on questions related to pattern recognition and data analysis, which includes areas such as machine learning, statistical learning theory, and applied statistics. Professor Buhmann took on important administrative functions at ETH, including the roles of Vice Rector for Study Programmes (2014-2018) and Head of the Institute for Machine Learning (2014-2023). Since 2017, he has also been a member of the Research Council of the Swiss National Science Foundation.
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“AI helps us to grasp more and more complex facts”
His dissertation in biophysics led Joachim Buhmann into the then still exotic terrain of machine learning in the mid-1980s. Since 2003, when he became an ETH professor, he has helped shape the explosive development of his field. It is not technical progress that worries him, but how society deals with it. Shortly before his retirement, he looks back on his academic career, in which, in addition to teaching and research, administrative functions have also been of great importance.
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Joachim Buhmann, why did you become a scientist?
Buhmann: There is a great answer from Luc Ferry, a French philosopher and former Minister of Education. It’s about the question of why people want to leave something behind after they die. This can be achieved by producing and raising offspring or educating and inspiring others as teachers. According to Ferry, however, the greatest legacy is left by scientists, as they make a lasting contribution to humanity as a whole through the knowledge they gain. Whether I was successful or not is for others to judge, and that may only become clear later. However, I believe that as a scientist I have at least tried to answer important questions and gain new insights, and some of my doctoral students have certainly taken away new knowledge that they have then developed further.
Did you already know at the beginning of your career that you wanted to do research at a university?
Buhmann: It was a kind of ideal, but I was never obsessed with the idea of becoming a professor. After my time as a postdoc in California, I was quite open to the idea of becoming a professor because my children were already older. My wife and I had our children in our 20s, and I became an associate professor at the University of Bonn at the age of 32. I am convinced that luck played a role in my career. Things could certainly have turned out very differently.
Would you have had a plan B?
Buhmann: My plan B would have been to go into a research laboratory or industry. There were already options in the field of machine learning in the 1990s, although not as many as there are today. I had received offers from the private sector, but then decided to go down the research route. It’s crucial to have a plan B. I am convinced that there is no one right path and that many doors open in life. You just need to choose a very good one for you. However, I believe that you can make good use of most options in life.
When students ask me for career advice, I always recommend them not to get too fixated on one option. This is probably a hindrance in the long run. Because then you miss the chance to try out new paths and are less open to other possibilities that could also lead to success. If you look at the biographies of successful people, you realise that most of them did not plan their careers. It’s important to keep an open mind and be willing to explore different routes in life.
Is there one piece of advice you wish you had received at the beginning of your academic career?
There certainly are many, and I probably would have ignored them all myself. One important piece of advice for success is to be passionate about what you do. If you realize that you can’t muster the passion, you should quickly take action and keep looking. Once you’ve found something you’re passionate about, stick with it and don’t let yourself be influenced by current trends.
In my opinion, success often only sets in when you have had to endure a certain amount of suffering to achieve it. Otherwise, the dream was probably not ambitious enough. It is therefore also important to develop a certain resilience in the face of failure. Failure is a crucial indicator of one’s abilities. If you never succeed, you are probably overestimating your capabilities. If you never fail, you probably haven’t challenged yourself enough.
If you’re fortunate enough to be endowed with more talents than others, then you have a certain responsibility to give something back. These talents should be nurtured by the people in your environment, such as parents or teachers who support you in your personal development.
Let’s go back in time a little. You first studied physics at the Technical University of Munich and later completed a Ph.D. in theoretical biophysics. How did you get into machine learning?
My doctoral supervisor was a theoretical biophysicist, but my research focused on the memory capacity of “Hopfield networks”. These are a special type of artificial neural network. If you study such models, then you are essentially already in the academic territory of computer science. It’s no longer just pure physics because it’s not about inanimate matter, but about information processing. This area was not yet fully established in computer science at that time, but it is clearly part of the subject. Later in my career, I moved to Bonn and continued to work in the field of neural networks as an associate professor of practical computer science.
Why did you come to ETH?
I was an associate professor in Bonn and had no prospects of being promoted there. At the age of 43, I got the opportunity to become a full professor at ETH Zurich. ETH had, as it does now, an excellent reputation, although the University of Bonn in Germany was also outstanding in mathematics, which was the home of computer science at the time. My wife and I had already built a house in Bonn, but as our children were almost finished with school, it was an obvious choice at that time. I probably wouldn’t have moved to a less important university, as every move requires considerable personal resources.
What have you worked on in your research career?
Even before I came to ETH Zurich, I was working on the question of how clustering algorithms assign their data to different groups.
