Science at Cambridge: Stuff matters – understanding how materials behave

When I came to Cambridge I thought I’d end up in Physics, but I’m currently in my third year doing Materials Science! I’d barely even heard of materials science before I did Natural Sciences – the closest I’d come to it was the book Stuff Matters by Mark Miodownik, but it was interesting enough that I would choose materials science as one of my options in first year. Quite literally speaking, it made me notice the things around me, and I wanted to know more.

Essentially, materials science is about how different materials behave, both on a macroscopic level (like how beams bend) and on a microstructural level (like how metals are basically made up of tiny grains), and how these macroscopic properties emerge from different kinds of microstructure (which are also often different for different materials). Add in electrical properties, magnetic properties, manufacturing processes, the effect of temperature, corrosion, mechanical stresses and much more, and the result is an interdisciplinary subject that combines physics, chemistry and engineering to explain matter and use stuff well. There’s enough theory in materials science to keep the physicist in me relatively satisfied, and there’s enough practical applications that the everyday relevance more than makes up for any fulfilment deeper theoretical intricacy would otherwise bring.

One of the best things about studying materials science is being able to see the way scientific concepts fit together and are used in items that we take for granted every day.

In second year, I had the chance to take apart a kettle and use equipment in the lab to identify which materials were used, how they were made, and why they were chosen, and all of this using methods that we’d been taught in our practicals and lectures. It was challenging, fun and gratifying to basically pick something apart and figure out how and why it worked.

More recently, I’ve enjoyed working on a literature review, in which we get to pick a topic and have several weeks to read up on the area and summarise and evaluate it. I was reading many papers on the many ways people are attempting to induce magnetism in graphene, and although this started off as quite intimidating, by the end of it I’d learnt so much that I’d begun to get excited about the possibilities if graphene could be used in this way – including significant applications for spintronics (where a particle’s intrinsic spin is used to store and manipulate data, instead of its charge, as in conventional electronics), which would allow massive improvements in current data manipulation capabilities.

Studying materials science – especially in Cambridge – has been such an enriching experience, partly because it’s so interdisciplinary and partly because it allows a much deeper appreciation of the way the world physically works.

I have definitely enjoyed myself for the past three years, and would recommend it for any curious mind!

Danielle Ho En Huei
Undergraduate Student

Science at Cambridge: Blurring the boundaries – Psychological and Behavioural Sciences

Inever really classed myself as a scientist; after all, I was arty, a writer, a people person and more into ‘why’ than ‘how’. Art and English literature were ‘my thing’, and quite honestly still are.  At school, Biology interested me – but only the stuff on things like the brain or hormones, so when I found psychology I felt as though I had hit the jackpot. Now, with my time at Cambridge nearly up, I can conclude that studying Psychological and Behavioural Sciences (PBS) has wonderfully blurred the boundaries between the arts and sciences, giving me the freedom to pursue whatever has taken my fancy.

Over the years, I have taken Natsci (Natural Sciences) papers, Sociology papers and Bio-Anth papers, learning about things from the lifecycle of an Angiosperm, to visual phototransduction and families created through assisted reproduction. It has been a learning curve, and at times I have wondered if I was in the right lectures. As this degree is still relatively new, it has been very open to feedback on what works and what doesn’t, and I feel that the students have been actively involved in shaping the content and structure of the course. It feels as though we have a voice beyond our essays, which was a welcome surprise coming to Cambridge.

Autism has always been the area of psychology that has interested me the most, and this year I have chosen to focus on it for my dissertation.

As well as analysing the data and drawing conclusions, I am also involved in the actual collection, conducting tests on language skills and motor ability with low-functioning, non-verbal children with autism. It is a big commitment, and requires a lot of effort and attention, but is  very hands on and I love the applied nature of this final year – I can put what I’ve learned in textbooks into the real world, and the idea that I am actively making a difference, no matter how small, is amazing.

I am graduating in 3 months, and have no firm plans – I may study Clinical Mental Health Sciences at UCL, I may have a year out travelling or get a job on the Isle of Wight. At first this worried me, but I feel as though my degree has not only equipped me with a huge and wide depth of knowledge but given me a new perspective on how I go about my daily life. I often catch glimpses of babies as evolutionarily designed information absorbers, London tube journeys as social experiments or my friends as bizarre machines at the mercy of their brains.

It’s been transformative, and now, I am confident in saying I am a scientist.

What you should expect for PBS:

-You can’t escape statistics no matter how hard you try.

-You will hear about Phineas Gage and attachment at least once a week.

