Prof Andrew H. Jaffe

20160201_121719 Andrew jaffeI was born in New York, USA, and after getting my undergraduate degree at Yale, took a year off to work on the atmospheric chemistry of the ozone hole before going to the University of Chicago to get my PhD with Mike Turner and Josh Frieman. I did my first postdoc at CITA in Toronto where I was lucky enough to work with Nick Kaiser and Dick Bond, and where I got my first taste of analyzing cosmological data.

I then went to Berkeley for another postdoc where I worked with the MAXIMA and BOOMERANG CMB ballooning teams and then finally on to Imperial College. Nowadays, I still work on the CMB and other projects in cosmology in general and data analysis in particular (and try to be as rigorous a Bayesian as possible!). I still spend most of my time working as a co-Investigator in the Planck collaboration, where I work on subjects as disparate as measuring the telescope’s optical beams and trying to use the data to put limits on the large-scale topology and geometry of the Universe.

I live in London with my amazing wife and our beautiful new baby daughter.

What inspired you to study science, and astrophysics?

One of my earliest memories was of an Apollo moon launch in the early 1970s, sitting in nursery school and watching the rocket rise up to space on a tiny black and white television. Ever since then, I’ve wanted to do something related to space and science.

Do you have a role model in science?

When I was in high school, I read Freeman Dyson’s “Disturbing the Universe”, and was lucky enough to hear him speak at our school, only 50 or so miles away from his base at Princeton’s Institute for Advanced Study. Although I could never be as simultaneously insightful and broad-ranging as he has been through his long career, his life and career has always been an inspiration.

A quote that inspires you?

“The whole point of science is that most of it is uncertain… The public, of course, imagines science is just a set of facts. But it’s not. Science is a process of exploring, which is always partial. We explore, and we find out things that we understand. We find out things we thought we understood were wrong. That’s how it makes progress.” – Freeman Dyson (see above)

Research interests

Cosmology, data analysis, Bayesian Statistics, gravitational waves

Favourite reference papers:

The statistics of peaks of Gaussian random fields, J M Bardeen, J Richard Bond, N Kaiser, and A S Szalay, ApJ, 1986 vol. 304 pp. 15-61, http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1986ApJ…304…15B

 

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Prof Nikolaos Tsamis

20150831-PP-Nicholas-Tsamis-001

I was born in Athens, Greece and am married with one child. I studied physics at Brown University (BA), Harvard University (PhD) and did my post-doctoral research work at Stanford University, CERN and Ecole Polytechnique.

I then returned to Greece and I’m currently a Professor of Theoretical Physics at the University of Crete.

What inspired you to study science, and astrophysics?

My high-school teachers.

Do you have a role model in science?

My PhD thesis advisor, Steven Weinberg.

A quote that inspires you?

Πάν μέτρον άριστον

Research interests

Elementary Particle Theory, Cosmology, Quantum Field Theory, Quantum Gravity

Favourite reference papers:

Principles of Quantum Mechanics, P. A. M. Dirac

Quantum Field Theory, Steven Weinberg

Gravitation and Cosmology, Steven Weinberg

 

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New Opening in Big Data: Postdoctoral Researcher position

The Cosmology and Astronomy group at the African Institute for Mathematical Sciences (http://www.aims.ac.za) invites applications for a post-doctoral Fellowship entitled Big Data: Inference and Machine Learning in Astronomy.  The successful applicant will have the choice to join one of our ongoing projects which include:
  • BIRO: The new Radio Astronomy Bayesian inference formalism being developed for the SKA (see [1] and [2])
  • Machine Learning Classification of light curves and spectra – preparing for the LSST and SKA data deluge (see e.g.  [3], [4], [5])
  • Machine Learning for Radio Frequency Interference (RFI) excision for MeerKAT and the SKA
  • Mining the Arxiv and scholarly web: understanding the academic web through machine learning

Although preference will be given to candidates with existing machine learning and Bayesian inference skills we will consider applicants wanting to switch fields to these areas as we can provide suitable training.

