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Martin WainwrightView profile

Professor

• High-dimensional statistics
• Information theory
• Statistical machine learning
• +3 more

Martin Wainwright is a Professor at the University of California at Berkeley with joint appointments in the Department of Statistics and the Department of Electrical Engineering and Computer Sciences (EECS). His research spans high-dimensional statistics , information theory , statistical machine learning , and optimization theory . He has made significant contributions to understanding computational and statistical trade-offs in high-dimensional settings, as well as developing advanced message-passing algorithms for graphical models. His educational background includes a Bachelor's degree in Mathematics from the University of Waterloo and a Ph.D. in EECS from MIT . His work has been recognized with prestigious awards such as the COPSS Presidents' Award (2014) , IEEE Joint Paper Award (2012) , and Sloan Research Fellowship (2005) . He has advised numerous prominent researchers, including Nihar Shah , John Duchi , and Yuchen Zhang . Publications by Wainwright reflect trends in machine learning , high-dimensional data analysis , and graphical model inference . Notable works include advancements in Markov Chain Monte Carlo algorithms , pairwise comparison models , and distributed computation methods . He has also contributed extensively to signal processing and LDPC codes . COPSS Presidents' Award (2014) IEEE Joint Paper Award (2012) Institute of Mathematical Statistics Fellow (2011) NSF CAREER Award (2006) Okawa Research Grant (2005) Sloan Research Fellow (2005)

Inbar FijalkowView profile

Professor

• Signal Processing
• Wireless Communications
• Optimization
• +3 more

Inbar Fijalkow is a Full Professor at the National School of Electronics and Computer Science (ENSEA) within CY Cergy Paris University. She is a member of the ETIS Research Unit (UMR 8051), focusing on signal processing for wireless communications, optimization, and machine learning applications. Her research bridges theoretical advancements with practical implementation in emerging communication systems. Education & Career: PhD in Signal Processing from TelecomParisTech (1993) Postdoctoral Fellow at Cornell University (1994–1995) Professor at ENSEA since 1999 Former Head of ETIS Research Unit (2004–2013) Research Interests: Signal processing for wireless communications Optimization techniques in massive MIMO and NOMA systems Machine learning applications in communication systems Nonlinear effects mitigation in high-power amplifiers Community & Awards: Member of CoNRS Section 7 (National Committee for Scientific Research) Chevalier de l’Ordre National du Mérite (2015) Founder of the CY Alliance Women in Science Prize (2017) Recent Projects: Active in ANR-funded initiatives (e.g., EcoBioH2, AI4code) and EU projects (e.g., PERSEUS). Her work emphasizes sustainability and AI-driven communication systems. Teaching: Teaches signal processing and wireless communications at ENSEA. Supervises PhD students and master’s theses in communication systems and signal processing.

• Communication Theory
• Channel Coding
• Low-Density Parity-Check (LDPC) Codes
• +3 more

Richard D. Wesel is an Associate Dean for Academic and Student Affairs at a College of Engineering, where he has held leadership roles such as Vice-Chair of the Electrical Engineering Department. With Ph.D. and M.S. degrees in Electrical Engineering from Stanford University and MIT respectively, his research focuses on communication theory and channel coding, particularly low-density parity-check (LDPC) codes and turbo codes for efficient data transmission over noisy channels. Education: MIT (B.S., M.S.), Stanford (Ph.D.) Leadership: Associate Dean, Former Vice-Chair of Electrical Engineering Wesel's research explores advanced techniques for broadcast and multiple-access communication systems, emphasizing applications in wireless LANs, satellite communications, and optical networks. His work integrates machine learning with traditional decoding algorithms, as seen in recent publications on neural-network-optimized LDPC decoding and parallel trellis-stage-combining methods for high-throughput systems. His articles demonstrate a focus on finite-blocklength coding, including CRC-aided list decoding for convolutional and polar codes, and voltage optimization strategies for flash memory reliability. Wesel has authored over 100 publications and led a research group that won the 2006 Design Automation Conference's top award for optical multiple access design. Scientific Awards: National Science Foundation CAREER Award Okawa Foundation Award As an educator, Wesel received the TRW Excellence in Teaching Award in 2000 and has contributed to academic governance through roles on the Faculty Executive Committee and Undergraduate Council. His work bridges theoretical communication systems with practical implementations, including FPGA-based decoders and adaptive coding for fading channels.

