SIGMOD 2020 conference experience

This is my brain dump of SIGMOD 2020 conference experience.

First of all, I really like virtual conference. I have been to conference once in the past. One big lesson learned from that experience is that there is no way to attend all sessions simply due to it’s not physical possible. Sessions are running concurrently at the same time. It’s cumbersome to navigate through the venue and get around the crowd to reach the session you like in time. However, with Zoom, the magic happens. I can open up all sessions I’m interested in and mute the speaker via drop audio setting in Zoom. If I find the topic I want to hear more, I can instantly switch to the desired Zoom window, reset the audio setting, and listen to the talk. Analogously, the experience feels like watching some streaming marathon on Twitch or watching the International from your laptop. Also, virtual means no visa headache 🙂 Another good thing about having sessions in Zoom is that I can easily ask questions whether via directly chiming in (Thanks Boon Thau Loo for promoting me to the panel to ask question live) or through typing. Asking questions in person offline can be challenge but being able to type questions online creates a relax environment for me to interact with speakers.

Another big advantage of virtual conference is the cost. Thanks to the COVID, this year’s SIGMOD is free. All I need is to sign up and then I can get into the internal system to attend any sessions I’m interested in. My perception on the academic conference is that everyone gathers at some fancy resort, enjoy the social interaction for a week, and then fly back home. I would imagine how costly this can be given the flight expense, hotel room, and registration fee. The zero cost conference means outreach; means reaching out wide audience. I would really love the organizer publish some stats on how many people actually virtually attend conference. I’ll be in huge surprise if we don’t see a huge number jump there. In addition, the free cost feels like welfare for me: I don’t have to pay a few hundred dollars to get myself motivated for the PhD journey ahead of me. I can see an accessible conference like SIGMOD this year will be a huge morale booster to someone who is struggling in their PhD and will motivate people to do good work.

Another big win for me is the recording part meaning that each presenter records their talk before hand and the session chair simply plays the videos one by one. In some session, I do experience some technical issue like there is video with no audio. But the issue is fixed within 5 minutes and the small distraction doesn’t impact the whole session experience at all. Pre-recording means high talk quality. The speaker can give his best performance for his talking. I think many of the speakers probably record their talks several times to pick the one they think can best deliver their idea in their work. Another great part is that the presenter is actually standing by to take questions and can give replies in the Zoom chat and even in the Slack channel several hours after the talk. This feature is very nice because we can keep the discussion in asynchronously fashion; both question and answer can be written out for further digest. Talking about Slack, I see PC chairs in SIGMOD and PODS are quite busy: you can see them across almost all Slack channels. Constantly saving questions and comments from Zoom to Slack to spark more discussion; spread Zoom link and session context. I think they deserve some kudos.

A Slack screenshot on a PC chair organized&pasted the content from Zoom to Slack

Being virtual means there will be a lot of writing communication: whether it is through Zoom chat or through Slack. This is huge benefit. I assume important information can easily get lost in the offline conversation. For example, it’s really too early for me to get up at 7am Friday to attend New Researchers Symposium given an 8 hours work ahead of me. However, thanks to being virtual, lots of discussion actually happen both during the Zoom live and more importantly, on Slack. People use Slack to ask questions and some panelist is nice enough to write their answer on Slack thread as well. This is good for me because now, during the lunch break, I can scan through the Slack channel and get some information from the past discussion. I figure this would not be possible if the conference is offline.

I’m not sure how the conference is run in the past but I think this year’s organizer puts huge effort to organize all-in-one page with Zoom link, Slack link, and schedule in one page so that I can easily find the information I want.

A peek of all-in-one schedule web page. Very useful.

As a first time academic conference attendee, matching actual people with their name in paper is a huge win for me. In some way, this does feel like handshake events for Japanese idol. Indeed, those “Japanese idol” are in fact quite approachable. I really do enjoy Anastasia Ailamaki’s smiley face from the camera and her persistent typing to answer the questions both from Zoom chat and Slack channel. There are several sessions I really like from this perspective are SIGMOD Plenary Panel: “The Next 5 Years: What Opportunities Should the Database Community Seize to Maximize its Impact?”, Mohan’s Retirement Party, and Industry Panel: Startups Founded by Database Researchers. So many names that I saw both from paper and from internal codebase. It’s very cool to see some roast and teasing online.

