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Debmalya PanigrahiView profile

Professor

Brown UniversityUnited States

Debmalya Panigrahi is a Professor and Associate Chair in the Department of Computer Science at Duke University. He holds a PhD in Theoretical Computer Science from MIT and has prior affiliations with Microsoft Research, Bell Labs, and the Simons Institute for Theory of Computing. His research focuses on algorithms, particularly graph algorithms, algorithms under uncertainty, and learning-augmented methods. He has received NSF CAREER and other awards, and his work spans peer-reviewed publications in top venues like STOC, FOCS, and SODA. He advises PhD students and mentors postdocs, emphasizing theoretical contributions with practical applications. His teaching includes courses on approximation algorithms, graph algorithms, and discrete mathematics. Education: PhD (MIT, advised by David Karger), MSc (Indian Institute of Science, advised by Ramesh Hariharan), BSc (Jadavpur University). Research highlights include fastest algorithms for graph connectivity, learning-augmented approximation methods, and online algorithms. Funded by NSF, ARO, Google, and others. Current projects explore network reliability, hypergraph algorithms, and algorithmic fairness. His lab collaborates across theory, AI/ML, and databases at Duke. Recent Grants: NSF CCF-2006512, CCF-1618286, CCF-1350537 Labs/Teams: Duke Algorithms Lab, Theory Group, Collaborations with CS-Econ and AI/ML groups Publications span 150+ papers, with 5+ journal articles in SIAM Journal of Computing and ACM Transactions. Recent focus on integrating machine learning into classical algorithms to improve worst-case performance bounds. Advised 10+ PhD students, many now in academia (e.g., UI Chicago, UT Dallas) and industry (Google, Microsoft).

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Aram HarrowView profile

Professor

Massachusetts Institute of TechnologyUnited States

Aram Harrow is a Professor of Physics at the Massachusetts Institute of Technology (MIT) , affiliated with the MIT Center for Theoretical Physics and MIT Center for Quantum Engineering . He focuses on quantum information science and quantum algorithms , with additional interests in representation theory and optimization . His recent work explores quantum computing applications in chemical physics and statistical mechanics . Undergraduate and graduate degrees in Physics at MIT Faculty positions: MIT (2013-present), University of Washington (2010-12), University of Bristol (2005-10) Research Interests: His work bridges quantum information theory and many-body physics , including: Quantum algorithm design for chemistry and optimization Quantum circuit complexity and t-designs Entanglement dynamics in quantum systems Quantum-classical hybrid computing models Key Publications: Recent articles demonstrate quantum speedups for biomolecular free energy calculations , Hamiltonian simulation , and jet clustering algorithms . His research combines quantum complexity theory with practical implementations on near-term quantum devices. Scientific Awards: 2023 Simons Investigator 2018 APS Bennett Award 2017 IEEE Best Paper Award 2016 Kavli Frontiers Fellow Mentorship: He advises current PhD students Shankar Balasubramanian , Angus Lowe , and Norah Tan , with 12 former advisees including Anand Natarajan and Saeed Mehraban . His 2026 recruitment seeks one new graduate student.

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David AllcockView profile

Assistant Professor

University of OregonUnited States

David Allcock is an Assistant Professor in the Department of Physics at the University of Oregon, part of the College of Arts and Sciences. His research focuses on ion trapping, quantum computing, and hybrid quantum systems, with an emphasis on manipulating atomic and molecular systems using electric and magnetic fields for quantum information applications. He leads the Ion Trapping Lab at UO, where he develops scalable quantum technologies and open-source control systems like ARTIQ and Sinara. His work bridges experimental physics with engineering, addressing challenges in qubit control, error mitigation, and large-scale quantum computer design. Education: MPhys from the University of Oxford (2007), D.Phil. in Physics from Oxford (2012). Prior to UO, he was a Lindemann Fellow at the National Institute of Standards and Technology (NIST) in Boulder, CO. His research includes innovations in trapped-ion qubit control, including laser-free entangling gates, scalable architectures, and applications in quantum sensing and dark matter detection. Key research themes include metastable qubit systems, photon scattering error mitigation, and the integration of superconducting detectors for state readout. He collaborates on open-source hardware-software stacks for quantum experiments and mentors students in quantum engineering through programs like the Quantum Technology Master’s Internship. Current projects explore hybrid quantum-classical interfaces and ultra-stable ion trap fabrication. His lab’s contributions span theoretical and experimental domains, with recent advances in geometric phase gates, microwave-driven control, and error-resilient qubit operations. The group also engages in interdisciplinary work linking quantum computing with precision measurement, such as SPUD (SPectroscopy for Ultralight Dark matter) and bosonic sensing tools.

