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Adrian WellerView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Explainability
• +9 more

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

• Networked Systems
• Distributed Systems
• Social Computing
• +5 more

Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.

Peter DindaView profile

Professor

• Parallel and Distributed Systems
• Virtualization
• Operating Systems
• +8 more

Peter Dinda is a Professor in the Department of Computer Science at Northwestern University , with a secondary appointment in the Department of Electrical and Computer Engineering . He has authored over 130 scientific papers, holds five patents, and is a Fellow of the IEEE . As the former head of the Computer Engineering and Systems division, he has contributed extensively to experimental computer systems. Education: B.S. in Electrical and Computer Engineering from the University of Wisconsin Ph.D. in Computer Science from Carnegie Mellon University Research Focus: Experimental computer systems, particularly parallel and distributed systems , virtualization , operating systems , and empathic systems that integrate user satisfaction with systems-level decision-making. His work also spans compiler design, memory management, and hardware-software co-design for performance optimization. Recent Trends: His publications emphasize virtualization efficiency, memory protection frameworks, parallel programming language design, and power management in heterogeneous computing environments. Key areas include exascale systems, IoT privacy, and physiological sensor-based user modeling. Scientific Awards: Fellow, IEEE Leadership: Served as Director of Graduate Studies and previously led the Computer Engineering and Systems division.

Bill GroppView profile

Professor

• High Performance Computing
• Parallel Computing
• Scientific Computing
• +3 more

William Gropp is the Grainger Distinguished Chair and Director of the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Computer Science from Stanford University (1982) and has contributed extensively to parallel computing, software for scientific computing, and numerical methods for PDEs. His research focuses on high performance computing, programming models, and scalable algorithms. Education: B.S. Mathematics (Case Western Reserve, 1977), M.S. Physics (University of Washington, 1978), Ph.D. Computer Science (Stanford, 1982). Research Interests: Gropp's work spans HPC, parallel computing, and numerical methods. He co-developed the MPI standard and MPICH implementation, and contributed to the PETSc library. His current projects include the Delta and DeltaAI supercomputers, Illinois Computes, and exascale initiatives. Scientific Awards: AAAS Fellow (2018), ACM/IEEE-CS Ken Kennedy Award (2016), SIAM/ACM Prize (2015), and 2024 ACM Software System Award. Member of the National Academy of Engineering. Grants & Leadership: Director of NCSA, leader of the Midwest Big Data Hub, and contributor to NSF-funded projects. His teams support AI/ML infrastructure and exascale computing. He advises on HPC policy and serves on committees like the Computing Community Consortium. Labs/Teams: NCSA, Siebel School research groups, collaborations with DOE, NSF, and industry partners. His work drives advancements in cyberinfrastructure and computational science.

Oliver GasserView profile

Researcher

• Internet Measurement
• IPv6
• BGP
• +9 more

Oliver Gasser is a researcher at the Max Planck Institute for Informatics (MPI-INF) and the Technical University of Munich (TUM), where he has co-lectured courses such as Advanced Computer Networking and Master Course Computer Networks. His research focuses on Internet measurement, security, and privacy, with a strong emphasis on IPv6, DNS, BGP, and web tracking technologies. His research interests include Internet measurement, IPv6 deployment, BGP security, DNS infrastructure, web tracking, privacy technologies, and network resilience. He has made significant contributions to understanding IPv6 hitlists, router fingerprinting, hypergiant content delivery networks, and consent banner manipulation on the web. His work combines large-scale active measurements with data analysis to uncover systemic issues in Internet infrastructure and privacy practices. The recent publications highlight a consistent focus on measurement-driven research across networking, security, and privacy. Trends include analyzing IPv6 adoption patterns, detecting covert tracking mechanisms in web cookies, evaluating security of Internet protocols like DNS and BGP, and improving measurement methodologies for large-scale network studies. His work often involves developing open tools and datasets that advance reproducibility in networking research. PAM 2023 Best Paper Award TMA 2023 Best Paper Award TMA 2023 Fast Track Award PAM 2018 Best Paper Award IRTF Applied Networking Research Prize 2018 IMC 2017 Community Contribution Award TMA 2017 Best Dataset Award CoNEXT 2023 Community Contribution Award Oliver Gasser has advised or co-advised over 40 master’s and bachelor’s theses at MPI-INF and TUM, demonstrating strong mentorship in academic research. He has led several measurement projects that have received external funding, including development of the IPv6 Hitlist Service and tools for analyzing web tracking. His community service includes extensive participation in program committees for major networking conferences such as IMC, CoNEXT, PAM, and TMA. He leads and contributes to several open research initiatives including the IPv6 Hitlist Service, SNMPv3 Measurement Service, MPTCP Measurement Service, DNS Observatory, and the BannerClick tool for automated cookie banner interaction. These platforms provide valuable resources for the global networking research community and support reproducible science.