Social wise, I really like Zoomside Chat series. For example, “Zoomside Chat with Jian Pei”. It feels like coffee break and the topic is very relaxing. This is the place where some “ungraceful” question get asked. Also, “Zoomside Chat with Tamer Özsu” is also fun. I really wish there would be more time allocated for this type of social events.

Research wise, taking a look of the accepted papers beforehand is really helpful. On my laptop, I have a list of papers written on the Notes. I write down my thoughts and comments for each corresponding paper on the list. Due to my personal interest, Wednesday and Thursday sessions interest me the most. Luckily, my targeted papers spread out quite evenly through two days. My biggest regret is to not go through the papers I’m interested in beforehand. The result of not doing so is to get lost in sessions that may seem tangible to my research direction. This is worth improving for the next time to make most out of conference. Having said that, I’m still able to learn some useful benchmarks that I can run related to my research. Also, sitting through the talks (even lost) help me to further refine my paper list for future reference. Another thing I notice is that workshops are much nicer for learning. Research sessions usually have only 10 minutes for each speaker. Speaker has to move very fast and cover part of material on paper. However, workshop speaker has 25 minutes (at least for aiDM workshop) and the pace is much slower compared to normal research sessions. Lastly, attending sessions is a great way to discover the knowledge gap: even they may not relate to my research direction, it is still fun to learn for pleasure.

Some downside of this year’s virtual conference is Gather. I don’t know how useful it is for others but it is not quite useful for a working professional like me. First of all, my company “bans” the website: I can get into the room but it will take forever to load the venue floor plan and see other people. If I really want to use Gather, I have to disconnect from company’s VPN. I want to walk around the venue while waiting for the build. However, VPN-unfriendly Gather is not quite helpful here. Another disadvantage for virtual means I don’t have to participate “fully”: I can run errands; check work emails; fix some code bugs for the work. I don’t have to give out full energy to the conference. I guess this is really my bad.

Overall, I’m very grateful for this virtual experience at SIGMOD this year. The overall experience is excellent. I’m hoping they will do something similar next time; maybe partial virtual? However, I surely will miss the chance to see people and attend sessions. That motivates me the most to do good work because I want to attend next time (maybe as a presenter).

UPDATE (06/19/20):

Received an email from organizer

The last workshop has finished, and SIGMOD/PODS 2020 is now history. We suspect it will be a landmark in most of your minds, separating SIGMOD/PODS Conferences into those pre-2020 and those post-2020. Even before all the adjustments brought on by the COVID-19 crisis, we planned to stream more of the sessions. Our registration of ~3000 shows that there is high demand for online access to the conference. If our community is serious about fostering diversity and inclusion, then remote participation should become a permanent option.

Looking forward to the remote participation in the future.


This section collects some useful comments I gathered from Slack. It is for my future reference; might be useful to you as well.

From SIGMOD Plenary Panel: “The Next 5 Years: What Opportunities Should the Database Community Seize to Maximize its Impact?” on whether researcher should be “customer obsession” and solve real problem:

Joe Hellerstein11:19 AM
I’m going to take a somewhat different tack than @AnHai Doan.

I would never discourage work that is detached from current industrial use; I think it’s not constructive to suggest that you need customers to start down a line of thinking. Sounds like a broadside against pure curiosity-driven research, and I LOVE the idea of pure curiosity-driven research. In fact, for really promising young thinkers, this seems like THE BEST reason to go into research rather than industry or startups. The best reward is the joy of the idea.

But I think I share some of Anhai’s concern about improving the odds of impact and depth in our community.

What I tend to find often leads to less-than-inspiring work is variant n+1 on a hot topic for large n. What Stonebraker calls “polishing a round ball”. The narrow gauge for creativity in a busy area makes it really hard to find either inspiring insights or significant impact on practice; but at the same time the threshold for publication is often low because social factors in reviewing favor hot topics (competition, familiarity, and yes — commercial relevance of the topic, which can lead to boring research too!) That’s something we can try to address constructively.