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Samir H. MushrifView profile

Professor

University of AlbertaCanada

Dr. Samir H. Mushrif is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta . Prior to this role, he served as faculty at the School of Chemical and Biomedical Engineering at Nanyang Technological University (NTU), Singapore . He holds a PhD in Chemical Engineering from McGill University and completed postdoctoral research at the University of Delaware, USA . Education : PhD (Chemical Engineering, McGill University), Postdoc (University of Delaware) His research focuses on computational catalysis , molecular modeling , and reaction engineering for biomass conversion and CO2 reduction . He develops novel catalysts, solvents, and reactor systems using integrated quantum mechanical and classical molecular simulations , synergized with experimental data to enable sustainable energy and chemical production . Recent publications highlight trends in condensed phase chemistry for biomass reactions, machine learning applications in solvent configuration prediction, and mechanistic studies of lignin-carbohydrate complex deconstruction. His work bridges methane activation on metal oxides, hydrodeoxygenation of bio-oil compounds, and polymerization pathways in lignin structures. Scientific Awards include: NSERC Doctoral and Post-doctoral Fellowships Discovery International Award 2017 (Australian Research Council) NANYANG EDUCATION AWARD 2016 (Singapore) SCBE Teaching Excellence Awards (Silver 2015, Gold 2016) Bharat Gaurav (Pride of India) Award 2014 Dr. Mushrif's NSERC Discovery Grant (2018), CFI John R. Evans Leaders Fund Grant (2022), and AcRF Tier-2 Grant (Singapore, 2015) have advanced his work. Current PhD and Master's students include José Carlos Velasco Calderón , Arul Mozhi Devan Padmanathan , and Sagar Bathla , among others. The CARES Lab (Catalysis Research for Sustainability) under his leadership combines ab initio molecular dynamics , machine learning potentials , and Density Functional Theory to design materials for renewable energy . Collaborations span institutions in France , Canada , India , and the UK .

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Daniel GottesmanView profile

Professor

University of Maryland, College ParkUnited States

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.

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Frank RudziczView profile

Associate Professor

Dalhousie UniversityCanada

Dr. Frank Rudzicz is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research lies at the intersection of artificial intelligence, natural language processing, and healthcare, with a focus on developing machine learning systems that improve clinical decision-making, patient outcomes, and accessibility in medicine. He holds a BSc from Concordia University (2004), an MEng from McGill University (2006), and a PhD from the University of Toronto (2011). His research interests include Natural Language Processing, Machine Learning, Healthcare, Speech Technologies, Explainable AI, and Fairness in ML. Dr. Rudzicz's recent publications span a wide range of topics, including Alzheimer's detection through speech analysis, surgical outcome prediction, mental health monitoring, privacy in AI, and the application of large language models in clinical settings. His work consistently emphasizes ethical AI, patient privacy, and real-world clinical integration. He has received several awards, including a Best Paper award at EMNLP 2020, a Best Student Paper award at ICASSP 2021, and the ISCA Best Student Paper award in 2013. His research has been published in top-tier journals such as Nature Scientific Reports , JAMA Network Open , IEEE Access , and Frontiers in Human Neuroscience , as well as leading conferences including NeurIPS, ACL, ICML, and Interspeech. Dr. Rudzicz supervises a dynamic research group working on AI for health, with active projects in voice-based diagnostics, ambient clinical documentation, explainable AI for surgery, and wearable-based monitoring for chronic diseases. He collaborates widely across disciplines, including with clinicians, neuroscientists, ethicists, and public health experts. He is also involved in major initiatives such as the Genetics Navigator study and Bridge2AI-Voice, aiming to build ethically sourced, diverse biomedical datasets. His lab actively explores the societal implications of AI in healthcare, including fairness, trust, and resistance to malicious fine-tuning.