Glenn GulakView profile

Professor

• Homomorphic Encryption
• Quantum Cryptography
• CMOS Biosensors
• +4 more

Glenn Gulak is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds the Canada Research Chair in Signal Processing Microsystems and the Edward S. Rogers Sr. Chair in Engineering. A Senior IEEE Member and Professional Engineer in Ontario, he received his Ph.D. from the University of Manitoba. His research spans: Digital Communication Systems: VLSI implementations of MIMO detectors, lattice reduction algorithms, and homomorphic encryption accelerators Lab-on-Chip Microsystems: CMOS biosensors for rapid pathogen detection and integrated fluorescence imaging Recent publications (2019-2025) demonstrate a dominant focus on privacy-enhancing technologies, with 73% concentrated in cryptographic hardware and homomorphic encryption. This reflects industry-aligned work on confidential computing and secure data processing. Awards & Honors: IEEE Millennium Medal (2001) Canada Research Chair in Signal Processing Systems (Tier 1, 2005-2012) Edward S. Rogers Sr. Chair (2005-2010) RBC Research Prize L. Lau Chair (1999-2004) Teaching Award (1999) He has supervised 44+ graduate students (PhD/MASc) with thesis topics spanning VLSI communication systems, CMOS biosensors, and cryptographic accelerators. Notable industry collaboration includes serving as CTO of a semiconductor startup (2001-2003). His lab develops hardware for quantum cryptography and secure medical computation.

• Architectures for fault-tolerant quantum computing
• Topological error correcting codes
• Magic states
• +5 more

Professor Earl T. Campbell is a Professor of Quantum Computation at the School of Mathematical and Physical Sciences, University of Sheffield, and serves as VP of Quantum Science at Riverlane. His research focuses on practical quantum technologies with capabilities beyond classical physics, particularly in fault-tolerant quantum computing. His educational background includes: Physics & Philosophy MSc (2001-2005, Bristol University) Quantum computing PhD (2005-2008, Oxford University) Professor Campbell's research centers on the central question 'What are the optimal blue-prints for large-scale, reliable quantum technologies?' His work spans architectures for fault-tolerant quantum computing, topological error correcting codes, magic states, gate synthesis, and Majorana fermions. His team designs protocols and software to operate future quantum computers reliably despite noise and decoherence, including development of error correction codes, magic state factories (which determine quantum computer processing speed), and algorithm compilation into machine-level commands. His recent publications demonstrate a strong focus on practical aspects of quantum error correction, with significant contributions to surface codes, homological product codes, and efficient decoding algorithms. His work bridges theoretical quantum information science with practical implementation challenges, aiming to reduce resource requirements for fault-tolerant quantum computing. His notable scientific achievements include: Royal Commission of Great Exhibition of 1851 fellowship (2008-2010) EPSRC early career Research Fellow (2015-present) Professor Campbell supervises PhD student Luke Heyfron and collaborates with postdoc Mark Howard. He has received significant grant funding including an EPSRC early career fellow project 'Towards fault-tolerant quantum computing with minimal resources' (EP/M024261/1, £675,867) and Royal Commission fellowship funding (~£100,000). He is an active member of the EPSRC Peer Review College and the Institute of Physics QQQ group Committee, and has organized the FTQT2016 workshop in Benasque, Spain.

Katie MorrisonView profile

Professor

• Mathematical Neuroscience
• Neural Networks
• Neural Coding
• +3 more

Katie Morrison is an Associate Chair and Professor in the Department of Mathematical Sciences at the University of Northern Colorado (UNC), part of the College of Natural and Health Sciences. Her research focuses on mathematical neuroscience, neural networks, and algebraic coding theory, with a particular emphasis on applying matrix codes and graph-based algorithms to understand neural dynamics. She has been funded by NSF DMS-1951599 and NIH R01 EB022862 grants. Education: Ph.D. in Mathematics (University of Nebraska-Lincoln, 2012), M.S. in Mathematics (2008), B.A. in Mathematics and Psychology (Swarthmore College, 2005), and studies at Budapest Semesters in Mathematics. Her work bridges abstract mathematics with applied neuroscience, including projects like the CTLN initiative. Research trends in her publications emphasize graph-theoretic approaches to neural network dynamics, pattern completion in threshold-linear networks, and algebraic analysis of neural codes. Collaborations with computational topologists and neuroscientists highlight interdisciplinary strengths. Grants and advising: Active in securing federal research funding and mentoring students in interdisciplinary projects. Her lab explores connections between network connectivity and emergent behaviors, with applications to both theoretical and applied neuroscience.

