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Selected research interests of Susanne Still
The purpose of this text is to give interested readers a synopsis of some of my work. Obviously, there is a large body of relevant work done by others in these areas, references can be found in my papers.

Fundamental physical limits to information processing
Optimal data representation and the Information Bottleneck framework
Quantum machine learning
Rules of Information Acquisition and Processing
Selected Applications

Students interested in doing a project or joining the group should email me.

Fundamental physical limits to information processing

Motivation

The laws of physics result in fundamental limits to information processing. In particular, energy consumption cannot be made arbitrarily small--the second law of thermodynamics sets limits on efficiency. An irreversible one-bit operation has to dissipate at least kT ln(2) joules (this is often called "Landauer's principle"), and conversely, one bit of information about the state of a system can be leveraged to extract useful energy (work) in the amount of at most kT ln(2) joules from a heat bath at temperature T (k is the Boltzmann constant).

These ultimate limits are achievable under conditions that are unrealistic for real world information processing systems: (i) operations have to be performed arbitrarily slowly, (ii) all relevant information has to be observable, (iii) all actions have to be chosen optimally. But interactive observers (often called "agents") in the real world usually do not have the luxury of operating arbitrarily slowly. On the contrary, speed is crucial, and these systems are therefore mostly not in thermodynamic equilibrium. They also typically find themselves in partially observable situations, and usually face constraints on what they can do, having limited control.

What are general bounds on thermodynamic efficiency that apply to real world systems? When those bounds are optimized over all possible strategies with which an agent can represent data and act on it, do general rules for optimal information processing emerge? Can this optimization furthermore give us concrete design principles for learning algorithms?

Implications from this line of reasoning are broad. On the one hand, it could lead to a unifying theory of learning and adaptation that is well grounded in physical reality, on the other hand it could lead to design principles for novel, highly energy efficient, computing hardware.

Thermodynamics of prediction

We study the energetics of learning machines using far-from-equilibrium thermodynamics, an area that has gained increasing traction since Jarzynski's work relation was published in 1997. We addressed the thermodynamics of prediction [6] for systems driven arbitrarily far from thermodynamic equilibrium by a stochastic environment. These systems, by means of executing their dynamics, implicitly produce models of the signal that drives them. We showed that there is an intimate relation between thermodynamic inefficiency, measured by dissipation, and model inefficiency, measured by the difference between memory and predictive power. As a corollary, Landauer's principle, generalized to systems that operate arbitrarily far away from thermodynamic equilibrium, is refined: processes running at finite rates encounter an extra cost that is proportional to the non-predictive information they retain. The dynamics of far from equilibrium systems with finite memory thus have to be predictive in order to achieve optimal efficiency.

Thermodynamics of inference and optimal data representation

More recent work has focused on generalizations of the above treatment. Common wisdom places information theory outside of physics, highlighting the broad applicability of a discipline rooted in probability theory. The connection to statistical mechanics is, however, tight, as was emphasized, for example, by E. T. Jaynes. I've shown how both Shannon’s rate-distortion theory and Shannon’s channel capacity can be directly motivated from thermodynamic arguments [5]: The maximum work potential a data representation can have is directly proportional to Shannon’s channel capacity. The least effort required to make a data representation (consisting of measurement and memory) is governed by the information captured about the data by the representation. Inference methods that extract relevant information are thus not outside of physics, but have, instead, a very tangible physical justification: minimizing the least physical effort necessary for representing given data, subject to a fixed fidelity (or utility), produces an encoding that is optimal in the sense of Shannon’s rate-distortion theory [5].  It is not inconceivable that von Neumann, Wiener and Shannon had these ideas in the back of their minds when they developed measures of information. However, the analysis we use here hinges upon the notion of nonequilibrium (or generalized) free energy, which emerged much later, and which is now becoming a common tool in the study of systems operating far from thermodynamic equilibrium (such as living systems).

In the most general setup, information-to-work conversion happens within partially observable systems. I showed that the thermodynamic efficiency of generalized, partially observable, information engines is limited by how much irrelevant information is retained by the data representation [1]. Optimizing for energy efficiency thus leads to a general rule for data acquisition and information processing: retain only information that is predictive of the quantities to be inferred. In other words: predictive inference can be derived from a physical principle. The generalized lower bound on dissipation can be directly minimized over all possible data representation strategies to yield strategies that least preclude efficiency. Mathematically, this procedure results in the derivation of a concrete, known and widely used, method for lossy compression and machine learning, called Information Bottleneck (Tishby, Pereira and Bialek, 1999).