Candidates who are interested in exploring connections to industry are particularly encouraged to apply. The cosmology group at AIMS runs the regular Machine Learning JEDI workshops which bring together students and academics from a wide range of departments to work on concrete applications of machine learning to the real world (see e.g. https://cosmoaims.wordpress.com/2014/10/25/the-jedi-workshops/).
The group consists of Prof Bruce Bassett (joint with SAAO and UCT) and Prof. Romeel Dave’ who holds a SARChI Chair jointly between AIMS, UWC and SAAO.
AIMS attracts some of the best lecturers in the world and has a very active visitors program that currently includes Prof. Martin Kunz as an SKA visiting professor. Prof. Roy Maartens (UWC and Portsmouth) and Dr. Nadeem Oozeer (SKA) hold visiting faculty positions.
The cosmology group at AIMS is actively involved in a number of international cosmology surveys (MeerKAT, DES, GAIA, CANDELS, EUCLID) and forms part of the larger AIMS network which now has centres in Ghana, Senegal, Cameroon, Tanzania as well as South Africa (see e.g. http://www.nature.com/news/education-africa-s-counting-house-1.11757). If they so desire the successful candidate will have the opportunity to broaden their CV significantly in preparation for a permanent position by, for example, teaching across the AIMS network, supervising the small research projects of AIMS students or helping to organise TEDxAIMS.
AIMS is situated about 50m from one of the top beaches in Cape Town, a city that regularly attracts top awards for its beauty (see e.g. http://www.capetown.gov.za/en/achievementsandawards/Pages/Listofaccolades.aspx). AIMS is particularly suited to candidates who wish to make a real difference in Africa and the world or those seeking a balanced lifestyle. Hiking, swimming, surfing and other water sports are all available within a 10 minute walk. The story of AIMS is described in the TED prize acceptance speech by Neil Turok: http://www.ted.com/talks/neil_turok_makes_his_ted_prize_wish?language=en
Queries regarding the position should be sent  to Prof. Bruce Bassett (bruce.a.bassett@gmail.com) while applicants should send a CV, brief statement of research interests and email addresses of three potential referees to Rene January (Rene@aims.ac.za).
The successful candidate will be expected to take up the post starting October 2015, though a later a later start date is possible. The Fellowship offers a competitive salary allowing for a good quality of life.
Applications are now closed.
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Dr. Adam Amara

D-PHYS KolloquiumI was born in the desert of Morocco and moved to the north of England when I was 10. The transition from a dry desert climate to one that was chilly and wet was a bit of shock for me as a young boy.

I did my undergraduate major in Physics at the University of York. During my studies, I spent my third university year at UC Davis as part of an exchange program with the University of California. As is usually the case with exchange programs, such an opportunity to study and live abroad was an extraordinary year for me. It was during this year that I found my passion for cosmology, and I spent much of my time at UC Davis in their newly formed cosmology group. After this, I did my PhD at the Institute of Astronomy at the University of Cambridge, which was then, as it is now, a vibrant and stimulating place to study astronomy.

While at Cambridge I met my wife Bonny Ling, and we are now parents to two wonderful children. Like me, Bonny comes from multiple backgrounds. Not only have we been fortunate enough to travel extensively, we have also lived in many places, and this tends to lead to new insights. Since graduating from Cambridge, I have lived and worked in Monrovia, Paris, Hong Kong and Zurich. One of the joys of doing astronomy is that no matter where one goes, people love to talk about the Universe. This means there is always an interesting conversation that awaits, no matter where I am.

What inspired you to study science, and astrophysics?

During my studies, I found that I was much more interested in working things out rather than learning lots of facts. The wonderful thing about physics is that once you have the basics in place, it becomes a process of detective work and applying common sense. With a full stomach and a decent night of sleep, you have a good chance of making your way through any physics question presented to you, even if you have not met the topic before. It turns out that working things out is the cornerstone of research work, so I very happy to be working in this area.

Do you have a role model in science?

Not really. I believe science and progress is made through collective actions. What interests me the most is the way that small groups of people can come together to work creatively on new ideas. Different people have different styles, but what matters to me is that they are smart, enthusiastic, decent and kind.

A quote that inspires you?

I like two related but different quotes. They are:

You will never do anything in this world without courage. It is the greatest quality of the mind next to honor.

—Aristotle

You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something – your gut, destiny, life, karma, whatever.

—Steve Jobs

Research interests

My research largely focuses on studying large-scale structures in the Universe so as to better understand things such as dark matter. I like to think in an experimental way. This means one starts with the questions that one wants to address and then tries to develop an experiment that will test this. For this reason, I find that I am drawn to experiment design and opportunities for new measurements.

