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SCS
Student
Seminar
Series
abstracts
previous talks
scs seminars
SCS
CMU
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All SSS talks can be carried out either in person or through Zoom.
Fall 2022 Schedule
Mon, Aug 29 |
GHC 6501 |
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available |
Fri, Sep 02 |
GHC 6501 |
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available |
Mon, Sep 05 |
GHC 6501 |
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available |
Fri, Sep 09 |
GHC 6501 |
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available |
Mon, Sep 12 |
GHC 6501 |
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available |
Fri, Sep 16 |
GHC 6501 |
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available |
Mon, Sep 19 |
GHC 6501 |
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available |
Fri, Sep 23 |
GHC 6501 |
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available |
Mon, Sep 26 |
GHC 6501 |
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available |
Fri, Sep 30 |
GHC 6501 |
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available |
Mon, Oct 03 |
GHC 6501 |
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available |
Fri, Oct 07 |
GHC 6501 |
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available |
Mon, Oct 10 |
GHC 6501 |
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booked |
Fri, Oct 14 |
GHC 6501 |
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available |
Mon, Oct 17 |
GHC 6501 |
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available |
Fri, Oct 21 |
GHC 6501 |
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Not available |
Mon, Oct 24 |
GHC 6501 |
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available |
Wed, Oct 26 |
GHC 6501 |
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available |
Fri, Oct 28 |
GHC 6501 |
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Detection and Location Uncertainty in Multi-agent Active Search
Presented by Arundhati Banerjee
https://cmu.zoom.us/j/91744959037?pwd=Nm85VzE3S0ZHWjVuRHo5NjYxeXVNQT09
Active search, in applications like environment monitoring or disaster response missions, involves autonomous agents detecting targets in a search space using decision making algorithms that adapt to the history of their observations. Active search algorithms must contend with two types of uncertainty: detection uncertainty and location uncertainty. The more common approach in robotics is to focus on location uncertainty and remove detection uncertainty by thresholding the detection probability to zero or one. In contrast, it is common in the sparse signal processing literature to assume the target location is accurate and instead focus on the uncertainty of its detection. In this talk, I will present a multi-agent active search algorithm that can jointly handle both target detection and location uncertainty for state estimation from noisy sensor measurements, without the need for a central controller or synchronous inter-agent communication. Our results indicate noticeable improvements in target recovery performance over baselines that only account for either target detection or location uncertainty separately. I will also demonstrate the real world transferability of our algorithm using a realistic simulation environment created on the Unreal Engine platform.
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Mon, Oct 31 |
GHC 6501 |
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Memento: Architectural Support for Ephemeral Memory Management
Presented by Ziqi Wang
Serverless computing is an increasingly attractive cloud paradigm due to its ease of use and fine-grained pay for-what-you-use billing.
However, serverless computing poses new challenges to system design due to its short-lived execution model.
Our detailed analysis reveals that memory allocations in serverless functions are typically small and ephemeral: i.e., freed
shortly after allocation. Unfortunately, these functions pay the full critical-path costs of memory management in both user space and
the operating system without the opportunity to amortize these costs over their short lifetimes.
In this talk, I will present Memento, a hardware-centric design to management that alleviates the overheads of ephemeral memory management.
Memento achieves its design goal with two key mechanisms. The first is a hardware object allocator that performs in-cache object allocation and free operations.
The second is a hardware page allocator that manages a small pool of physical pages and handles physical memory allocation.
We evaluate Memento with full-system simulations across a diverse set of serverless workloads. The results show
that, on average, achieves a 14% (and up to 22%) speedup of function execution time, and reduces the runtime pricing cost by 24%.
Memento also eliminates most of the memory management work from the critical path and almost accomplishes the theoretically optimal result that any optimization
scheme can achieve.
