DC1 - Shiyuan Piao

Eindhoven University of Technology

DC1: Physics Guided Learning and Control for EMC

Shiyuan Piao has recently joined the PATTERN project as a Doctoral Candidate (DC1), focusing on physics guided learning and control for EMC. After graduating from the Dalian University of Technology with a master’s degree in Control Science and Engineering, he joined the Eindhoven University of Technology to pursue his PhD under the supervision of Prof. Roland Toth and Dr. Amritam Das. 

Shiyuan is interested in integrating physics knowledge into AI models to develop more reliable and effective approaches for solving complex EMC problems. Outside of research, Shiyuan enjoys traveling, chess, and football. 

He obtained master’s degree in Control Science and Engineering at Dalian University of Technology, focusing on deep learning methods for partial differential equations.  He is currently eager to continue his studies at the Eindhoven University of Technology, where he will conduct research on physics-guided learning and control for electromagnetic compatibility (EMC) under the guidance of Professor Roland Toth and Dr. Amritam Das.

In his free time, he enjoys traveling, chess, and football.

DC 2 - Milad Momeni

Eindhoven University of Technology

DC2: AI to combine and model EMNF applied to cables

Milad Momeni has recently joined the PATTERN project as a Doctoral Candidate (DC2), focusing on AI-driven modeling of electromagnetic noise footprints in cables for integrated EMC design in medical products.

Milad initially aspired to contribute directly to cutting-edge biomedical technologies. He pursued an MSc in Electrical Engineering (Fields & Waves) at the University of Tehran. He conducted research on transcranial magnetic stimulation (TMS) coil design, and discovered true interest in combining physics-based modeling with AI to tackle complex electromagnetic phenomena. This led him to join the PATTERN project at Eindhoven University of Technology as a Doctoral Candidate, where he focuses on AI-driven modeling of electromagnetic noise in cables to enable integrated EMC design in medical products. 

He transitioned from the University of Tehran, where he completed his MSc in Electrical Engineering (Fields & Waves) and conducted research on transcranial magnetic stimulation (TMS) coil design, to Eindhoven University of Technology to begin his PhD research under the supervision of Prof. Anne Roc’h and Prof. Sander Bronckers.

Outside of research, Milad enjoys cycling, hiking, chess, and cooking, and he also loves traveling and gaining new experiences. 

DC 3 - Jennifer Dimov

CNRS Rennes

DC3: Sensitivity propagation in AI-developed tools for EMI analysis

Jen joined DN PATTERN as a doctoral candidate for DC3, which focuses on statistical propagation of uncertainty in efficiently modeling electromagnetic systems for EMC design purposes.

DC3 aims to identify statistical techniques for more accurately predicting the electric field emissions from complex real-life sources (from PCBs to aircraft) without consuming untenable quantities of computational resources, using iterative machine learning to train surrogate models to meet or exceed the accuracy of conventional deterministic modeling techniques. 

She obtained a BSc from the University of the Sciences in Philadelphia and a MSc Technical Management from Johns Hopkins University in Baltimore, and has returned to academia at INSA Rennes to study with Dr. Philippe Besnier after over ten years of industry experience in EMC at NASA Goddard Space Flight Center and as a Senior Member of the IEEE EMC Society. 

Outside of work and study, Jen enjoys reading, hiking, video games, and combat simulation sports, and is very happy so far with the beautiful country of France and its extremely functional trains.  

DC 4 - Hugo Vitry

Eindhoven University of Technology

Originally from La Réunion, a French island in the Indian Ocean. During his studies, he discovered the joy of antenna and PCB design. Passionate about science and technology, he strongly believes that AI can bring the world to the next level. He joined PATTERN focusing on AI to combine and model ElectroMagnetic Noise Footprint (EMNF) applied to PCB Tracks.

His main hobby is participating in robotics tournaments. He’s deeply interested in space exploration. He also enjoys cooking, traveling and playing the guitar.