Favourite reference papers:

For me nothing beats talking to people directly about their work and asking lots of questions.

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Prof Stefano Liberati

photo stefano liberatiI was born in Rome in1970. M.Sc at the University of Rome “La Sapienza” in 1995. PhD in 2000, Supervisor D.W. Sciama, on “Vacuum effects in Strong Gravitational Fields”. Postdoc at the University of Maryland from 2000 to 2003. Research Associate in SISSA from 2003 till 2010.

Associate Professor at SISSA since then, Head of the Astroparticle PhD/Research group at SISSA since 2011.

What inspired you to study science, and astrophysics?

Passion and curiosity. I wanted to do science since I was 10.

Do you have a role model in science?

My former supervisor Dennis Sciama is probably what comes closer to a role model to me.

A quote that inspires you?

“All truths are easy to understand once they are discovered; the point is to discover them… “. Galileo Galilei

I like it because it conveys what I feel is a profound truth. Nature can be difficult to understand but once you get a true understanding its laws re always elegant and somewhat consequential. We always end up saying “Of course it had to be so!”

Research interests

Quantum Gravity Phenomenology; Analogue models of Gravity; Quantum Aspects of Black Holes; Quantum Field theory in Curved Spacetimes; Theory of gravitational theories

Favourite reference papers:

Relativity: The Special and the General Theory; A Popular Exposition by Albert Einstein

Wald –General Relativity

Hawking-Ellis: The Large Scale Structure of Spacetime

 

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The JEDI workshops

Heisenberg reflecting on the famous 1927 Solvay conference remembered that “the most intense discussions took place not during the conference sessions but over meals…” [1]. Why is that most conferences suck so badly and can one do better?

The search for an antidote to conference ennui  has been a decade-long journey for me. We have recently finished the 2nd Machine Learning JEDI workshop during which time I learned a great deal – like PCA for tensors and EMD analysis, which I did not even know existed beforehand. I also enjoyed myself very much, as always, but like most of the participants I suspect, am now exhausted — but in the good, “TEDache“, kind of way. This is the 15th JEDI-style workshop, school or conference we have run since early 2006. For years I have been meaning to write about it, why I think it works and how to run your own version, but have never got round to it because I was still experimenting, or perhaps I was just protective of something that has been an important part of my life. I think now is the right time to change that…

I will perhaps describe the history of how the JEDI concept was born elsewhere but here I will focus on the main idea. Boiled down to its essence the JEDI is an attempt to cure the usual conference woes:  the one-way, top-down communication where sometimes 80% of the audience have their heads buried in their laptops because they are tired of poorly delivered talks and their bodies are aching after being in the same seat for hours  on end with no movement except for the 30 minutes where they crowd around the coffee and biscuit table in a desperate search for relief and connection.

Typically conference organisers try to get round these horrible features by choosing a beautiful venue for the conference, only to force the participants to stay indoors for the majority of the day! In a way it is worse – you know there is beauty just outside but instead you are forced into dull, serial monogamy with a string of lacklustre speakers.

On the rare occasion that I attend “normal conferences” these days I am struck by how dysfunctional and painful they are for the most part, yet they continue as standard because of social norms. But can one do better? On our path to try to do better we have evolved, through trial and error, the following key elements to the JEDI recipe:

  • Very few formal talks (< 15% of the total available time), but lots and lots of 2-way, 3-way and n-way  informal discussions between participants.
  • Participants work on a selection of open research problems. This is the core of the JEDI concept and ensures that people are really engaged in something that interests them. The fact that the problem is unsolved means the participants are doing real research, which is very exciting. Hopped-up on the excitement of the chase, participants learn using “just-in-time” methods and then immediately apply the learning to solve the problem in front of them. As Aristotle said “What we have to learn to do we learn by doing”.
  • Research projects are done by teams that form spontaneously based on the interests of participants (everyone interested in project 1 works together, etc…). This mixes students, post-docs and faculty who then have to decide how to tackle the project together. Seeing how senior researchers tackle the problem provides a wonderful learning experience for younger participants.  If enough progress is made during the JEDI, the paper is completed by the participants working long-distance, over the following few months or longer. Using this model eight papers have been published and numerous new research links made.
  • Team discussion of spectroscopic data reduction...

    Team discussion of spectroscopic data reduction at the 1st Namibian JEDI, 2014.

