Hey, I passed another exam!

· Posted in ISP

Today, I have completed three years of having a blog. I took to blogging as a way to document my PhD experiences (and for learning to write :D). Though, it was very satisfying to see tens of thousands of visitors finding posts of their interest here. As a coincidence I also passed my PhD comprehensive exam today and wanted to do a post to help future students. As a PhD student you take so many courses and exams but you still need to pass a few extra special ones. Different departments and schools have their own requirements but the reasoning behind having each of these milestones is the same.

ISP has three main exams on a way to PhD. You first finish all your coursework and take a preliminary exam, or prelims, with a 3-member committee of your choice. The goal here is to prove your ability to conduct a research paper by presenting the work you’ve done till then. At this point, you already have or are on your way toward your first publication in the program. After taking this exam and completing the coursework, you are eligible to receive your masters (or second masters) degree.

This is how an average timeline for a PhD student in my department looks like.

This is how a typical timeline for a PhD student in my department looks like. Of course you can expect everyone to have their own custom versions of it.

Next is the comprehensive exam (comps). The committee structure is similar to prelims, but here you first pick three topics related to your research and decide a member responsible for each. By working with your committee members, you prepare a reading list of recent publications, important papers and book chapters.

Each of the committee members will select a list of questions for you to answer. You get 9 days to answer these questions. It may be challenging to keep up with all the papers in the list if it has a lot of items. Usually it is a good idea to include those papers that you have referred to in your research.

I immensely enjoyed this process and was reminded of the Illustrated guide to a PhD by Matt Might. Specially the one about “Reading research papers takes you to the edge of human knowledge”. If you haven’t seen those posts and intend to pursue a PhD, I would definitely recommend them.

Most of the questions in my exam were subjective, open-ended problems. Except the first one which made me wonder if I was interpreting it correctly. I guess, it was only there as a loosener [1] .

After you send in your written answers, you do an oral presentation in front of all three committee members. I was also asked a few follow-up questions based on my responses. Overall, it went smoothly and every one left pleased with the presentation.

Footnotes

  1. A term used in cricket for an easy first ball of the over ^


Learning from multiple annotators

· Posted in HCI, ISP, Machine Learning

I recently prepared a deck of slides for my machine learning course. In the presentation, I talk about some of the recently proposed methods on learning from multiple annotators. In these methods we do not assume the labels that we get from the annotators to be the ground truth, as we do in traditional machine learning, but try to find “truth” from noisy data.

There are two main directions of work in this area. One focuses on finding the consensus labels first and then do traditional learning, while the other approach is to learn a consensus model directly. In the second approach, we may estimate the consensus labels during the process of building a classifier itself.

Here are the slides for the presentation. I would be happy to receive your comments and suggestions.


Talk: Human-Data Interaction

· Posted in HCI, ISP, Talks

This week I attended a high energy ISP seminar on Human-Data Interaction by Saman Amirpour. Saman is an ISP graduate student who also works with the CREATE Lab. His work in progress project on the Explorable Visual Analytics tool serves as a good introduction to this post:

While this may have some resemblance with other projects such as the famous Gapminder Foundation led by Hans Rosling, Saman presented a bigger picture in his talk and provided motivation for the emergence of a new field: Human-Data Interaction.

Big data is a term that gets thrown around a lot these days and probably needs no introduction. There are three parts of the big data problem, involving data collection, knowledge discovery and communication. Although we are able to collect massive amounts of data easily, the real challenge lies in using it to our advantage. Unfortunately, we do not enough sophistication in our machine learning algorithms that can handle this as yet. You really can’t do without the human in the loop for making some sense of the data and asking intelligent questions. And as this Wired article points out, visualization is the key for allowing us humans to do this. But, our present-day tools are not well suited for this purpose and it is difficult to handle high dimensional data. We have a tough time to intuitively understand such data. For example, try visualizing a 4D analog of a cube in your head!

So now the relevant question that one could ask is that if Human-data interaction (or HDI) really any different from the long existing areas of visualization and visual analytics? Saman suggests that HDI addresses much more than visualization alone. It involves answering 4 big questions on:

  • Steering To help in navigate the high dimensional space. This is the main area of interest for researchers in the visualization area.

But we also need to solve problems with:

  • Sense-making i.e. how can we help the users to make discoveries from the data. Sometimes, the users may not even start with the right questions in mind!
  • Communication The data experts need a medium to share their models that can in-turn allow others to ask new questions.
  • And finally, all of this needs to be done after solving the Technical challenges in building the interactive systems that support all of this.

