Hasan Ferit Enişer I am a first year PhD student in MPI-SWS advised by Maria Christakis. I am a member of Practical Formal Methods Group.

My research interests lie in Program Analysis and Machine Learning. In my PhD study, I focus on testing and verification of machine learning, especially deep learning. I apply widely used software testing/verification practices to deep neural networks.

Currently, I am investigating fault localization/tolerence for deep neural networks. Other than that, I am interested in many other aspects of safe AI.

I graduated from Bogazici University in 2017 with a M.Sc. in computer science. Before that, I did my B.Sc. studies in computer science at the same university.



DeepFault: Fault Localization For Deep Neural Networks
H. F. Eniser, S. Gerasimou, A. Sen,
FASE 2019
Virtualization of Stateful Services via Machine Learning
H. F. Eniser, A. Sen,
Submitted to Software Quality Journal
Testing Service Oriented Architectures Using Stateful Service Virtualization Via Machine Learning
H. F. Eniser, A. Sen,
International Workshop on Automation of Software Test (AST), 2018.
FancyMock: Creating Virtual Services From Transactions
H. F. Eniser, A. Sen, S. O. Polat
ACM Symposium on Applied Computing SE Track, 2018.
Service Virtualization Using Recorded Interactions
H. F. Eniser
M.Sc. Thesis

Temporal Logic Motion Planning using POMDP with Parity Objectives
M. Svorenová, M. Chmelík, K. Leahy, H. F. Eniser, K. Chatterjee, I. Cerná, C. Belta
Hybrid Systems: Computation and Control (HSCC), 2015.
I can provide PDFs upon request.


Spring 2019 Digital Design
Taught by: Alper Sen
Fall 2018 Probability and Statistics
Taught by: Ethem Alpaydin
Spring 2018 Programming Languages
Taught by: Tunga Gungor
Spring 2018 Systems Programming
Taught by: Can Ozturan
Fall 2017 Probability and Statistics
Taught by: Ethem Alpaydin


Here are several presentations prepared by me. One can freely use them. If you want to make a correction or have sources contact me.

DeepXplore: Automated Whitebox Testing of Deep Learning Systems link to pdf
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars link to pdf
Feature-Guided Black-Box Safety Testing of Deep Neural Networks link to pdf
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation link to pdf
Testing Deep Neural Networks link to pdf
Concolic Testing for Deep Neural Networks link to pdf
DeepMutation: Mutation Testing of Deep Learning Systems link to pdf
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing link to pdf
Explaining and Harnessing Adversarial Examples link to pdf
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing link to pdf