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Jörn OstermannView profile

Professor

• Video Coding
• Computer Vision
• Machine Learning
• +9 more

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

Sayed Ahmad SalehiView profile

Assistant Professor

• VLSI Circuits
• Stochastic Computing
• DNA Computing
• +7 more

Sayed Ahmad Salehi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Kentucky's College of Engineering, where he directs the Computing with Unconventional Technologies (CUT) Lab. His research spans energy-efficient VLSI circuits for deep learning, stochastic computing, and DNA-based molecular programming. His research focuses on unconventional computing paradigms to address limitations in traditional electronics. Key areas include stochastic and approximate computing for energy-efficient deep learning hardware targeting IoT and edge devices, and biomolecular computing using DNA for environments incompatible with silicon. His lab develops tools like FUNSC (stochastic computing) and FUNDNA (DNA computing) that enable mathematical function computation through novel encoding schemes. Analysis of his recent publications reveals strong emphasis on in-memory computing architectures (particularly for DRAM and phase-change memory), stochastic-to-binary conversion techniques , and DNA circuit synthesis for mathematical operations. His work bridges theoretical computer science with practical hardware implementations for resource-constrained environments. Award highlights include: BIOMOD competition silver award (2023) Best paper nomination at Asilomar Conference (2022) ESWEEK Student Travel Grant (2019) He actively mentors PhD and undergraduate researchers, with recent graduates joining companies like Applied Materials. His lab secures significant NSF funding for interdisciplinary projects in stochastic computing and DNA-based systems. Current initiatives include CUT Lab's Kentucky iGEM team and collaborations on photonics-integrated computing. The CUT Lab operates at the intersection of computer architecture, molecular biology, and signal processing, with research impacting edge AI hardware, biocompatible computing, and next-generation memory systems.

Chee Yeow MengView profile

Professor

• Coding Theory
• Combinatorics
• Data Science
• +3 more

Chee Yeow Meng is the Provost and Chief Academic & Innovation Officer at the Singapore University of Technology and Design (SUTD), effective April 2025. He holds a Professor rank and oversees curricular, research, and innovation affairs. Previously, he served as Vice President (Innovation and Enterprise) at NUS and held senior roles in academia and public service, including leadership positions at the National Computer Board and Infocomm Development Authority. His education includes a BMath (Hon) and MMath from the University of Waterloo (1988-1989), followed by a PhD in Computer Science (1996). His research focuses on combinatorics' intersection with computer science, particularly coding theory, extremal set systems, and AI applications. He has pioneered work in error-correcting codes, combinatorial cryptography, and data science. Prof Chee is an associate editor for IEEE Transactions on Information Theory and a Fellow of the Institute of Combinatorics and its Applications. His awards include the Public Administration Medal (Silver, 2018) and the Long Service Award (NTU, 2018). He has founded multiple startups and holds patents in coding technologies. His recent publications emphasize innovations in coding theory applications (e.g., domain wall memories, DNA storage) and machine learning for combinatorial optimization. He actively contributes to national committees and international forums on innovation policy and AI ethics.

• Discrete Mathematics
• Coding Theory
• Algorithms
• +6 more

Assoc. Prof. Zlatko Georgiev Varbanov is a distinguished academic at the Faculty of Mathematics and Informatics, University of Veliko Tarnovo (Bulgaria). With expertise spanning discrete mathematics, coding theory, and software development, he has established himself as a prominent researcher in both theoretical and applied computer science domains. His scholarly work bridges mathematical theory with practical implementations, particularly in DNA coding, quantum error correction, and software engineering principles. Dr. Varbanov's research interests encompass a diverse range of topics including Discrete Mathematics, Coding Theory, Algorithms, Programming, DNA codes, Quantum codes, and Information security. His scholarly work demonstrates a consistent focus on the intersection of algebraic structures and practical coding applications, with particular emphasis on self-dual codes over finite fields and their applications in DNA computing and quantum information theory. In software engineering, he has made significant contributions to design patterns, asynchronous programming in C#, and static site generation techniques. Analysis of Dr. Varbanov's recent publications reveals a dual research trajectory: one strand focusing on theoretical aspects of coding theory (particularly DNA codes and quantum codes), and another addressing practical software engineering challenges. His work on design patterns, SOLID principles, and asynchronous programming in C# demonstrates his commitment to improving software development practices. The publications on static site generation and data storage services reflect his engagement with contemporary web development technologies and their optimization. Dr. Varbanov has been actively involved in numerous research projects, with current engagements extending into 2025. These projects focus on AI-driven information systems, mobile technologies for students with special needs, digital accessibility for visually impaired users, and enhancement of research indicators for faculty members. His collaborative work spans multiple institutions and demonstrates a strong commitment to both theoretical research and practical applications that address real-world challenges.