Nikos HardavellasView profile

Professor

• Computer Architecture
• Quantum Computing Systems
• Parallel Systems
• +4 more

Nikos Hardavellas is a Professor of Computer Science and Electrical and Computer Engineering at Northwestern University, affiliated with the McCormick School of Engineering. He leads the Parallel Architecture Group at Northwestern (PARAG@N), focusing on energy-efficient parallel computing and quantum systems. His research spans quantum computing systems, fault-tolerant quantum error management, memory-centric architectures, and photonics-based interconnects. Education: Ph.D. Computer Science, Carnegie Mellon University (2009) M.S. Computer Science, Carnegie Mellon University (2006) M.S. Computer Science, University of Rochester (1997) B.S. Computer Science, University of Crete (1995) Research Interests: Quantum system software stack and error mitigation Memory-centric computing and programmable memory systems Energy-efficient architectures and dark silicon Photonics and optical interconnects Parallel systems and compiler-hardware co-design Key Contributions: Developed SupermarQ, a scalable quantum benchmark suite Pioneered optical cache hierarchies (Pho$) and energy-proportional photonic networks Advanced compiler-driven virtual memory systems (CARAT) and MPI autotuning (ACCLAiM) Awards & Honors: NSF CAREER Award (2015) Future CRA Leader (2024) Best Paper Awards at HPCA (2022) and ISLPED (2021 nomination) Test-of-Time Award at EDBT (2019) Grants & Service: Secured $4.8M in research funding from NSF, industry partners, and university initiatives Executive Committee member of Northwestern’s INQUIRE Institute for Quantum Research General Co-chair of IEEE/ACM MICRO 2022 Extensive service on departmental committees and thesis advisory boards Labs & Teams: Directs PARAG@N, collaborating on quantum computing, photonics, and energy-efficient architectures. Engages with industry partners like AMD, Intel, and Synopsys.

• Comparative Psychology
• Developmental Psychology
• Primate Behavior
• +6 more

Professor Zanna Clay is a leading comparative and developmental psychologist at Durham University, specializing in primate behavior and human evolutionary origins. She holds a joint appointment as an Associate Fellow in the Institute of Advanced Study. Her work bridges primatology, developmental psychology, and conservation, focusing on empathy, communication, and social learning in great apes and humans. Clay completed her doctoral work at the University of St Andrews, followed by postdoctoral research at Emory University and the University of Neuchatel. She has extensive field experience studying bonobos in the wild and captivity, including roles at the Lola ya Bonobo Sanctuary. Her research explores the evolutionary roots of human social cognition through comparative studies of bonobos, chimpanzees, and human infants. Key research interests include empathy development, language evolution, primate vocal communication, and the role of gesture in early human communication. She leads projects on cross-cultural empathy in infants (UK and Uganda) and has pioneered studies on emotion contagion using thermal imaging. Her work also addresses conservation challenges for bonobos through behavioral insights. Clay supervises PhD students investigating primate behavior, socio-emotional development, and conservation. She collaborates globally, including with the MPI-EVA in Congo and Birmingham University. Notable publications include studies on bonobo empathy, primate vocal flexibility, and the evolutionary origins of imitation.