  • Team building and bonding is actively encouraged as a direct outcome: long-term friendships and collaborations are great for humans and key for careers, especially for students who lack contact with top researchers.  Typically all participants live and eat together in self-catered apartments and usually the cooking is done communally. In the recent Machine Learning JEDI 2 the dinners were made by the participants – we had a braai (barbecue), Indian curry and fresh pasta – as well as delicious breakfast burritos on the final morning. There is nothing like making a meal with someone to build rapport and break down formal barriers: while an MSc student may feel intimidated by a professor when it comes to science, they can probably match them in chopping an onion!
IMG_9080

Participants at ML JEDI 2 making fresh pasta for dinner…

  •  Hold your workshop in a creative environment that nurtures creativity, hard work and social bonding (like walking on the beach) while getting away from distractions and “work associations”. Google’s policy of hiring good people and then treating them really well (not something typical of universities in my experience) inspired me to get out of the typical University department and go somewhere nice. Just as important, progress on JEDI research projects requires that the participants are fully engaged and dedicated to the project during the workshop. Trying this inside a department, where people can find you with trivial admin requests, kills the group dynamic and any real progress.
  • The spirit of the workshop is based on the belief of the power of sharing and communal good. This is of course not new: Linux and the open source community is a wonderful example. However, in the realm of conferences it is still somewhat foreign. At their worst, conferences can be akin to a used-car auction in which car dealers try to sell their work to other dealers trying to do the exactly the same.

Of course many people have recognised the issues with conferences and this has spawned the “unconference” model. How is the JEDI model similar and different? Unconferences and JEDI share their community spirit and favour lots of participant interaction. Unconferences sometimes have a “hack” day where teams work to build something together. From this point of view JEDI workshops effectively are 80% hack while at many unconferences the hack component is less than 20%.

I was lucky to attend the famous Google Sci Foo Camp unconference this year which, like most unconferences, was almost all built around parallel discussion sessions with amazing participants from all spheres of science. My problem with unconferences in general is that they can feel a little superficial and random at times because although the agenda is created by the attendees, sessions are often hijacked by one or two people, and usually nothing deep is produced in the end. At JEDIs the focus is usually on trying to solve some hard but concrete problem and on really learning research skills and the difficulties this entails leads to connections that last long after the end of the workshop.

In the decade that we have been using the JEDI model participants have published the following papers:

The secret to getting publications out is to choose projects that are sufficiently well-defined, contained and small that the participants can see the end goal in sight, which motivates them to finish.

The JEDI model is very flexible: we have run them for undergraduates and inexperienced graduate students using simple problems in Mauritius, Namibia and Kenya, as half of a more standard cosmology school and for faculty at the SuperJEDI in Mauritius. I hasten to add that the JEDI model can definitely still be improved.  I often leave them feeling a vague sense of dissatisfaction and a feeling that one could still do better. It is partly the hope that we will crack the code on the next one, along with the positive reactions of participants, as well as our own growth and enjoyment, that drives us to organise new events.

It seems appropriate to close with some quotations from a couple of the past participants:

Thank you so much for organizing such a beautiful and original conference…I am also looking forward to test some of your ideas (like the speed meetings) as team building for the next edition of a school I am organizing. The whole conference has been a total boost in motivation. This is just so much more than I could expect from any conference”  – participant at SuperJEDI 2013.

“The JEDI V conference was the most enriching experience I ever had so far.” –  student at JEDI V

At the end of the SuperJEDI – the JEDI format conference targeted at established researchers – we asked participants to fill out a survey to rate various aspects of the conference on a scale from 1 to 5. Overall the SuperJEDI was rated 4.6 out of 5. How good is that? Well the SuperJEDI was held at a top Mauritian resort where the food, water sports and drinks were all included for free and which respondents rated as being 4.2, 3.5 and 3.9 respectively. Anytime your conference rating is beating the free food, drinks and water-sports on an island paradise, then you’re probably doing OK!

SuperJEDI 2013 group photo - Mauritius

SuperJEDI 2013 group photo – Mauritius

 


Acknowledgements

It is a pleasure to thank all the people who have helped organise and fund the JEDI workshops over the years and to thank the participants themselves, without which it would be a just an intellectual construct. There are far too many wonderful people to name but I would particularly like to thank Nadeem Oozeer who in the last few years has put in a great deal of effort to expand the JEDI into Africa: Mauritius, Kenya and Namibia, while bringing with him the true spirit of the workshops.

