Archive for the ‘students’ Category

h1

To good health!

January 10, 2018

So enrolment sessions start soon for our incoming Master of Data Science students.  I know its a stressful time for some students in terms of “life”.  I usually talk briefly about staying healthy, and Monash offers various services to support this.  But for PhD students I think its important to take this on as a lifestyle objective.  They are undertaking a knowledge intensive career path and brain health will be critical for their future career.

Disclaimer:  Now, this page is full of opinions and pointers to, in some case, controversial material.  I’m just a little old computer science professor, so my opinions have no real backing, and I have no recognised expertise. All care but no responsibility for what I say! 

The fact is, keeping health and understanding how to keep healthy in the modern world is a subject fraught with challenges.  To understand this, consider the following:

  1. The official Australian government position on colds and flu prevention, and the official USA government position:  hygiene and vaccines.  What’s missing:  discussion of healthy diet and exercise to strengthen and repair the immune system.
  2. The Time magazine reports extensive research shows vitamin D helps prevent colds and flu, so some sunshine is also important.  No mention of this in the official government positions above!
  3. Believe it or not, in the USA prescription drugs are the third leading cause of death!  There is a larger issue here in that most published medical research findings are false.  Note, I see this is a systemic thing not limited to medical research, and the medical research community given its importance has extensive, concerted efforts like systematic reviews to address the issue.
  4. Tobacco science is a term used to describe fake science protecting an industry.  Read about the Tobacco Institute and see the movie The Insider.  How much of this goes on in the food industry?
  5. Sugar is now known to be very damaging to the health.  Here is a hard hitting discussion about it, though note quite a few of these claims are considered controversial.  But it is known that sugar suppresses the immune system.  Figuring out your sugar consumption is challenging.   There are rumors (in movie form) of tobacco science going on here too.
  6. Energy drinks rot the teeth, like soft drinks.  Its due to the high acid content.  Its certainly not clear they give any energy.
  7. Artificial sweeteners are not a substitute, in fact evidence suggests they have poor health impacts, and they mess up the brains analysis of your food intake.
  8. Fats are the subject of a massive onslaught from advertisers.  For years we were told to avoid butter and use margarine instead, but now it seems butter is good.  Current conflicting advice is being broadcast about the humble coconut.
  9. The health of organic produce is currently a propaganda battleground.   None other than former tobacco scientist Henry I Miller (he was a founder of TASSC) has claimed its an expensive scam.  Hint:  organics are also lower in toxic pesticide residue, but no mention of that.
  10. The commercial world has taken on healthy eating big time, and it is the fastest growing segment of the food industry.  Monash University has done a wonderful job of getting really good fast food vendors at the Caulfield campus food court.

Summary:  There is lots of conflicting and bad advice out there!  Heck, even the government websites seem to have errors of omission.

Now, if we consider the specific position of someone who wants their brain to function well, then consider the following:

  1. Short term exercise is known to boost mental performance.
  2. Meditation and mindfulness is also known to boost performance in exams.
  3. Long term sitting is considered to be as bad for health as smoking!  Here is a poster of the dangers.
  4. There are also lifestyle recommendations about studying from scientists:  don’t cram for subjects, learn slowly over the semester.
  5. Recent studies show the brain can be encouraged to grow new cells.
  6. The brain is mostly fat, so we need healthy fats to work well.  Don’t believe a lot of what you read about fats!  Cholesterol is also important for the brain.
  7. Sugar consumption (e.g., soft drinks, commercial juices, commercial cereals, flavoured yogurts, etc. etc. etc.) is bad for the brain, as well as the immune system.
  8. Canola oil is bad for the brain.  This one is important because most cheap salad oils, margarines and many food products are loaded with it.
  9. All sorts of food and chemicals are bad for the brain.  Here’s a TEDx talk on details.  Note TEDx means not official (is this a reliable information source?).
  10. Deep sleep is the basis for memory, learning and health.   In particular, without deep sleep, your brain will not be functioning properly and your memory will be impaired.  Here is a disquieting Google talk on health and sleep (along the lines of the hideous anti-smoking adverts some countries have), but there are many more on this.
  11. Adult neurogenesis is the process by which we adults gain new brain cells.  Not surprisingly, this is very popular amongst the Silicon Valley crowd, and I suspect is also a domain where snake-oil salesman like to peddle.  Nonetheless, here is a video on it:  a TED talk.