The way this allocation works differs from that of classification algorithms. In classification algorithms, data is usually annotated manually by a human, and the algorithms are then trained with these annotations. For example, you want to automatically classify images of dogs and cats into two groups and use a training dataset to specify that an image should either be classified in the “dog” group or the “cat” group.
With clustering algorithms, there are no such labels, so there is no predefined “dog” or “cat” class. Nevertheless, the algorithm is supposed to eventually assign a label to every object. I wanted to find out how the algorithms carry out the clustering when there is no quality measure that they can use as a guide. I then applied this theory to various biological and medical projects. The approach of the clustering algorithms reflects the situation of a doctor who is tasked with predicting the probability of survival of a patient based on an X-ray image and other sources of information.
How has your field of research at ETH Zurich changed over the last 20 years?
I had not foreseen that my field of research would develop so dramatically in the last 15 years. These are incredibly exciting times. The current rise of artificial intelligence affects all disciplines of science and, for me as a trained physicist, it is comparable to the introduction of quantum mechanics in physics. As a student, I wanted to know enough in my profession to see such a revolution emerge in my field of research and perhaps even contribute to it myself. It is an absolute stroke of luck for me that I was able to witness this time and actively participate in it as a researcher.
When I joined the Department of Computer Science, hardly anyone was interested in machine learning. Some professors, even in other research areas, did use machine learning in their research, but never to the extent that it is used today. And no one in the Department of Computer Science had made machine learning their core area. Things are different nowadays. There is now an Institute for Machine Learning with 11 professors.
Do you view these developments in the field of artificial intelligence with concern or enthusiasm?
I’m not worried about the scientific development itself. My concern, if any, is that society may not sufficiently understand or anticipate the consequences of these scientific advances. New technologies – including artificial intelligence – have the potential to do a lot of good, but they can also be misused. Artificial intelligence is a technology that improves human thinking by enormously expanding the limits of the human capacity to store facts and grasp complexity. This is because the human brain tends to ignore details and focus on the big picture, i.e. to abstract. Enabling society to learn how to use these systems in an ethically correct way is an important educational task. New procedures need to be developed to ensure transparency, responsibility and fairness in the use of these programs.
At ETH Zurich, you were both a researcher and a lecturer and took on some administrative roles. How do you look back on your time as Vice Rector?
I was Vice Rector for Study Programmes at ETH for four years. The role required a lot of time and empathy. But there are also incredibly interesting issues that come your way and that involve enormous responsibility. You are confronted with questions that are at the interface between the preconceived set of rules and an empathetic, ethically correct assessment of individual cases. The decisions you make can result in significant restrictions on someone’s life options. For example, you must decide whether a student should be expelled from their programme. This must be grounded on a very good reason rather than the randomness of any given processes. The role of Vice Rector was certainly a challenge, but I think I was able to contribute reasonable solutions. My academic background has certainly helped me in this administrative role. Essentially, being Vice Rector is about monitoring the processes that take place. This has a lot to do with computer science, where we develop automated processes that are then executed by the computer.
Is there anything you have learned during your time as Vice Rector?
First and foremost, I became a scientist to do research. However, in addition to producing new knowledge, as a university lecturer I also have the responsibility to pass on existing knowledge. During my time as Vice Rector, I learned that the university’s priority is always teaching, and that research comes second. However, as the quality of research is easier to measure, it is often given more importance than teaching. Students at universities should first and foremost be trained to become intelligent problem solvers who can make reasonable decisions even in conditions of great uncertainty – regardless of whether they go on to work in industry or stay in academia.
Something else I have learned in this context: everything we do is a service to society. This is true of administrative tasks just as it is of research and teaching. In this sense, I believe that all three areas should be valued equally.
You are retiring in July. What will you miss most in retirement?
When you’ve been in a place for a while, you tend to long for that familiar environment and the community of colleagues, doctoral students, postdocs and students. I will certainly miss many things, but I don’t think I will suffer from it. This is currently an optimistic assessment and if you ask me again in a year’s time, my opinion may have changed. But I am convinced that new opportunities will arise in the future.
Do you have any concrete plans?
My family is relatively large. We are expecting our eighth grandchild soon. I’m sure I’ll have a few tasks ahead of me. Professionally, I haven’t prepared myself for a direct follow-up job and I’m not actively looking for one. However, I would like to maintain my contacts with the institute and try to make myself useful as an emeritus professor. I also think that I will continue to do research, but probably less than now. I would also like to contribute my time and expertise to public relations work to support society in this digital transition.