[1] The Strangest Man: the hidden life of Paul Dirac, Mystic of the Atom, Graham Farmelo.

 

 

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Gilad Amar

Gilad AmarHaving grown up in the concrete jungle of Johannesburg, I did my undergraduate in Nuclear Engineering followed by Physics Honours at The University of the Witwatersrand (Wits). This was followed by some brief research work for the Wits High Energy Astrophysics (HEP) Group on the Higgs boson. Then in 2014 I moved down to Cape Town in order to take part in the National Astrophysics and Space Science (NASSP) masters degree at UCT. Now after finishing the introductory coursework my research is on Machine Learning, specifically Deep Learning, applied to astronomy data under the supervision of Bruce Bassett.

What inspired you to study science, and astrophysics?

My first career ambition was to be a lion.When I learnt that this wasn’t possible I had to come up with an alternative.

You know how some kids always keep asking ‘why?’ ad nauseam, well for me that has never stopped. The ‘Big Questions’ that cosmology tackles in trying to explain the very existence of the universe as it is are fascinating. Considering the above it seemed like a great Plan B to study science, and perhaps be a lion some other time.

Do you have a role model in science?

I don’t have a particular role model, but have learnt from and been inspired by several teachers, lecturers and many scientists who share their insights with the world. That said, I must admit I’m curious about everything characters like Einstein and Feynman had to say about anything.

A quote that inspires you?

I do not feel obliged to believe that the same God who has endowed us with senses, reason, and intellect has intended us to forgo their use. – Galileo Galiliei

Research interests

Cosmology and Artificial Intelligence

Favourite reference papers:

My favourite papers can mostly be found at http://www.improbable.com/ for their research that first makes you laugh and then think. I hope to win an Ig Nobel Prize someday.

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Arun Kumar Aniyan

ArunI was born in a beautiful place in South India called Kerala, more often known as “ Gods own Country ”. I did my Bachelors in Physics from St. Thomas College, Kozhencherry and then my Masters in Applied Electronics from Mahatma Gandhi University, Kottayam. Soon after my Masters I got the opportunity to work as a Junior Research Fellow in a major research project funded by the Indian Space Research Organization (ISRO). I then went on to do my PhD in Computational Physics under the guidance of Dr.Ninan Sajeeth Philip. Currently I work on Machine learning methods for different problems in astronomy in collaboration with the CRTS team at Caltech.

 

What inspired you to study science? 

As a kid I grew up watching my father working on different mechanical machines. I was amazed how they worked and my curiosity grew day by day. I was always curious how things work which inspired me to learn science and take up a research career.

 Do you have a role model in science?

My computer science teacher at school Rajesh sir and my PhD advisor Dr. Sajeeth are the ones who I admire the most.

A quote that inspires you?

“What we have to learn to do we learn by doing” – Aristotle

 

Research interests

The arXiv

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Mark G. Jackson

Mark-Jackson-Research-Profile-20140303-webI was born and raised in beautiful Portland, Oregon. I did my undergraduate degree at Duke University, then completed my Ph.D. in theoretical physics at Columbia University under Brian Greene. My research in superstring theory and cosmology continued with postdoctoral positions at the Fermi National Accelerator Laboratory, the Lorentz Institute for Theoretical Physics, the Paris Centre for Cosmological Physics, the Institut d’Astrophysique de Paris, and my current position at AIMS. I just founded the world’s first physics fundraising agency, Fiat Physica, Latin for “Let Physics Be Done.”

When not doing physics I also love traveling, improv comedy, music by Ennio Morricone, the smell of rain, opera, daydreaming on public transportation, reading novels with plot twists and Indian food. I can speak 1.5 languages (including English).

What inspired you to study science, and astrophysics?

Newton once said, “If I have seen further than others it is because I have stood on the shoulders of giants.” This is often quoted as an example of how modest he was, but actually it was the opposite: Newton was making fun of his hunchback rival Robert Hooke. Yes, even brilliant scientists can be jerks sometimes.

But the spirit of the quote is true. Each generation learns the knowledge available at the time, then questions it, teases it, pulls it, pushes it, squishes it until cracks form. We are then required to produce an answer capable of explaining everything known before, but withstanding the problems. What seems obvious to us now was once a revolutionary insight by a single person, and what seems impossible to us now will one day be laughed at.

