Members

Members

Prof. Dr. John Paul Strachan
Head of Institute
E-Mail: j.strachan@fz-juelich.de

John Paul Strachan directs the Peter Grünberg Institute (PGI-14) for Neuromorphic Compute Nodes at Forschungszentrum Jülich and is Professor in the faculty of Electrical Engineering and Information Technology at RWTH Aachen University. Before this, John Paul led the Emerging Accelerators team within the A.I. Lab at Hewlett Packard Labs, where he was a Distinguished Technologist. He received Bachelors degree in Electrical Engineering with Computer Science and Physics from MIT, a Masters in Electrical Engineering and Computer Science from MIT, and a Ph.D. from Stanford University in Applied Physics. He took a post-doc at Hewlett Packard Labs and joined the permanent staff. He has over 50 patents and has authored or co-authored over 90 peer-reviewed papers. He previously studied nanomagnetic devices for memory for which he was awarded the Falicov Award from the American Vacuum Society, and has developed sensing systems for precision agriculture in a company which he co-founded. Since 2014, he has been leading a team to explore novel computing architectures that exploit emerging device technologies for increased energy efficiency and performance. He serves in professional societies including IEEE IEDM ExComm, the Nanotechnology Council ExComm, and past program chair and steering member of the International Conference on Rebooting Computing. Since July 2021, John Paul moved to Germany to ramp up a new institute continuing his exciting research and benefiting from the many rich collaborations at Forschungszentrum Jülich and RWTH Aachen University.

Petra Wagner
Executive Assistant to Prof. Dr. John Paul Strachan
Room: C3.16
Contact: +49 241/92-780421

Members

Dr. Michael Schiek

Room C3.19
My educational background is theoretical physics, which I have applied mainly in medical research and neuroscience. In my previous work, I have also been involved in the acquisition and management of third-party funded research and development projects. The goal of my work in PGI-14 is to apply insights from dynamical systems theory to the realization of bio-inspired information processing systems. In parallel, my work aims to explore application fields for these novel bioinspired information processing systems and to identify funding opportunities.

Members

Dr. Roman Levchenko

Room C3.22
In 2011, I completed my PhD in Computer Science focusing on automation of parallel computing at Taras Shevchenko National University, Kyiv, Ukraine. Although my educational background is in computer engineering, network infrastructure and security. Since then, I have worked as an engineer, architect and manager in a number of IT companies from Germany, Switzerland and the USA involved in the development of Artificial Intelligence (AI)/Intelligent Automation (IA). As an Associate Professor in Ukraine, I taught the areas of programming and administration of supercomputer systems, including GPU solutions.

I have been actively involved in NLP research as well as mathematical research in the field of chaos. My last position was as Scientific Solution Architect in the BigBrain project (Forschungszentrum Jülich), where I was involved in the automation of large data processing.

Currently, as Scientific IT Manager of PGI-14 and PGI-15, I am responsible for the scientific IT infrastructure of both institutes. I am responsible for the computing infrastructure of the institutes, be it supercomputer equipment, software, management of computing resources or the specific technical needs of our researchers. Therefore, I have a real interest in participating in modern AI/mathematics research, working with big data, and finding new ways to apply the latest supercomputing technologies.

Mohammad Hizzani

Room: C3.25
I did a BSc and MSc in Electrical Engineering at Philadelphia University and Princess Sumaya University for Technology (PSUT) respectively. My master’s thesis title was “Design of a Modular Multiplier for Public-Key Cryptography Applications Using Residue Number System and Signed-Digit Representation”. My interests are software/hardware co-design, application-specific design, neuromorphic computation, optimization problems, machine learning, cryptography, and computer arithmetic operation. I am currently working on neuromorphic-based solvers for combinatorial optimization problems.

Members

Sara Ameli

Room: C3.18
My background is in nonlinear dynamics in complex networks, control theory, and memristive materials. In PGI-14, I explore software-hardware codesign for associative memory using content-addressable memories.

Paul Manea

Room C3.26
I received my B.Sc. and M.Sc. at the University of applied science Mannheim. I was previously working as an electrical engineer in a space tech company before starting my Ph.D. position at Forschungszentrum Jülich in April 2022. I am working on hardware implementations of memristive analog Content-Addressable-Memories (aCAM) as well as their possible implementation and usage. I am interested in chip design, machine learning, and neuroscience.

Dr. Ming-Jay Yang

Room: C3.24
I am currently interested in exploring probabilistic computing and model-based machine learning from hardware-algorithm co-design aspects. My previous topics in Taiwan include device physics and quantum computing.

Members

Dmitrii Dobrynin

Room: C3.14
I received my BSc at Moscow State University and MSc at RWTH Aachen in Physics. I am currently working on physics-aware neuromorphic computing algorithms and hardware for solving NP-hard optimization problems.

Members

Pezhman Ebrahimzadeh

Room: C3.17
My educational background is in mathematical physics which I have applied for uncovering the underlying physical principles of biological and artificial neural networks. I am currently working on identifying the role of different dynamical regimes in neural computation and working memory.