-The degree doesn’t teach you how to read minds.

-Never mention Freud in an essay without saying he’s wrong.

-You’ll learn great chat up lines (Roses are red, Violets are blue. If you were a null Hypothesis, I would fail to reject you).

-…And even better jokes (Who is the most emotional woman in the world? Amy G. Dala).

– Even the best and brightest often can’t spell ‘Pycholology’.

Meg Fairclough
Undergraduate student

Science issue – Big Data: Friend or Foe?

16b-rebecca-stanley-standingWhen studying phenomena outside the controlled conditions of the lab there are a whole host of variables that can affect your results. How an animal population behaves one season to the next, or why one set of patients responds well to a drug while others don’t, can come down to a number of factors that are difficult to control for in experimental design. This is why sample size is such a key factor in research, ensuring that you have enough data points, covering a variety of different scenarios, can help to smooth out background noise and identify the key trends you’re looking for.

The era of “Big Data” we are now living in has helped with this problem of sample size as the vast amount of data available allows us to investigate different problems and ideas using rich and varied datasets.

Big Data is a exactly that, extremely large datasets – the threshold is a moving target but is generally considered to be in the petabytes, for context, one petabyte worth of MP3-encoded music would take 2,000 years to play! The types of data we can now bring together is only limited by the type of sensors we have; from all the different apps on a smartphone to heart monitors, weather stations and CCTV cameras. This vast and varied data requires new ways of thinking and new analytical tools to reveal relationships between variables and conduct predictive analysis of what might happen in the future.

The applications of this analysis are endless, from translation: Google Translate is based on Big Data statistical analysis of text, to personalised medicine: exploring healthcare data to see which drugs are most effective in which types of people.

I work for a company called Carbon Credentials and our mission is to use data to drive sustainable practices. By connecting carbon emissions data to operational data from buildings you can work out what is consuming the most energy and why. Is it a cultural issue: do people just need to get better at switching their equipment off before they leave for the day? Or is it an operational issue with the building: is the air conditioning and heating on at the same time? (This happens more often that you’d think). We work with large datasets from universities, businesses and hospitals to create tailored sustainability performance programs to optimise the use of buildings to reduce energy wastage and increase user comfort.

This type of data analysis may seem overwhelming, complicated or just dull! But it is revolutionising our lives for better or worse. We can expand scientific research, improve healthcare and reduce carbon emissions but our personal information is no longer our own – if you want to use pretty much any app on your phone you have to give access to your location, photos, contacts and more.

How many sites do you go on that make you accept the “cookies” that personalise your content and ads?

A University of Cambridge research group hit headlines in 2015 for creating an online tool that can predict your relationship status, intelligence levels, political & religious beliefs and sexual preferences just by analysing your Facebook likes. We offer up a lot of this information readily to Facebook but this analysis demonstrates how what you like, what you search for and what you buy can generate a profile which can determine factors such as what news articles will appear in searches. How this information is being used is helping to confirm existing biases and narrow our experience of the online world.

In a world that is increasingly polarising to the extremes of left and right I think it’s important that we consider how data can improve and connect the world rather than isolate and divide it.

Rebecca Stanley
Alumna

Career Path: Understanding dark matter – a collaborative venture

16a-sarah-williamsAs a researcher on the ATLAS experiment at the Large Hadron Collider at CERN, one of the things I love about my job is that on a day-to-day basis I get to interact with scientists from different backgrounds all around the world. The ATLAS collaboration includes around 3000 physicists from over 175 institutes around the world, all working together to answer fundamental questions about the elementary particles and interactions in the universe.

My work focuses on searches for new particles at the LHC, and in particular those that could help explain what makes up Dark Matter in the universe.

Astrophysicists now believe that dark matter makes up nearly 25% of the mass energy content of the universe (with only 4% being normal `baryonic matter’ which we can explain and the rest being dark energy, a mysterious substance that actually causes the expansion of the universe to accelerate). Although we don’t currently know what dark matter is made of, there is strong evidence that it could constitute a “weakly interacting massive particle” (or WIMP) which could thus be searched for in the high energy collisions at the LHC.

The LHC collides bunches of protons together around 40 million times a second, and recording these collisions requires enormous detectors (the ATLAS detector is around half the size of Notre Dam Cathedral in Paris and weighs as much as the Eiffel tower). Most particles produced in collisions decay instantly so we can only indirectly infer their existence by trying to reconstruct information from their decay products. The data is read off the detector, reconstructed and stored at large computing sites all over the world waiting to be analysed offline by particle physicists.