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The Swiss Federal Institute of Technology in Zürich [ETH Zürich] [Eidgenössische Technische Hochschule Zürich] (CH) is a public research university in the city of Zürich, Switzerland. Founded by the Swiss Federal Government in 1854 with the stated mission to educate engineers and scientists, the school focuses exclusively on science, technology, engineering and mathematics. Like its sister institution The Swiss Federal Institute of Technology in Lausanne [EPFL-École Polytechnique Fédérale de Lausanne](CH) , it is part of The Swiss Federal Institutes of Technology Domain (ETH Domain)) , part of the The Swiss Federal Department of Economic Affairs, Education and Research [EAER][Eidgenössisches Departement für Wirtschaft, Bildung und Forschung] [Département fédéral de l’économie, de la formation et de la recherche] (CH).
The university is an attractive destination for international students thanks to low tuition fees of 809 ₣ per semester, PhD and graduate salaries that are amongst the world’s highest, and a world-class reputation in academia and industry. There are currently students from over 120 countries, many of which are pursuing doctoral degrees. In the QS World University Rankings ETH Zürich is ranked very highly in the world and very highly by the Times Higher Education World Rankings. In the QS World University Rankings by subject it is ranked very highly in the world for engineering and technology, earth & marine science.
Nobel laureates, Fields Medalists, Pritzker Prize winners, and Turing Award winners have been affiliated with the Institute, including Albert Einstein. Other notable alumni include John von Neumann and Santiago Calatrava. It is a founding member of the IDEA League and the International Alliance of Research Universities (IARU) and a member of the CESAER network.
ETH Zürich was founded on 7 February 1854 by the Swiss Confederation and began giving its first lectures on 16 October 1855 as a polytechnic institute (eidgenössische polytechnische schule) at various sites throughout the city of Zurich. It was initially composed of six faculties: architecture, civil engineering, mechanical engineering, chemistry, forestry, and an integrated department for the fields of mathematics, natural sciences, literature, and social and political sciences.
It is locally still known as Polytechnikum, or simply as Poly, derived from the original name eidgenössische polytechnische schule, which translates to “federal polytechnic school”.
ETH Zürich is a federal institute (i.e., under direct administration by the Swiss government), whereas The University of Zürich [Universität Zürich ] (CH) is a cantonal institution. The decision for a new federal university was heavily disputed at the time; the liberals pressed for a “federal university”, while the conservative forces wanted all universities to remain under cantonal control, worried that the liberals would gain more political power than they already had. In the beginning, both universities were co-located in the buildings of the University of Zürich.
From 1905 to 1908, under the presidency of Jérôme Franel, the course program of ETH Zürich was restructured to that of a real university and ETH Zürich was granted the right to award doctorates. In 1909 the first doctorates were awarded. In 1911, it was given its current name, Eidgenössische Technische Hochschule. In 1924, another reorganization structured the university in 12 departments. However, it now has 16 departments.
ETH Zürich, EPFL (Swiss Federal Institute of Technology in Lausanne) [École polytechnique fédérale de Lausanne](CH), and four associated research institutes form The Domain of the Swiss Federal Institutes of Technology (ETH Domain) [ETH-Bereich; Domaine des Écoles polytechniques fédérales] (CH) with the aim of collaborating on scientific projects.
Reputation and ranking
ETH Zürich is ranked among the top universities in the world. Typically, popular rankings place the institution as one of the best universities in continental Europe and ETH Zürich is consistently ranked among the top universities in Europe, and among the best universities of the world.
Historically, ETH Zürich has achieved its reputation particularly in the fields of chemistry, mathematics and physics. Nobel laureates are associated with ETH Zürich, the most recent of whom is Richard F. Heck, awarded the Nobel Prize in chemistry in 2010. Albert Einstein is perhaps its most famous alumnus.
The QS World University Rankings placed ETH Zürich very high in the world. ETH Zürich has ranked very highly in the world in Engineering, Science and Technology, just behind The Massachusetts Institute of Technology, Stanford University and The University of Cambridge (UK). ETH Zürich also ranked very highly in the world in Natural Sciences, and in Earth & Marine Sciences.
The Times Higher Education World University Rankings has ranked ETH Zürich very highly in the world in the field of Engineering & Technology, just behind
The Massachusetts Institute of Technology, Stanford University, The California Institute of Technology, Princeton University, The University of Cambridge (UK),
Imperial College London (UK) and
The University of Oxford (UK).
In a comparison of Swiss universities by swissUP Ranking and in rankings published by CHE comparing the universities of German-speaking countries, ETH Zürich traditionally is ranked very highly in natural sciences, computer science and engineering sciences.
In the survey CHE Excellence Ranking on the quality of Western European graduate school programs in the fields of biology, chemistry, physics and mathematics, ETH Zürich was assessed as one of the institutions to have excellent programs in all the considered fields, the other two being Imperial College London (UK) and the University of Cambridge (UK), respectively.