EE6367 Topics in Data Storage and Communications

1. Assessment (tentative):
2. Instructor:
3. Class timings:
4. Primary references:
5. Tentative list of topics
6. General comments regarding presentations/reports
7. Academic honesty and plagiarism

Welcome to EE6367 (Topics in Data Storage and Communications).

This is the official webpage of the EE6367 course for 2023. All classes will be conducted in person. Online resources and references will be shared here. Much of our online interaction (homework submissions, announcements, off-classroom discussions, etc) will be on Google classroom. Invites will be sent to registered students by the first week of class. If you have not received an invite by the second class, please send me an email.

This is an elective course where we will go over some basic principles underlying a class of problems on storage and communications. Each year will follow a particular theme. The theme for this year is statistical inference with communication constraints.

With the advent of smartphones, low-cost sensors and smart devices, we are generating huge volumes of data. This has brought about the need for efficient algorithms that allow us to learn from this. A key feature in the settings mentioned above is that data is generated in a distributed fashion, but learning is done by a central entity. A simple example is to design predictive text algorithms for keyboards, or automatic face recognition from smartphone images. Sharing data directly requires a huge amount of communication, and in many cases violates privacy. This has led to new techniques such as federated learning.

In this course, we will explore the problem of distributed statistical inference using a mathematical approach. We will focus largely on distributed estimation wherein a large number of users observe samples drawn from a distribution, and a central entity wants to estimate a parameter. We will assume that each user can send only a limited number of bits to the central entity, and the goal is to design algorithms, and obtain fundamental bounds on the tradeoff between accuracy and communication constraints.

The course will be mathematical in nature, and some of the assignment questions will involve programming.

Prerequisites

Strong foundation in probability and random processes
Strong foundation in information theory
Prior exposure to detection/estimation theory is helpful, but not mandatory
Comfort with programming in python

1. Assessment (tentative):

Each student will be expected to

attend classes and participate actively
scribe lecture notes
solve homeworks
present a paper

Scribing lecture notes	25%
Homeworks	50%
Paper presentation	25%

2. Instructor:

Name	Dr. Shashank Vatedka
Email	shashankvatedka@ee.iith.ac.in
Office	446, Academic block C

3. Class timings:

Slot B: Mondays 10:00-11:00, Wednesdays 09:00-10:00, Thursdays 11:00-12:00
Class venue:

4. Primary references:

5. Tentative list of topics

Brief introduction to detection and estimation theory
Fundamentals of lossy compression and rate distortion theory
Techniques for efficient lossy compression with mean squared error distortion
General techniques for proving lower bounds
Distributed mean estimation with limited communication

6. General comments regarding presentations/reports

In addition to learning cool stuff, you should also learn to present your work. The final presentation is an opportunity to develop your presenting skills. You must be able to convey your ideas and thoughts clearly to another person. Here are some general comments for making good presentations:

Make sure that you acknowledge all the references/material that you used (papers/code/etc.) While it does not matter much for the purposes of this class, this is very important to keep in mind when you are delivering presentations at bigger venues
In a presentation, always introduce yourself and your team members/collaborators in the very beginning.
If much of the presentation is based on on one/two papers/references, explicitly state which references you are using in the first/second slide.
Avoid saying “We did this/we showed that…” when you are in fact talking about some other authors’ work. Unless you have derived something/programmed something yourself, always say “they/the authors did/showed/observed that…”
A presentation/report is like telling a story: even before preparing the slides/document, imagine that you want to explain the work to a friend in 15-20 mins, identify exactly what you want to convey, and only then proceed.
Make sure that you explain the problem clearly.
When reading a paper, you should first understand what the problem is, what makes it challenging, and why it is worthwhile solving the problem. Then, you should identify what is new about the paper, and what the contributions are. To understand the rest of the paper, you should then try to understand the constructions/proof techniques/ideas. Your presentation should also bring out these aspects. See this article and this one on how to read a paper.
There is no single best rule for presentations, but generally in any presentation, the viewer/audience’s attention is maximum in the first 5-10 minutes and then gradually decreases. So it is helpful to get to the punchline in the first few minutes.
Citations should be treated differently in papers and slides. When you are making a reference to a particular paper/book/website (for e.g., when you say that result A can be found in paper B) in your slides, mention the paper/book/website as a footnote rather than saying [1], and putting the reference in the last slide.
Do not put too much text on your slides. This is bad practice. Each slide should contain at most 3-5 points. You should absolutely avoid long sentences.
Do not copy text directly from the paper.
Do not read out your slides word-to-word.
Similarly, do not put too many lemmas in a slide. Each slide should convey a single idea. Unless very important, do not put too much math/long derivations in a short presentation. It is more important to convey ideas/intuition. But at the same time, there should be a significant amount of technical content. It is therefore very useful to instead explain using examples.
Make sure that the fonts/diagrams/labels/plots are clear. Label all the axes on your plots.
Speak clearly, and make sure that the audio quality is fine. Check your mic volume. Do a couple of dry runs.
If you are using Google meet to record, then make sure that you “pin” the slides since meet might automatically switch displaying you/your icon instead of the presentation.
Make sure that you check, and double check the slides and report for typos, technical and grammatical errors.
If your group has more than one person, then the presentation must be shared equally.

7. Academic honesty and plagiarism

Students are encouraged to discuss with each other regarding class material and assignments. However, verbatim copying in any of the assignments or exams (from your friends or books or an online sources) is not allowed. This includes programming assignments. It is good to collaborate when solving assignments, but the solutions and programs must be written on your own. Copying in assignments or exams, will be dealt with severely.

See this page (maintained by the CSE department), this page, and this one to understand more about plagiarism.