Reinhard HeckelView profile

Professor

• Machine Learning
• Deep Learning
• Medical Image Processing
• +3 more

Reinhard Heckel is a Professor of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM). His career includes positions as a Tenure-Track Assistant Professor at Rice University (2017–2019) and postdoctoral fellow at UC Berkeley's Berkeley Artificial Intelligence Research Lab. He holds a PhD from ETH Zurich (2014) and conducted doctoral research at Stanford University’s Statistics Department. Recognitions include being named one of Germany's 'Top 40 under 40' (2022) and the Werner von Siemens Ring Foundation Award (2022). Education & Professional Background: PhD in Computer Science, ETH Zurich (2014) Visiting Doctoral Fellow, Stanford University (Statistics Department) Postdoctoral Fellowship, UC Berkeley (EECS Department) Research Focus: His work bridges theoretical foundations and practical applications in machine learning, including: Algorithm development for deep learning and medical image processing Mathematical foundations of machine learning DNA data storage technology (error correction, synthesis methods) Computational imaging and inverse problem solutions Awards & Highlights: 2022: Capital 40 under 40, Werner von Siemens Ring Foundation Award 2015: ETH Zurich Medal for Doctoral Thesis, IBM Invention Achievement Award Grants & Collaboration: His research has been supported by grants focusing on DNA storage scalability and MRI reconstruction. He collaborates with institutions like IBM Research and the Berkeley AI Lab. Key projects include developing DNA synthesis methods and AI-driven medical imaging tools. Labs & Teams: Leads TUM's machine learning initiatives in computational imaging and biological data storage systems. Active in interdisciplinary teams bridging computer science, bioengineering, and statistics.

Eitan YaakobiView profile

Professor

• Information Theory
• Coding Theory
• Non-Volatile Memories
• +5 more

Eitan Yaakobi is a Professor in the Computer Science Department at the Technion – Israel Institute of Technology, with a courtesy appointment in the Electrical and Computer Engineering (ECE) Department. He holds affiliations with the University of California, San Diego (UCSD) Center for Memory and Recording Research and previously served at the Technical University of Munich’s Institute of Advanced Studies under a Hans Fischer Fellowship. His roles include Associate Editor for IEEE Transactions on Information Theory and IEEE Transactions on Molecular, Biological, and Multi-Scale Communications. Education: B.A. and M.Sc. in Computer Science and Mathematics from Technion (2005–2007), Ph.D. in Electrical Engineering from UCSD (2011). Postdoctoral research at Caltech (2011–2013). Awards include the Marconi Society Young Scholar Award and Intel Ph.D. Fellowship. He has secured grants such as the ERC Consolidator Grant and EIC Pathfinder Challenge. Research focuses on coding and information theory applications in non-volatile memories, DNA storage, distributed systems, and privacy-preserving data retrieval. Teaching spans courses like Coding Algorithms for Memories and Seminars on DNA Data Storage. Office hours: Wednesdays 11:30–12:30.

Anouar YatribiView profile

Research Fellow

• Coding and Information Theory
• Error Correcting Codes
• Wireless Communication Systems
• +5 more

Anouar Yatribi is a Postdoctoral Research Associate at Imperial College London's Department of Electrical and Electronic Engineering within the Information Processing and Communications Lab. He holds a PhD from ENSIAS, Mohammed V University (Morocco, 2021), and prior degrees in Physics (BSc, 2011) and Electrical Engineering (MSc, 2013) from Mohammed V University. His research focuses on Coding Theory, Error Correcting Codes, and their applications in wireless communications and DNA data storage, alongside discrete mathematics and optimization algorithms. His academic contributions include pioneering work on iterative decoding techniques, majority-logic decodable codes, and hybrid ARQ protocols. He has authored/co-authored over a dozen peer-reviewed publications in IEEE journals and conferences, with notable work on DNA storage error correction and LDPC code optimization. Yatribi serves as a reviewer for IEEE Access and the American Journal of Networks and Communications. Research trends in his publications emphasize improving code performance through algorithmic innovations (e.g., genetic algorithms for code construction) and exploring novel applications like non-binary codes and finite geometry-based coding. His work bridges theoretical coding advancements with practical implementations in real-world communication systems. Yatribi's professional activities include lab leadership within the Information Processing group and active participation in international conferences (e.g., WINCOM, WCCCS). His lab focuses on advancing communication systems through interdisciplinary approaches combining coding theory, optimization, and machine learning.