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Jonathan M. BakerView profile

Assistant Professor

University of Texas at AustinUnited States

Jonathan M. Baker is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin, holding the Advanced Micro Devices Chair in Computer Engineering. His research centers on quantum computer architecture with emphasis on practical quantum error correction implementation across the quantum computing stack. His educational background includes a Ph.D. in Computer Science from the University of Chicago (advised by Fred Chong) and dual B.S. degrees in Mathematics and Chemistry and Computer Science from the University of Notre Dame. Baker's research spans quantum compilation, logic synthesis, multi-radix architectures, and error mitigation for both near-term and fault-tolerant quantum systems. His work addresses critical challenges in quantum hardware-software co-design, with particular focus on optimizing quantum circuits for real-world hardware constraints and noise characteristics. Current projects emphasize qudit-based computing, neutral atom architectures, and efficient error correction implementations. His publication record shows strong focus on quantum architecture innovations, with recent work exploring qudit advantages, modular chiplet designs, and dynamic noise adaptation. Key trends include hardware-aware compilation techniques, communication optimization across quantum systems, and practical approaches to fault tolerance. Best Paper Award Runner Up, MICRO 2020 IEEE Micro Top Pick, 2020 (Virtualized Logical Qubits) IEEE Micro Top Pick, 2020 (Extending Frontier with Qutrits) IEEE Micro Top Pick, 2021 (Emerging Technologies) Best Poster Award, MICRO 2018 Baker actively mentors graduate students in quantum computing architecture research and serves on conference review committees including MICRO and ASPLOS. His teaching includes specialized quantum systems courses at UT Austin and online EdX modules covering quantum computation fundamentals and architecture. He collaborates with the Duke Quantum Center and maintains strong industry connections through the AMD Chair position, focusing on bridging academic research with practical quantum computing implementations.

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GONG JiangbinView profile

Professor

National University of SingaporeSingapore

GONG Jiangbin serves as Professor and Head of Department at the National University of Singapore (NUS), holding the prestigious Provost's Chair Professorship (2020-2026). He is a Principal Investigator at the Centre for Quantum Technologies (CQT) with office S12-02-10 and contact email phygj@nus.edu.sg. Education: PhD, University of Toronto, Canada (2001) His research program centers on topological quantum phenomena, with primary focus on novel topological phases of matter and their applications in quantum computation and information transfer. The group actively investigates quantum dynamics control, quantum simulation frameworks for metrology/sensing applications, disorder physics in few/many-body systems, quantum chaos, and emerging quantum machine learning paradigms to bridge theoretical advances with practical quantum technologies. Analysis of recent publications (2018-2024) reveals consistent expertise in topological quantum systems, spanning non-Abelian braiding in Majorana time crystals, Thouless pumping with single spins, exotic Floquet semimetals, acoustic topological platforms, and KPZ physics in Anderson localization - demonstrating deep integration of theoretical modeling with experimental quantum platforms. Scientific Awards: Provost's Chair Professorship (2020-2026) National Research Foundation Investigatorship (class of 2017) No explicit information regarding student advising or specific research grants was provided in the source material, though his leadership role suggests significant mentorship responsibilities and grant oversight. Gong leads a multidisciplinary research group at CQT/NUS that synergizes theoretical quantum physics with experimental quantum technologies, maintaining active collaborations across quantum simulation, topological materials, and quantum information science to advance next-generation quantum computing architectures.

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Martin Willis MonroeView profile

Assistant Professor

University of New BrunswickCanada

Martin Willis Monroe is an Assistant Professor in the Department of Classics and Ancient History at the University of New Brunswick. His research focuses on the ancient Middle East, particularly cuneiform cultures, with an emphasis on the history of scholarly knowledge in Babylonian and Assyrian societies. He specializes in Mesopotamian astronomy and astrology, as well as quantitative approaches to historical data. Dr. Monroe holds a PhD in Assyriology from Brown University (2016), an MPhil and BA in Ancient Near Eastern Studies from the School of Oriental and African Studies (2008, 2007). Previously, he served as a postdoctoral fellow and research associate at the University of British Columbia (2016–2023). His current projects include the publication of his Hellenistic astrology research under contract with Brill, titled Celestial Schemata: A Series of Astrological Tables from Seleucid Babylonia , and his role as associate director of the Database of Religious History , a global initiative to quantitatively analyze religious and cultural data. He has contributed to excavations at the Neo-Assyrian site of Tušhan in southeastern Turkey and advocates for responsible qualitative-to-quantitative data conversion in historical scholarship. Dr. Monroe’s research bridges traditional philological approaches with digital humanities, focusing on topics such as cuneiform astral diagrams, the intersection of ‘scientific’ and ‘religious’ texts in antiquity, and the application of computational methods to undeciphered scripts. He teaches courses on ancient civilizations, Near Eastern history, and archaeology.