I love that science gives us this connection with previous generations, and something to pass on to future generations. It’s like one of those “Generation Quilts” in which each thread is a little bit of knowledge. And if you’re lucky enough to add a few threads of your own, they will be there for all time.

Do you have a role model in science?

Linus Pauling. The only person to have won two unshared Nobel Prizes (Chemistry and Peace). And fellow Oregonian.

A quote that inspires you?

“We are all of us in the gutter, but some of us are looking at the stars.” — Oscar Wilde

Research interests

I completed my doctorate in physics researching whether superstring theory can naturally produce the three large spatial dimensions that we observe. Since then I have turned my attention to other topics including cosmic superstrings, signatures of new physics in the cosmic microwave background, holography and spin-statistics.

Favourite reference papers:

I always loved the article by Gross and Mende about stationary phase solutions to the scattering amplitudes. When you study the equations of particle interactions they are often very complicated, especially at high energies, sometimes requiring powerful computers to estimate the answer. But what this article finds is that superstring interactions can get *more simple* at high energies. In fact they can become so simple they can be solved with pencil and paper, and there is a very satisfying physical picture of how the strings behave during the interaction. I loved the way it turned the usual lore on its head.

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Kai Staats

kai_pebblebeach-4x6

I am an entrepreneur, writer, film maker and now, a student once more.
In 1999 I co-founded and for ten years was CEO of Terra Soft Solutions, developer of Yellow Dog Linux. My company’s systems were used to process images from the Mars rovers at NASA JPL; to conduct real-time sonar imaging on-board the U.S. Navy submarines; to train both military and commercial pilots for Boeing, and conduct bioinformatics research at DoE labs. It was an amazing learning experience, and incredibly challenging. In 2008 I sold the company to an industry leading, Japanese firm.

Now working as a professional filmmaker, I have returned to my passion for science as a storyteller, capturing the curiosity, passion, and drive of those who work a lifetime to better understand the inner workings of the universe around them.

In the past calendar year I have complete a film for Caltech and another for the National Science Foundation, both about the Laser Interferometer Gravitational-wave Observatory (LIGO). More at www.facebook.com/ligofilm

What inspired you to study science, and astrophysics?

When I was in high school I fell victim to a voice in my head which said, “I am not good at mathematics,” when in fact the voice should have said, “I need to learn a different way, hands-on, applied.”

My return to academia after a two decades break is to not only demonstrate to myself that I can enjoy applied mathematics, but to expand my foundation in the sciences in order that I will be better equipped to produce effective science outreach films.

Overall, I maintain an insatiable, child-like desire to know how it all works. Being here, in the AIMS Cosmology Research Group under Professor Bassett has fuelled that desire even more.

Do you have a role model in science?

Carl Berglund, a former engineer at NASA JPL, my high school physics professor Dan Heim, and life-long mentor and friend Ron Spomer, a renowned wildlife conservationist, writer, and photographer.

A quote that inspires you?

“It takes a village to raise a child,” because it reinforces the reality that no amount of social networking, no advanced gadget or supercomputer will ever replace our intrinsic need for human parents, peers, mentors, and associates. As our population grows (out of control), it will be this fundamental parameter of healthy child rearing that will give our species the best chance for long-term survival, both here on Earth and as we head to the stars.

Research interests

In July of 2014 I switched research topics to join a small team which is applying machine learning algorithms to the automated isolation of radio frequency interference (RFI) in radio astronomy data at SKA.

The first six months of our work (bringing 2014 to a close) brought us into a stronger understanding of the data while granting hands-on experience with machine learning.

My research will now move into genetic programming, developing code which simulates the random mutation of biological organisms in order to find a potential solution to a given problem.

This is wonderfully engaging, and equally challenging.

 

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AIMS Seminar: Dr Ignacy Sawicki

This week we had a stimulating seminar by Iggy Sawicki, our new postdoc, who packed out our cosy seminar room with a talk entitled: “Testing dark energy as a function of scale”. You can see the slides of his talk below,  admire his relaxed delivery style and read his humorous take on life and cosmology here.

photo (96)

 

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Machine learning and the future of science

A while ago I gave a talk at IAP in their wonderful amphitheatre (slides at the bottom). The colloquium series at IAP is  a very serious affair with lots of top scientists so I felt pressure to try and say something interesting (Jim Peebles gave a lovely talk the following week on the future of cosmology to celebrate the 75th anniversary of IAP).