Jonathan ScarlettView profile

Associate Professor

• Information Theory
• Machine Learning
• High-Dimensional Statistics
• +3 more

Jonathan Scarlett is an Associate Professor at the National University of Singapore (NUS), holding joint appointments in the Department of Computer Science, Department of Mathematics, and the Institute of Data Science. His research focuses on information theory, machine learning, and high-dimensional statistics, with contributions to topics such as group testing, Bayesian optimization, and coding theory. He has received notable awards including the NUS Presidential Young Professorship and MIT TR35 Innovators (Asia Pacific) recognition. Scarlett advises numerous PhD students, including Yang Sun, Zihan Li, and Arpan Losalka, whose theses address challenges in inverse problems, kernel-based optimization, and safety in Bayesian optimization. His work spans theoretical foundations and practical applications, with recent publications exploring optimal group testing algorithms, error exponents in DNA storage, and robust bandit optimization. He leads collaborative projects on distributed statistical estimation and adversarial machine learning, leveraging interdisciplinary methods from signal processing and information theory. His academic journey includes a postdoc at the LIONS laboratory (2014–2017) before his promotion to Associate Professor in 2024. Scarlett actively contributes to the academic community through editorial roles, conference organization, and over 100 peer-reviewed publications. His lab develops novel frameworks for handling uncertainty in data-driven systems, with applications to healthcare, communication networks, and autonomous systems.

• Electrical and Electronic Engineering
• Communications Technologies
• Artificial Intelligence
• +3 more

Roman Sokolovskii is a Research Fellow at Imperial College London's Department of Computing within the Faculty of Engineering. His work intersects Electrical and Electronic Engineering , Artificial Intelligence , Communications Technologies , and Distributed Computing , with a focus on innovative data storage solutions. Key research trends include advancements in DNA-based data storage , coding theory , and nanopore sequencing . His publications emphasize error-correcting codes (e.g., SC-LDPC), finite-length scaling , and adaptive sampling for efficient data retrieval. Recent work explores combinatorial motif-based DNA storage , PCR-free random access , and end-to-end coding schemes with nanopore-sequenced reads, reflecting his commitment to bridging computer science and molecular biology in novel storage media.

• Communications Engineering
• Signal Processing
• Information Theory
• +3 more

Masoumeh Karvar is a Research Fellow at the Chair of Line Transmission Technology (Prof. Norbert Hanik) within the Institute for Communications Engineering at the Technical University of Munich. Her work focuses on advanced communications systems and theoretical foundations of data transmission. Her research spans Communications Engineering with emphasis on Signal Processing, Information Theory, and Coding Theory. The Institute maintains leadership in coding applications including DNA-based data storage and quantum communication networks, organizing annual workshops like the Munich Workshop on Coding and Cryptography that attract global researchers. Current initiatives explore 6G technologies and molecular communications through collaborations with DLR (German Aerospace Center). Dr. Karvar contributes to the institute's mission of advancing transmission technologies for next-generation communication networks through both theoretical research and practical implementations.

• Information Theory
• Coding Theory
• Communication Systems
• +8 more

Ragnar Thobaben is a Professor in Communication Theory at the Division of Information Science and Engineering, part of the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. He has held this position since December 2006 and is affiliated with the CERCES Center for Resilient Critical Infrastructures. His research focuses on information and coding theory, with applications to secure communication systems, machine learning generalization, and DNA-based data storage. He has also contributed to data-driven decision tools for critical care medicine, such as in sepsis management. Education: Ragnar earned a Dipl.-Ing. (M.Sc.) in Electrical Engineering from Christian Albrechts University, Kiel, Germany in 2001, followed by a Dr.-Ing. (Ph.D.) in Electrical Engineering in 2007. His academic journey is complemented by extensive editorial roles, including Editor for IEEE Transactions on Communications (2016–2020) and IEEE Transactions on Information Forensics and Security (2020–2023). He has also served as Technical Program Committee member for major IEEE conferences like GLOBECOM, ICC, and PIMRC, and held organizational roles such as Publicity Chair for the 2011 IEEE Swedish Communication Technologies Workshop and Local-Arrangement Chair for the 2019 IEEE Information Theory Workshop. Research Interests: His work bridges theoretical and applied domains, emphasizing robustness and reliability in communication systems. Key topics include physical-layer security for critical infrastructures, information-theoretic PAC-Bayes bounds in machine learning, and microwave engineering innovations like glide symmetry in antenna design. He also explores interdisciplinary applications, such as data-driven tools for ICU patient monitoring and secure wireless power transfer. Advising & Grants: Thobaben has advised multiple PhD students, including current co-supervision of Martin Lindström (machine learning) and collaborations with Karolinska Institute on medical informatics projects (Anna Sundelin and Navid Korah Soltani under main supervisor Johan Mårtensson). His prior advisees span topics from generalization theory to secure relaying in cognitive networks. His recent publications (2024–2022) highlight advancements in PAC-Bayes bounds, sepsis clinical studies, and antenna engineering.