It is very rare to perform a LHC search on your own, it normally takes a group of a dozen or more physicists working together to produce the final result.

For example, I tend to work a lot on the statistical analysis, which considers quantitatively the level of agreement between the observed data and the prediction based on the Standard Model (which encapsulates our current understanding of the elementary particles on the universe).

Another aspect of my work that I appreciate is the variety of skills that I have gained and used over the years in carrying out my research. The large volumes of LHC data makes computer programming unavoidable, so I have had to learn a variety of programming languages including c++ and python. In addition to that, working in such a large collaboration requires strong communication skills. Before I started my PhD I had very limited experience of public speaking however I very quickly became accustomed to presenting on a weekly basis. I have also had the opportunity to present at international conferences around the world, including in Taipei, Moscow and later this month in Adelaide.

There are so many fundamental questions within the sciences that are waiting to be answered.

Challenges range from finding alternative energy sources that can be exploited on a global scale, to developing new techniques for the diagnosis and treatment of life-threatening diseases.  We need young women (and men) with a passion for new knowledge, the creativity to solve problems and the personal qualities to engage effectively in interdisciplinary teams.  It’s an exciting and rewarding field and can provide you with many unexpected opportunities along the way.

Sarah Williams
Alumna

Science issue: A machine that can learn to speak to you

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News
Have you ever talked to Siri and asked yourself how one builds such a system? Some time ago, when I was pursuing my MPhil degree in Cambridge, Prof. Steve Young demonstrated a spoken dialogue system during a talk. I was fascinated by the idea that one could make a computer speak and understand human speech. I thought I must get into this research and so I applied for a PhD at the Department of Engineering’s Dialogue Systems Group.

A spoken dialogue system normally has three parts: speech understanding, which decodes the meaning from the user’s speech; dialogue management, which tries to come up with a good response; and speech generation, which turns the answer into natural speech. All of these modules can be data-driven: machine learning methods allow us to build systems that become better at their tasks the more data they have.

This is very exciting because in today’s world we are generating data at the biggest pace ever.

There are two distinct kinds of machine learning methods that we use for this research. One is called supervised learning.  This is how we learn ourselves when we have a teacher to provide examples. The system simply tries to imitate the teacher.  Another is called reinforcement learning, and one can think of it as learning from interaction. In this approach, the system can explore different possibilities.  Whenever it makes a good decision, it gets a reward from the user.  Over time, it tries to maximise that reward.  Just like a child learns from trial and error.

This kind of learning through interaction in the context of dialogue systems really intrigues me. The problem is that such learning methods normally need a huge number of interactions before the system starts to behave reasonably well. So I’ve been working on ways to speed up this process, so that the system can learn directly from talking to a human. And indeed I was the first researcher to show that this is possible.

Applications for this technology include every area where we currently see human-computer interaction, and it will make such interaction possible in the future in areas where we can’t imagine it today.  Currently, I am particularly interested in applications in the health sector.  To support such systems, we need to develop algorithms capable of supporting much more complex interactions than what is possible today.  But if successfully built, such systems would have a huge benefit for society.

Dr Milica Gašić
Lecturer in Dialogue Systems, Department of Engineering
Fellow, Murray Edwards College

See my interview for The Naked Scientists: http://www.thenakedscientists.com/HTML/interviews/interview/1001757/

Or check out my website:

http://mi.eng.cam.ac.uk/~mg436/

References

Gašić and S. Young “Gaussian Processes for POMDP-based dialogue manager opimisation”, IEEE Transactions on Audio, Speech and Language Processing, 2014

Gašić, F. Jurcicek, B. Thomson, K. Yu and S. Young. “On-line policy optimisation of spoken dialogue systems via live interaction with human subjects”, ASRU, Hawaii, 2011

Career Path: Blending research and patient care as a GP

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Career
The World Health Organisation defines general practice as providing ‘continuous, comprehensive, co-ordinated and personalised whole-patient care to individuals, families and their communities’. As soon as I completed my clinical training I joined a GP training scheme in Oxfordshire, and have loved being a GP for more than 30 years since then. At its core it’s about being comfortable with being a ‘generalist’ and have some expertise across all clinical conditions, rather than a being a ‘specialist’ with in-depth expertise in one (often very focused) area. When patients first seek help in primary care their problems may be vague or ill-defined- a GP’s expertise lies in working out whether this needs further investigation or referral, or whether the patient can be reassured. One of the most fulfilling parts of being a GP is that we often care for a number of family members over many years. Interestingly, analyses of data from the US, UK and Europe have shown that having more GPs is associated not only with better health outcomes, but also with better patient experience.