Strongly coupled systems - marginalized and conditioned Second Law

Learning machines that can use feedback to interact with the process they are learning about form a strongly coupled system with the environment (and/or with each other if the driving process is another agent). Much recent work has been devoted to understanding the thermodynamics of strongly coupled, interacting systems. The second law of thermodynamics was originally stated by Clausius as “The entropy of the universe tends to a maximum.” In practice, measuring the entropy of the entire universe is difficult. Alternatively, the second law can be applied to any system isolated from outside interactions (a universe unto itself). Of course, perfectly isolating any system or collection of systems from outside influences is also difficult. Over the last 150 years, thermodynamics has progressed by adopting various idealizations which allow us to the isolate and measure that part of the total universal entropy change that is relevant to the behavior of the system at hand. These idealizations include heat reservoirs, work sources, measurement devices, and information engines. We showed that we do not need, in principle, to resort to the usual idealizations [4]: Conditional and marginalized versions of the Second Law hold locally, even when the system of interest is strongly coupled to other driven, non-equilibrium systems.

The theory we developed here lays the foundation for our ongoing investigations into the thermodynamics of interactive learning (more about interactive learning below).

Realistic information engines

Recently, much activity has focused on designing thought experiments, centered around machines that convert information (which is presented as physical realizations of bits on a tape) into work by extracting energy from a heat bath. Some of these imagined machines seem to be capable of extracting a significant amount of work by utilizing temporal correlations. It is important to ask if these models are realistic, in a sense that they could, in principle, be built. It turns out that this is, unfortunately, not always the case. We point out that time-continuous dynamics are necessary to ensure that the device is physically realizable. Enforcing this constrains possible designs, and drastically diminishes efficiency [2]. We show that these problems can be circumvented by means of applying an external, time-varying protocol. This turns the device from a "passive" free-running machine into an "actively" driven one that requires input work to function. It is perhaps not surprising that actively driven machines are ubiquitous in biology.

N particle q-partition Szilard engine

Szilard's famous 1929 Gedankenexperiment serves as a foundation for studying information-to-work-conversion. We calculated the maximal average work that can be extracted when not one, but rather N particles are available, and the container is divided into q partitions [3]. For a work extraction protocol that equalizes the pressure, we find that the average extracted work is proportional to the mutual information between one-particle position and the counts of how many particles are in each partition.

Real world information engines

To test the predictions of [1, 2, 6], and others made for information engines that run at finite speed, we proposed a series of experiments that should allow us to either develop concrete building blocks for a "thermodynamic computer", or to clarify why and how reversible computation is infeasible. This is a joint project with John Bechhoefer (experiment) and David Sivak at SFU, funded by the Foundational Questions Institute. We are currently interviewing candidates for a postdoctoral position. Interested candidates should send me an email.

Publications:

Funding: 

(2013-2015) "Foundations of information processing in living systems" Foundational Questions Institute.
(2018-2020) "Thermodynamics of Agency"(partly); Foundational Questions Institute with the Fetzer Franklin Fund.
(2019-2021) "Maxwell's demon in the real world"; with John Bechhoefer (PI) and David Sivak; Foundational Questions Institute.

Talks:

Invited Conference Talks and Summer Schools

  1. 11/14-18/2019 Montreal Artificial Intelligence and Neuroscience (MAIN), Montreal, Canada.
  2. 07/20-25/2019 The Foundational Questions Institute 6th International Conference, Tuscany, Italy.
  3. 07/11-12/2019 The Physics of Evolution, Francis Crick Institute, London.
  4. 08/26-31/2018 Runde Workshop, Runde Island, Norway.
  5. 02/08/2018 Non-equilibrium dynamics and information processing in biology, Okinawa Institute of Science and Technology, Japan (remote talk).
  6. 11/18/2016 Statistical Physics, Information Processing and Biology, Santa Fe Institute, Santa Fe, NM
  7. 09/25/2016 Information, Control, and Learning--The Ingredients of Intelligent Behavior, Hebrew University, Jerusalem, Israel (remote talk).
  8. 08/20/2016 Foundational Questions Institute, 5th International Conference, Banff, Canada.
  9. 04/25/2016 Spring College in the Physics of Complex Systems International Center for Theoretical Physics (ICTP), Trieste, Italy.
  10. 7/14-17/2015 Conference on Sensing, Information and Decision at the Cellular Level ICTP
  11. 5/4-6/2015 Workshop "Nature as Computation". Beyond Center for Fundamental Concepts in Science.
  12. 4/8-10/2015 Workshop on Entropy and Information in Biological Systems National Institute for Mathematical and Biological Synthesis (NIMBioS).
  13. 10/26-31/2014  Biological and Bio-Inspired Information Theory Banff, Canada.
  14. 7/5-8/2014 Seventh Workshop on Information Theoretic Methods in Science and Engineering
  15. 5/8-10/2014 Statistical Mechanics Foundations of Complexity–Where do we stand? Santa Fe Institute.
  16. 1/14-16/2014 The Foundational Questions Institute Fourth International Conference, Vieques Island, PR.
  17. 6/26-28/2013 Modeling Neural Activity (MONA) Kauai, HI.
  18. 01/2011 Workshop on measures of complexity Santa Fe Institute, Santa Fe, NM
  19. 01/2011 - Berkeley Mini Stat. Mech. Meeting.

