Takeshi Ikenaga is a Professor at Waseda University’s School of Fundamental Science and Engineering and Graduate School of Information, Production and Systems . He earned his Ph.D. in Information & Computer Science from Waseda University in 2001, following B.E. and M.E. degrees in Electrical Engineering (1988–1990). His career spans roles at NTT LSI Laboratories (1990–2002), Kitakyushu Foundation for Advancement of Industry, Science and Technology (FAIS) (1999–2002), and visiting researcher at the University of Massachusetts (1999–2000). Research Interests : Application-specific SoCs for video/image processing, including compression (H.264/AVC, H.265/HEVC), filters (super-resolution, noise reduction), recognition systems (feature detection, object tracking), and communication (UWB, LDPC). He also works on many-core processor design, ultra-low-delay vision systems, and sports analytics (volleyball, figure skating) with real-time 3D pose estimation and ball tracking. Awards : Recipient of the Furukawa Sansui Award (Waseda University, 1988) IEICE Research Encouragement Award (1992) Multiple Best Paper/Presentation Awards (2006–2022) at conferences including DAC/ISSCC, LSI IP Design, ISOCC, ISPACS, and CVIT APSIPA Distinguished Lecturer Certificate (2015) Waseda University Presidential Teaching Award (2020)
Daniel Gottesman is the Brin Family Endowed Professor in Theoretical Computer Science at the University of Maryland, affiliated with the Department of Computer Science, Institute for Advanced Computer Studies (UMIACS), and the Joint Center for Quantum Information and Computer Science (QuICS). He holds a Ph.D. in Physics from Caltech (1997) and has held positions at institutions like the Perimeter Institute and Quantum Benchmark. His research focuses on quantum computing, quantum error correction, and fault-tolerant systems, with contributions to stabilizer codes and quantum teleportation-based gates. Education: Bachelor's in Physics, Harvard University (1992) Ph.D. in Physics, California Institute of Technology (1997) Research Interests: Quantum error correction and fault-tolerant architectures Quantum cryptography and secure communication protocols Quantum complexity theory and algorithm design Applications of stabilizer codes and topological quantum computing Scientific Awards: Fellow of the American Physical Society CIFAR Senior Fellow in Quantum Information Science Three U.S. Patents (e.g., quantum key distribution systems) Advising & Grants: Supervised over 30 students/postdocs and served on numerous thesis committees. Active in securing funding for quantum research through endowed professorships and industry partnerships (e.g., Quantum Benchmark). Labs/Teams: Member of QuICS and UMIACS, collaborating on quantum hardware-software integration and error correction challenges.
Joachim Rosenthal is a Professor of Applied Mathematics at the University of Zurich's Department of Mathematics, leading the Applied Algebra Group. His research integrates coding theory, cryptography, and algebraic geometry with applications in communications and security systems. His primary research explores algebraic coding theory including convolutional codes, subspace codes, and post-quantum cryptography. He develops algorithms for error correction, code optimization, and cryptographic protocol analysis, with emphasis on mathematical structures in finite fields and rings. Professor Rosenthal serves as President of the Swiss Mathematical Society (2024-2025) and sits on IEEE Information Theory Society's Board of Governors. He has organized multiple international conferences on coding theory and cryptography, including the Zurich COST Meeting and Workshop on Convolutional Codes. His editorial contributions include roles at Archiv der Mathematik and SIAM Journal on Applied Algebra and Geometry. Recent publications focus on code construction methods, complexity analysis in group-based cryptography, and algebraic approaches to error-correcting codes.
Eugene Tang is an Assistant Professor in the Department of Mathematics and Physics at Northeastern University. His research focuses on quantum information theory and the theoretical limitations of quantum computing, particularly quantum error correction and efficient protocols using high-rate codes. He received his PhD from the California Institute of Technology in 2021. Dr. Tang's research interests include quantum error correction, the development of efficient quantum protocols surpassing conventional schemes, and the study of quantum algorithms such as QAOA. He explores the theoretical boundaries of quantum computing, with a focus on optimizing error detection and decoding methods for quantum LDPC codes and subsystem codes. His work also intersects with quantum gravity, particularly in the context of black hole interiors and bulk geometry construction through tensor methods. His recent publications highlight advancements in quantum error correction, including optimal locality in subsystem codes and efficient decoding strategies for quantum LDPC codes. His work on variational quantum optimization addresses challenges in scalability, such as QAOA's performance at large qubit scales and symmetry-related obstacles. Earlier contributions include research on superoscillations and hybrid quantum-classical algorithms for graph coloring. No scientific awards or grants are explicitly mentioned in the provided information. No specific labs or teams are associated with his work in the given data.
