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Secure & Intelligent Systems (SIS) Lab

At SIS Lab, we do interdisciplinary Machine Learning (ML) research with a focus on Real-Time Inference and Security Analysis for delay-sensitive applications.

ml

Research Areas

Exploring cutting-edge topics at the intersection of Real-time ML and Cybersecurity.

We work on different formulations of Sequential Learning and Inference problems, namely real-time anomaly detection, reinforcement learning, and supervised learning (parametric hypothesis testing and change detection) with applications in several interdisciplinary topics, such as communication assistant for stroke-related speech disorders in collaboration with medical doctors, sea level rise scenario planning in collaboration with urban planners, and permafrost thaw monitoring with geoscientists.

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vad

Video Anomaly Detection

Detecting unexpected events in real-time.

aml

Adversarial Machine Learning

Analyzing vulnerabilities of ML algorithms and developing defense mechanisms.

forecast

Time Series Forecasting

Predicting future values of multivariate time series and detecting anomalies in them.

auth

Hardware-based Authentication

RF fingerprinting and PUF signature design for authentication.

remote

Remote Sensing

Landscape characterization for environmental monitoring.

md

Medical AI

AI for medical applications.

Recent Publications

HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.
Tripathi, A., Waqas, A., Schabath, M.B., Yilmaz, Y. and Rasool, G.
npj Digital Medicine, 2025
GitHub
Additively Manufactured RF Electronics With Structurally Integrated Physically Unclonable Functions for Wireless System Security.
Pendino, A., Nguyen, N., Nouma, S., Wang, J., Yavuz, A., Yilmaz, Y. and Mumcu, G.
IEEE Access, 2025
Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers
Mumcu, F. and Yilmaz, Y.
Transactions on Machine Learning Research (TMLR), 2025
GitHub
Wpmixer: Efficient multi-resolution mixing for long-term time series forecasting
Murad, M.M.N., Aktukmak, M. and Yilmaz, Y.
AAAI Conference on Artificial Intelligence, 2025
GitHub
Hardware and Deep Learning-Based Authentication Through Enhanced RF Fingerprints of 3D-Printed Chaotic Antenna Arrays
McMillen, J., Razak, F.A., Mumcu, G. and Yilmaz, Y.
IEEE Access, 2025
Invisibility Cloak: Hiding Anomalies in Videos via Adversarial Machine Learning Attacks
Karim, H. and Yilmaz, Y.
Winter Conference on Applications of Computer Vision (WACV), 2025

Our Team

yasin
Assoc. Prof. Yasin Yilmaz
Principal Investigator
PhD, 2014, Columbia University, New York, NY
BSc, 2008, Middle East Technical University, Turkiye
Google Scholar
murad
Md Mahmuddun Nabi Murad
PhD Student
Time series forecasting and anomaly detection, Environmental monitoring, Intelligent transportation systems
justin
Justin McMillen
PhD Student
Remote sensing, Multimodal data fusion, Semantic Segmentation, Efficient ML
fawaz
Fawaz Abdul Razak
PhD Student
RF fingerprinting, Hardware-based authentication
lokman
Lokman Bekit
PhD Student
Video anomaly detection, Efficient ML
furkan
Furkan Mumcu
PhD Student
Video anomaly detection, Adversarial ML
hamza
Hamza Karim
PhD Student
Video anomaly detection, Adversarial ML
nick
Nghia (Nick) Nguyen
PhD Student
Physcially unclonabel functions (PUFs), Hardware-based authentication, Video anomaly detection
nick
Nikolas Koutsoubis
PhD Student, co-advised with Dr. Ghulam Rasool (Moffitt Cancer Center)
Federated learning, Uncertainty quantification, Medical image processing
nick
Delwende Eliane Birba
PhD Student, co-advised with Dr. Yoga Balagurunathan (Moffitt Cancer Center)
AI for oncology, Medical image processing
nick
Siddharth Sivaram
PhD Student, co-advised with Dr. Ghulam Rasool (Moffitt Cancer Center)
AI for oncology

Alumni:

Mehmet Necip Kurt
PhD Graduate, 2020 (Columbia University, co-advised with Dr. Xiaodong Wang)
Lead Applied Scientist at Visual Concepts (previously Research Scientist at Amazon)
Mehmet Aktukmak
PhD Graduate, 2020
Machine Learning Engineer at Micron Technology (previously AI Engineer at Intel; co-advised with Dr. Ismail Uysal)
Almuthanna Nassar
PhD Graduate, 2021
Staff Data Scientist at Walmart Global Tech
Keval Doshi
PhD Graduate, 2022
Applied Scientist II at Amazon
Salman Sadiq Shuvo
PhD Graduate, 2023
Research Scientist at Pacific Northwest National Lab
Sabeen Ahmed
PhD Graduate, 2025
Machine Learning Engineer at Moffitt Cancer Center (co-advised with Dr. Ghulam Rasool)
Aakash Tripathi
PhD Graduate, 2025
Machine Learning Engineer at Moffitt Cancer Center (co-advised with Dr. Ghulam Rasool)
Ammar Haydari
MS Graduate, 2025
Machine Learning Engineer at Meta

Latest News

Updates from our lab

Nov 2025

Research Grant

Chih Foundation is providing $10K gift to support our research at SIS Lab!

May 2025

Highly Ranked Scholar

Dr. Yilmaz has been recognized as Highly Ranked Scholar in Anomaly Detection by ScholarGPS, Top 0.05% of all scholars worldwide, Prior 5 years, [link]

March 2025

AAAI

Murad presented his work "Wpmixer: Efficient multi-resolution mixing for long-term time series forecasting" at the 39th Annual AAAI Conference on Artificial Intelligence.

Video Anomaly Detection

We investigate detecting anomalies in video streams (e.g., for surveillance systems, autonomous vehicles, social media content moderation) by designing state-of-the-art deep neural networks and more recently with LLMs, VLMs, and agentic AI systems.

Sample Publications:

  • Mumcu, F., Jones, M., Yilmaz, Y. and Cherian, A., 2025. ComplexVAD: Detecting Interaction Anomalies in Video. Winter Conference on Applications of Computer Vision Workshops (WACVW).[pdf]
  • Karim, H. and Yilmaz, 2024. Real-Time Weakly Supervised Video Anomaly Detection. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) [pdf]
  • Doshi, K. and Yilmaz, Y., 2023. Towards interpretable video anomaly detection. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (WACV).[pdf]
  • Doshi, K. and Yilmaz, Y., 2021. Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate. Pattern Recognition[pdf]
  • Doshi, K. and Yilmaz, Y., 2020. Continual learning for anomaly detection in surveillance videos. IEEE/CVF conference on computer vision and pattern recognition workshops.[pdf]

Sponsor: NSF

vad-llm

Adversarial Machine Learning

Attackers can deceive AI systems by designing adversarial data samples or cyberattacks. We investigate potential vulnerabilities of various AI systems and propose solutions.

Sample Publications:

  • Mumcu, F. and Yilmaz, Y., 2025. Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers. Transactions on Machine Learning Research (TMLR) [pdf]
  • Karim, H. and Yilmaz, Y., 2025. Invisibility Cloak: Hiding Anomalies in Videos via Adversarial Machine Learning Attacks. Winter Conference on Applications of Computer Vision (WACV).[pdf]
  • Mumcu, F. and Yilmaz, Y., 2024. Sequential architecture-agnostic black-box attack design and analysis. Pattern Recognition.[pdf]
  • Mumcu, F. and Yilmaz, Y., 2024. Multimodal attack detection for action recognition models. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).[pdf]
  • Mumcu, F. and Yilmaz, Y., 2024. Fast and lightweight vision-language model for adversarial traffic sign detection. Electronics.[pdf]
  • Mumcu, F., Doshi, K. and Yilmaz, Y., 2022. Adversarial machine learning attacks against video anomaly detection systems. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).[pdf]

Sponsor: NSF
noise-amp

Time Series Forecasting

Time series forecasting and anomaly detection problems appear in a wide range of applications, such as environmental monitoring, ship route prediction, energy optimization, etc.

