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Mostafa AmmarView profile

Professor

• Network Architectures
• Protocols and Services
• Multicast Communication
• +17 more

Mostafa Ammar is a Regents' Professor and Interim Chair at the School of Computer Science , Georgia Institute of Technology. He holds a Ph.D. from the University of Waterloo and degrees (S.B., S.M.) from MIT. His career spans academia, industry collaboration, and leadership in networking research. Research Interests : Network architectures, protocols, and services; multicast communication; multimedia streaming; content distribution networks; disruption-tolerant networks; mobile cloud computing; network virtualization; HTTP adaptive streaming; video quality of experience (QoE); encrypted traffic analysis; vehicular networks; peer-to-peer systems; overlay networks. Funding : Supported by NSF, DARPA, AFOSR, CISCO, IBM, Intel, BellSouth, Sprint, and others. His research focuses on video QoE estimation using network measurements, mobile cloud computing , and network agility through virtualization. Recent work includes machine learning approaches for encrypted traffic analysis and scalable techniques for network performance. Key scientific awards include: IBM Faculty Partnership Award (1996), Best Paper at WWW '98, IEEE Fellow (2002), ACM Fellow (2003), GT Outstanding Doctoral Thesis Advisor (2006), IEEE TCCC Service Award (2010), ACM Mobihoc Best Paper (2012), College of Computing Awards (2015, 2018), IFIP Best Paper (2018), and multiple teaching excellence awards (2013-2017, 2022 CIOS Award). Dr. Ammar has advised 39 PhD students , many of whom hold prominent positions at institutions like UC Santa Barbara, Emory University, and companies including Google, Microsoft, and Facebook. His editorial leadership includes Editor-in-Chief of IEEE/ACM Transactions on Networking (1999-2003) and roles in conference committees.

Peter ZollerView profile

Professor

• Theoretical Physics
• Quantum Optics
• Many-Body Quantum Physics
• +3 more

Peter Zoller is a Professor of theoretical physics at the University of Innsbruck and Scientific Director at IQOQI Innsbruck (Austrian Academy of Sciences). His research focuses on quantum optics, many-body quantum physics, and quantum information science, with a strong emphasis on quantum simulation of gauge theories and atomic systems. He has trained 34 PhD students and hosted 57 postdoctoral researchers, fostering collaborations between theory and experiment. His group, the Zoller Group, explores quantum phenomena such as lattice gauge theories, entanglement dynamics, and topological order using advanced quantum simulation techniques. Key research interests include atomic physics, quantum gases, and applications of quantum technologies to high-energy physics problems. Recent work addresses string breaking in quantum simulators, entanglement Hamiltonians, and scalable architectures for fermionic quantum processors. Collaborations span institutions like Harvard, MIT, and the University of Innsbruck’s experimental teams. His contributions bridge foundational physics with cutting-edge quantum technologies, aiming to solve problems inaccessible to classical methods.

Huadong MoView profile

Senior Lecturer

• Power and energy systems
• Cyber-physical systems
• Manufacturing systems
• +6 more