Tools that can sufficiently address these challenges are the way to go in future. They can truly help the humans in their sense-making processes by providing them with responsive and interactive methods to not only test and validate their hypotheses but also communicate them.

Saman devoted the rest of the talk to demo some of the tools that he contributed towards and gave some examples of beautiful data visualizations. Most of them were accompanied by a lot of gasping sounds from the audience. He also presented some initial guidelines for building HDI interfaces based on these experiences.


Talk: The Signal Processing Approach to Biomarkers

· Posted in ISP, Machine Learning, Talks

A biomarker is a measurable indicator of a biological condition. Usually it is seen as a substance or a molecule introduced in the body but even physiological indicators may function as dynamic biomarkers for certain diseases. Dr. Sejdić and his team at the IMED Lab work on finding innovative ways to measure such biomarkers. During the ISP seminar last week, he presented his work on using low-cost devices with simple electronics such as accelerometers and microphones, to capture the unique patterns of physiological variables. It turns out that by analyzing these patterns, one can differentiate between healthy and pathological conditions. Building these devices requires an interdisciplinary investigation and insights from signal processing, biomedical engineering and also machine learning.

Listening to the talk, I felt that Dr. Sejdić is a researcher who is truly an engineer at heart as he described his work on building an Asperometer. It is a device that is placed on the throat of a patient to find out when they have swallowing difficulties (Dysphagia). The device picks up the vibrations from the throat and does a bit of signal processing magic to identify problematic scenarios. Do you remember the little flap called the Epiglotis that guards the entrance to your wind pipe, from your high school Biology? Well, that thing is responsible for directing the food into the oesophagus (food pipe) while eating and preventing it from going into wrong places (like the lungs!). As it moves to cover the wind pipe, it records a characteristic motion pattern on the accelerometer. The Asperometer can then distinguish between regular and irregular patterns to find out when should we be concerned. The current gold standard to do these assessments involve using some ‘Barium food’ and X-Rays to visualize its movement. As you may have realized, the Asperometer is not only unobstrusive but also appears to be a safer method to do so. There are a couple of issues left to iron out though, such as removing sources of noise in the signal due to speech or even breathing through the mouth. We can, however, still use it in controlled usage scenarios in the presence of a specialist.

The remaining part of the talk briefly dealt with Dr. Sejdić’s investigations of gait, handwriting processes and preference detection, again with the help of signal processing and some simple electronics on the body. He is building on work in biomedical engineering to study age and disease related changes in our bodies. The goal is to explore simple instruments providing useful information that can ultimately help to prevent, retard or reverse such diseases.


Talk: Understanding Storytelling

· Posted in ISP, Talks

This week I attended a very interesting talk by Dr. Micha Elsner. Yes, this was one of those full-house ISP seminars. I was glad that I reached the venue a bit earlier than the usual. Dr. Elsner started his talk by giving us an overview of the bigger goals he is looking at. His work is helping us formally understand storytelling and develop computational methods for it. If you have ever used Auto Summarize in Word, you’ll have an intuitive idea about how it works: It finds sentences with frequently used words to make a summary of the document. It can generate satisfactory summaries for articles that merely state some facts, but would fail miserably in trying to understand and summarize a story.

Dr. Elsner’s approach focuses on observing social relationships between characters as the story unfolds, to understand the high level plot. He uses two basic insights about common plots in a story: a) it has an emotional trajectory, i.e. over time, we see a variation in negative and positive emotions, and b) characters interact with each other and have a social network just like in real life.

To begin his analysis, Dr. Elsner would first parse the text to identify characters from the noun phrases in the sentences. This step itself is not an easy one. For example, one character may be referred to by several different names through the chapters like – Miss Elizabeth Bennet, Miss Bennet, Miss Eliza, Lizzy and so on. Once we have that, we could try understanding the relationships between different characters over course of time. Simple functions measuring nearby mentions (co-occurrence) of the characters and their emotional trajectory curves are used to build a complex similarity measure. Emotion trajectory is plotted by finding words with “strong sentiment” cues. This makes up the first-order character kernel for measuring similarity. Now, he adds social network features to build the second order kernel. Characters are more similar if they each have close friends who are also similar.

I think that the method for testing the similarity the proof of concept was also an ingenious one. Dr. Elsner artificially re-orders the chapters of a book, and attempts to distinguish it from the one in the original form. Success here would imply that we indeed been able to gather some understanding about a plot by using this method. A corpus of novels from Project Gutenberg is used as a training data for this purpose. Do go through the links in the section below to find out more!