Now I am guilty of going deep and narrow sometimes myself, e.g. in distributed transactions and consistency in the last many years. But it’s been rewarding and fun, and I like to think we had a new lens on things that let us hit paydirt a few times. Certainly outside my group, work like Natacha Crooks’ beautiful paper on client-centric isolation in PODC 17 demonstrates there is still room for major breakthroughs there. So some topics do merit depth and continued chipping away for gold.

Bottom line, my primary advice to folks is to do research that inspires you.

Joe Hellerstein  11:32 AM

To align with @AnHai Doan a bit more, if you are searching for relevance, you don’t need to have a friend who is an executive at a corporation. Find 30-40 professionals on LinkedIn who might use software like you’re considering, and interview them to find out how they spend their time. Don’t ask them “do you think my idea is cool” (because they’ll almost always say yes to be nice). Ask them what they do all day, what bugs them. I learned this from Jeff Heer and Sean Kandel, who did this prior to our Wrangler research, that eventually led to Trifacta. It’s a very repeatable model that simply requires different legwork than we usually do in our community.

AnHai Doan  12:51 PM

As for where to find the customers, I really like your suggestion. Another thing I may add is that one can go talk to the domain scientists in the SAME university. Many of them now have tons of data and are struggling to process them. These domain scientists are often sitting just ten minutes from one’s office, and they are dying for any help. Talk with them to really understand the kind of data problems they have. Often at the start those are very mundane basic problems, such as querying a big amount of data. But if one can help them solve those basic problems, then often many more interesting problems come up.

AnHai Doan  12:56

Yet another thing to do is go talk to companies in the SAME TOWN. They often are downed in data too and would love to get some help. One can very quickly get to know the kinds of problems they have. This has worked at least for me. My group started out working on data problems with several domain science groups at UW-Madison. We developed solutions that were used by them and in turn they gave us feedback. Then we took those solutions to local companies (insurance, health, heating/cooling, three companies), and they helped us improve the solution. Then we got funding to do a startup. This is perhaps also a possible roadmap.

New Researchers Symposium with a question asking about how to fix “the despicable but common toxicity of the database community in the tone of their reviews and often during in-person questions/discussions?” and ask about how to write a good review.

Joe Hellerstein7 hours ago
I sense there are stories here, and I’m sorry to hear this. In my experience, the face-to-face interactions in our community have gotten more professional over the years. I’m very sorry if the questioner has witnessed bad public behavior.

But in my experience the reviews have actually gotten worse in recent years. I believe we need a process change. The root of the problem is that reviewers are generally not held accountable for what they write.

One thing I learned in my startup is that team members who voice concerns are not very valuable — startups are risky by nature, and concern-mongering just contributes to negativity. However, team members who voice concerns and propose solutions are gold. We should ask the same of reviewers.

Anecdote: Doug Terry often signs his reviews. I went through an exercise last CIDR where I decided to try that, and I found instantly that I became much more helpful and constructive in my reviewing, even for papers that I did not recommend for acceptance. It didn’t feel OK just to criticize; I felt more responsible to suggest and encourage changes.

I’ve heard arguments why this isn’t a reasonable global solution—e.g. junior researchers could face retribution for signing negative reviews. But we might consider other mechanisms for ensuring that the reviewers are (a) held to account and (b) required to be constructive.

My critical mistakes in Academia and reflections

This post is a summary and reflection of the critical mistakes I have made throughout my post-secondary academia career. This is a gift for my child (if there is one) and it might be helpful for others.

Diverse interests without focus

I have three majors from economics, computer science, and mathematics after I finish my undergraduate degree. I often get wowed from other people. However, the more I focus on one field, the more I feel three majors are diversified enough to have no focus. Even I have a major in computer science, I didn’t take courses in computer architecture, operating systems, networks, compilers, which are essential courses for a computer science major. I have to take hard way to catch up with those missing material: reading classics. This process takes a long time and I’m still on my way finish studying them. If I have an end goal of becoming a computer scientist, there certainly no need to obtain a major in economics and I should become more focus on the mathematical branch related to topology, combinatorics, and logic. Taking extra unnecessary courses may not be the only waste. Lacking of background incurs extra cost during the PhD and job applications. Even though I manage to secure a position in industry, I still need lots of work to catch up.

