Research Talk Schedule
- Summer 2013
- Spring 2013
- Winter 2013
- Fall 2012
- Fall 2013
- Winter 2014
- Spring 2014
- Summer 2014
- Fall 2014
- Winter 2015
- Spring 2015
- Summer 2015
- Fall 2015
- Winter 2016 (active tab)
- Spring 2016
- Fall 2016
- Winter 2017
The CWDS seminar series is centered around intellectual exchange and interaction, and the audience is encouraged to ask questions during presentations. The goal is a seminar that looks less like a lecture and more like a spirited discussion of issues raised in a relatively brief presentation of a paper or a research project.
All seminars will be held at 12:30 on Wednesdays in TLB 307B on the third floor of the Tioga Library Building. We will aim to conclude by 1:30.
If you are interested in learning more about our seminar series please contact Mohamed Ali.
|Title||Date of Presentation||Speaker||Affiliation||Research Focus|
|Song Li, Co-founder and CTO of NewSky Security||01/13/2016||Song Li||NewSky Security - https://newskysecurity.com||
Abstract - In this talk I will cover the two major game-changing factors in mobile and IoT age: wireless and small or no screen. Switching to wireless, we left two things behind us: the wires, and the trust infrastructure of internet. To make things worse, devices with small or no screens makes the weakest link in security - human - even weaker. I will provide some live demo to help explaining the topics.
|Janine Terrano, CEO of Topia Technology Inc.||01/20/2016||Janine Terrano||Topia Technology Inc.||
Bio - Janine Terrano, is the CEO of Topia Technology Inc. which she founded in 1999 to meet the growing demand for software solutions addressing data security challenges. Terrano spent the last decade developing and piloting programs for securely moving and managing data in complex distributed environments with the US Army, FAA, Air Force and TSA. Each of these customers required the highest level of security coupled with strict performance metrics—challenges met by Terrano and Topia’s seasoned engineering team.
|Distributed Optimization on Apache Spark||01/27/2016||Naveen Ramakrishnan||Data Science Research group at Bosch Research and Technology Center||
Abstract - Most machine learning algorithms involve solving a convex optimization problem. Traditional in-memory convex optimization solvers do not scale well with the increase in data. In this work, we identify a generic convex problem for most machine learning algorithms and solve it using the Alternating Direction Method of Multipliers (ADMM). We implement this framework in Apache Spark and compare it with the widely used Machine Learning LIBrary (MLLIB) in Apache Spark 1.3.
|Understanding Automatic Source Code Summarization||02/03/2016||Paul McBurney||Microsoft Research||
Abstract - Programmers rely on software documentation. Software documentation tells a programmer how to use a system, and how the system functions. However, software documentation is time-consuming to write and often becomes incomplete or outdated. To address the limitations and costs of software documentation, researchers have begun producing automatic source code summarization approaches. In my proposal, I discuss my ongoing and future work towards understanding and improving automatic source code summarization.
|Privacy Preserving Machine Learning Scoring: The Case of Decision Trees||02/10/2016||Prof. Anderson C A Nascimento, Ph.D.||Center for Data Science, University of Washington, Tacoma||
Abstract - Privacy preserving machine learning scoring deals with the problem of scoring data x hold by Alice against a model M hold by Bob so that, at the end of the protocol, Alice should obtain the desired result M(x) and Bob should learn nothing about Alice’s input. Moreover, Alice should obtain no knowledge on the model M beyond what can be efficiently computed from her input x and M(x).
|Mining sociotechnical information from software repositories||02/17/2016||Marco Gerosa||University of São Paulo||
Abstract - A large amount of data is produced during collaborative software development. The analysis of this data sets a great opportunity to better understand software engineering from the perspective of evidence-based research. Mining software repositories studies have contributed to the discovery of important information about software development and evolution, considering both technical and social aspects.
|Machine Reading for Cancer Genomics||02/24/2016||Hoifung Poon||Microsoft Research||
Abstract - Advances in sequencing technology have made available a plethora of genomics data for cancer research, yet the search for disease genes and drug targets remains a formidable challenge. Biological knowledge such as pathways can play an important role in this quest by constraining the search space and boosting the signal-to-noise ratio. The majority of knowledge resides in text such as journal articles, which has been undergoing its own explosive growth, making it mandatory to develop machine reading methods for automating knowledge extraction.
|Autonomic Management of Cost, Performance, and Resource Uncertainty for Deployment of Applications to Infrastructure-as-a-Service (IaaS) Clouds||03/02/2016||Wes Lloyd||Colorado State University||
Abstract - Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous, and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing presents challenges forcing practitioners to balance cost and performance tradeoffs to successfully host applications in the cloud.
|Social Media Users Modeling towards Personalized Advertisement||03/09/2016||Golnoosh Farnadi||Ghent University and Katholieke Universiteit Leuven||
Abstract - Nowadays web users actively generate content on different social media platforms. A large number of users requiring personalized services creates a unique opportunity for researchers to explore user modelling. To distinguish users, recognizing their attributes such as personality, age and gender is essential. To this end, substantial research has been done by utilizing user generated content to recognize user attributes by applying different machine learning techniques.