Henry D. Pfister is the Addy Family Professor of Electrical and Computer Engineering at Duke University, with a secondary appointment in Mathematics. He holds affiliations with the Pratt School of Engineering and the Duke Quantum Center. His research focuses on information theory, error-correcting codes, quantum computing, and machine learning applications in communications. Pfister earned his Ph.D. from UC San Diego and has held prior roles at Texas A&M University, École Polytechnique Fédérale de Lausanne, and Qualcomm. Education: Ph.D. in Electrical Engineering, UC San Diego (2003); M.S. degrees in Public Policy and Environmental Management from Duke University; J.D. and additional degrees from UNC Chapel Hill. Research interests include Reed-Muller codes, quantum error correction, neural decoders for DNA storage, and capacity-achieving coding schemes. Recent work highlights include proving Reed-Muller codes achieve capacity on binary-erasure channels and developing quantum-enhanced classical communication protocols. Publications span topics like polar codes for quantum channels, belief-propagation algorithms, and neural network-based decoding. Notable grants include NSF funding for DNA storage coding and quantum simulation projects. Pfister has advised over 20 graduate students and is a recipient of the STOC Best Paper Award and NSF CAREER Award.
Venkatesan Guruswami is a Chancellor's Professor in the Department of EECS and a Senior Scientist at the Simons Institute for the Theory of Computing at UC Berkeley . He also holds a Professor position in the Department of Mathematics . His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras (1997) , followed by a Ph.D. in Computer Science from the Massachusetts Institute of Technology (2001) . After a Miller Research Fellowship at UC Berkeley (2001–02), he held faculty roles at the University of Washington and Carnegie Mellon University before returning to UC Berkeley in January 2022. Education : B.Tech, IIT Madras (1997) Ph.D., MIT (2001) Professional Affiliations : Chancellor's Professor, UC Berkeley (EECS) Senior Scientist & Interim Director, Simons Institute Professor, UC Berkeley (Mathematics) Guruswami's research spans multiple domains within Theoretical Computer Science , focusing on Error-Correcting Codes , Approximation Algorithms , Randomness in Computing , Probabilistically Checkable Proofs , and Computational Complexity . His groundbreaking work in List Decoding has enabled codes with minimal redundancy for correcting worst-case errors, while recent advancements include Polar Codes , Deletion-Correcting Codes , and Constraint Satisfaction Problems . He has also contributed to Quantum Coding Theory , Locally Recoverable Codes , and Approximation Hardness in various computational contexts. His publications reflect a deep engagement with interdisciplinary topics. Key trends include: Quantum Information Theory : Quantum LDPC codes, transversal gates, and quantum storage. Algebraic Coding : Reed-Solomon codes, AG codes, and polynomial-based constructions. Computational Complexity : Hardness of approximation, CSPs, and parameterized intractability. Data Transmission : Polar codes, deletion channels, and feedback mechanisms. Algorithmic Techniques : Spectral methods, semirandom models, and Lasserre hierarchy applications. Guruswami has received numerous accolades, including the Simons Investigator Award , Presburger Award , Packard Fellowship , Sloan Research Fellowship , ACM Doctoral Dissertation Award , and the IEEE Information Theory Society Paper Award . He is an ACM Fellow (2017) and IEEE Fellow (2019) , with recent honors like the Guggenheim Fellowship (2023) and AMS Fellow (2023) . As an advisor, he has mentored over 25 PhD and postdoctoral researchers , including Atri Rudra , Prasad Raghavendra , and Peter Manohar , whose work has won awards like the Edmund M. Clarke Doctoral Dissertation Award and CRA Outstanding Undergraduate Researcher Award . His research is supported by grants from the National Science Foundation , Packard Foundation , and Sloan Foundation . He also serves as Editor-in-Chief of the Journal of the ACM and holds leadership roles in IEEE and arXiv moderation. Guruswami is actively involved in Simons Institute programs and co-organized workshops on Coded Computation and Information Theory . His work bridges theoretical advancements with practical applications in Cloud Storage , Quantum Computing , and Group Testing , including pandemic-era contributions like AC-DC: Amplification Curve Diagnostics for SARS-CoV-2 .