Doesn’t know the end goal

Even though I have foreshadowed this point in the previous paragraph, I want to emphasize how important it is to know the end goal. Ideally, people should discover their interests from high school. However, start the exploration in college is not too late. But, the exploration should end after freshman so that there is enough time to become specialized and concentrate on something. Knowing the end goal cannot happen immediately but at least, it should happen by Junior. During the college, I hopped around three majors with no common theme at all: what I want to do for my future? I avoid to answer this question by taking the majors that may seem to offer the greatest flexibility in the future. However, the cost of doing so is the lack of depth. In addition, I didn’t know what I want to do for the computer science career: research or software engineer? That leads to one huge mistake detailed in “Failure in seizing ‘the’ opportunity” section. Knowing the end goal is very very important and the book “The 7 habits of Highly Effective People” should help.

Doesn’t engage in research with long term vision early

People often emphasize how important to get involved with research in college mainly because research is a critical component of higher education. I certainly did but my mistake is that I’m involving in research in ad-hoc way: I did research in math, in psychology, and in statistics without a common theme that connects them all together. In math, I did research in probability; in psychology, I did research in early childhood education; in statistics, I did research in fMRI. Those research experience is helpful only in the sense that they help me to discover what I don’t like. I always admire the people who can discover their interests early: there are lots of options; how can one settle on one without trying out others first? That’s my unresolved question. Technically speaking, I don’t think this section should be considered as a mistake but certainly, it is something that incurs lots of detours in my short-lived academia career.

Failure in seizing “the” opportunity

I started to compose this post when I was on the spring break trip in Alaska. I ran into a group of people who were from my undergraduate institution – University of Wisconsin-Madison. I had a brief chat with them. One question I asked one of them who happened to be a CS major was: does Wisconsin start to set bar for people who want to declare CS major? “No! Everyone can do it! That’s the amazing part of Wisconsin: the university gives everyone opportunities to try!” She answered. “I know a friend who transferred to Wisconsin from University of Washington to study CS because he cannot study CS at UW. Students in UW can study CS only when they are admitted to CS directly from high school.” Her replies don’t surprise: that’s the same impression I have about Wisconsin. However, her answer stirs a huge pain in my heart. I suddenly have guts to admit a huge mistake I have made during my first year study at UT-Austin.

I’m unsure about what to do with summer: whether I want to go to a research lab to prepare my PhD application or finding an internship in industry. As you can see, here I have the mistake of not knowing my end goal: I’m not sure whether I want to pursue a career in research or in software engineering. I contacted one of my former professors in Wisconsin and he was kind enough to offer me a position in his lab over the summer. He is a famous researcher and people are dying to work with him. But, guess you already know, I blow up the chance and work on a software engineering internship over the summer. Of course, the professor is unhappy but he is kind enough to not saying that explicitly. In the following Fall, I applied for PhD programs and I asked him for a letter. Without big surprise, I got rejected by all the programs including the school the professor is in. After learning the admission results, I keep lying to myself about all the drawbacks of attending a PhD program and I constantly have debate in my heart about whether I have made a good decision for the summer. After talking with the girl from my school during the trip, I suddenly realized that how upset I am in my heart and how I keep avoiding facing the fact that I have made a huge mistake and blow up “the” opportunity. I couldn’t help to imagine that if everything works out over the summer, I may already have the admission from his lab to have the privilege to study for PhD program. Of course, in real life, there is no “if”. Failure in seizing “the” opportunity can be treated as a pivot point in my life. A person’s life might be settled after a few pivotal decisions. I think I just made a mistake in one of them.