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Fri, Nov 04 |
GHC 6501 |
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available |
Mon, Nov 07 |
GHC 6501 |
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available |
Fri, Nov 11 |
GHC 6501 |
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available |
Mon, Nov 14 |
GHC 6501 |
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available |
Fri, Nov 18 |
GHC 6501 |
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SpectraNet: multivariate forecasting and imputation under distribution shifts and missing data
Presented by Cristian Challu
In this work, we tackle two widespread challenges in real applications for time-series forecasting that have been largely understudied: distribution shifts and missing data. We propose SpectraNet, a novel multivariate time-series forecasting model that dynamically infers a latent space spectral decomposition to capture current temporal dynamics and correlations on the recent observed history. A Convolution Neural Network maps the learned representation by sequentially mixing its components and refining the output. Our proposed approach can simultaneously produce forecasts and interpolate past observations and can, therefore, greatly simplify production systems by unifying imputation and forecasting tasks into a single model. SpectraNet achieves SoTA performance simultaneously on both tasks on five benchmark datasets, compared to forecasting and imputation models, with up to 92% fewer parameters and comparable training times. On settings with up to 80% missing data, SpectraNet has average performance improvements of almost 50% over the second-best alternative.
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Mon, Nov 21 |
GHC 6501 |
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Snapshotting Concurrent Data Structures
Presented by Hao Wei
Abstract: Most work on concurrent data structures has focused on supporting single-point operations, such as insert, delete, and lookup, but many applications require supporting these alongside multi-point queries, such as searching for ranges of keys, finding the first key that matches some criteria, or checking if a collection of keys are all present. In this presentation, I'll cover a general technique for adding linearizable multi-point queries to existing concurrent data structures. This technique maintains the time bounds and progress properties (e.g. lock-freedom/wait-freedom) of the original data structure. Furthermore, multi-point queries written with this technique are wait-free and take time proportional to their sequential complexity plus a contention term representing the number of update operations concurrent with the query.
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Mon, Nov 21 |
GHC 6501 |
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available |
Fri, Nov 25 |
GHC 6501 |
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available |
Mon, Nov 28 |
GHC 6501 |
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available |
Fri, Dec 02 |
GHC 6501 |
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available |
Mon, Dec 05 |
GHC 6501 |
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available |
Fri, Dec 09 |
GHC 6501 |
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available |
Mon, Dec 12 |
GHC 6501 |
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available |
Fri, Dec 16 |
GHC 6501 |
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available |
Mon, Dec 19 |
GHC 6501 |
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available |
Fri, Dec 23 |
GHC 6501 |
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available |
General Info
The Student Seminar Series is an informal seminar
for SCS graduate students to communicate ideas.
Each meeting starts at noon and lasts 1 hour. Lunch is
provided by the Computer Science Department (thanks to
Debbie Cavlovich!). At each meeting, a different student
speaker will give an informal, 40-minute talk about his/her research,
followed by questions/suggestions/brainstorming. We try to attract
people with a diverse set of interests, and encourage speakers to
present at a very general, accessible level.
So why are we doing this and why take part? In the best case
scenario, this will lead to some interesting cross-disciplinary work
among people in different fields and people may get some new ideas
about their research. In the worst case scenario, a few people will
practice their public speaking and the rest get together for a free
lunch.
Previous years
2022Fall,
2022Spring,
2021Fall,
2021Spring,
2020Fall,
2020Spring,
2019Fall,
2019Spring,
2018Fall,
2018Spring,
2017Fall,
2017Spring,
2016Fall,
2016Spring,
2015Fall,
2015Spring,
2014Fall,
2014Spring,
2013Fall,
2013Spring,
2012Fall,
2012Spring,
2011Fall,
2011Spring,
2010Fall,
2010Spring,
2009Fall,
2009Spring,
2008Fall,
2008Spring,
2007Fall,
2007Spring,
2006Fall,
2006Spring,
2005Fall,
2005Spring,
2004Fall,
2004Spring,
2003Fall,
2003Spring,
2002Fall,
2002Spring,
2001Fall,
2001Spring,
2000Fall,
2000Spring,
1999Fall,
1999Spring,
1998Fall,
1998Spring,
SSS Coordinators
Juncheng Yang, CSD
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