Peter JaxView profile

Professor

• Speech and Audio Processing
• Acoustic Signal Enhancement
• Machine Learning for Audio Systems
• +5 more

Peter Jax is a Full Professor at RWTH Aachen University , leading the Chair of Communication Systems . His research focuses on speech and audio processing , with expertise in active noise control , spatial audio , and machine learning applications for acoustic systems. Diploma in Electrical Engineering (1997), RWTH Aachen University PhD (2002), RWTH Aachen University Research areas include binaural direction-of-arrival estimation , MIMO acoustic system identification , and adaptive filtering for consumer and medical audio applications. Recent articles highlight innovations in ambisonics upscaling , noise control for UAVs , and data-driven uncertainty modeling in headphones. Scientific honors : Distinguished Member of Technicolor Fellowship Network (2010) Johann-Philipp-Reis Preis Borchers Medal E-Plus Award for Best Dissertation With over 25 patents in speech/audio processing and leadership roles in industry (Deutsche Thomson OHG, 2005–2015), he bridges academic research and industrial innovation.

Madhur TulsianiView profile

Professor

• Theoretical Computer Science
• Complexity Theory
• Algorithm Design
• +3 more

Madhur Tulsiani is a Professor at the University of Chicago's Department of Computer Science and a researcher at the Toyota Technological Institute at Chicago (TTIC). His research focuses on theoretical computer science, particularly complexity theory and algorithm design, with applications in coding theory and information theory. He has been supported by NSF grants 1254044, 1816372, and 2326685. Education: Bachelor’s in Computer Science, IIT Kanpur (2001-2005) Ph.D. in Computer Science, UC Berkeley (2005-2009), advised by Luca Trevisan Postdoctoral fellowships at the Institute for Advanced Study (IAS) and Princeton University Research Interests: Mathematical foundations of computation Complexity theory and algorithm design Coding theory and error-correcting codes Sum-of-Squares hierarchies and approximation algorithms Recent Contributions: Pioneering work on list decodable codes and expander-based constructions Advances in approximation algorithms for high-dimensional expanders Lower bounds for Sum-of-Squares algorithms using high-dimensional expanders Teaching: Information and Coding Theory Mathematical Toolkit (linear algebra/probability) Summer REU programs in theoretical computer science Students: Advised PhD students including Fernando Granha Jeronimo, Goutham Rajendran, and Shashank Srivastava Co-advised students with Sasha Razborov, Janos Simon, and others Labs/Groups: Member of the Theoretical Computer Science Group at TTIC and UChicago, contributing to cross-disciplinary research in algorithms and complexity.

Ramy H. GoharyView profile

Assistant Professor

• Machine-to-machine communications
• Internet-of-Things (IoT)
• MIMO wireless communication systems
• +9 more

Ramy H. Gohary is an Assistant Professor in the Department of Systems and Computer Engineering at Carleton University. His research focuses on advanced wireless communication systems, including machine-to-machine communications, Internet-of-Things (IoT), MIMO systems, convex optimization, differential geometry applications in signal processing, and information-theoretic aspects of multiuser systems. Principal Investigator for Ericsson-Carleton Strategic Partnership projects on 5G+ wireless networks Research emphasizes fairness, cross-layer design, cooperative communications, and jamming-resistant detection techniques His recent work explores direction-of-arrival estimation, non-coherent communication in jamming environments, and distributed MIMO architectures. Publications highlight applications of differential geometry, optimization techniques, and sparse signal recovery in modern wireless systems. He actively contributes to advancements in channel modeling, beamforming, and energy-efficient network design.

Lei WangView profile

Associate Professor

• Cyber-Physical Systems
• Aquatic Systems
• Embedded Computing with Renewable Energy
• +3 more

Lei Wang is the F. L. Castleman Associate Professor in Engineering Innovation at the University of Connecticut's School of Engineering, Department of Electrical and Computer Engineering. He holds a PhD from the University of Illinois at Urbana-Champaign (2001), an MS (1996) and BS (1992) from Tsinghua University, China. His research focuses on cyber-physical systems , embedded computing with renewable energy , and nanoscale integrated circuit design . Key areas include microbial fuel cells , memristor-based hardware security , and low-power signal processing architectures . Recent work trends involve Quantum-dot transistor applications for in-memory computing Adaptive LDPC decoder optimization Hardware security leveraging memristor properties Energy-efficient power management systems for underwater sensors Scientific recognition includes National Science Foundation CAREER Award (2010) F. L. Castleman Term Professorship in Engineering Innovation Professional roles encompass editorial and committee positions at IEEE and ACM journals. His work spans interdisciplinary domains in renewable energy integration , VLSI design , and bio-inspired computing systems .

