Our paper (with Dr. Candido Cabo) on “Promoting students’ social interactions results in an improvement in performance, class attendance and retention in first year computing courses” has been accepted for publication at the 48th IEEE Annual Frontiers in Education (FIE) 2018 Conference. The conference will be held in San Jose, California, from Oct 4th-6th, 2018. The program for the conference can be found at: https://edas.info/p23986

I am pleased to inform that a discussion session titled “Pros and Cons of Using Data Analytics for Predicting Academic Performance in Computer Science Courses” (with co-authors J. Bivens and J. Chen), and a Poster titled “Building a Community of First Year Students Improves Student Retention and Performance in Computing Courses” (with co-author C. Cabo) are both accepted for publication at the ACM SIGCSE 2018 conference to be held in Feb 2018.

Our new paper (with Dr. Candido Cabo) on “Building Community Improves Student Performance in First-Year Computing Courses” has been accepted for presentation at the  American Educational Research Association (AERA) 2018 annual meeting. AERA received over 13,000 submissions this year. The conference will be held in New York City, New York, from Friday, April 13 to Tuesday, April 17, 2018. More details about the conference can be found at: http://www.aera.net/Events-Meetings/Annual-Meeting/2018-Annual-Meeting-General-Information

Paper titled “Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis” is now published. This is work done in collaboration with Math professors (Nadia Benakli, Boyan Kostadinov, Satyanand Singh) at CityTech. 
Abstract: The goal of this paper is to promote computational thinking among mathematics, engineering, science and technology students, through hands-on computer experiments. These activities have the potential to empower students to learn, create and invent with technology, and they engage computational thinking through simulations, visualizations and data analysis. We present nine computer experiments and suggest a few more, with applications to calculus, probability and data analysis, which engage computational thinking through simulations, visualizations and data analysis. We are using the free (open-source) statistical programming language R. Our goal is to give a taste of what R offers rather than to present a comprehensive tutorial on the R language. In our experience, these kinds of interactive computer activities can be easily integrated into a smart classroom. Furthermore, these activities do tend to keep students motivated and actively engaged in the process of learning, problem solving and developing a better intuition for understanding complex mathematical concepts.

Paper titled “Performance modeling of CMOS inverters using support vector machines (SVM) and adaptive sampling” is accepted at the Journal of Microprocessors and Microsystems (Elsevier).

Abstract: Integrated circuit designs are verified through the use of circuit simulators before being reproduced in real silicon. In order for any circuit simulation tool to accurately predict the performance of a CMOS design, it should generate models to predict the transistor’s electrical characteristics. The circuit simulation tools have access to massive amounts of data that are not only dynamic but generated at high speed in real time, hence making fast simulation a bottleneck in integrated circuit design. Using all the available data is prohibitive due to memory and time constraints. Accurate and fast sampling has been shown to enhance processing of large datasets without knowing all of the data. However, it is difficult to know in advance what size of the sample to choose in order to guarantee good performance. Thus, determining the smallest sufficient dataset size that obtains the same accurate model as the entire available dataset remains an important research question. This paper focuses on adaptively determining how many instances to present to the simulation tool for creating accurate models. We use Support Vector Machines (SVMs) with Chernoff inequality to come up with an efficient adaptive sampling technique, for scaling down the data. We then empirically show that the adaptive approach is faster and produces accurate models for circuit simulators as compared to other techniques such as progressive sampling and Artificial Neural Networks.