Jessica BariffiView profile

Researcher

• Coding Theory
• Cryptography
• Information Theory
• +4 more

Dr. Jessica Bariffi is a postdoctoral researcher at the Technical University of Munich (TUM) within the Associate Professorship of Coding and Cryptography led by Prof. Antonia Wachter-Zeh. Her work focuses on coding theory applications for DNA storage and post-quantum cryptography. Previously, she earned her PhD from the University of Zurich under Prof. Joachim Rosenthal while conducting full-time research at the German Aerospace Center (DLR)'s Quantum-Resistant Cryptography Group. Her research interests span coding theory intersections with probability, number theory, combinatorics, algebra, and cryptography. Notable collaborations include work with Hannes Bartz, Violetta Weger, and Thomas Jerkovits on Lee metric codes, sum-rank metrics, and MacWilliams identities. Recent publications emphasize sphere-packing bounds in sum-rank metrics, LDPC code performance analysis, and cryptographic code design. She actively presents at conferences such as IEEE Globecom and SIAM Applied Algebraic Geometry.

• Cryptography
• Distributed Learning
• Secure Computing
• +3 more

Christoph Hofmeister is a doctoral researcher at the Technical University of Munich (TUM), affiliated with the Associate Professorship of Coding and Cryptography under Prof. Antonia Wachter-Zeh. He holds an M.Sc. in Electrical and Computer Engineering from TUM (2021) and a B.Eng. from Munich University of Applied Sciences (2019), with a dual study program at Infineon Technologies (2015–2019). His research focuses on distributed learning systems, federated learning, secure computing, and coding theory. Key areas include Byzantine-resilient distributed learning, privacy-preserving federated learning, and secure matrix multiplication protocols. Hofmeister’s work addresses challenges such as fault tolerance, communication efficiency, and information-theoretic security in distributed systems. He has contributed to over 15 peer-reviewed publications, including works on DNA-based storage systems, secure aggregation in federated learning, and privacy-preserving algorithms. His teaching includes courses on coding theory, channel coding, and distributed computing at TUM. Current research initiatives include exploring fundamental limits of Byzantine-resilient learning and optimizing compression techniques for federated learning. Hofmeister is also involved in advising doctoral and master’s theses on topics like non-uniform secure aggregation and coordinate-based compression methods.

Jehoshua BruckView profile

Professor

• Information Theory
• Distributed Storage Systems
• DNA Data Storage
• +4 more

Jehoshua (Shuki) Bruck is the Gordon and Betty Moore Professor of Computation and Neural Systems & Electrical Engineering at the California Institute of Technology. He leads the Distributed Information Systems Group and serves as the founding director of Caltech's Information Science and Technology (IST) program. His work bridges information theory, storage systems, and computational biology. Bruck holds B.Sc. (Summa cum Laude) and M.Sc. degrees from the Technion-Israel Institute of Technology (1982-1985) and a Ph.D. in Electrical Engineering from Stanford University (1989). He has held academic positions at Caltech since 1994, advancing from Associate Professor to Moore Professor. Research Focus: His dual-track research explores Physical implementations of coding schemes for flash memories (rank modulation, rewriting codes) Biological computation mechanisms, particularly DNA sequence evolution via replication Secure distributed storage systems and RAID schemes Awards & Recognition: Recipient of the Feynman Teaching Prize (2008), Sloan Fellowship, NSF Young Investigator Award, and multiple IEEE best paper awards. Holds over 40 patents and authored 350+ publications. Industry Contributions: Co-founder of storage startups Rainfinity (acquired by EMC) and XtremIO, which pioneered flash storage solutions. Collaborates with NASA-JPL on non-volatile memory for space missions.

Xavier PicView profile

Research Fellow

• DNA Data Storage
• Image Coding
• Data Compression
• +3 more

Xavier Pic is a Research Fellow at EURECOM's Data Science department. His work focuses on advancing DNA-based data storage technologies, particularly in the intersection of computer science, bioengineering, and machine learning. He specializes in developing innovative coding algorithms, entropy coders, and neural network architectures for robust image and media storage on synthetic DNA. Key research areas include implicit neural representations, multiple description coding, and compressive autoencoders. His contributions address challenges in long-term data preservation, error resilience, and efficient encoding-decoding processes. Xavier actively explores applications ranging from molecular biology-driven storage solutions to hybrid systems combining classical algorithms (e.g., JPEG) with modern deep learning techniques. His research portfolio reflects interdisciplinary collaboration, bridging computer science with synthetic biology. Prior to his current role, he contributed to text alignment projects using deep learning and dynamic programming for historical document analysis.