• Florida Cybersecurity Center Collaborative Seed Award (2018-2019)
  • Best Paper Award at the 10th International Conference on Foundations of Privacy & Security FPS (2017)
  • UCF Predictive Analytics Innovation Fellow (2017-2018)
  • Air Force Office of Scientific Research Young Investigator Award (2016-2019)
  • UCF Faculty Senate (2016-2017)
  • National Science Foundation Computing and Communications Foundations -- Exploiting Parallelism & Scalability (2014-2017)
  • National Science Foundation Computing and Communications Foundations -- Software & Hardware Foundations (2014-2018)
  • Best Paper Award at IEEE International Conference on Computational Advances in Bio and Medical Sciences ICCABS (2014)
  • ASE/Air Force Summer Faculty Fellowship (2014)
  • Charles N. Millican Faculty Fellow (2013)
  • IEEE Orlando Outstanding Engineering Educator Award (2013)
  • Air Force Information Directorate Visiting Faculty Fellowship Program (2013)
  • Best Paper Award at IEEE International Conference on Computational Advances in Bio and Medical Sciences ICCABS (2011)

Recent Publications

S. Raj, S. K. Jha, L. L. Pullum, and A. Ramanathan, “ SATYA: Defending against Adversarial Attacks using Statistical Hypothesis Testing ,” in The 10th International Symposium on Foundations and Practice of Security (FPS 2017), Nancy, France. (BEST PAPER AWARD), 2017.Abstract

The paper presents a new defense against adversarial attacks for deep neural networks. We demonstrate the effectiveness of our approach against the popular adversarial image generation method DeepFool. Our approach uses Wald's Sequential Probability Ratio Test to sufficiently sample a carefully chosen neighborhood around an input image to determine the correct label of the image. On a benchmark of 50,000 randomly chosen adversarial images generated by DeepFool we demonstrate that our method SATYA is able to recover the correct labels for 95.76% of the images for CaffeNet and 97.43% of the correct label for GoogLeNet. 


D. Chakraborty, S. Raj, J. C. Gutierrez, T. Thomas, and S. K. Jha, “ In-Memory Execution of Compute Kernels using Flow-based Memristive Crossbar Computing ,” in IEEE International Conference on Rebooting Computing 2017, Washington D.C., 2017.Abstract


Rebooting computing using in-memory architectures relies on the ability of emerging devices to execute a legacy software stack. In this paper, we present our approach of executing compute kernels written in a subset of the C pro- gramming language using flow-based computing on nanoscale memristor crossbars. Our framework also tests the correctness of the design using the parallel Xyces electronic simulation software. We demonstrate the potential of our design methodology by designing and testing a compute kernel for edge detection in images. 


S. Raj, A. Ramanathan, L. L. Pullum, and S. K. Jha, “ Testing Autonomous Cyber-Physical Systems using Fuzzing Features Derived from Convolutional Neural Networks ,” in ACM SIGBED International Conference on Embedded Software (EMSOFT), Seoul, South Korea, 2017.Abstract


Autonomous cyber-physical systems rely on modern machine learning methods such as deep neural networks to control their interactions with the physical world. Testing of such intelligent cyber-physical systems is a challenge due to the huge state space associated with high-resolution visual sensory inputs. In this paper, we demonstrate how fuzzing the input using patterns obtained from the convolutional lters of an unrelated convolutional neural network can be used to test the correctness of vision algorithms implemented in intelligent cyber-physical systems. Our method discovers interesting counterexamples to the pedestrian detection algorithm implemented in the popular OpenCV library. Our approach also unearths counterexamples to the correct behavior of an autonomous car similar to NVIDIA’s end-to-end self-driving deep neural net running on the Udacity open-source simulator. 