The only takeaway: Never ever give up your interest

I write the following in Chinese to my parents:

如果有孩子 我一定教育他不要因为钱和客观因素就轻易放弃梦想 因为放弃梦想的感觉真的很难受 即使最后你没有钱 但是你至少知道你为了梦想努力过 那种踏实的感觉是用钱买不回来的

Basically, it says that there is no such thing has higher worth than one’s dream. After getting rejected by all PhD programs, I know that getting an internship in industry over the summer signifies my give-up my interests in becoming a researcher for the money. I didn’t upset at the very beginning but the more I think about, the more I think I should stick with my interests no matter how poor or how old I am. Now, I’m in a situation about I should hog onto something that is not my interest: money in this case. I’m not sure eventually, I can have a way to switch back to my dream but I know it’s going to be a long and hard way

End Semester Recap

I just finished all the exam and papers today. It has been a long day (wake up at 6) and I feel very exhausted. However, I want to do a quick recap of this semester before my judgment affected by my final grades.


CS 380D Distributed System

My first exam is a disaster. The exam is all about system design + understanding of RAFT. I didn’t get used to the system design in general. All I do is to remember every detail of some system implementations, which usually don’t matter from a design perspective. Vijay has been emphasized this point a lot but I didn’t get it until the second half of the course. The course is good and the biggest takeaway for me is two:

  • Can comfortably read distributed system paper. I cannot claim I can read all types of system paper but for distributed system paper, I begin to get the momentum and start to know where to focus on during the reading. Takes a lot of struggling to get this point but I’m happy overall after reading more than 30 papers.
  • Got intrigued by the distributed system and storage system. In the past, I have been struggling to find my research interests.  But, thanks to this course, I become more intrigued with the combination of distributed system and storage. Right now, I like storage more. I read tons of LSM-based storage paper to find a topic for my final course project. I really enjoy the moment to read LevelDB and PebblesDB’s code and enhance them in some way. That further makes me want to know more about SSDs and HDDs.

CS 388 Natural Language Processing

I trade this course with algorithm class. I have a mixed feeling right now. On one hand, unlike the NLP course that I take in the previous semester, which looks at NLP from models perspectives (HMM, CRF, different networks). this semester’s course is from more traditional linguistics + machine learning perspectives. I really like this part. Overall, I strongly believe linguistics domain knowledge should play the key role in NLP study not various deep learning manic.  First two homework, we look at language models and LSTM based on the intuition of prediction can be two ways. I really like Mooney’s view that you always think about intuition whether the model can work or not instead of mindlessly applying models.  Like last semester’s NLP class, my interests with class declines as the semester progresses partly due to the fact that the material is no longer relevant for homework and exam. That is my bad.

The final project is on VQA, which mostly done by my partner. I only gather the literature and survey the field plus some proofreading. I’m OK with that as I want to have more time working on my system project and my partner wants to work alone in the modeling.  This leads to my lesson learned from the class:

  • Graduate school is about research, not class. Pick the easiest courses and buy yourself time to work on the research problem that attracts you.

If I look back right now, I want to take algorithm class instead. My thoughts to NLP is that I want to start from the dumbass baseline and know the history of the field. If you think about NLP, the most basic technique is just regular expression pattern matching. But, how do we go from there to more complex statistical models is the most interesting point I want to learn.

LIN380M Semantics I

The course is taught by Hans Kamp, which I believe invents the Discourse Representation Theory (DRS). Really nice man. I learn the predicate logic, typed lambda calculus, Montague grammar and DRS. Very good course for the logic-based approach to derive the semantic meaning of a sentence. However, I do feel people in this field put a lot of efforts in handling rule-based exceptions like how do we handle type clash in Montague grammar. When I turn in the final exam, Hans is reading some research paper. He is still doing research and that inspires me a lot.