Invited Seminars and Colloquia

  1. 08/2018 - Institute for Theoretical Physics (ITP), ETH Zuerich, Switzerland.
  2. 08/2018 - Institute for Neuroinformatics, University of Zuerich, Switzerland.
  3. 07/2018 - IST, Austria.
  4. 06/2018 - Google Deepmind, Montreal, Canada.
  5. 06/2018 - Facebook AI, Montreal, Canada.
  6. 11/2016 - Condensed Matter Seminar, UC Santa Cruz.
  7. 08/2016 - Biophysics Seminar, Simon Fraser University, Vancouver, Canada.
  8. 06/2013 - Max Planck Institute for Dynamics and Self-organization, Göttingen, Germany.
  9. 04/2013 - Scuola Internazionale Superiore di Studi Avanzati (SISSA) Trieste, Italy.
  10. 03/2013 - Physics Department, The University of Auckland, Auckland, NZ.
  11. 03/2013 - Physics Department, The University of the South Pacific, Suva, Fiji.
  12. 11/2012 - Center for Mind, Brain and Computation Stanford University.
  13. 09/2012 - Physics Colloquium University of Hawaii at Manoa.
  14. 10/2011 - Redwood Center for Neuroscience, University of California at Berkeley.
  15. 08/2011 - Institute for Neuroinformatics, ETH/UNI Zürich, Switzerland.
  16. 11/2011 - Symposium in honor of W. Bialek’s 50th Birthday, Princeton University, Princeton, NJ.

Press:

Optimal data representation and the Information Bottleneck framework

As we have seen above, the Information Bottleneck method arises naturally from an energy efficiency argument. It is also conceptually satisfying, as it makes minimal assumptions (in particular no assumptions about the statistics of the underlying process are necessary), yet versatile (such knowledge can be built in, and the method can also be combined with other estimation methods), and widely applicable. 

Information Bottleneck framework in practice.

We have studied how this method can be used when learning from finite data [11], and derived a criterion akin to the AIC, used to prevent over-fitting. We showed that this procedure produces the desired results on synthetic data [9, 11], as well as used it in practice to rule out overly complicated models in practical applications [15, 17]. I generalized the Information Bottleneck method to recursive, interactive learning [10]. This generalized Information Bottleneck framework provides not only a way to better understand known models of dynamical systems [9, 10], but also a way to learn them from data. We showed mathematically that the core concepts of Jim Crutchfield's ``computational mechanics" can be derived as limiting cases of the generalized Information Bottleneck framework, applied to time series [7, 9]. Overall, the generalized Information Bottleneck framework provides not only a constructive method for predictive inference from which learning algorithms can be derived, but also a general information theoretic framework for data processing that is well grounded in physics, as I have argued in [6].

Predictive inference in the presence of feedback from the learner

Living systems learn by interacting with their environment, in a sense they "ask questions and do experiments", not only by actively filtering the data but also by perturbing, and, to some degree, controlling the environment that they are learning about. Ultimately, one would like to understand the emergence of complex behaviors from simple first principles. To ask about simple characteristics of policies which would allow an agent to optimally capture predictive information, I extended the Information Bottleneck approach to the situation with feedback from the learner, and showed that optimal encoding in the presence of feedback requires action strategies to balance exploration with control [10]. Both aspects, exploration and control, emerge in this treatment as necessary ingredients for behaviors with maximal predictive power. The reason why they both emerge is the feedback itself.