S. Raj and S. K. Jha, “ Predicting Success in Undergraduate Parallel Programming via Probabilistic Causality Analysis ,” in 8th NSF/TCPP Workshop on Parallel and Distributed Computing Education (EduPar-18) co-located with the 32nd IEEE International Parallel & Distributed Processing Symposium, Vancouver, Canada, 2018.Abstract


We employ probabilistic causality analysis to study the performance data of 301 students from the upper-level under- graduate parallel programming class at the University of Central Florida. To our surprise, we discover that good performance in our lower-level undergraduate programming CS-I and CS- II classes is not a significant causal factor that contributed to good performance in our parallel programming class. On the other hand, good performance in systems classes like Operating Systems, Information Security, Computer Architecture, Object Oriented Software and Systems Software coupled with good performance in theoretical classes like Introduction to Discrete Structures, Artificial Intelligence and Discrete Structures-II are strong indicators of good performance in our upper-level un- dergraduate parallel programming class. We believe that such causal analysis can be useful in identifying whether parallel and distributed computing concepts have effectively penetrated the lower-level computer science classes at an institution.


A. U. Hassen and S. K. Jha, “ Free Binary Decision Diagram Based Synthesis of Compact Crossbars for in-Memory Computing of Boolean Functions ,” in International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 2018.Abstract

We introduce a new computer-aided design  approach based on Free Binary Decision Diagrams (FBDDs) for  implementing Boolean functions on crossbars using flow-based computing. Our crossbar synthesis procedure uses generalized FBDDs to design crossbars for a Boolean formula such that there is a flow of current from an input nanowire to an output nanowire through the sneak paths in the crossbar if and only if the Boolean formula evaluates to true.  Generalized FBDDs are more succinct representations of Boolean formula that traditional Reduced Ordered Binary Decision Diagrams (ROBDDs) because they do not require the same variable ordering along all paths of the decision diagram.   Our experimental results with the middle bit of a multiplier show that our designs are 69.9% more succinct than flow-based crossbar computing approaches designed using ROBDDs. 

A. Velasquez and S. K. Jha, “ Fault-Tolerant In-Memory Computing Using Paths-Based Logic and Heterogeneous Components ,” in Design, Automation, and Test in Europe (DATE), Dresden, Germany, 2018.Abstract


The memory-processor bottleneck and scaling difficulties of the CMOS transistor have given rise to a plethora of research initiatives to overcome these challenges. Popular among these is in-memory crossbar computing. In this paper, we propose a framework for synthesizing fault-tolerant computation- in-memory circuits based on bounded model checking. The resulting designs can be used to compute Boolean formulas using a constant number of read and write cycles. We demonstrate the effectiveness of the approach by generating addition and comparator circuits in the presence of common crossbar faults. 


A. Ramanathan, L. L. Pullum, S. Raj, Z. Husein, S. Pattanaik, and S. K. Jha, “ Adversarial attacks on computer vision algorithms using natural perturbations ,” in Tenth International Conference on Contemporary Computing, New Delhi, India, 2017, pp. in press.Abstract


Verifying the correctness of intelligent embedded systems is notoriously difficult due to the use of machine learning algorithms that cannot provide guarantees of deterministic correctness. In this paper, we investigate the histogram of oriented gradients (HOG) based human detection algorithm implemented in the popular OpenCV computer vision framework. Our validation efforts demonstrate that the OpenCV imple- mentation is susceptible to errors due to both malicious perturbations and naturally occurring fog phenomena. To the best of our knowledge, we are the first to explicitly employ a natural perturbation (like fog) as an adversarial attack using methods from computer graphics and demonstrate that computer vision algorithms are also susceptible to errors under such naturally occurring minor perturbations. Our methods and results may be of interest to the designers, developers and validation teams of intelligent cyber-physical systems such as autonomous cars. 


D. Chakraborty, S. Raj, and S. K. Jha, “ A Compact 8-bit Adder Design using In-Memory Memristive Computing: Towards Solving the Feynman Grand Prize Challenge ,” in 13th ACM/IEEE International Symposium on Nanoscale Architectures, Newport, USA, 2017, pp. in press.Abstract

We introduce a new compact in-memory computing design for implementing 8-bit addition using eight vertically-stacked nanoscale crossbars of  one-diode one-memristor 1D1M switches. Each crossbar in our design only has 5 rows and 4 columns. Hence, the design may be used to fabricate a compact 8-bit adder that meets the size constraint of  50nm x 50nm x 50nm imposed by the electrical component of the Feynman Grand Prize. The potential availability of sub-5nm nanoscale memristors and single-molecule diode devices coupled with the ability to fabricate high-density nanoscale memristor crossbars suggests that our design may eventually be fabricated to meet the size constraints of the  Feynman Grand Prize.