Further Reading

  1. Micha Elsner. Character-based Kernels for Novelistic Plot Structure. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), Avignon, France. Available: http://aclweb.org/anthology-new/E/E12/E12-1065.pdf
  2. Presentation slides are also available on Dr. Elsner’s page: http://www.ling.ohio-state.edu/~melsner/slides/novelpres.pdf

Talk: ISP Seminar

· Posted in ISP, Machine Learning, Talks

Turn-Taking Behavior in a Human Tutoring Corpus by Zahra Rahimi

In their research, Zahra and Homa, analyze turn-taking behavior between students in a human-human spoken tutoring system. This analysis could be helpful in understanding how users from different demographics interact with a tutor. In this study, they use sequences of speech and silence over time to mark ‘Active’ and ‘In-active’ states the dialogues between the tutor and the student. Considering both the tutor and student together we have four different combinations of these states, in which each one of them being active or inactive. The next step is to learn (using a semi-Markov process) a model from the dialogues. Using this model, they are able to measure the association of these models with features such as gender, scores obtained in the pre-test etc. The experiments provide some interesting results such as female students speak simultaneously longer with the tutor than male students; while their activities are less than their male counterparts. Also, for the students with a lower pre-test scores, the tutor tended to speak for longer time.

Content-Based Cross-Domain Recommendations Using Segmented Models by Shaghayegh Sahebi

Sherry presented her work on the job recommendation systems in her talk. This was done as part of her internship at LinkedIn last summer. The site originally used a single model to make job recommendations to the users by selecting features from their profiles. But, these profiles tend to vary a lot according to the job function the users play and the industry they are in. Professionals in academia, for example, may put a very different set of information on their resume as opposed to a banking executive. With this new study, they wish to segment users using these very features (current job function and industry etc.) before sending them to the recommender systems. This allows them to develop an efficient method of feature augmentation and adapt their algorithms.

The model was built and evaluated based on some pre-collected data. They evaluated the accuracy of the system in recommending the jobs that the users applied to. This, however, restricted them to a certain extent and an online A/B testing is still under process. We’ll have to wait and watch for the results to find out if they do better than the one-size-fits-all model that is currently in place.

Further Reading

  1. Z. Rahimi, Homa B. Hashemi “Turn-Taking Behavior in Human Tutoring Corpus.” AIED 2013. Available: http://link.springer.com/chapter/10.1007%2F978-3-642-39112-5_111

Talk: Understanding Social Dynamics of Emergent Hashtag

· Posted in ISP, Social Computing, Talks

This post is about a talk titled, “#Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtag” by Dr. Yu-Ru Lin in the ISP Colloquium Series. You may browse all such posts under the Talks category in the archives.

Hashtags could be simply defined as words that are a prefixed by a “#” sign. They serve as a means to group meaningful messages together on social media. Twitter (and recently Facebook) makes it possible for users to search for specific hashtags to look at all the relevant posts on a topic. While Twitter wasn’t the first to use this concept, it has unarguably gained more popularity since its use on the micro-blogging site.

Dr. Lin’s research concerns with studying the rise of new hashtags (such as #bigbird) during the 2012 US Presidential Election debates. She presents an analysis on the emergence and evolution of such hashtags and in turn the topics that they represent. Posts were analyzed during the periods when new never-before-used hashtags were created, used and shared by other people.

Since different people may be tweeting on the same topic around the same time, we can have several different candidates (eg. #bigbird, #supportbird, #savebigbird etc.) but a few gain more popularity amongst the fellow tweeters (or twitterers, take your pick!). Dr. Lin and her colleagues put them into two classes: ‘winners’ and ‘also-rans’. A ‘winner’ hashtag is considered to be the one that emerges more quickly and is sustained for longer periods of time.

Now the question to be asked is that what factors are influential in making a hashtag, a ‘winner’? Here are two of the important results from the study:

  • A hashtag is adopted faster when re-tweeted more. It also depends on the size of the audience that gets to read them.
  • More replies and diversity amongst the tweeters using them imply longer persistence.

I think that apart from the results above (which should be studied carefully by people involved in making promotional campaigns etc.), there is a lot more to take back from research like this. It not only gives us insights into the dynamics that come into play on social networks (which may be interesting to the social sciences researchers) but also give us tools and methods to analyze big data. It serves as example data-driven computational and statistical approaches to make sense of the conversations on social networking sites like Twitter.

Further Reading

  1. Y.-R. Lin, D. Margolin, B. Keegan, A. Baronchelli and D. Lazer, #Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtag, In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM 2013), 2013. Available: http://arxiv.org/pdf/1303.7144v1.pdf