He received his Master’s degree in Aeronautical and Space Telecommunications Engineering from the École Nationale de l’Aviation Civile (ENAC) in Toulouse.

DC 5 - Alberto Novati

Ghent University

Alberto Novati has recently joined the DN PATTERN project as a Doctoral Candidate (DC5), focusing on developing novel ML-based Sequential Sampling Techniques for Electromagnetic Compatibility Measurements (EMC).

Before joining the PATTERN Project, he worked in industry as a Data Scientist, where he helped clients in adopting state-of-the-art AI models and workflows. He recently started his PhD at IDLab (UGent – imec) under the supervision of Prof. Ir. Tom Dhaene. Alberto will now combine his expertise in Physics, mathematical modelling and AI in order to develop new cutting-edge AI techniques to enhance the testing and design process of microwave devices, optimizing them for both safety and efficiency.

He earned his Bachelor’s degree in Physics from the University of Pavia and his Master’s degree in Physics of Complex Systems at the University of Turin. There he grew more and more interested in modelling a variety of real-world, complicated systems by means of mathematical techniques and Machine Learning ones. He did research for his Master Thesis at CENTAI where he worked on developing ML techniques for making Agent Based Models (ABM) capable of statistical inference via learning from data.

Alberto, in typical italian fashion, likes to surround himself with good company and good food, alongside with some good (belgian) beer or (italian) wine. Outside his research activity, he enjoys books, videogames and travelling, always seeking for new opportunities to grow both professionally and personally.

DC 6 - Nazim Talibzade

Hamburg University of Technology

Nazim Talibzade has joined the PATTERN project as a Doctoral Researcher (DC6) at the Hamburg University of Technology (TUHH) in collaboration with NEXPERIA and Prof. Dr. Matthias Mnich. His academic and research background includes machine learning and multimodal AI, with several projects focused on object detection and model evaluation. Now, under the supervision of Prof. Dr. Christian Schuster, his PhD focuses on the generation of adaptive database for machine learning-based EMI and SI prediction and optimization of high speed electronic packages, aiming to integrating AI into electronic design automation.

Driven by a deep enthusiasm for machine learning and its limitless potential across diverse fields, Nazim sees AI as a driving force for the future of innovation. He believes through collaboration and knowledge sharing within the PATTERN network, it will possible to reach new heights in research and create technologies that will make a great impact on our life.

He joined ADA University in Azerbaijan to study Master of Science in Computer Science and Data Analytics as part of his dual degree program in corporation with the George Washington University in the USA. During his studies, he became passionate about machine learning and AI, with several projects focused on training an object detection model for detecting objects in real-time and model evaluation.

DC 7 - Nagapoornima S. Murthy

Eindhoven University of Technology

Recently, Nagapoornima S. Murthy has joined DN PATTERN as a doctoral candidate (DC7), where her research focuses on AI for EMI filters. She believes that, when applied in the right way, AI can be effectively integrated into EMC, significantly reducing time-to-market. AI is widely recognized as a valuable tool for enhancing efficiency, early problem detection, and optimization in EMC and electrical design, with strong potential to accelerate R&D cycles.

Nagapoornima holds a Bachelor’s degree in Electrical Engineering from India and an MSc in Embedded Systems from Germany. After gaining several years of industry experience across software, hardware, and system integration, she has returned to academia to pursue doctoral research under the supervision of Anne Roc’h at Eindhoven University of Technology.

Beyond her research, Nagapoornima enjoys playing cricket, reading, and hiking.

DC 8 - Samuel Gelvez Osorio

CNRS Rennes

Samuel has recently joined the PATTERN project as a Doctoral Candidate (DC8), focusing on electromagnetic compatibility of wiring systems using artificial intelligence methods.

He begins his PhD research under the supervision of a PhD researcher. Philippe Besnier and Prof. Jean-François Dupuy at the Institut National des Sciences Appliquées of Rennes. With enthusiasm, dedication, and empathy, Samuel hopes to collaborate with his research colleagues as he advances toward his goal of becoming a university professor.