Members

Dr. Sebastian Siegel

Room: G0.11
Here at PGI-14, I am investigating Field-Programmable Analog Arrays (FPAA), the analog computing counterparts of FPGAs. I am especially interested in memristive FPAAs and possible applications in various fields ranging from neuromorphic and artificial neural networks to analog computing and storage. Here, I can apply my previous research that evolved from the investigation of memristive materials and devices during my Bachelors and Masters at RWTH Aachen University to the investigation of learning rules for CMOS-memristive co-integrated circuits during my PhD at PGI-10 of Forschungszentrum Jülich.

Members

Dr. Chirag Sudarshan

Room: C3.15
I am a zealous Post-Doctoral Research Fellow with over 6 years of extensive academic and research experience. My educational journey began with a Bachelor of Engineering in Electronics and Communication Engineering from Ramaiah Institute Of Technology, Bangalore, India. I further refined my skills by completing my MSc in Electrical and Computer Engineering at Rheinland-Pfälzische Technische Universität (RPTU), Kaiserslautern, Germany.

Following this, I successfully earned my PhD in Engineering at RPTU, before joining Forschungszentrum Jülich as a Post-Doc.Throughout my academic and professional endeavors, my primary focus has been on designing innovative memory-centric computer architectures tailored for emerging data-intensive applications, particularly in the realm of Neural Networks. My doctoral research equipped me with a profound understanding of DRAM memory sub-systems and Neural Networks, cultivating strong RTL design skills. I have contributed significantly to the field, having authored numerous publications and secured patents.

In my current role at Forschungszentrum Jülich, I am dedicated to pioneering advancements in the design of novel neuromorphic hardware accelerators, utilizing cutting-edge non-volatile memories. My commitment to pushing the boundaries of technology and contributing to the forefront of research continues to drive my endeavors in the field.

Members

Alexandru Ciobanu

Room: G0.13
I received my BSc and MSc from IST Lisbon in Engineering Physics. My masters research focused on topological Plasmas. I worked as a model-based engineer at an IT company, before coming to Forschungszentrum Jülich. Here I apply ideas from statistical field theory in order to make better solvers for the computational problem k-SAT. 

Members

Rishabh Mallik

Room: C3.17
I received my BTech in Engineering Physics from the Indian Institute of Technology Delhi (IIT Delhi) and MSc in Physics from Ludwig-Maximilians-Universität Munich (LMU) . My research at PGI-14 and PGI-15 involves using machine learning methods in the modelling and control of highly nonlinear dynamical memristors and memristor circuits.

Members

Mauricio Tedeschi

Room: G0.13
I am pursuing a Bachelor's in Computer Science and Physics at Northeastern University in Boston, US. I am a research intern at Forschungszentrum Jülich. I am working on solvers for combinatorial optimization problems using neuromorphic algorithms and architecture. I am interested in quantum computing, machine learning, optimization, and formal methods.

Members

Vaishnavi Chaturvedi

Room: G0.13
I am studying Electrical Engineering and Information Technology at RWTH Aachen University and am currently doing my BSc thesis at Forschungszentrum Jülich. We are developing a concept for the implementation of complex neuron models in integrated circuits (ICs). This concept can be used for various applications in neuroscience, artificial intelligence and electronics. I find it very interesting to use this research as a basis for my Bachelor's thesis and to further refine and validate it.

Members

Ermanno Fiorillo

Room: G0.11
I have completed my Bachelor of Science in Biomedical Engineering at Università di Genova (UniGe), Italy. I am currently pursuing my Master of Science degree in Electrical Engineering and Information Technology at RWTH Aachen. As a student assistant at PGI-14, I am developing electronic circuits for an in-memory computing implementation of structured state-space sequential models. The goal is to characterize these circuits for seamless integration into machine learning frameworks, enhancing computational efficiency and model performance. My research aims to advance hardware-efficient machine learning by leveraging in-memory computing to accelerate state-space model processing, offering potential improvements in speed and energy consumption for machine learning tasks.

Collaborations across Forschungszentrum Juelich

We work closely with:

PGI-15, led by Prof. Dr. Emre Neftci

PGI-7 / PGI-10, led by Prof. Dr. Rainer Waser

ZEA-2, led by Prof. Dr. Stefan van Waasen

INM-6, led by Prof. Dr. Markus Diesmann

External Collaborators

Our research is the product of many fruitful collaborations world-wide!

  • Hewlett Packard Labs, HPE: Catherine Graves, Paolo Faraboschi, Xia Sheng, Giaocomo Pedretti, Thomas Van Vaerenbergh, Martin Foltin, Dejan Milojicic, Sergey Serebryakov
  • University of Hong Kong: Professor Can Li
  • Sandia Labs: Matthew Marinella, Sapan Agarwal, Zhiyong Li, Suhas Kumar
  • University of Utah: Rajeev Balasubramanian
  • Purdue University: Aayush Ankit, Kaushik Roy
  • UIUC: Wenmei Hwu, Izzat El-Haj
  • Polimi: Daniele Ielmini, Piergiulio Mannocci
  • University of Michigan: Professor Wei Lu
  • USC: Professor Joshua Yang
  • University of Massachusetts: Professor Qiangfei Xia

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Last Modified: 04.12.2024