Computer Science Research Symposium

Friday 6th May 2016, Filene Auditorium, Moore Hall



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Come find out about the latest research advances being made by our students and faculty.

Talks

Listen to our students and faculty talk about their latest research results..

Posters

Talk to the students one-on-one about their work.

Demos

Checkout the cool demos.

Computer Science Research Symposium is an annual exhibition of the research happening in the computer science department. Meet and talk with the researchers in our department about the bleeding edge techniques!

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Program

Time Speaker/Event Title
15:00 - 15:10 Lorenzo Torresani Opening Remarks
15:10 - 15:40 Tom Cormen Dense Gray Codes, or Easy Ways to Generate Cyclic and Non-Cyclic Gray Codes For the First $n$ Whole Numbers.
15:45 - 16:05 Jack Holland A new definition of contact provides insight into protein structure prediction.
16:10 - 16:30 Zhao Tian The DarkLight Rises: Visible Light Communication in the Dark
16:30 - 17:45 Posters session - Moore Basement Foyer
18:00 - 18:20 Deeptak Verma Developing next-generation biotherapeutics using computational methods.
18:25 - 18:45 Karim Ahmed Network of Experts for Large-Scale Image Categorization.
18:50 - 19:10 Tianxing Li Practical Human Sensing in the Light
19:30 - 22:00 Award Ceremony and Dinner DOC House
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Speakers

Tom Cormen

Dense Gray Codes, or Easy Ways to Generate Cyclic and Non-Cyclic Gray Codes For the First $n$ Whole Numbers.

Dense Gray Codes, or Easy Ways to Generate Cyclic and Non-Cyclic Gray Codes For the First $n$ Whole Numbers.

The standard binary reflected Gray code gives a sequence of binary numbers in the range 0 to $n-1$, where $n$ is a power of 2, such that each number in the sequence differs from the preceding number in only one bit. We present two methods to compute Gray codes containing exactly $n$ numbers in the range 0 to $n-1$---that is, a permutation of $\langle 0, 1, \ldots, n-1 \rangle$ in which each number differs from the preceding number in only one bit---where $n$ is unconstrained. The first method produces a Gray code that is not cyclic: the first and last numbers in the sequence differ in more than one bit. The second method produces a cyclic Gray code if $n$ is even, so that the first and last numbers differ in only one bit, at the expense of a slightly more complicated procedure. Both methods are based on the standard binary reflected Gray code and, as in the binary reflected Gray code, each number in the output sequence can be computed in a constant number of word operations given just its index in the sequence. Joint work with Jessica C. Fan ’17.

Jack Holland

A new definition of contact provides insight into protein structure prediction.

A new definition of contact provides insight into protein structure prediction.

A protein comprises a chain of amino acids specified by DNA. Predicting how these chains fold in the cell based on their constituent sequence of amino acids is a longstanding problem in cellular biology, and the inverse problem (predicting a sequence of amino acids that fold into a specified structure) is equally important in biological engineering. One approach to solving this dual problem is taking advantage of the wealth of data provided by experimentally determined structures. Statistical rules can be derived from a database of these structures and used to predict how likely it is to observe a given sequence fold into a particular structure (and vice-versa). However, the utility of these statistical rules depends on the relevance of the structural features they are based on. We introduce a novel definition of contact between amino acids that captures structural information useful for deriving statistical rules. The predictive value of these rules is tested by applying them to sets of protein structures: in each set, one structure is a real experimental structure, while the rest are decoys that do not reflect the structure that their sequence would fold into in an actual cell. Our new definition of contact and the statistical rules derived from it were tested on commonly used sets of decoys. In many situations, they predict which structure is the real one when state of the art methods do not. This success in decoy discrimination suggests that the rules we have developed are useful aids for evaluating how likely it is that particular sequences will fold into particular structures.

Zhao Tian

The DarkLight Rises: Visible Light Communication in the Dark.

The DarkLight Rises: Visible Light Communication in the Dark.

Visible Light Communication (VLC) emerges as a new wireless communication technology with appealing benefits not present in radio communication. However, current VLC designs commonly require LED lights to emit perceptible light beams, which greatly limits the applicable scenarios of VLC (e.g., in a sunny day when indoor lighting is not needed), and brings high energy overhead and unpleasant visual experiences for mobile devices to transmit data using VLC. We design and develop DarkLight, a new VLC primitive that allows light-based communication to be sustained even when LEDs emit extremely-low luminance. The key idea is to encode data into ultra-short, imperceptible light pulses. We tackle challenges in circuit designs, data encoding/decoding schemes, and DarkLight networking, to efficiently generate and reliably detect ultra-short light pulses using off-the-shelf, low-cost LEDs and photodiodes. Our DarkLight prototype supports 1.3-m distance with 1.6-Kbps data rate. By loosening up VLC's reliance on visible light beams, DarkLight presents an unconventional direction of VLC and fundamentally broadens VLC's application scenarios.