• Cybersecurity
• Machine Learning
• Network Security
• +6 more

Dr. Zhiyuan Tan is an Associate Professor in the School of Computing at Edinburgh Napier University (ENU), specializing in cybersecurity research. He holds a PhD in Computer Systems from the University of Technology Sydney (UTS), Australia (2014), an MEng from Beijing University of Technology, China (2008), and a BEng with high distinction from North-eastern University, China (2005). Before joining ENU in 2016, Dr. Tan held research positions at the University of Twente (Netherlands), University of Technology Sydney (Australia), and La Trobe University (Australia). Dr. Tan's research focuses on cybersecurity, machine learning, data analytics, virtualisation, and cyber-physical systems. His work has resulted in over 44 scholarly publications with an H-Index of 13 and more than 830 citations according to Google Scholar. His recent publications demonstrate a continued focus on network security, intrusion detection systems, and the application of machine learning techniques to cybersecurity challenges, with publications spanning from 2022-2025 in top venues including IEEE Transactions and international conferences. Dr. Tan has received significant research funding, including AUD 27,800 from CSIRO and UTS for autonomous network intrusion detection research and £6,987 from ENU for securing future 5G health care systems. His research has been recognized with awards including the National Research Award 2017 from the Research Council of the Sultanate of Oman, a Best Paper Award, and the Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award. National Research Award 2017 from the Research Council of the Sultanate of Oman Best Paper Award Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award Dr. Tan has mentored 9 PhD students over the past 5 years, with 6 successfully completing their studies. His students have produced 12 journal and 10 conference publications. He has also served as an editorial board member for international journals, organized special issues, and participated as a technical program committee member for major international conferences. Dr. Tan is currently recruiting PhD students for research projects on network security, adversarial machine learning for anomaly/malware detection, virtualization security, and IoT security.

• 5G/6G Physical Layer signal processing algorithms
• Machine Learning techniques for Physical Layer signal processing
• Ultra-Reliable Low-Latency Communications
• +5 more

Dr. Beeshanga Abewardana Jayawickrama is a Senior Lecturer and Data Science Engineering Course Director at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). He holds a BEng in Telecommunications Engineering (Hons I) and a PhD in Electronic Engineering (Wireless Communications) from Macquarie University, Sydney, Australia, completed in 2011 and 2015 respectively. As a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), he maintains active research and industry collaborations. His educational background includes: BEng in Telecommunications Engineering (Hons I), Macquarie University (2011) PhD in Electronic Engineering (Wireless Communications), Macquarie University (2015) Dr. Jayawickrama's research focuses on cutting-edge wireless communication technologies with particular emphasis on 5G/6G Physical Layer signal processing algorithms, Machine Learning techniques for Physical Layer signal processing, Ultra-Reliable Low-Latency Communications, Non-Terrestrial Networks, Compressed Sensing (Sub-Nyquist Sampling), and spectrum sharing. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and industry collaborations. He has published over 40 prestigious conference and journal papers while developing algorithms that have been incorporated into commercial 5G base stations. Analysis of his recent publications reveals a strong focus on satellite communications, particularly cognitive GEO-LEO satellite networks, where he explores spectrum sharing, beam design, and interference management. His research increasingly integrates machine learning techniques with traditional signal processing approaches, especially for spectrum sensing and channel estimation in next-generation wireless systems. The trend shows growing emphasis on practical implementation and experimental validation of theoretical concepts. His scientific recognition includes: Macquarie University Medal in Engineering Vice-Chancellor's Commendation for Academic Excellence Outstanding Teacher Award (2021) Multiple competitive scholarships from Macquarie University and CSIRO In terms of teaching and supervision, Dr. Jayawickrama has taught numerous undergraduate and postgraduate subjects including Advanced Telecommunication Engineering, 4G/5G Mobile Technologies, Communication Systems, and Engineering Research Thesis. His current research is supported by significant grants including the AI-SSPCAS project (CSIRO), Smart Flood and Storm Intelligence Sensing Initiative (NSW Department), and CogSat: Cognitive Satellite Radio (SmartSat CRC). Previously, he has secured research funding from Intel Corporation and Nokia Research Centre. Dr. Jayawickrama has held leadership roles including Course Director for Data Engineering since 2021 and UTS IEEE Student Branch Counsellor from 2017-2020. His industry experience includes research positions at Ericsson in Sweden (working on 5G New Radio receiver algorithms) and Intel Labs in the USA (working on Licensed Shared Access and Citizens Broadband Radio Service).