Other Lesson Learned

  • “Don’t be afraid to fail, be afraid not to try”. I learn a lot from my final system project partner. Reading complex code can be daunting but we can always start to play around even when we cannot understand the code fully. There is a great deal of psychological barrier to be overcome. My partner always starts with reading and then writing. Once bug happens, he is happy because the bug is an indicator of progress, which eventually leads to working code.
  • Work independently. When I got stuck for a while, I always want to seek help instead of counting on myself to solve the problem. It seems that I can never trust myself ability to solve the problem. By observing how my project partner solves the problem, I learn a lot. Start to trying and always seek for the root cause of the problem and situation changes as long as you start trying.
  • Some tips about system paper writing:
    • Use hatch on the bar graph. People may print out their paper in black and white. Use hatches on the bar graph help them to distinguish which bar is your system and which bar is the baseline system.
    • Add more descriptions to each figure and table below. I used to think that there should be only one line of description for each picture. But, as pointed out by my another project partner, people need instructions when read the graphs. People love the pictures and they hate to go to the paragraphs to search for the instructions to understand the graph. Thus, put instructions directly below the picture. Great insight!
  • I really want to know how to measure a system accurately.  From my system project, I realize that measuring the system performance is really hard. Numbers fluctuate crazily and you have no clue why is that because there are some many layers of abstraction  & factors in the experiment environment that can potentially impact the system measurement. I really want to know more about this area during my own study and summer internship.
  • System improvement without provable theoretical guarantees will be very unlikely successful. Overhead or the constant factor hidden in the big-O model usually dominate the actual improvement you might think you can get. For example, there are overhead in spawning threads. We need to compare how much we can get by having multiple threads running in parallel to do the subtask vs. having one single thread do the whole thing. PebblesDB’s paper on the guard and improvement to compaction ultimately prove that we really need to think more before getting our hands dirty. By reading the paper, I get the feeling that they know the system will work before even implementing one because they can clearly show that their functionality works before writing a single line of code. I need to develop more sense about this and taking more theory class.


Ok. Time to pack and catch the flight.

On Reading CS Papers – Thoughts & Reflections

Be forewarned:

  • This is not an advice post. There are tons of people out there who desperately want to give people advice on reading papers. Read theirs, please.
  • This post is a continuous reflection on the topic “how to read a CS paper” from my personal practice. I will list out my academic status before each point so that it may be interesting to myself on how my view on the matter has changed as time goes forward.


The first year of my CS master program. Just get started on CS research.

  • It’s OK to not like a paper

In my first semester, I majorly read papers on Human Computation and Crowdsourcing.  Very occasionally, I read papers on NLP. Some papers on NLP are from extra readings in Greg’s course. Some are related to Greg’s final project, which deals with both code and language.  I don’t really like and want to read papers back then. In NLP class, I prefer to read textbooks (Jufrasky’s one) and tutorial posts that I can find online. One roadblock for me to read papers is that there is certain background knowledge gap I need to fill and I just simply don’t know how to read a paper. So, for Greg’s NLP course, I only read some papers related to my final project. This paper is the base paper for my final project. I got this paper from professors in linguistics and software engineering and they want me to try out the same idea but using neural network model instead. I read this paper several times and the more I read, the more I want to throw up.  I just think this paper hides many critical implementation details and the score 95% is just too high for me to believe. The authors open source their code but their code has some nasty maven dependencies, which won’t compile under my environment. Their evaluation metric is non-standard in NLP and many “junk words” wrap around their results. Of course, the result of my experiment is quite negative.  I often think it is just a waste of life to spend your precious time on some paper you dislike.  Here, I’m more of talking about paper writing style and the reproducibility of papers’ results. I probably want to count shunning from some background gap as a legitime reason not like a paper.

  • Try to get most of the paper and go from there

I got this message from Matt’s Crowdsourcing class. In the class, I have read a very mathematical heavy paper, which invokes some combinations of PGM and variational inference on the credibility of fake news. I’m worried back then about how should I approach a paper like this one, which I’m extremely lack of background and mathematics formula looks daunting.  I pose my doubts on Canvas and Matt responds in class and gives the message.  I think the message really gives me some courage on continuing read papers.

  • It’s OK to skip (most) parts of a paper.  Remember: paper is not a textbook!

This semester I’m taking a distributed system class. To be honest, distributed system paper can be extremely boring if they are from industry. Even worse, system paper can be quite long: usually around 15 pages, double column. So, if I read every word from beginning to end, I’ll be super tired and the goal is not feasible for a four-paper-per-week class. So, I have to skip. Some papers are quite useful maybe just for one or two paragraphs. Some papers are useful maybe just because of one figure. As long as your expectation about a paper gets met, you can stop wherever you want.