Dr. Huadong Mo is a Senior Lecturer at the School of Systems and Computing, University of New South Wales (UNSW) Canberra, Australia. He holds a B.E. degree in automation from the University of Science and Technology of China (2012) and a Ph.D. in systems engineering and engineering management from the City University of Hong Kong (2016). Prior to his current position, he was a research associate at ETH Zurich's Reliability and Risk Engineering Lab (2016-2019) and a Lecturer at UNSW Canberra (2019-2021). Dr. Mo's educational background includes a strong foundation in systems engineering with international experience across China, Switzerland, and Australia. His career trajectory demonstrates a progression from academic research to faculty positions with increasing responsibilities in teaching and research leadership. His research focuses on enhancing the resilience, performance, and security of complex systems using learning-based algorithms, primarily in power and energy systems, cyber-physical systems, and manufacturing systems. He applies data analytics to understand system evolution under uncertainties, with particular emphasis on prognostics and health management, sustainable transportation, robust operation of power systems under extreme events, and reinforcement learning-based asset management. His work bridges theoretical advances with practical applications in critical infrastructure. Analysis of Dr. Mo's recent publications reveals a strong focus on energy systems, particularly in the integration of machine learning with power grid management, battery storage systems, and resilience against cyber threats. His research shows a clear trajectory toward increasingly complex system integration, with growing emphasis on multi-vector energy communities, cross-domain prediction, and uncertainty-aware energy management. The interdisciplinary nature of his work spans electrical engineering, computer science, and operations research. 2024 IEEE SMC Early Career Award 2023 Visiting Research Fellowship (Jean d'Alembert Pour Fellowship) Gold Medal in 2024 China International College Student Innovation Competition (as supervisor) Arc PGC Supervisor Award (2021) IEEE SMC Outstanding Chapter Award (2021) Alumni Achievement Award from City University of Hong Kong (2019) Dr. Mo actively supervises numerous HDR students working on cutting-edge research topics including battery health monitoring, quantum control, reinforcement learning for power systems, and explainable AI for energy management. He leads multiple significant research grants totaling over 3 million AUD, including projects funded by ARC, Energy Innovation Fund, and international collaborations with institutions like ETH Zurich, Cambridge, and Tsinghua University. His research group maintains strong international connections, facilitating student exchanges and collaborative research. As Postgraduate Course Coordinator of Systems Engineering and Chair of IEEE SMC ACT Chapter, Dr. Mo plays a significant role in academic leadership and professional community building. His research team collaborates with industry partners on practical implementations of their theoretical work, particularly in the energy sector.

• Artificial Intelligence
• Data Systems
• Large Language Models
• +9 more

Joseph E. Gonzalez is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, co-director of the Sky Computing Lab and RISE Lab, and member of the Berkeley AI Research (BAIR) group. His work bridges artificial intelligence and data systems with significant impact on large language model infrastructure and deployment. His research focuses on large language models (LLMs) including tool use, RAG, and agent systems; LLM deployment infrastructure; edge-based machine learning; cloud computing innovations; and computer vision applications. He addresses the full machine learning lifecycle from training to serving, emphasizing real-time decision systems and secure execution environments. Scientific Awards: Okawa Research Grant NSF Expedition Award NSF CAREER Award Professor Gonzalez mentors 17 current graduate students and numerous former students/post-docs across AI and systems research. His work is funded by the NSF Expedition grant for the RISE Lab, an NSF CAREER Award, and industrial sponsors including major technology companies. He co-directs the RISE Lab advancing real-time intelligent secure execution systems, and the Sky Computing Lab pioneering cloud computing abstractions. These initiatives tackle low-latency systems, online learning algorithms, and security frameworks for intelligent decision-making in physical environments.

Frederic GardesView profile

Professor

• Silicon Photonics
• Integrated Optics
• Photonic Integrated Circuits
• +2 more

Professor Frederic Gardes is a leading academic in the Department of Electronics and Computer Science within the Faculty of Engineering and Physical Sciences at the University of Southampton. He holds a PhD and serves as Principal Investigator on multiple high-impact photonics research projects funded by EPSRC and the European Union. His research spans critical areas of modern photonics: Silicon photonics for energy-efficient communications Integrated optical circuits using silicon nitride platforms Quantum dot systems for sensing and information processing High-speed electro-optic modulators Nonlinear optical phenomena in integrated waveguides Professor Gardes' recent publications (2024-2025) demonstrate a concentrated focus on advancing silicon nitride photonics, particularly in developing low-loss interfaces, high-speed modulators operating at 100 Gbit/s, and broadband wavelength conversion techniques. His work bridges fundamental nonlinear optics with practical applications in telecommunications and sensing, consistently emphasizing CMOS compatibility and manufacturing feasibility. He actively supervises seven PhD students pursuing cutting-edge research in photonic integration. His leadership extends to major collaborative projects including QUDOS, C-PIC, and Horizon Europe initiatives like DOLORES and PIXEurope, where he works alongside Professors Graham Reed, David Thomson, and Goran Mashanovich. Professor Gardes maintains active industry engagement through speaking roles such as his 2023 presentation on Advanced Silicon Nitride Integration for CMOS Photonic Circuits , demonstrating the translational impact of his research.