• VLSI Design
• Wireless Communications (5G/6G)
• Massive MIMO
• +5 more

Mohsin Abbas is an Assistant Professor (Tenure Track) in the Computing Sciences department at Tampere University, Finland. His research focuses on high-throughput, low-latency, and energy-efficient VLSI architectures for baseband processing systems, particularly channel code decoders. He has held roles including Research Assistant Professor at HKUST, Postdoctoral Fellow at McGill University, and Lead Engineer at ASTRI. Education: PhD in Electronics and Computer Engineering (2017), HKUST, under Prof. Chi-Ying Tsui MSc in Computer Science and Engineering (2011), Hanyang University, South Korea BSc in Computer Engineering (2007), University of Engineering and Technology Taxila, Pakistan Research Interests: VLSI Design (ASIC/FPGA) Wireless Communications (5G/6G) Massive MIMO and Compute-In-Memory (CiM) Channel Coding and Hardware Architecture Low-Latency Decoding Algorithms (e.g., GRAND) Energy-Efficient Systems and Green Communication Teaching Experience: HKUST: Digital Circuits and Systems (ELEC 2200), VLSI Design Automation (EESM 5020) Labs/Teams: Integrated Systems for Information Processing (ISIP) Lab, McGill University (2019–2022) HKUST’s ECE Department and ISIP Lab collaborations

Øyvind YtrehusView profile

Professor

• Coding Theory
• Error-Correcting Codes
• Information Theory
• +7 more

Øyvind Ytrehus is a Professor at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Bergen (UiB), Norway. He is actively engaged in research and academic supervision, with a focus on coding theory and its applications in communication systems. Research Interests: His primary research areas include Coding Theory , Error-Correcting Codes , Information Theory , Network Coding , Cryptography , and RFID Communication . His work bridges theoretical foundations with practical implementations in wireless and networked systems. The recent publications (2024–2008) reflect a consistent trajectory in coding for communication security, RFID systems, and iterative decoding. Key trends include the use of formally unimodular lattices for secrecy gain, LDPC and turbo codes for erasure channels, and network coding for multicast and delay optimization. His work frequently intersects with physical-layer security and energy-efficient communication. Scientific Contributions: Co-edited special issues on coding theory and applications. Authored foundational work on convolutional codes, stopping sets, and generalized Hamming weights. Contributed to RFID and inductively coupled channel modeling. Advising and Grants: He has supervised several PhD candidates, including Bjørn Møller Greve, Christian W. Otterstad, and Mohsen Toorani. While specific grants are not listed, his extensive publication record and editorial roles suggest active grant involvement in coding and communication research. Labs and Teams: While no specific lab name is mentioned, his collaborations with researchers at Simula UiB, University of Valladolid, and Lancaster University indicate participation in interdisciplinary research networks focused on coding and communication systems.

Michael E. O'SullivanView profile

Professor

• Coding Theory
• Algebraic Geometry
• Error-Correcting Codes
• +6 more

Michael E. O'Sullivan is a Professor and Chair in the Department of Mathematics and Statistics at San Diego State University. He has taught advanced courses such as Commutative Algebra and Algebraic Geometry (Math 621) and Groups, Rings, and Fields (Math 620), with a focus on integrating computational tools like Sage, Maple, and Magma into his teaching. Research Focus: His work centers on Coding Theory , particularly Algebraic Geometry Codes , Hermitian Codes , and LDPC Codes . His research includes developing decoding algorithms (e.g., Groebner basis methods, Sudan's list decoding), analyzing code properties (e.g., minimum distance, redundancy), and optimizing code constructions. Publications: His most recent articles (2014–2006) address unique decoding of AG codes, list decoding techniques, and applications of algebraic structures to code optimization. These span journals like IEEE Transactions on Information Theory and Designs, Codes and Cryptography , as well as conferences such as IEEE International Symposium on Information Theory and Canadian Workshop on Information Theory . Emails: Contact him at mosullivan@mail.sdsu.edu or m.osullivan@math.sdsu.edu .