This study resulted in a novel algorithm for recursively learning optimal models and policies from data, which my student Lisa Miller has applied to selected problems in robotics (work in progress). In the context of reinforcement learning this approach allowed us to study [8] how exploration emerges as an optimal strategy, driven by the need to gather information, rather than being put in by hand as action policy randomization.

Publications:

Funding: 

"Foundations of information processing in living systems";  Foundational Questions Institute: "Physics of Information" Program, Large Grant.

Talks:

Invited Talks at Conferences and Summer Schools

  1. 09/2010 Eigth Fall Course on Computational Neuroscience}, Bernstein Center for Computational Neuroscience, and Max Planck Institute for Dynamics and Self-Organization, Goettingen, Germany.
  2. 08/2009 Keynote Lecture. 2nd International Conference on Guided Self-Organization (GSO)}, Leipzig, Germany.
  3. 07/2009 Chaos/Xaoc, Conference Center of the National Academy of Sciences in Woods Hole, MA.
  4. 08/2008 Sante Fe Institute Complex Systems Summer School at the Institute of Theoretical Physics, Chinese Academy of Sciences (CAS), Beijing, China.
  5. 09/2008 Ecole Recherche Multimodale d'Information Techniques & Sciences (ERMITES); Universite du Sud Toulon-Var, Laboratoire des Sciences de l'Information et des Systemes, Association Francaise de la Communication Parlee; Giens, France.
  6. 09/2009 European Conference on Complex Systems, Warwick (ECCS ‘09), Workshop on Information, Computation, and Complex Systems.
  7. 04/2006 Bellairs Reinforcement Learning Workshop, Barbados.
  8. 12/2005 Neural Information Processing Systems (NIPS), Workshop on ``Models of Behavioral Learning'', Vancouver, BC, Canada.
  9. 07/2004 Kavli Institute for Theoretical Physics (KITP), University of California, Santa Barbara. Program: Understanding the Brain.

Invited Seminars and Colloquia

  1. 04/2010 University of British Columbia, Canada, Physics Colloquium.
  2. 03/2010 University of Victoria, Canada, Physics Colloquium.
  3. 01/2010 University of California at Berkeley, Redwood Center for Theoretical Neuroscience.
  4. 12/2009 Universitaet Koeln, Germany, Physics Department.
  5. 11/2009 International Center of Theoretical Physics (ICTP), Trieste, Italy.
  6. 04/2009 University of California at Davis, Computational Science and Engineering Center, Davis, CA.
  7. 10/2008 Max Planck Institute for Biological Cybernetics, Machine Learning Seminar, Tuebingen, Germany.
  8. 09/2007 University of Montreal, Montreal, Canada. Department of Computer Science.
  9. 09/2007 McGill University, Montreal, Canada. McGill-UdeM-MITACS Machine Learning Seminar.
  10. 03/2007 University of California at Davis, Computational Science and Engineering Center, Davis, CA.
  11. 01/2007 TU Munich, Institute of Computer Science, Munich, Germany.
  12. 01/2007 ETH Zuerich, Institute for Neuroinformatics, Zuerich, Switzerland.
  13. 01/2007 IDSIA, Institute for Artificial Intelligence (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), Lugano, Switzerland.
  14. 01/2007 ETH Zuerich, Institute of Computer Sciences, Zuerich, Switzerland.
  15. 01/2007 University of Hawai'i at Manoa, Physics Colloquium.
  16. 07/2006 Max Planck Institute for Biological Cybernetics, T\"ubingen, Germany.
  17. 06/2006 McGill University, Montreal, Canada. Department of Computer Science.
  18. 04/2005 University of Hawai'i at Manoa, Honolulu, HI, Mathematics Colloquium.
  19. 09/2005 University College Dublin, Dublin, Ireland.
  20. 04/2005 University of Hawai'i, Hilo, Hilo, HI, Department of Computer Science.
  21. 04/2005 University of Hawai'i, Manoa, Honolulu, HI, Department of Electrical Engineering.
  22. 03/2003 University of British Columbia, Vancouver, Canada, Department of Physics.
  23. 08/2003 Humboldt University, Berlin, Germany, Theoretical Biology Seminar.
  24. 08/2003 Hamilton Institute, National University of Ireland, Maynooth, Ireland. Machine Learning and Cognitive Neuroscience Seminar.
  25. 08/2003 University of Hawai'i, Honolulu, HI. Department of Electrical Engineering.
  26. 07/2003 Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, Machine Learning Seminar.
  27. 07/2003 ETH Zuerich, Switzerland, Institute for Neuroinformatics.
  28. 04/2003 Columbia University, New York, NY, Applied Mathematics Seminar.