Don ZagierView profile

Professor

• Number Theory
• Algebraic Geometry
• Modular Forms
• +3 more

Don Zagier is a distinguished mathematician holding positions as Scientific Member and former Director at the Max Planck Institute for Mathematics in Bonn. He has been a Professor at the University of Bonn, University of Maryland, Kyushu University, University of Utrecht, and Collège de France. His work bridges number theory, algebraic geometry, topology, and mathematical physics. Born in 1951, he studied at MIT (1966–1968), earned a D.Phil. from Oxford (1971), and completed his habilitation at Bonn (1975). He has held roles in the Sonderforschungsbereich (SFB) Theoretische Mathematik, served as ICTP Distinguished Staff Associate since 2014, and is an Honorary Member of the London Mathematical Society (2019). Research interests include modular forms, zeta functions, quantum invariants, and algebraic geometry. Notable works include studies on the Birch-Swinnerton-Dyer conjecture, polylogarithms, and the theory of Jacobi forms. His contributions span over 200 publications, including foundational texts like Zetafunktionen und quadratische Körper (1981) and co-edited volumes on modular forms. His awards include the Carus Prize (1984), Frank Nelson Cole Prize (1987), and Karl Georg Christian von Staudt Prize (2001). Research trends in his articles focus on modular forms, knot theory, quantum topology, and the interplay between number theory and geometry. Grants and collaborations are extensive, though specifics remain unspecified. He leads research at the MPI and contributes to global mathematical communities through educational initiatives and editorial roles in journals like Communications in Number Theory and Physics .

Antonino Di PiazzaView profile

Professor

• Quantum Electrodynamics
• Strong-Field QED
• High-Energy Physics
• +12 more

Antonino Di Piazza is a Professor of Physics at the Department of Physics and Astronomy, University of Rochester, and a Distinguished Scientist at the Laboratory for Laser Energetics (LLE). He is also a Guest at the Theory Division of the Max-Planck-Institut für Kernphysik (MPI-K), where he previously served as a Group Leader from 2009 to 2023. His academic journey includes a PhD from the University of Trieste and habilitations in both Germany and Italy, establishing his expertise in theoretical physics. His research interests lie at the forefront of quantum electrodynamics, particularly in strong and ultra-intense laser fields. He investigates fundamental phenomena such as radiation reaction, vacuum polarization, nonlinear Compton scattering, and Breit-Wheeler pair production. His work often bridges classical and quantum electrodynamics, exploring the limits of theoretical models like the local constant field approximation and developing new frameworks for tightly focused and flying-focus laser pulses. The 15 most recent publications highlight a consistent focus on strong-field QED processes. Key themes include the behavior of particles in flying focus pulses, quantum vacuum effects like birefringence, and the analytical and numerical modeling of radiation processes. His work combines deep theoretical insight with relevance to cutting-edge experimental facilities, aiming to test QED in previously inaccessible regimes. He has not received any scientific awards listed in the provided text. Dr. Di Piazza is an active educator, currently teaching Quantum Mechanics II at the University of Rochester. He has a history of lecturing and tutoring at Heidelberg University and leading numerous doctoral schools and workshops on strong-field physics. He has secured research support through collaborations with major institutions like LLE, ELI-NP, and MPI-K, enabling his theoretical work to inform and be informed by high-power laser experiments. His extensive list of invited seminars and conference talks underscores his leadership in the field. He leads the 'High-Energy Quantum Electrodynamics' research group, which is affiliated with the University of Rochester and the Max-Planck-Institut für Kernphysik. This group focuses on theoretical investigations of quantum vacuum and particle dynamics in extreme electromagnetic fields, contributing significantly to the global effort in high-field science.

Frank NIELSENView profile

Professor

• Data science
• Geometric science of information
• Information geometry
• +4 more