Deeptak Verma

Developing next-generation biotherapeutics using computational methods.

Developing next-generation biotherapeutics using computational methods.

T cell driven recognition of non-"self" proteins presents a major obstacle in the development of next-generation biotherapeutics. In order to mitigate the immune response to T cell epitopes within an exogenous protein, we have developed a suite of powerful computational protein design algorithms that globally optimize variants for simultaneous function and immunogenicity. The talk describes our computational methods and presents experimental results from application to therapeutic candidates, demonstrating our ability to disrupt broadly distributed immunogenic epitopes without compromising protein function. In one such application, we have recently engineered epitope-depleted variants of lysostaphin, a highly potent but immunogenic anti-staphylococcal enzyme. In constrast to wild-type, our variants maintain low antibody titers and are able to repeatedly rescue humanized mice from challenges with methicillin resistant Staphylococcus aureus (MRSA). This work provides the first controlled demonstration that depletion of T cell epitopes from a biotherapeutic agent leads to a reduced antibody response and consequently enhanced efficacy in an immune competent disease models.

Karim Ahmed

Network of Experts for Large-Scale Image Categorization.

Network of Experts for Large-Scale Image Categorization.

We present a tree-structured network architecture for large-scale image classification. The trunk of the network contains convolutional layers optimized over all classes. At a given depth, the trunk splits into separate branches, each dedicated to discriminate a different subset of classes. Each branch acts as an expert classifying a set of categories that are difficult to tell apart, while the trunk provides common knowledge to all experts in the form of shared features. The training of our "network of experts" is completely end-to-end: the partition of categories into disjoint subsets is learned simultaneously with the parameters of the network trunk and the experts are trained jointly by minimizing a single learning objective over all classes. The proposed structure can be built from any existing convolutional neural network (CNN). We demonstrate its generality by adapting 3 popular CNNs for image categorization into the form of networks of experts. Our experiments on CIFAR100 and ImageNet show that in each case our method yields a substantial improvement in accuracy over the base CNN, and gives the best reported result on CIFAR100. Finally, the improvement in accuracy comes at little additional cost: compared to the base network, the training time of our model is about 1.5X and the number of parameters is comparable or in some cases even lower.

Tianxing Li

Practical Human Sensing in the Light.

Practical Human Sensing in the Light.

We present StarLight, an infrastructure-based sensing system that reuses light emitted from ceiling LED panels to reconstruct fine-grained user skeleton postures continuously in real time. It relies on only a few (e.g., 20) photodiodes placed at optimized locations to passively capture low-level visual clues (light blockage information), with neither camera capturing sensitive images, nor on-body devices, nor electromagnetic interference. It then aggregates the blockage information of a large number of light rays from LED panels and identifies best-fit 3D skeleton postures. StarLight greatly advances the prior light-based sensing design by dramatically reducing the number of intrusive sensors, overcoming furniture blockage, and supporting user mobility. We build and deploy StarLight in a 3.6 m x 4.8 m office room, with customized 20 LED panels and 20 photodiodes. Experiments show that StarLight achieves 13.6 degrees mean angular error for five body joints and reconstructs a mobile skeleton at a high frame rate (40 FPS). StarLight enables a new unobtrusive sensing paradigm to augment today's mobile sensing for continuous and accurate behavioral monitoring.

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Posters and Demos

Srivamshi Pittala Predictive Modeling of Vaccine Mediated Protection Against HIV/SIV.
Lixing Lian Out-of-Core Suffix Arrays Using FG.
Athina Panotopoulou Perceptual Models of Preference in 3D Printing Direction.
Rawan Alghofaili Fabricating a Medical Cast Using Upper Extremity Motion Models.
Srinath Ravichandran Control Variates for Linear Light Sources in Participating Media.
Shruti Agarwal Degrade It!
Du Tran Deep End2End Voxel2Voxel Prediction.
Mohammad Haris Baig Coupled Depth Learning.
Anup Joshi Estimating Partition Function with Better Proposal Distribution.
Chuankai An Improving Local Search with Open Geographic Data.
Xi Xiong Customizing Your Wireless Coverage via A 3D Fabricated Reflector
Rui Wang CrossCheck: Towards Passive Sensing and Detection of Mental Health Changes in People with Schizophrenia.
Prashant Anantharaman Namespace and Cryptographic Complexity in the Smart Grid.
Varun Mishra Sensing Stress Levels
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Organizers

Sagar Kale, Suman Bera, Athina Panotopoulou.

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