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Qing LiView profile

Associate Professor

Carnegie Mellon UniversityQatar

Qing Li is an Associate Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. He holds a B.E. in Electronics Engineering from Tsinghua University (2006) and a Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2013). Prior to CMU, he worked as a postdoctoral researcher at the National Institute of Standards and Technology (NIST), where he developed quantum frequency conversion and microresonator-based optical systems. His research focuses on light-matter interactions in integrated photonics, emphasizing nonlinear optics and quantum information processing. He has pioneered silicon carbide and aluminum nitride platforms for chip-scale quantum technologies and optical metrology. Dr. Li has been recognized with prestigious awards including the Darpa Young Faculty Award (2019), OSA Paul F. Forman Team Engineering Excellence Award (2020), and Sigma Xi Best Ph.D. Thesis Award (Georgia Tech). His work bridges classical and quantum information systems, with applications in secure communication, atomic systems interrogation, and high-precision frequency synthesis. He actively contributes to the Pittsburgh Quantum Institute (PQI), advancing regional quantum engineering initiatives. His research group’s key projects include developing compact optical frequency synthesizers, soliton microcombs for communication grids, and entangled photon pair sources for quantum networks. Grants and collaborations support his exploration of novel photonic materials and devices, targeting advancements in both fundamental science and applied technologies.

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Brian Leeds DeMarcoView profile

Professor

University of Illinois Urbana-ChampaignUnited States

Professor Brian Leeds DeMarco is a leading academic in the Department of Physics at the University of Illinois Urbana-Champaign (UIUC), serving as the IQUIST Director and Bardeen Faculty Scholar. He specializes in Atomic, Molecular, and Optical Physics, with a focus on quantum simulation using ultracold atoms in optical lattices to address problems in condensed matter physics. His research has led to groundbreaking discoveries, including the first quantum-degenerate Fermi gas and three-dimensional Anderson localization of matter, earning him prestigious awards like the NSF CAREER Award and Sloan Fellowship. DeMarco holds a B.A. in Physics from SUNY Geneseo (1996) and a Ph.D. from the University of Colorado Boulder (2001). His postdoctoral work at NIST with David Wineland advanced quantum computing with trapped ions. At UIUC since 2003, he has held roles such as Associate Head for Undergraduate Programs and currently chairs NASA's Fundamental Physical Sciences Standing Review Board. His research explores quantum simulation, quantum computing architectures, and ultracold matter. Notable achievements include identifying phase-slip crossover in optical lattices and pioneering 3D Anderson localization. He has published over 30 high-impact articles, with recent work focusing on quantum simulators and hybrid quantum systems. Awards: APS Fellow, University of Illinois Scholar, NASA Review Board Chair, Defense Sciences Study Group member. Grants: NSF, ONR, Sloan Foundation support for quantum simulation and ultracold atom research. DeMarco leads the DeMarco Group, advancing quantum technologies and training students in experimental physics. His lab’s work on quantum testbeds and trapped ion processors positions him at the forefront of quantum information science.

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John H. ShawView profile

Professor

Harvard UniversityUnited States

John H. Shaw is the Harry C. Dudley Professor of Structural and Economic Geology and Professor of Environmental Science & Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS). He specializes in structural geology, earthquake hazards, and geomechanics, with a focus on thrust fault systems, fault-related folding, and seismic risk assessment in regions like California and China. His research integrates field observations, 3D modeling, and geomechanical simulations to understand fault dynamics and their implications for societal safety. Shaw's work emphasizes quantitative analysis of fault geometry, slip rates, and rupture processes. Key projects include modeling ground deformation during earthquakes, assessing seismic hazards in fold-thrust belts, and investigating reservoir-induced seismicity. He leads the Structural Geology & Earth Resources Group and contributes to collaborative initiatives like the Southern California Earthquake Center (SCEC). His articles highlight advancements in fault system modeling, including 3D structural reconstructions, distinct element method applications, and coupling geomechanical models with fluid flow simulations. Recent studies focus on the Wilmington blind-thrust fault beneath Los Angeles, the Ventura fault system, and tectonic evolution of the Canadian Rockies and Qaidam Basin. Shaw's research also addresses interdisciplinary challenges such as stochastic velocity modeling for earthquake ground motion prediction and developing open-source tools like the SCEC Unified Community Velocity Model (UCVM). His work bridges fundamental structural geology with applied seismic hazard mitigation strategies.