Frank NIELSEN is a Professor at École Polytechnique with expertise in information geometry, data science, and machine learning. He holds a PhD (1996) and HDR (2006) in computer science and has established himself as a leading researcher in geometric approaches to information science. His educational background includes a PhD in computer science (1996) followed by a Habilitation à Diriger des Recherches (HDR) in 2006, the highest academic qualification in France that qualifies one to supervise doctoral candidates. Dr. NIELSEN's research focuses on the Geometric Science of Information , where he develops theoretical frameworks for understanding data through geometric and information-theoretic lenses. His work bridges Computational information geometry Statistical manifold theory Bregman divergences and their applications Machine learning with geometric foundations High-dimensional data analysis He aims to address the challenge of inappropriate data representation in current Data Science by building a theory of Computational Information Geometry to enable Intrinsic Data Science with principled distances. His extensive publication record shows a clear trend toward developing geometric frameworks for understanding statistical divergences, with recent work focusing on Bregman geometry, Fisher-Rao metrics, and their applications in machine learning. His research spans theoretical developments in information geometry to practical implementations like the pyBregMan Python library, demonstrating both theoretical depth and practical relevance. Dr. NIELSEN has made significant contributions through his teaching and publications. He has taught courses at École Polytechnique including INF442, INF517, and INF591. His authored textbooks include Introduction to HPC with MPI for Data Science (2016), A Concise and Practical Introduction to Programming Algorithms in Java (2009), and Visual Computing: Geometry, Graphics, and Vision (2005). He has also edited influential volumes such as Computational Information Geometry for Image and Signal Processing (2016) and Geometric Theory of Information (2014). He actively organizes and participates in academic events, serving on program committees for major conferences including GSI (Geometric Science of Information), CVPR, and ICCV. His work has established him as a key figure in the growing field of geometric approaches to information science.

Michalis VazirgiannisView profile

Professor

• Machine Learning
• Deep Learning
• Graph Analysis
• +11 more

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique in France, where he leads the Data Science and Mining group (DaSciM). He holds a degree in Physics and a PhD in Informatics from Athens University (Greece), and a Master's degree in AI from Heriot Watt University, Edinburgh (UK). His academic career spans multiple prestigious institutions including Fraunhofer and Max Planck MPI in Germany, INRIA/FUTURS in Paris, AUEB in Greece, Telecom-Paristech, ENS in France, Tsinghua and Jiaotong Shanghai in China, and Deusto University in Spain. Professor Vazirgiannis's research focuses on machine and deep learning methods for graph analysis, including community detection, graph clustering, node embeddings, and influence maximization. His work in text mining encompasses Graph of Words, word embeddings with applications to web advertising and marketing, event detection, and summarization. He has active collaborations with industrial partners in analytics and machine learning for large-scale data repositories across various application domains such as recommendations, meeting summarization, influence metrics for scientific and social networks, and predictive maintenance. His recent publications demonstrate a strong emphasis on Graph Neural Networks, multilingual NLP (particularly for French and Arabic), and applications of deep learning to diverse domains including social networks, legal text, and biomedical data. There's a clear trajectory toward developing more efficient, explainable, and specialized models that address real-world challenges in data analysis. ERCIM fellowship Marie Curie EU fellowship Tencent "Rhino-Bird International Academic Expert Award" (2017) Best Paper Award at IJCAI 2018 Best Paper Award at CIKM 2013 Professor Vazirgiannis has supervised 29 completed PhD theses and has attracted significant R&D funding from national and international sources, including research agencies and industrial partners such as Google, Airbus, Huawei, Deezer, BNP, and LVMH. He leads or has led several academic research chairs including DIGITEO (2013-15), ANR/HELAS (2020-25), and AXA (2015-2018). The DaSciM research group, which he leads at École Polytechnique, has extensive experience in real-world R&D projects involving large-scale data mining. The team maintains active collaborations with major industrial partners including AIRBUS, Google, BNP, Tencent, and Tradelab, working on cutting-edge machine learning projects. The group has co-organized major conferences such as ECML PKDD 2011 and ECML/PKDD 2017 and participates in the senior organization of AI and data mining events like AAAI and IJCAI.