He earned his bachelor’s degree in electrical engineering and his Master’s degree in electrical engineering at the Universidad Nacional de Colombia. There, he acquired his knowledge of EMC theory and, at the LIATER laboratory, had the opportunity to manage EMC computer simulation projects related to low-frequency interference and environmental emissions from transmission lines and substations.

In his free time, he likes to walk outdoors, enjoy nature, and spend time with his family and friends.

DC - 9 Mikheil Kvizhinadze

Hamburg University of Technology

Mikheil Kvizhinadze has recently joined the PATTERN network as a Doctoral Candidate (DC9). My research focuses on developing AI-based methods for signal integrity (SI) and electromagnetic interference (EMI) optimization in high-speed interconnects. He is pursuing his PhD at the Hamburg University of Technology (TUHH) in collaboration with Rosenberger.

He has recently joined the PATTERN network as a Doctoral Candidate (DC9). My research focuses on developing AI-based methods for signal integrity (SI) and electromagnetic interference (EMI) optimization in high-speed interconnects. He is pursuing his PhD at the Hamburg University of Technology (TUHH) in collaboration with Rosenberger.

DC 10 - Sasi Rekha Paramaguru

Tomas Bata University in Zlín

Sasi Rekha Paramaguru has recently joined the DN PATTERN project as a doctoral candidate (DC10); her research focuses on developing efficient electromagnetic shielding designs and optimizing them using AI-driven evolutionary computation methods.

She completed her Bachelor’s in Mechatronics Engineering and Master’s in Engineering Design. During her master’s, she worked on the design and analysis of metastructures using mathematical modeling. Her area of interest encompasses materials and composites, design for manufacturing, design optimization, and additive manufacturing. Now that she has set foot in her PhD, Sasi is curious to expand her expertise into electromagnetic shielding under the supervision of Dr. Stanislav Kovar at Tomas Bata University in Zlin.

In her spare time, Sasi likes to sit with a good novel and appreciates the art of storytelling.

DC 11 - Mete Turhan

KU Leuven

He studied Aeronautical Engineering, where he developed an interest in computational fluid dynamics and computational analysis on physical systems. Later, his curiosity about applying AI to solve physical phenomena led him to pursue a master’s in Computer Science, specializing in AI. He is now continuing his academic journey along this long-standing interest, with a growing motivation for AI-based solution techniques. He has recently joined DN PATTERN as a Doctoral Candidate (DC11), focusing on the development of AI diagnostic tools to identify the origin of EMI.

He completed his bachelor’s degree in Aeronautical Engineering at Istanbul Technical University, where he was first introduced to electromagnetic theory. Later, his interests shifted toward AI, and he pursued a master’s degree in Computer Science and Engineering at the University of Oulu in Finland. During his studies, he also worked as a research assistant in the Biomimetics and Intelligent Systems Research Group, specializing in Data AI and contributing to multiple projects ranging from wearable devices to cybersecurity, with AI consistently serving as the core tool for addressing challenges and generating insights. Recently, he began his PhD under the supervision of Prof. Davy Pissoort in the WaveCore Research Group at KU Leuven, Belgium.

Beyond his professional journey, he loves traveling and exploring nature, discovering the beauty of his new place in Ghent and destinations around the world.

DC 12 - Nianfei Ao

Nova School of Science and Technology

Nianfei Ao has recently joined the “Development of AI tools to assess accurately the influence of EMI on biosignals” project as a Doctoral Candidate, focusing on the development of AI tools to accurately assess the influence of EMI on biosignals. Nianfei Ao is a dedicated researcher with a background in both industry and academia, specializing in the application of artificial intelligence to complex biological data. His Master’s thesis involved developing deep learning models for medical projects, after which he spent a year in industry. He is passionate about leveraging AI to solve meaningful challenges. Nianfei believes this research will have a significant impact on the future integration of AI into biosignal monitoring devices and aligns with his long-term career goals.