David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)
Jossy Sayir is an Affiliated Lecturer and Senior Research Associate in the Department of Engineering at the University of Cambridge . Holding a Dipl. El.-Ing. ETH and Dr. Techn.-Wiss. from ETH Zurich, Sayir’s work bridges Information Theory and Bioinformatics , focusing on DNA-based data storage and error correction systems. They serve as Director of Studies in Engineering at Newnham College and coordinate Engineering Admissions. Interdisciplinary collaboration with the European Bioinformatics Institute Research on DNA data storage efficiency and cost reduction Expertise in channel coding, source coding, and 5G algorithms Teaching spans mathematics and information engineering modules in Part I Engineering Tripos, with Part II contributions on information theory, error control coding, and cryptography. Sayir also oversees data compression labs and serves as Wine Committee Chair, reflecting diverse interests in food, coffee, wine, music , and jazz . Best Lecturer Award, 2017-18 Research Fellowships in coding theory Key research trends include DNA storage encoding , LDPC decoders , polar code optimization , and Sudoku-inspired constraint coding . Sayir’s work addresses both theoretical and practical challenges in high-density data storage and next-generation communication protocols .
Venkatesan Guruswami is a Chancellor's Professor in the Department of Electrical Engineering and Computer Sciences and Professor in the Department of Mathematics at the University of California, Berkeley. He previously served as faculty at Carnegie Mellon University for 13 years and held a Miller Research Fellowship at UC Berkeley. His research focuses on Theoretical Computer Science , particularly in Error-Correcting Codes , Approximation Algorithms , Quantum Computing , and Hardness of Approximation . Guruswami has made groundbreaking contributions to list decoding and quantum code constructions, with works featured in Science Magazine and the Journal of the ACM (where he serves as Editor-in-Chief). Education : B.Tech (1997, IIT Madras), Ph.D. (2001, MIT), Miller Research Fellowship (2001-02, UC Berkeley) Research Areas : Theory of error-correcting codes, approximation algorithms, pseudorandomness, probabilistically checkable proofs, and quantum coding theory Guruswami's recent work explores quantum LDPC codes , parameterized inapproximability , and stream decodable codes . He has received prestigious awards including the NSF CAREER award , David and Lucile Packard Fellowship , and Sloan Research Fellowship . His advising spans a wide range of students and postdocs, with notable contributions to coding theory and computational complexity .
Moinuddin Qureshi is a Professor of Computer Science at Georgia Institute of Technology, affiliated with the School of Computer Science and involved in the Online Master of Science in Computer Science (OMSCS) program. He holds a Ph.D. and M.S. from the University of Texas at Austin. His research focuses on computer architecture, memory systems, hardware security, and quantum computing, with notable contributions to mitigating rowhammer vulnerabilities and advancing quantum error correction. Previously, he was a Research Staff Member at IBM T.J. Watson Research Center (2007–2011), where he contributed to caching algorithms for Power-7 processors. He has held leadership roles, including Program Chair of MICRO 2015 and Selection Committee Co-Chair of Top Picks 2017. His work has been recognized with prestigious awards, including the 2019 Persistent Impact Prize and multiple best paper awards. Key research areas include secure memory design (e.g., rowhammer mitigation techniques like MINT and Moat), quantum computing (e.g., Flag-Proxy Networks and Élivágar), and hardware vulnerability analysis (e.g., Roguerfm attacks and COAXIAL memory systems). His publications span 2009–2025, addressing topics like error correction, secure tracking, and quantum annealing optimization. Awards include membership in ISCA, MICRO, and HPCA Hall of Fame, alongside contributions to conferences like HiPC and IEEE MICRO. His work bridges theoretical advancements with practical implementations in both classical and quantum domains.
Andreas Peter Burg is a Tenured Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Telecommunications Circuits Laboratory (TCL) within the School of Engineering. He holds multiple academic and administrative roles at EPFL including Associate Professor in Teaching (SEL, EDMI, EDEE), Director of SEL Management, and Member of the Doctoral Program Committee for Electrical Engineering. Dr. Burg received his Dipl.-Ing. degree in 2000 and Dr. sc. techn. degree in 2006 from ETH Zurich. His academic career includes positions as SNF Assistant Professor at ETH Zurich (2009-2011) before joining EPFL in January 2011 as a Tenure Track Assistant Professor, where he was promoted to Tenured Associate Professor in June 2018. His research focuses on circuits and systems for telecommunications , with particular expertise in silicon implementation of communication technologies, communication algorithms optimization for hardware, low-power VLSI signal processing, and digital integrated circuits. His work bridges theoretical communication concepts with practical circuit implementations, addressing challenges in wireless and wired communication systems. His recent publications (2024-2025) demonstrate a strong focus on next-generation communication technologies including 6G systems, advanced error correction coding, wireless sensing applications, and ultra-low power circuit design. These works span multiple subfields from LDPC and polar code decoding to RF signal processing and machine learning applications in wireless systems. Willi Studer Award (2000) ETH Medal for diploma thesis (2000) ETH Medal for Ph.D. dissertation (2006) Swiss National Science Foundation Assistant Professorship grant (2008) Dr. Burg has been involved in the development of more than 25 ASICs throughout his career and co-founded Celestrius, an ETH spinoff in MIMO wireless communication. His laboratory work focuses on practical implementations of communication algorithms with emphasis on power efficiency and hardware optimization. Current research directions include 6G technologies, wireless sensing applications, and novel error correction techniques for next-generation communication systems.