Quantum machine learning

All systems ultimately have to obey quantum mechanics. With the advent of quantum computers, and with mounting evidence for the importance of quantum effects in certain biological systems, understanding efficient use of quantum information has become increasingly important.

We generalized the Information Bottleneck framework to quantum information processing [3]. With Renato Renner (ETH Zurich), and members of his group, we are now working towards extending the approach I proposed in [1] to quantum systems.

Students interested in this research should email me for possible projects. This would be on a Master's or PhD thesis level.

Publications:

Funding: 

(2018-2020) "Thermodynamics of Agency"(partly); Foundational Questions Institute with the Fetzer Franklin Fund.
(08/24-10/31, 2019) Pauli Center for theoretical studies, ETH Zuerich, Switzerland.

Rules of Information Acquisition and Processing

Observers, biological and man made alike, do not gain anything by choosing strategies to acquire and represent data which would not allow them to operate, in principle, as close to the physical limits as possible. This does not mean that they always will operate optimally--in certain situations that might be either not possible or a disadvantage (for example, to make sure a process runs in one direction, excess dissipation might be necessary). It just means that observers should choose the structure of their strategies, not the execution, to allow for achieving the limits. Then they have the freedom to invest other resources to achieve the limits whenever necessary (e.g. invest time to achieve energy efficiency).

Hence, we may postulate the following principle: Observers chose the general rules they use to acquire and process information in such a way that the fundamental physical limits to information processing can be reached as closely as possible.

(Again, remember that "can be reached" and "will be achieved" are two different statements, and the second one obviously would not yield a reasonable postulate, as counter examples exist in nature).

Applied to energy efficiency, we know that the "rule" that emerges is to write down predictive information and leave out irrelevant information. What rule(s) might emerge from speed limits? What are the speed limits for mesoscopic and macroscopic observers? What rules emerge from limits on robustness? What are those limits, and how should we even quantity robustness of information processing? Is it possible that the emerging set of rules might serve as axioms for an operational approach to quantum mechanics?

Funding: 

(2018-2019) "Thermodynamics of Agency"(partly); Foundational Questions Institute with the Fetzer Franklin Fund.

Selected Applications

With students and collaborators, we apply the theory developed in the lab, together with other machine learning methods, to problems of interest. I try to keep the focus on applications that are of scientific relevance and/or have some potential positive impact on society.

Regularized Portfolio Optimization

Since 2008, the world has been reminded of the importance of having a stable global financial system. It is usually the poor that suffer most from crashes, and therefore, preventing instability becomes a moral imperative. As scientists, we have little or no control over most relevant factors, such as political decision making. But, it does fall into my area of expertise to work on improving the mathematical tools used in the finance sector.

Textbook portfolio optimization methods used in quantitative finance produce solutions that are not stable under sample fluctuations when used in practice. This effect was discovered by a team of physicists, lead by Imre Kondor, and characterized using methods from statistical physics. The instability poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In the bigger picture, instabilities of this type show up in many places in finance, in the economy at large, and also in other complex systems. Understanding systemic risk has become a priority since the recent financial crisis, partly because this understanding could help to determine the right regulation.

The instability was discovered in the regime in which the number of assets is large and comparable to the number of data points, as is typically the case in large institutions, such as banks and insurance companies. I realized that the instability is related to over-fitting, and pointed out that portfolio optimization needs to be regularized to fix the problem. The main insight is that large portfolios are selected by minimization of an emperical risk measure, in a regime in which there is not enough data to guarantee small actual risk, i.e. there is not enough data to ensure that empirical averages converge to expectation values. This is the case because the practical situation for selecting  large institutional portfolios dictates that the amount of historical data is more or less comparable to the number of assets. The problem can be addressed by known regularization methods. Interestingly, when one uses the fashionable "expected shortfall" risk measure, then the regularized portfolio problem results in an algorithm that is closely related to support vector regression. Support vector algorithms have met with considerable success in machine learning and it is highly desirable to be able to exploit them also for portfolio selection. We gave a detailed derivation of the algorithm [18], which slightly differs from a previously known SVM algorithm due to the nature of the portfolio selection problem. We also show that the proposed regularization corresponds to a diversification ''pressure". This then means that diversification, besides counteracting downward fluctuations in some assets by upward fluctuations in others, is also crucial for improving the stability of the solution. The approach we provide here allows for the simultaneous treatment of optimization and diversification in one framework which allows the investor to trade-off between the two, depending on the size of the available data set.