Note, for each of these, there are 10’s-100’s of good articles and scientific literature to back it up, though oftentimes conflicting scientific literature as well.  I’m just giving generally readable and somewhat respectable accounts.  A lot of these issues remain controversial, and possibly there is some tobacco science going on, but its hard for us non-experts to really know.

Anyway, I hope from this you understand the complexity of trying to stay health, and trying to keep your brain functioning well in the modern world.

I’m probably a bit extreme but I say,

About eating and food:

  • Try and cook your own meals from real ingredients.  After a while, it becomes easy and its a great way to wind down with friends.
  • If someone’s great grandmother (anyone’s, Fiji, Vietnam, Sweden, …) didn’t make the food 100 years ago, its probably not good for you.
  • Don’t take dietary or health advice from Big Food.  In fact, looking at the government advice (listed above) on the flu, I’d say their’s is missing some major points too for some issues.
  • Try and avoid packaged meals, fast food, and canned and bottle drinks.  Likewise, avoid most commercial fruit juices which have way too much sugar and have lost too much of the fabulous nutrients in the original fruit.
  • Go low sugar, low refined carbohydrates and healthy fats.  Its a lifestyle thing, not a diet.  Once you do, you’ll discover all the amazing subtle flavours you’ve missed from traditional foods and realise how horrible standard breads, sweet deserts, snack bars and cakes really are:  the sugar masks the real flavour, and it gives you a longer term bad after taste, and refined carbohydrates have removed a lot of the flavours
    • Healthy fats is challenging to maintain because Big Food likes to put unhealthy canola oil in everything:  most salad oils, hummus and deli mixes are mostly canola oil, as is margarine.
  • Just avoid artificial sweeteners.  Once you’ve gone cold turkey and got off the sugar addiction you wont be craving it and you’ll feel better for it.
  • Health slogans on food products, “low fat”, “low cholesterol” often mean its bad for you!   Low fat usually means high sugar, for instance.

About other aspects of health:

  • Get exercise, and make it a lifestyle thing.  When you’re older, you’ll discover you cannot function well as a knowledge worker without it.
  • Don’t sit at your desk for long hours.  You need to get up and move around every hour!  Also, become aware of your posture.   Don’t become a hunchback!  Some 2nd years are already heading that way.
  • When you’re mentally worn out, a quick nap or a brisk walk does wonders, and both have scientific backing.
  • Make sure you are getting proper sleep.  That can mean organising your assignments and study properly so you don’t need a to do a bunch of all-nighters to get through.  But it also means setting up the right environment at home for sleep.
  • I know of few cases where drugs and alcohol support good health or brain functioning, including so-called smart drugs or nootropics.  Most are dangerous to the liver, as are many medicines.  Headache and pain medicine is far more dangerous and damaging than many other things!
  • Routine … that’s what the body needs.  For sleep, for eating, for study, for exercise, routine is critical part of making it function well.

Anyway, I have said too much already.  In case you’re wondering, I am now on holidays.  No time for a Data Science professor to talk about this stuff during semester!  But keep in mind, I have no qualifications or expertise when it comes to health.  These are mere opinions!

Advertisements
h1

Picking Conferences

January 7, 2018

As a PhD student starting out, you do have some career options.  Likewise, as a typical junior academic, with limited budget and research time, you have similar career options.  A main one which I’ll discuss here is:  Which conference(s) should I got to?  This is peculiar to computer scientists whose conferences are competitive publications (say 20-25% acceptance rate) and count as publications.