  • Multiple views of reading a paper

I didn’t get the point until very recently. I did quite terrible on the first midterm of my distributed system class. The exam is about how to design a system to meet a certain requirement. In the first half of the course, I focus on the knowledge part presented by the paper but that doesn’t work out well. Until then, I realize that I need to read those systems paper from a system design point of view: what problems they need to solve, what challenges they have, how they solve the challenges.  OF course, those papers are valuable from knowledge perspective: how consistent hashing works, for example. But, depends on the goal of reading paper, I can prioritize different angles of reading a paper. If I need to implement the system mentioned in the paper, I probably need to switch to a different paper reading style.

  • Get every bit of details of paper if you need to

It’s time again for the final course projects. Again, I need to generate some ideas and find some baseline papers. In this case, “skip parts” and “get most out of the paper and move on” strategy probably won’t work well. All in all, I need to understand the paper and those are rely on the details from the paper. In this case, I need to sit through the whole journey and remove any blockers that I may encounter.

Freedom of speech

Piazza is an online forum tool that is heavily used in the academia. It is used to help students ask questions and get feedback from both peers and instructors. It has a goal that is similar to Slack in the sense that they both try to cut the duplicate emails sent by several people for the same or similar type of request. It is a good tool but every tool that comes with power has its own consequence.

Instructors can perform the following configuration when they setup the forum for the course.

Screen Shot 2018-02-09 at 11.58.12 PM

Basically, this option means that when you make a post, whether you can choose to be “Anonymous” to both your peers and instructors or to your peers only (instructors can still see who makes the post).  The following picture shows what this option looks like from student’s perspective:

Screen Shot 2018-02-09 at 11.58.33 PM

The intention for this option I guess is that some students may feel embarrassed to ask questions. They might think their questions are dumb and will make them look bad in front of peers or instructors. I think this option is used as a way to encourage students to ask questions bravely.

However, this option may get abused. From my observation, Piazza is used as a way for instructors to show off their teaching quality. This is important for Assistant Professors because teaching still means something (if teaching quality doesn’t matter, why institution asks for the teaching statement at the very first place?). In addition, the teaching quality in some sense is an important indicator for students to evaluate you as a person. This is important for professors who are looking for graduate students because research publication is only part of the story and how those professors interact with students may be a crucial indicator to how good a professor as a human being is (evaluation may be a better indicator but it is confidential). Thus, if some potential students look at the piazza that his interested professor teaches gets a lot of complaints. The students may have a second thought on whether he should work with him for research (maybe he is a very bad person even he is doing a good research).

Thus, the instructors have a strong motivation to censor the posts on the piazza. This scares the students because they don’t have a secure way to provide feedback to the instructor. Let’s assume that the majority of students has a good heart: they won’t say bad stuff to the instructor who actually really cares about students. Thus, the time that something slightly negative appears on the Piazza may be a very important signal to the instructor that something wrong with his teaching. However, due to the strong motivation for instructors to show off their teaching quality through Piazza, the instructors may start to censor the speech on the Piazza by turning the option off.

I didn’t realize this thing last semester. Last semester, the instructor from one course sets this option off and I was thinking maybe he wants to know the students who are shy to ask questions and provide some individual attention. However, this semester, the instructor from one of my course initially turn the option on so that everyone can truly ask questions as “Anonymous”. Then, until one day, someone makes the below post and the option is turned off. Now, no students dare to make slightly negative posts.

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I fully understand the interests conflict between students and instructors on the use of Piazza: students may think Piazza is a secure way to provide anonymous feedback while instructor may think bad posts on the forum make them look bad. However, I still think there should be a better way to address this conflict to protect both students and instructors especially with the technology we have nowadays. But, (unintentional) censorship is not something we want to culture especially in the Academia. By the way, for this course, I still think the instructor is good but the material is quite challenging without laying down a solid theoretical foundation beforehand. He went through the material again after this post but too bad the truely “Anonymous” is gone.