Sample Publications:

  • Murad, M.M.N., Aktukmak, M. and Yilmaz, Y., 2025. Wpmixer: Efficient multi-resolution mixing for long-term time series forecasting. AAAI Conference on Artificial Intelligence [pdf]
  • Murad, M.M.N., Yirenya-Tawiah, D.K., Weller, T. and Yilmaz, Y., 2025. Multi-Resolution Mixer Network for Localization of Multiple Sensors from Cumulative Power Measurements. IEEE Wireless Communications and Networking Conference (WCNC). [pdf]
  • Doshi, K., Abudalou, S. and Yilmaz, Y., 2022. Reward once, penalize once: Rectifying time series anomaly detection. In 2022 International Joint Conference on Neural Networks (IJCNN). [pdf]

Sponsor: NIFA

Hardware-based Authentication

Hardware signatures can significantly complement software-based authentication. We introduce enhanced RF fingerprints and Physically Unclonable Functions (PUFs) through additively manufactured antenna arrays and digital circuits, respectively.

Sample Publications:

  • Pendino, A., Nguyen, N., Nouma, S., Wang, J., Yavuz, A., Yilmaz, Y. and Mumcu, G., 2025. Additively Manufactured RF Electronics With Structurally Integrated Physically Unclonable Functions for Wireless System Security. IEEE Access. [pdf]
  • McMillen, J., Razak, F.A., Mumcu, G. and Yilmaz, Y., 2025. Hardware and Deep Learning-Based Authentication Through Enhanced RF Fingerprints of 3D-Printed Chaotic Antenna Arrays. IEEE Access. [pdf]
  • McMillen, J., Mumcu, G. and Yilmaz, Y., 2023. Deep learning-based rf fingerprint authentication with chaotic antenna arrays. IEEE Wireless and Microwave Technology Conference (WAMICON). [pdf]
Sponsor: Army
puf

Remote Sensing

We use drone-based lidar and SAR data, as well as satellite-based data, for landscape characterization and environmental monitoring.

Sample Publications:

  • McMillen, J. and Yilmaz, Y., 2025. FuseForm: Multimodal Transformer for Semantic Segmentation. Winter Conference on Applications of Computer Vision Workshops (WACVW). [pdf]
  • McMillen, J. and Yilmaz, Y., 2025. SegGen: An Unreal Engine 5 Pipeline for Generating Multimodal Semantic Segmentation Datasets. Sensors. [pdf]
Sponsor: Army Corps of Engineers
remote

Medical AI

We collaborate with medical doctors and physical theraphists on medical image segmentation, multimodal data fusion, and federated learning.

Sample Publications:

  • Tripathi, A., Waqas, A., Schabath, M.B., Yilmaz, Y. and Rasool, G., 2025. HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings. npj Digital Medicine. [pdf]
  • Abudalou, S., Choi, J., Gage, K., Pow-Sang, J., Yilmaz, Y. and Balagurunathan, Y., 2025. Challenges in Using Deep Neural Networks Across Multiple Readers in Delineating Prostate Gland Anatomy. Journal of Imaging Informatics in Medicine. [pdf]
  • Koutsoubis, N., Waqas, A., Yilmaz, Y., Ramachandran, R.P., Schabath, M.B. and Rasool, G., 2025. Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging. Radiology: Artificial Intelligence. [pdf]
  • Tripathi, A., Waqas, A., Venkatesan, K., Yilmaz, Y. and Rasool, G., 2024. Building flexible, scalable, and machine learning-ready multimodal oncology datasets. Sensors. [pdf]
  • Baldeon-Calisto, M., Wei, Z., Abudalou, S., Yilmaz, Y., Gage, K., Pow-Sang, J. and Balagurunathan, Y., 2023. A multi-object deep neural network architecture to detect prostate anatomy in T2-weighted MRI: Performance evaluation. Frontiers in Nuclear Medicine. [pdf]
Sponsor: Moffitt Cancer Center
database

Contact Us

Department of Electrical Engineering

University of South Florida

4202 E Fowler Ave, ENG 030, Tampa, FL 33620

Email: yasiny@usf.edu

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