Archive for the ‘talk’ Category
today Prof David Krackhardt (CMU) gave a very very nice talk titled “Simmelian Ties in Organizations “. david krackhardt greatly contributed to the discipline of cognitive social networks and has extensively studied the power of simmelian ties in organizations (his bio). here is the result of my live blogging during his talk:
Ushahidi (blog) is an open source platform for collecting, visualizing, and distributing information related to a crisis or ongoing public problem, such as swine flu, election fraud, and political violence:
- 4-minute TED talk
- Explaining Swift River – video on developing more sophisticated news filtering
- API – if you like to play with data
Plus, there is also the OMC – it is all about open source mobile phone software, with a focus on humanitarian needs.
(from Brad Karp’s email)
Dr Ranveer Chandra
[Talk title TBA]
10:30 AM, Thursday, 16th April
Roberts G06 (Sir Ambrose Fleming LT)
Bio: Ranveer Chandra is a researcher in the Networking Research Group at Microsoft Research. He completed his undergraduate studies from the Indian Institute of Technology, Kharagpur and a PhD in Computer Science from Cornell University. He was the recipient of the Microsoft Graduate Research Fellowship during his PhD and his dissertation on VirtualWiFi was nominated by Cornell for the ACM Dissertation Award. VirtualWiFi has been downloaded more than 100,000 times and is the third most downloaded software ever to be released by Microsoft Research. Ranveer has authored more than 25 research papers and filed more than 30 patents. He is active in the networking and mobile systems community, and has served on the program committees of several conferences.
Professor Michael Mitzenmacher
Some Results on Coding for Flash Memory
11:15 AM, Thursday, 16th April
Roberts G06 (Sir Ambrose Fleming LT)
Abstract: Flash memory is rapidly becoming the technology of choice for storage in several settings. But flash memory behaves differently than other memory systems, making us rethink the basic ways we represent data. In this talk we’ll consider the question of how to code data for flash memory systems. Although our framework will be primarily theoretical, it will shed light on some of the basic issues underlying the use of flash memory systems, including what considerations need to be kept in mind when designing algorithms or data structures for such systems.
Bio: Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Michael has authored or co-authored over 140 conference and journal publications on a variety of topics, including Internet algorithms, hashing, load-balancing, erasure codes, error-correcting codes, compression, bin-packing, and power laws. His work on low-density parity-check codes shared the 2002 IEEE Information Theory Society Best Paper Award. His textbook on probabilistic techniques in computer science, co-written with Eli Upfal, was published in 2005 by Cambridge University Press. This year, he is serving as chair of STOC 2009 and on the PC of SIGCOMM 2009.
Michael Mitzenmacher graduated summa cum laude with a degree in mathematics and computer science from Harvard in 1991. After studying math for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999.
Yesterday was the first day of RecSys 2008, and was dedicated to three very interesting tutorials:
1. Robust Recommender Systems. Robin Burke introduced the wide range of attacks that typical collaborative filtering algorithms are vulnerable to; scenarios that arise when people attempt to force, rather than express, opinions. An attack was strictly defined as a set of profiles intending to obtain excessive influence on others, which can be aimed at pushing (making recommendation more likely) or nuking (i.e. recommendation less likely) items. His talk was an interesting blend of attack strategies, knowledge that attackers need to have, and a high-level description of approaches aiming at preventing or fixing the system when attacked. Of course, there are strong overlaps between this work and work in other areas (p2p trust, adversarial information retrieval, search engine spam..); I particularly like this area as pushes the point that recommender systems are about people/dynamic datasets, and not just prediction.
2. Recent Progress in Collaborative Filtering. Yehuda Koren (who has recently moved from AT&T to Yahoo! Research) gave a tutorial about the leading approaches in the Netflix prize competition. The techniques he described blend matrix factorisation and neighbourhood models, and include a number of other factors (such as user biases and time) that result in techniques that have multiple-billions of parameters (and the resulting ranking of team BellKor in the competition). His work is remarkable and worthy of the progress prizes he has been awarded thus far. He also explored alternative techniques of evaluating recommender systems, explaining his take on evaluating top-N recommendation lists.