So you only get time to attend a few conferences.  Likewise, you only get time to write papers for a few.  So you want them to count.  Conferences each have their own style.  Best way to think of it is that a conference is a tribe where membership is part-time.  You have to take time to learn about the habits and preferences of the tribe, i.e., in terms of paper content.  If the tribe always starts off with 20% of detailed theoretical definitions then you have to as well.  If they do certain kinds of experiments, then so should you.  Think of these sorts of things as tribal markings.  To be innovative, you generally need to do so from inside the system.  I know this sounds conformist, and belief me, I am completely non-conformist myself, but generally its how conferences work, largely as a result of the reviewing system. If a trusted member of the tribe starts quoting classical, venerated philosophers, so will the others.  If a complete unknown person submits a paper quoting venerated philosophers, then it’ll be viewed as weird unless they have enough other tribal markings on their work to accepted.

I have a number of conferences I really like where I understand the general tribal markings and am happy to live with them.  So SIGKDD has solid experimental work, ICML has innovative new methods, ACL has applications of machine learning to real linguistic problems.  They sometimes have additional tribal markings that can be more or less problematic.

Anyway, as a junior academic, you have to target a few conferences and learn to become a reliable tribal member.  You might want to pick a few authors and build on their work.  Or you might want to pick a specialised problem.  Regardless, to publish in particular venues you’ll have to get to know the tribal preferences and adhere to them.  Doing good research is one thing, and really good research will usually speak for itself, but if your contribution is not outstanding, say “merely” at the top 25 percentile of work, then you have to follow the tribe to be accepted into the tribe.  That takes time.

Moreover, the vibe at the conference is always much, much more than the printed proceedings.  You need to be there:  hear the questions, watch the audience, chat to others in the breaks, see the quality of the presenters.  What is important and influential?  What is losing out, perhaps because it was trendy rather than productive?  All this happens at the conference.  You need to be there to see it.  Otherwise, you’ll be a year behind the others … new ideas for next year’s conference are often the germ of an idea at this year’s conference.  Moreover, it always helps to see the movers and shakers in action.  What sort of people are they?  How do they present their work?

So what does this mean to the junior academic?  You need early on to target a particular conference, subject or influential author’s/group’s body of work, and learn what it is they do.  That’ll take time.  So if you don’t see yourself as being involved in that community 5 years down the track, you probably shouldn’t be making that effort.  If you think their research doesn’t have a good future, then again, you probably shouldn’t be making that effort.  Pick some conferences with this in mind, and try and go along semi-regularly to keep track of things and pick up the vibe.

h1

MDSS Seminar Series: Doing Bayesian Text Analysis

August 4, 2017

Giving a talk to the Monash Data Science Society on August 28th.  Details here.  Its a historical perspective and motivational talk about doing text and document analysis.  Slides are here.

h1

On the “world’s best tweet clusterer” and the hierarchical Pitman–Yor process

July 30, 2016

Kar Wai Lim has just been told they “confirmed the approval” of his PhD (though it hasn’t been “conferred” yet, so he’s not officially a Dr., yet) and he spent the time post submission pumping out journal and conference papers.  Ahhh, the unencumbered life of the fresh PhD!

This one:

“Nonparametric Bayesian topic modelling with the hierarchical Pitman–Yor processes”, Kar Wai Lim , Wray Buntine, Changyou Chen, Lan Du, International Journal of Approximate Reasoning78 (2016) 172–191.

includes what I believe is the world’s best tweet clusterer.  Certainly blows away the state of the art tweet pooling methods.  Main issue is that the current implementation only scales to a million or so tweets, and not the 100 million or expected in some communities.  Easily addressed with a bit of coding work.

We did this to demonstrate the rich possibilities in terms of semantic hierarchies one has, largely unexplored, using simple Gibbs sampling with Pitman-Yor processes.   Lan Du (Monash) started this branch of research.  I challenge anyone to do this particular model with variational algorithms 😉   The machine learning community in the last decade unfortunately got lost on the complexities of Chinese restaurant processes and stick-breaking representations for which complex semantic hierarchies are, well, a bit of a headache!

h1

Basic tutorial: Oldie but a goody …

November 7, 2015

A student reminded me of Gregor Heinrich‘s excellent introduction to topic modelling, including a great introduction to the underlying foundations like Dirichlet distributions and multinomials.  Great reading for all students!  See

  • G. Heinrich, Parameter estimation for text analysis, Technical report, Fraunhofer IGD, 15 September 2009 at his publication page.
h1

Data Science Resources

October 26, 2015

For my main job, I am Director of the Master of Data Science.  This is a fast paced field that is just as much industry as academia, and a lot of the really exciting stuff is applications.  To keep up you need to monitor the media.  There are too many resources to name or list them all, or to attempt to do some kind of thorough tracking.  I recommend students, however, to install a news aggregator on their tablet/smart-phone/laptop and enrol in some of the better and more relevant RSS feeds, to keep track.