He transitioned from his Master’s research, where he developed AI tools for automated analysis of mouse pathology images, he spent a year working in the big data intelligent analysis department at GenScript. There, he developed programs for automatically processing Protein electrophoresis gel images. He now transitions to his PhD research under the supervision of Prof. Hugo Gamboa.

Outside of the lab, Nianfei relaxes by listening to music and tending to his plants. He values these grounding activities for the mental clarity and balance they provide.

DC 13 - Yuhang Wang

KU Leuven

Yuhang Wang has recently joined the PATTERN project as a Doctoral Candidate (DC13) at KU Leuven in Belgium, under the supervision of Prof. Mathias Verbeke.

The objective of this project is to develop AI-driven methods for real-time anomaly detection in electromagnetic compatibility (EMC) environments. Since anomalous events are rare and difficult to capture in large datasets, the research focuses on unsupervised and semi-supervised approaches that learn representations of normal electromagnetic behavior from regular operational data. By combining electromagnetic interference (EMI) footprints with modern machine learning and deep learning techniques, the project aims to establish an efficient framework for detecting anomalies and providing insights into their potential root causes.

Expected results: The expected outcome of this project is a flexible anomaly detection framework that can rapidly adapt to different electromagnetic environments. The framework will integrate advanced AI techniques with EMI-based representations to detect abnormal patterns while also providing interpretable insights into the model’s decision-making process, supporting reliable diagnostics and practical deployment in EMC applications.

Before joining PATTERN, he completed his Master’s degree at Leiden University in the Netherlands, where his research focused on graph neural networks and model deployment. His research interests lie at the intersection of artificial intelligence and real-world problem solving, particularly in developing intelligent systems that enhance efficiency, safety, and explainability in industrial environments.

DC 14 - Lorenzo Barsotti

University of Twente

Lorenzo Barsotti has recently joined the DN PATTERN project as a Doctoral Candidate (DC14), focusing on the development of AI diagnostic tools aimed at transforming EMI propagation modeling from post-design analysis to pre-design synthesis, by incorporating neglected design factors and empirical insights from experts.

He graduated in Physics and Astrophysics from the University of Florence, and in Applied Physics from the Alma Mater Studiorum – University of Bologna. In the latter degree he focused on applying machine learning to physics problems. He concluded his studies with an internship at ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development. Afterwards, he moved to Trento, where he spent two years in the Silicon Photomultiplier research group at Fondazione Bruno Kessler – FBK.

Outside of research, Lorenzo enjoys traveling, sports, listening to music and watching movies.

DC 15 - Jonathan Cedeño Chaves

KU Leuven

He has joined to DN PATTERN project as a Doctoral Candidate for DC15, which involves the development of a tool based on AI for the scanning of the near field of systems, as part of the test and post-deployment phase of electronics products. Under the supervision of Prof. Tim Claeys and Prof. Davy Pissoort at the KU Leuven, he is investigating on AI topics and near-field scanning from different electromagnetic radiation cases.

Born in Costa Rica in 1991. He received his bachelor’s and master’s degrees in Electronics Engineering at the Costa Rica Institute of Technology (ITCR), option in Microelectronics. Additionally, he worked at Intel Corporation for around 8 years as an Analog Engineer focused on the design and verification of analog circuits in memory interfaces, as well as the signal integrity analysis for client platform products. His current interests include the analysis of electromagnetic compatibility of digital systems, design and optimization of interconnections in high-speed links, and design and testing of analog circuits.

In his free time, he enjoys the sports, reading, expending time with family and friends.

DC 16 - Hamza Iqba

De Montfort University

Hamza is a Doctoral Candidate at De Montfort University (UK) within the MSCA-DN PATTERN project. His background is in Electrical Engineering with a strong focus on Electronics and AI. In PATTERN, he will work on human analogue training for neural networks, integrating Feature Selective Validation (FSV) with AI techniques to improve validation and assessment workflows.

He holds an Electrical Engineering background with research experience in machine learning, image processing and synthetic data generation.

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