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Sushmita RujView profile

Associate Professor

University of New South WalesAustralia

Sushmita Ruj is an Associate Professor in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney. She serves as the Faculty of Engineering Lead for the UNSW Institute for Cybersecurity (IfCyber) and as the Taste of Research (ToR) Coordinator within the School of Computer Science and Engineering. Her academic journey includes previous positions as a Senior Research Scientist at CSIRO's Data61 (2019-2022), Associate Professor at the Indian Statistical Institute, Kolkata, and Assistant Professor at the Indian Institute of Technology (IIT), Indore. Dr. Ruj's primary research interests focus on applied cryptography, post-quantum cryptography, cybersecurity, blockchains, and data privacy. She designs practical, efficient, and provably secure protocols for real-life applications, with particular emphasis on critical infrastructure including smart grids, cloud computing, ad hoc networks, and data sharing frameworks. As quantum technology advances, her work increasingly focuses on developing quantum-safe algorithms to ensure a more secure Internet infrastructure. Her research spans multiple domains including cryptographic key management, proofs of storage, verifiable computation, vector commitments, and privacy-enhancing technologies for cloud and IoT environments. Her recent publications demonstrate a strong trend toward post-quantum cryptography solutions, with particular emphasis on blockchain applications, DNS security, and privacy-preserving protocols for industrial IoT. The research shows increasing focus on practical implementations of theoretical cryptographic concepts, with applications across multiple sectors including finance, healthcare, and critical infrastructure. Her work bridges the gap between theoretical cryptography and real-world security challenges, with growing emphasis on the transition from classical to quantum-resistant systems. Best Paper Award at ACISP 2024 JNCA Best Survey Award (2023) NSW Innovation Award (iAward) Merit Winner (2022) Women in Science Award from CSIRO (2020) ACM Senior Member (2016) IEEE Senior Member (2015) Samsung GRO award (2014) Dr. Ruj has successfully mentored numerous PhD and Master's students, with many of her former students now holding academic positions at institutions like IIT Indore, TU Wien, and CISPA Helmholtz Center. She has secured significant competitive funding including multiple Australian Research Council (ARC) grants, Samsung GRO Award, NetApp Faculty Fellowship, Cisco Academic Grant, and IBM Research grant. Her current research portfolio includes projects on blockchain-based quantum-safe digital medical passports, embedding trust in digital IDs, and resilience of supply chain unstructured data. As Faculty of Engineering Lead for IfCyber, Dr. Ruj plays a key role in UNSW's cybersecurity research initiatives. She has served on editorial boards for prestigious journals including IEEE Transactions on Information Forensics and Security and has held leadership positions in major conferences such as ACISP 2021 and Indocrypt 2020. She was also a member of the working group on "Blockchain For Cybersecurity" for the National Blockchain Roadmap of Australia and the first Blockchain Working group set up by the Reserve Bank of India.

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Jianqi LiuView profile

Research Fellow

University of PennsylvaniaUnited States

Jianqi Liu is a Hans Rademacher Research Fellow in the Department of Mathematics at the University of Pennsylvania, affiliated with the School of Arts and Sciences. His research focuses on vertex operator algebras (VOAs) and 2D conformal field theory (CFT), with an emphasis on algebraic structures such as fusion rules, Zhu’s algebras, Borel-type subalgebras, and connections to the classical Yang-Baxter equation. He also explores geometric aspects of VOAs and their physical interpretations. He earned his Ph.D. in Mathematics from the University of California, Santa Cruz (2018–2023), under the supervision of Chongying Dong. Prior to his current role, he held teaching roles at both UPenn and UCSC, instructing courses ranging from advanced linear algebra to complex analysis and number theory. His research publications address topics such as twisted conformal blocks, Rota-Baxter operators, and the interplay between algebraic structures and integrable systems. Liu collaborates with researchers like Angela Gibney and Daniel Krashen, focusing on advancing theoretical frameworks in mathematical physics and algebraic geometry.

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Anders RantzerView profile

Professor

Max Planck Institute for Software SystemsGermany

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

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