All the big business and technology magazines have relevant sections on Data Science or Big Data:  Forbe’s, Harvard Business Review, O’Reilly, ZDNet, MIT Sloan Management Review, Information WeekWired, InfoWorld, TechCrunch (big data) and TechCrunch (data science), … Each of these has a particular perspective, which is useful in understanding their contributions.  For instance, TechCrunch is a technology startup magazine whereas Forbes targets Fortune 500 companies.  The articles in this class of magazines usually are good quality, although there are sometimes “commissioned” journalism or press releases for marketing.

Many technology blogs focus on Data Science.  The following are listed as most popular first:  KDNuggets.comDataScienceCentral.com and its offshoot AnalyticBridge.comDatafloq.comAllAnalytics.comPredictiveAnalyticsToday.com, Dataconomy.com, 101.DataScience.community, DataScienceWeekly.org.  The first, KDNuggets has been in the business for almost two decades.  Many of these have email and RSS subscription services and Twitter feeds.  Some of these have a low signal to noise ratio so it is easy to get drowned in content.  See also Quora’s What are the best blogs for data scientists to read?” for more discussion.

There are two weekly newsletters that you should sign up to for great content in your email. The Data Science Weekly Newsletter has more of a technology orientation with, for instance, some popular machine learning content.  The O’Reilly Data Newsletter is more about industry and is essential reading for anyone who wants to remain current.

Most of the blogs are also coupled with curated information sources.  Other site with curated information are Resources to Learn Data Science Online and Big Data and Applications Knowledge Repository.  This second one also has a good list of conferences.

A related category are the question answering sites: Quora has Data Science and Big Data channels, though many other discussions are useful too.  A site more in the Slashdot style is Datatau.com.

Pinterest.com is a site that records infographics.  e.g., queries for “data science” and “big data“.  These are seductive, and some certainly informative.  Datafloq.com also has an infographics section.  Some notables here that go way beyond infographics are cheat sheets: Machine Learning Cheat Sheet and the Probability Cheat Sheet.  These are handy academic references, and also a nice way to find out what you do not know.

Many sites give collections of data sets, so perhaps the  most notable here are: aws.amazon.com data setsKDNuggets.com awesome public datasets, Google’s public data directory, Quora.com large data sets, …  The Internet Archive is a long running source of free digital content (books, etc.).  There are many, many more such sites, especially as governments now support open data.

Finally, most terms and concepts are well explained in the Wikipedia, often with good diagrams and related discussions.  As one delves into the more esoteric aspects of statistics or computer science, the quality of Wikipedia’s entries drop’s off.  Wikipedia’s definition of Data Science, for instance, as “a continuation of the field data mining and predictive analytics” would be hotly contested by some, but others would find the distinctions not that important.

WikiBooks has now produced Data Science: An Introduction, which I haven’t looked at properly yet but the outline seems OK.  I am skeptical of such efforts because the typical academic author has a focused speciality and a list of axes to grind … not me of course, oh no, not me 😉

h1

Some diversions

July 4, 2015

Quora‘s answers on What are good ways to insult a Bayesian statistician?   Excellent.  I’m insulted 😉

Great Dice Data: How Tech Skills Connect.  “Machine learning” is there (case sensitive) under the Data Science cluster in light green.

Data scientist payscales: “machine learning”‘ raises your expected salary but “MS SQL server” lowers it!

Cheat sheetsMachine Learning Cheat Sheet and the Probability Cheat Sheet.   Very handy!