3. Context-Aware Recommendations. Gedas Adomavicius and Alex Tuzhilin introduced their work on incorporating context into recommender systems, including pre-, post-, and hybrid-filtering of recommendation algorithm results based on user context. A running example that was repeated throughout the tutorial was going to the theatre with your girlfriend on the weekend: if you always watch comedy, then your recommendations can be filtered to match what you did in previous instances of the same context (i.e. you can be recommended comedy). They have done a lot of cool stuff on multi-dimensional recommenders, extending the common rating scales into cubes of ratings, and stressed more than once that this is virgin territory. Their work is also impressive, but raised a few questions. For example, should context be described by a well-enumerated taxonomy? Moreover, if you always watch comedy at the theatre with your girlfriend on weekends, then why should you need a recommender system in the first place (especially a collaborative one- what happened to serendipity or diversity)? They have a number of papers that are worth reading before trying to answer these questions!
If you are running particularly long experiments like me, or are looking for something to watch for 45 minutes, then I suggest this video on youtube: a documentary about truth and wikipedia. It features interviews with big pro- and anti- web 2.0 names, and discusses the extent to which sites like wikipedia encourage truth, freedom, and democracy (or mob-rule, lies, and social fragmentation).
I was recently reading Dorris Lessing’s Nobel Prize for Literature acceptance speech (full text here). It’s an excellent read, a story of storytelling, that recalls her experiences in Africa and the influence of books on a writer. I strongly recommend all to read it. However, as inspirational as it is, there is also a strong feeling of cynicism towards the culture heralded on by the technology revolution, and some points worth thinking about. Here is a short quote:
“We are in a fragmenting culture, where our certainties of even a few decades ago are questioned and where it is common for young men and women, who have had years of education, to know nothing of the world, to have read nothing, knowing only some speciality or other, for instance, computers.
What has happened to us is an amazing invention – computers and the internet and TV. It is a revolution. This is not the first revolution the human race has dealt with. The printing revolution, which did not take place in a matter of a few decades, but took much longer, transformed our minds and ways of thinking. A foolhardy lot, we accepted it all, as we always do, never asked: “What is going to happen to us now, with this invention of print?” In the same way, we never thought to ask, “How will our lives, our way of thinking, be changed by the internet, which has seduced a whole generation with its inanities so that even quite reasonable people will confess that, once they are hooked, it is hard to cut free, and they may find a whole day has passed in blogging etc?”
I found it strange how she describes the printing revolution as something good, by allowing “voices unheard” to wield their talent, but the internet revolution as something meaningless, fragmenting, and wasteful. It seems to either imply that publishers have been given the divine gift of knowing what is good to publish, or that people (are dumb, and) lack the collective knowledge to find what is worth reading. Is there no such thing as collective wisdom? Does a change of medium naturally imply a change in content and quality? Perhaps her words reflect Toffler’s predictions, or it is impossible for her to find any quality in the chaotic community that we call the web?
After all, her message came to me by means of blogs and online news. Any thoughts?
Claudio (from IIIA and sponsored by MyStrands) gave a very interesting talk about poolcasting (pdf of his slides). Poolcasting is a web radio in which individuals may join different channels. Those subscribed to a channel will listen to the same stream of songs. The problem is how to select the songs on that stream. Claudio did so by combining the preferences of a channel´s listeners using Case-Based Reasoning.
The same approach may be used for mobile music. A bar may decide to play songs depending on the preferences of its customers (and preferences may be elicited from the playlists that customers store on their mp3 players or mobile phones).
A couple of questions that might be of interest to some of us: what if listeners do not share many songs in their playlists? Would it be possible to factor listeners´ reputation (trust) in deciding which songs to play?
A talk by Jean Camp at BT Adastral Park on the 1st of December.
The talk discusses privacy in ubicomp as a design, social, technical, and policy issue; outlines the research program at IU that is designed to meet the technical and social challenges of using sensor networks as a monitoring technology.