Théo WinterhalterView profile

Researcher

• Type Theory
• Proof Assistants
• Formal Verification
• +3 more

Théo Winterhalter is a researcher at INRIA Saclay and a member of the Laboratory of Mathematics and Computer Science (LMF) at ENS Paris-Saclay . He previously held a postdoctoral position at the Max Planck Institute for Security and Privacy (MPI-SP) and completed his PhD at the Gallinette research team in Nantes, supervised by Nicolas Tabareau and Matthieu Sozeau. Education PhD in Computer Science, 2017–2020, University of Nantes (Gallinette/Inria) MSc in Computer Science, École Normale Supérieure de Rennes Research interests include type theory , proof assistants , formal verification , and dependent types . He actively works on improving the safety and usability of proof assistants like Rocq (formerly Coq), focusing on rewrite rules, erasure, and cryptographic verification. His work often involves formalizing results within proof assistants and developing tools for verified programming. Contributions span conferences like POPL, ICFP, CPP, and TYPES. Recent projects include foundational verification of high-speed cryptography ( The Last Yard ), type-preserving rewrite rules ( The Rewster ), and modular cryptographic proofs ( SSProve ). His publications emphasize formal methods and computational assumptions in type theory. Teaching includes the Proof Assistants course at MPRI , a joint master’s program. He co-supervises PhD students like Yann Leray and has mentored interns on topics such as erased data implementation and Autosubst tooling. Labs and Teams : Deducteam (INRIA Saclay) – Developing deduction tools and formal verification LMF (ENS Paris-Saclay) – Laboratory for Mathematics and Computer Science Gallinette (former) – Team at INRIA Nantes MetaCoq Project – Collaborative effort on Coq verification

Théo WinterhalterView profile

Researcher

• Formal Methods
• Type Theory
• Proof Assistants
• +3 more

Dr. Théo Winterhalter is a researcher at the Deducteam within INRIA Saclay and the Laboratoire de Méthodes Formelles (LMF) at École Normale Supérieure Paris-Saclay. Previously, he held a postdoctoral position at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. He completed his PhD in computer science at the University of Nantes under the supervision of Nicolas Tabareau and Matthieu Sozeau, focusing on the formalization and meta-theory of type theory. His research centers on proof assistants, particularly Coq, and involves advancing formal methods for security, type systems, and dependent type theory. Winterhalter's work includes contributions to the MetaCoq project, enabling meta-programming and formal reasoning about Coq itself. He is also involved in developing verified cryptographic frameworks like SSProve and SecRef*, ensuring secure integration of verified and unverified code. His educational background includes a PhD in type theory and prior work in cryptographic algorithms, such as randomized scalar multiplication. He actively participates in academic communities, serving on program committees for ICFP, TYPES, and POPL. His teaching includes co-teaching the MPRI course on proof assistants with Yannick Forster. Supervision activities include PhD student Yann Leray and multiple interns focusing on Coq extensions and formal verification.

Walid HetabaView profile

Researcher

• Electron Microscopy
• X-ray Photoelectron Spectroscopy (XPS)
• Catalysis
• +3 more

Dr. Walid Hetaba is a Group Leader in the Scientific Infrastructure department at the Max Planck Institute for Chemical Energy Conversion (MPI CEC), specializing in Electron Microscopy and X-ray Photoelectron Spectroscopy (XPS). He leads a research group focused on advanced materials characterization, particularly for catalytic systems. His academic background includes a Diplom in Technical Physics (Dipl.-Ing.) and a Dr.techn. from TU Wien (2011–2015). Prior to his current role, he held postdoctoral positions at TU Wien, Universität Bielefeld, and the Fritz Haber Institute of the Max Planck Society (2016–2020). Dr. Hetaba's research emphasizes the structural and electronic characterization of materials at micro- and nanoscales, linking material properties to catalytic function. His group develops methodologies for TEM/XPS analysis, including ChemiTEM—a TEM optimized for chemistry and materials science. Key research areas include catalyst design, nanomaterial synthesis, and surface science, with applications in energy storage and conversion. His group operates state-of-the-art equipment such as the Thermo Scientific Talos F200X TEM, Phenom Pharos SEM, and NAP-XPS systems. They collaborate extensively with other research groups to advance catalysis and materials science. Current projects include the UniSysCat cluster on bimetallic nanocatalysts and FAIRmat data standardization initiatives. Dr. Hetaba has published extensively in journals like Advanced Energy Materials , ACS Catalysis , and Chemistry-Methods , focusing on topics such as magnetic catalysts, nanomaterial functionalization, and surface reactivity. His work bridges fundamental material science with applied catalysis, driving innovations in energy technologies.

Grey BallardView profile

Associate Professor

• Numerical Linear Algebra
• High Performance Computing
• Computational Science
• +6 more

Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.