Dr. Ying He is a Senior Lecturer at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). Her research focuses on wireless communication networks, particularly integrating machine learning with satellite and terrestrial systems. She holds a BEng from Beijing University of Posts and Telecommunications (2009) and a PhD from UTS (2017). Prior to her academic role, she worked on TD-LTE chip design at the Chinese Academy of Sciences. Affiliations : Faculty of Engineering and Information Technology Global Big Data Technologies Centre (GBDTC) Education : BEng in Telecommunications Engineering, Beijing University of Posts and Telecommunications (2009) PhD in Engineering (Telecommunications), UTS (2017) Her research interests include satellite communication (GEO-LEO integration), spectrum sharing, vehicular communication, and applying machine learning to physical layer algorithms. Notable contributions include optimizing beam design in LEO networks and developing secure IoT systems. She supervises PhD/Master’s students and teaches courses like CCNA and capstone projects. Funded projects span satellite networks, IoT security, and supply chain tracking. Recent grants include SmartSat CRC initiatives and collaborations with industry partners like Intel and Ericsson. Her work addresses challenges in 6G, UAV-enabled computing, and resilient quantum algorithms.
Chih-Chun Wang is a Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University, with additional leadership roles as Associate Head for Facilities, Planning, and Staff. He earned his Ph.D. in Information Sciences and Systems from Princeton University in 2005, following an M.S. (2002) and B.S. (1999) in Electrical Engineering from Princeton and National Taiwan University, respectively. Research Interests: His work spans Network coding (graph-theoretic capacity, wireless network coding, feedback mechanisms) Coding theory (LDPC codes, Reed-Solomon decoding, iterative algorithms) Information theory (multi-user detection, network information theory) Signal processing (turbo equalization, space-time codes) Control theory (optimal stopping theory) Scientific Contributions: He has published extensively on Age-of-Information (AoI) minimization, low-latency coding, and multi-hop relay optimization. His research trends include Integrating machine learning with network coding Wireless security for Beyond-5G systems Distributed storage networks with intelligent helper selection Delay-constrained communication protocols Awards: Recognized as an IEEE Fellow in 2024 for contributions to network coding and information theory. Teaching: He teaches undergraduate courses like ECE301: Signals and Systems and graduate courses such as ECE639: Error Control Coding , with a focus on iterative decoding, LDPC codes, and network information theory. Advising: Supervised 15+ Ph.D. students, including current advisees Pin-Wen Su (delay-oriented coding), Wonjun Lee (cyber-physical systems), and Giles Bischoff (low-latency systems). Former students hold prominent roles at institutions like Google, Meta, and Intel.
Ken Duffy is a Professor and Chair of the Department of Mathematics at Northeastern University, with a joint appointment in the Department of Electrical and Computer Engineering. He joined Northeastern in 2023 and previously served as Interim Chair of the latter department. Previously, he was a professor at the National University of Ireland, Maynooth, where he directed the Hamilton Institute (2016–2022) and co-directed the Science Foundation Ireland Centre for Research Training in Foundations of Data Science. He earned a PhD in Mathematics from Trinity College Dublin. His research focuses on collaborative, multi-disciplinary algorithm design using probability and statistics, with applications in digital circuits, DNA, and network coding. Notable contributions include the Royal Statistical Society’s Applied Probability Section (co-founded in 2011) and numerous award-winning papers in IEEE conferences and journals. Recent work emphasizes decoding algorithms like GRAND (Guessing Random Additive Noise Decoding), applied to error correction, wireless systems, and biomedical imaging. His articles address topics like soft-output decoding, interference mitigation, and cellular lineage tracing. Awards: Best Paper Awards (IEEE ICC 2015, IEEE TNSE 2019), COMSNETS Best Demo (2022–2023), and the IEEE Ellersick Award (2024). Advising: The SFI Centre he co-directed funded over 120 PhD students. Labs/Teams: Hamilton Institute, Royal Statistical Society’s Applied Probability Section, and collaborative projects in cellular dynamics and secure communication.
John Preskill is the Richard P. Feynman Professor of Theoretical Physics at Caltech, specializing in quantum information science, quantum computing, and the physics of quantum error correction. He directs the Institute for Quantum Information and Matter. His pioneering work bridges quantum computing, gravitational physics, and quantum field theory, with recent focus on fault tolerance and quantum supremacy. He has authored over 500 publications and received the John Stewart Bell Prize (2024).
