Math & Computer Science Dept.
John Jay College of Criminal Justice
April 11, 12pm-1pm
In this 20-year retrospective, we discuss some of the challenges of dealing with distributed denial-of-service (DDoS) attacks from its origins in 1999 to the recent attacks in the late 2010s. We describe first architectures of DDoS agents, the challenges of DDoS agent/bot forensics, the variety of topologies and command-and-control mechanisms for botnets over the years, the different victim populations from scientific workstations to IoT devices, future and current Internet design considerations, as well as attack and defense mechanisms at the host and network levels.
Brooklyn College, Graduate Center
November 8, 12pm-1pm
Matching human performance is one of the most difficult problems for a variety of speech communication technologies, including automatic speech recognition, voice processing in hearing aids, and mobile telephony. One theory of human noise robustness is that listeners pick out reliable “glimpses” of a target sound and utilize contextual clues to fill in missing information using top-down knowledge. This talk presents work that brings both of these processes to machines.
Design Applications Development Manager
Perkins + Will
October 11, 12pm-1pm
Enterprises are embracing Virtual and Augmented Reality, using VR and AR to transform the way businesses work, train their staff, collaborate, communicate and interact with clients. We will examine the current trend of VR and AR usage from different industries such as Aviation, Automobile and Retail with a specific emphasis on use cases for the Architectural, Engineering and Construction industry.
Software Engineer Google
April 12, 12pm-1pm Room N923
In this talk we’ll go over TensorFlow, an open- source cross-platform machine learning library developed by Google, and explore its new feature: eager execution. We’ll go over how to use it to write dynamic models, to debug and profile models, and to learn deep learning.
Business Development, M& A
IBM Watson Group
In a world of open data and consumer platforms it is easy to forget the significant quantities of high-value data still held by entities who view or require those assets to be proprietary. A myriad of parties from corporations to government entities are keen to explore new advances in AI but do not recognize the challenges that will befall them as they try to protect the data assets that are their lifeblood. ‘Applied AI’ is the study of the novel techniques required to translate AI innovations into agents of value creation for this silent majority. We’ll explore the limitations of today’s most commonly applied AI techniques and discuss a variety of ways institutions are accommodating these shortfalls. We’ll also spend some time on exciting new results in the nascent field of few shot learning to inspire further hope for the future and show that there is core innovation in AI still to be accomplished.
Yu-Wen Chen, PhD
Assistant Professor, CST Dept.
December 7, 12pm-1pm, Room N907
Dr. Chen presents an introduction to smart grid and cloud computing as the foundation for the design of customer-oriented energy-efficient systems (EmaaS). These systems provide financial incentives to customers thus enhancing the renewable energy sources(solar, wind, electrical) integration with the smart grid community.
APRIL 7 @ 12:00 PM – 1:00 PM in N928
This work introduces faceted service discovery. It uses the Programmable Web directory as its corpus of APIs and enhances the search to enable faceted search, given an OWL ontology. The ontology describes semantic features of the APIs. We have designed the API classification ontology using LexOnt, a software we have built for semi-automatic ontology creation tool. LexOnt is geared toward non-experts within a service domain who want to create a high-level ontology that describes the domain. Using well- known NLP algorithms, LexOnt generates a list of top terms and phrases from the Programmable Web corpus to enable users to find high-level features that distinguish one Programmable Web service category from another. To also aid non-experts, LexOnt relies on outside sources such as Wikipedia and Wordnet to help the user identify the important terms within a service category. Using the ontology created from LexOnt, we have created APIBrowse, a faceted search interface for APIs. The ontology, in combination with the use of the Apache Solr search platform, is used to generate a faceted search interface for APIs based on their distinguishing features. With this ontology, an API is classified and displayed underneath multiple categories and displayed within the APIBrowse interface. APIBrowse gives programmers the ability to search for APIs based on their semantic features and keywords and presents them with a filtered and more accurate set of search results.
Knarig Arabshian is an Assistant Professor in the Computer Science Department at Hofstra University, since Fall 2014. Prior to that she was a Member of Technical Staff at Bell Labs in Murray Hill, NJ. She received her Ph.D. in Computer Science from Columbia University in 2008.
Professor Arabshian’s interests lie in the field of semantic web, service discovery and composition, context-aware computing and distributed systems. The goal of her research is to drive forward the idea of a personalized web. Her work explores ways of describing data meaningfully and designing frameworks and systems for efficient data discovery. During her tenure at Bell Labs, she worked on different aspects of ontology creation, distribution and querying.
MARCH 24 @ 12:00 PM – 1:00 PM in N928
Founded in 2010, NYC Media Lab is dedicated to driving innovation and ultimately job growth in media and technology by facilitating collaboration between the City’s universities and its companies. Comprised of a consortium including New York City Economic Development Corporation, New York University, Columbia University, The New School, CUNY, and Pratt Institute, NYC Media Lab’s goals include generating research and development, knowledge transfer, and talent development across all of the City’s campuses. Justin will describe NYC Media Lab, its projects, and the curiosities of its member companies.
Justin Hendrix connects companies seeking to advance digital media technologies with university capabilities in order to drive collaborative innovation. Before joining NYC Media Lab, Hendrix was Vice President, Business Development & Innovation for The Economist Group in the Americas, where he directed the Group’s innovation process, including prototyping, testing, and commercializing new digital media business concepts. Prior to this role, Hendrix directed brand marketing and communications and ran The Economist’s thought leadership events business in the Americas. He is a regular writer and speaker on media & innovation. Hendrix holds a Bachelor of Arts from the College of William & Mary and a Master of Science in Technology Commercialization from the McCombs School of Business, University of Texas at Austin. He lives in Brooklyn.
MARCH 10 @ 12:00 PM – 1:00 PM
In our research, we analyze digitized images of Hematoxylin-Eosin (H&E) slides equipped with tumorous tissues from patient derived xenograft models to build our bio-inspired computation method, namely Personalized Relevance Parameterization of Spatial Randomness (PReP-SR). Applying spatial pattern analysis techniques of quadrat counts, kernel estimation and nearest neighbor functions to the images of the H&E samples, slide-specific features are extracted to examine the hypothesis that existence of dependency of nuclei positions possesses information of individual tumor characteristics. These features are then used as inputs to PReP-SR to compute tumor growth parameters for exponential-linear model. Differential evolution algorithms are developed for tumor growth parameter computations, where a candidate vector in a population consists of size selection indices for spatial evaluation and weight coefficients for spatial features and their correlations. Using leave-one-out-cross-validation method, we showed that, for a set of H&E slides from kidney cancer patient derived xenograft models, PReP-SR generates personalized model parameters with an average error rate of 13:58%. The promising results indicate that bio-inspired computation techniques may be useful to construct mathematical models with patient specific growth parameters in clinical systems.
Aydin Saribudak received his Bachelor of Science degree, in 2005, from Electrical and Electronics Engineering Department of Middle East Technical University (METU), Turkey. After his graduation, he worked as software developer and researcher in telecommunication field for more than 5 years. Aydin is currently a Ph.D. candidate at the City College of the CUNY. His interests include biologically inspired computation algorithms, artificial intelligence, and their applications to personalized mathematical models for tumor growth and anti-cancer therapy.