In two follow-up papers [14, 16] we have characterized the typical behavior of the optimal liquidation strategies, in the limit of large portfolio sizes, by means of a replica calculation, showing how regularization can remove the instability. We furthermore showed how regularization naturally emerges when market impact of portfolio liquidation is taken into account. The idea is that an investor should care about the risk of the cashflow that could be generated by the portfolio if it was liquidated. But the liquidation of large positions will influence prices, and that has to be taken into account when computing the risk of the cash that could be generated from the portfolio. We showed which market impact functions correspond to different regularizers, and systematically analyzed their effects on performance [14]. Importantly, we found that the instability is cured (meaning that the divergence goes away) for all Lp norms with p > 1. However, for the fashionable L1 norm, things are more complicated. There is a way of implementing it that does cure the instability, but the most naive implementation may not - it may only shift the divergence.

Geospatial analysis

Geological surface processes are relevant in the context of understanding physical mechanisms underlying volcanism, and the temporal evolution of planets and moons in the Solar System. In a team including former students C. Hamilton (lead) and W. Wright, we analyzed the spatial distribution of volcanic craters on Io, a moon of Jupiter, believed to be more vulcanologically active than any other object in the Solar System. The extreme volcanism on Io results from tidal heating, but its tidal dissipation mechanisms and magma ascent processes are poorly constrained. Our results may help narrow down the possible mechanisms underlying Io's volcanism by putting constraints on physical models [17].

Social sciences/Document Classification

Large scale multi-disciplinary research efforts often face the problem that synergy might be impeded by lack of knowing which research from other disciplines relates (sometimes in unexpected ways) to, or may inspire ones own research. Document classification can be applied here to build visualization interfaces as library science tools to aid multi-disciplinary collaborations. This is of relevance, as it may help to increase communication between groups, increase synergy, reduce redundancy, and perhaps even occasionally spur scientific creativity. In the ``old days", each trip to the library could turn into an adventure as one walked down the aisles and got distracted from the main purpose of the trip by some interesting looking titles. Before one knew it, one was reading something unexpected, and had found a new idea, a new approach. The taste of these adventures has changed in the digital age. To a certain extent, they have been replaced by online browsing, but the sheer volume of information is often a limiting factor, and there may well be use for tools that help organize relevant information in an intuitive way. As part of a NASA funded study, we developed such a tool in the context of astrobiology, an area that spans many fields, from chemistry to biology, to astronomy, with my student L. Miller and my colleague R. Gazan [15].

Origin of life

With my student Elan Stopnitzky, we applied Gavin Crooks' notion of non-equilibrium maximum entropy hyperensembles to the abundancy of building blocks for life [13], and found that the spontaneous emergence of life looks less unlikely if one does not assume that conditions on early Earth were consistently in or near thermodynamic equilibrium. Heterogeneous non-equilibrium driving appears as a possible catalyst, the more so, the further away from equilibrium chemical reactions happened.

Volcano prediction and global volcanic activity profiles

Robert Wright of HIGP has 18 years of thermal emission data from 110 volcanoes around the Earth, recorded twice daily. We are analyzing this data with a variety of machine learning techniques, with the ultimate goal of predicting thermal output trends and classifying volcanoes by their activity profiles.

This project is perfect for a student in CS, or HIGP, or Physics, and can be done as 499 or 699.

Chemical composition of volcanic rocks and their classification

Dr. Thorvaldur Thordarson has built an impressive corpus of chemical composition data of volcanic rocks, mostly from Iceland. We have done some preliminary cluster analysis of this data and are interested in continuing work with the ultimate goal of predicting rock origin (which volcano and possibly which erruption) from chemical composition.

This project is perfect for a CS student, and could be done as 499 or 699.

Publications:

Talks (about regularized portfolio optimization):

  1. 11/2016 - Santa Fe Institute, NM.
  2. 11/2011 - Applied Math Seminar, City College New York, NY.

Funding: 

(2018-2021) NASA, ``A 30 Year, Multi-Sensor Analysis of Global Volcanic Thermal Unrest" (co-PI). PI: Robert Wright (HIGP, UHM),
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