After working as a GP partner for more than 12 years, I moved to Cambridge and soon met the newly appointed Foundation Chair of General Practice (Ann Louise Kinmonth, also a New Hall alumna) – she encouraged me to consider a clinical academic career. While continuing to work as a part-time GP, I completed a Masters course and then a doctorate. I was fascinated to find that most of the evidence that we used to care for our primary care patients had arisen from less relevant research from specialist care, and that there was a real need for evidence from the primary care setting.

I now lead the Primary Care Cancer Research group at the University of Cambridge- so, it’s never too late for a mid-career change!

While the career of an academic GP can be demanding, it is also very rewarding. I still work as a GP, but only for one day a week. The rest of the week is spent mainly on research, with some under- and post-graduate teaching. My research focuses on developing patient and GP interventions to help diagnose cancer earlier, as there is plenty of evidence that, for most cancers, a timely diagnosis allows curative treatment and better outcomes. Current projects are researching cancers of the skin, oesophagus, stomach, brain, breast and pancreas. I feel very privileged to work alongside world leaders in cancer screening, early detection and treatment on the Cambridge Biomedical campus, and some of my research findings have already led to changes in NHS guidance for patient care.

What’s next? My research will continue to focus on new and cost-effective approaches for preventing and diagnosing cancer.

One example is the impact of technological advances on patient access to health information, and on the monitoring of symptoms and treatments by both patients and GPs. We need more clinical academics in general practice to take this important work forward.

Dr Fiona Walter MA MD FRCGP
Alumna

Fiona Walter (New Hall 1976) is Principal Researcher (Reader) in Primary Care Cancer Research at the University of Cambridge. She leads studies investigating cancer prevention, diagnosis and follow-up care, was Fellow of Lucy Cavendish College, Cambridge, and is Honorary Clinical Associate Professor at the University of Melbourne, Australia.

Career Path: The fascination of neuroscience and the teenage brain

7A Stephanie Burnett Heyes (2 - portrait)

CareerMy life as a scientist is varied, hectic and rewarding. I feel tremendously privileged to be doing what I do.

I’m a cognitive neuroscientist, which means that I study the brain and the mind. To do this, I use a mixture of psychology and brain imaging.

The teenage brain and mind is my main research topic. I’m interested in finding out how basic mental abilities, such as short term memory, as well as more complex abilities, such as understanding emotions and social situations, alter during the teenage years. Sometimes, I use brain scanning to look at their neural basis.

Studying teenagers is hard. A teenager is so similar to an adult that it’s hard to spot the difference. Many changes take place during adolescence, so collecting good data and interpreting it correctly can be a challenge.

But… it’s both interesting, and important. Many mental health problems begin to take root during the teenage years, and we have very little real understanding of why this is and what we can do about it.

So that’s the big picture. But what do I do all day?

Short answer: A lot of different things. Here are some of the things I’ve been working on just this week:

  • Designing experiments.  This uses a unique mix of scientific reasoning and creative insight.  In science, there is a rule book but no manual.  You have to follow scientific principles to come up with something new.  At the moment, I am experimenting with combining psychology, game theory and social network analysis.
  • Learning a new coding language.  Coding is using maths to make computers do stuff for you.  I use it to analyse data and build experiments.  When I started doing science, I thought I was rubbish at coding so I should leave it to the experts.  Then I learned that everyone has to start somewhere.
  • Writing. When I’ve done an experiment and it worked, I write it up and send it to a journal. I find this hard. Luckily, scientists tend to work in teams, so when I get stuck I ask my colleagues for help. I work with some amazing people and I really respect their opinions. Sometimes, when I’ve sent a paper off, the journal sends it back with critical comments. Then I have to construct a watertight argument that will win them over. This is fun – like intellectual sparring.
  • Teaching university students. I write lectures, mark essays, and meet with students to give guidance on assignments and check they’re ok. Sometimes students are having a hard time for various reasons. If I can do my bit to help them achieve their goals, I find that deeply rewarding.
  • Public speaking. I never thought I’d say this, but I really enjoy giving a talk in front of a couple of hundred people. If I feel nervous, I interpret it as excitement. I prepare properly and I practice what I’m going to say. I get a real buzz if it goes well.

I don’t know any other job that has such a variety of activities in a single week. I honestly think I’d get bored doing anything else.

Dr. Stephanie Burnett Heyes
School of Psychology, University of Birmingham