I have been thinking a lot about the future recently, giving an AIMS public talk on the issues facing society in general due to the growth of Big Data and machine learning which you can watch here. I think this is really interesting and important, so I decided that should be the topic of my IAP talk.

The only problem was, I had no idea what to say! Machine learning, as it is currently applied in astronomy and cosmology, is fairly straight forward. Usually one has a classification problem and some training data. You pick some features that you think capture the important features of your data, you pick an algorithm (SVM, LDA, neural networks etc…), you train your algorithm with your training data,  apply the result to your data and then write your paper. Typically you would see how things change with different training data sets or algorithms and you end up with a paper something like this one which we did on supernova classification. Not very stimulating for a general audience.

So I decided to look forward 20 years and ask the question, will computers and/or robots ever do “real” science? As you may imagine, this turned out to be quite a controversial topic and the most vocal people were certainly against the idea, but that is probably not surprising.

It is very human and alluring to think that we are special, and that what our best scientists or artists do is somehow unique. Yet much of science is based on the refutation of these kinds of idea: the Copernican Revolution, Darwinian evolution and the scientific method are essentially all based on the rejection of the notion that you, I, we or anyone in particular, are fundamentally special.

The Copernican revolution rejects the idea that we live at a special place or time in the universe. Darwinian evolution rejects the idea that we are fundamentally different from the animals. The Scientific Method rejects knowledge that cannot be reproduced anywhere or anytime by anybody. And yet, many physicists fervently believe that some aspects of what humans do will never be done by “computers”. Often it is  “creativity” that comes up as the first candidate for a purely human activity.

But never is a dangerous term. Things change. A lot. It is worth remembering that the word “computer” goes back to the 1600’s and simply meant someone who computes. In the late 1800’s the word was typically used in astronomy to denote someone (often a woman) who would do tedious calculations. Now the idea of a human emulating a digital computer is strange (which is a tangent we could follow down the mechanical turking avenue but won’t!) but it illustrates how non-intuitive change can be over long timescales.

There has also been remarkable progress towards computer/robotic science and automated reasoning. The robotic scientist ADAM is able to implement the full scientific method, albeit in a well-defined search space, and produced what is probably the first non-human contribution to knowledge. You can see a video of ADAM in action here.

In preparing for my talk I came across a couple of very interesting online videos that are relevant to this question. The first is a talk by Gregory Chaitin that I actually saw in person at the Perimeter Institute on the search for the perfect language. The second is a talk by Douglas Hofstadter (of Godel, Escher, Bach fame) on analogy as the core of cognition.

So how would a computer do “great” science. One way would be to have a complete encoding or feature space for concepts or ideas. This is close to Leibniz’s idea of the Characteristica Universalis. Then a computer algorithm could simply apply some clever search algorithms to find concepts or ideas that fit the observable data best. To do so, it would need to be able to compute the implications of a given idea. For example, given an action, it would compute the observable implications, compare with available data, compute a likelihood, and then then jump to a new theory.

This is hard to imagine, but there has been remarkable progress in automated theorem proving software. I can (sort of!) imagine a robotic scientist that proposes theories through some encoding of the space of relevant concepts, derives logical derivations using allowed logical operations until it produces something that can be compared with data, computes the likelihood of the theory given the data, and then adapts the theory based on this outcome.

Perhaps this seems implausible and perhaps it is. It critically relies on the idea that one can cleverly parametrise the space of ideas, which might be impossible.   But a great deal of research today is fairly algorithmic and I suspect that at a minimum the “bottom 50%” least-creative research could be done by computers within 10-20 years. Peter Norvig has a very interesting discussion of related issues in response to Noam Chomsky here.

It is worth remembering that scientific papers are supposed to be as logical and clear as possible. They should follow infallible logic, starting with axioms, deriving propositions, comparing with data and drawing clear conclusions. In writing a paper,  humans attempt to emulate a digital ideal, interspersed with simple pictures and creative prose to excite and provide insight for their fellow analogue colleagues. A computer proving a theorem has no need of simple pictures or creative prose. If anyone is going to write a good logical paper, I think it is going to be a computer, with no need to fudge the results, fake the data or publish before it is ready for fear of perishing.

If you are interested you can see the slides of my talk here:

Updated 14 November 2013.

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