Zachariah AddisonView profile

Assistant Professor

• Quantum Condensed Matter Theory
• Topological Insulators
• Quantum Hall Effects
• +4 more

Zachariah Addison is an Assistant Professor of Physics at Wellesley College, specializing in quantum condensed matter theory. His research focuses on topological and geometric aspects of electronic dynamics, particularly in quantum materials like topological insulators, skyrmion phases, and chiral magnets. He explores phenomena such as anomalous Hall effects, nonlinear optical responses, and quantum transport using quantum field theory methods. Education: B.S. in Physics from MIT, M.S. and Ph.D. in Physics from the University of Pennsylvania. Addison teaches a range of physics courses emphasizing hands-on learning through computational tools (Mathematica, GUI) and experimental demonstrations. He actively involves students in research projects, fostering thesis work and publication opportunities. Professional contributions include peer review for journals like Physical Review B and editorship of an open-access journal special edition. He is developing a textbook series for introductory physics curricula. Outside academia, Addison is an avid classical violinist and chamber music performer. His recent research trends emphasize topological transport mechanisms in magnetic systems, with publications analyzing Hall effects in chiral magnets and quantum valley hall edge states in graphene. Key themes include the interplay of topology, spin-orbit coupling, and nonlinear responses in functional materials.

• Quantum Information
• Quantum Control
• Precision Spectroscopy
• +3 more

Dr. Fabian Schmid is a Researcher affiliated with the Institute for Quantum Electronics at ETH Zürich, working within the Professorship for Experimental Quantum Information . His research focuses on quantum control, precision spectroscopy, and optical frequency comb technologies. Key applications include molecular ion manipulation, laser cooling techniques, and advanced spectroscopic methods for atomic and molecular systems. His work bridges quantum physics and optics, with contributions to ultra-stable laser systems, low-repetition-rate frequency combs, and high-resolution spectroscopic measurements. Recent efforts target applications in trapped ion systems and new boson constraints via calcium isotope studies. Schmid's experimental setups often involve precision engineering of optical components and cavity-stabilized laser systems. Notable experimental achievements include demonstrating quantum control over single molecular ions (H₂⁺) and developing number-resolved detection methods for Coulomb crystals. His research also explores synergies between dual-species laser cooling and cavity-based technologies. While currently holding no listed academic awards, Schmid's contributions are evident through his prolific publishing record in top-tier physics journals. His lab work integrates cutting-edge quantum optics with atomic physics to advance fundamental understanding and precision measurement capabilities.

Nikolaus RajewskyView profile

Professor

• RNA Biology
• Gene Regulation
• Single-Cell Transcriptomics
• +6 more

Nikolaus Rajewsky is a leading Professor at the Max Delbrück Center for Molecular Medicine (MDC) and Charité – Universitätsmedizin Berlin , where he founded and directs the Berlin Institute for Medical Systems Biology (BIMSB) . His lab integrates experimental (biochemistry, molecular biology) and computational (bioinformatics, physics) approaches to study RNA regulation in gene expression , with applications to developmental biology, regeneration, neurodegenerative diseases, and cancer . Using model systems like C. elegans , planaria, and human brain organoids, his team pioneers cutting-edge methods such as MirDeep , DistMap , and FLAM-seq for RNA analysis. His research focuses on single-cell transcriptomics , spatial RNA sequencing , and circular RNA (circRNA) regulation , revealing novel roles for circRNAs like CDR1as in neuropsychiatric disorders. Recent work includes 3D tumor microenvironment mapping and computational modeling of RNA metabolism in diseases. Scientific Awards : Gottfried Wilhelm Leibniz Prize (2012) EMBO Membership (2010) Honorary PhD, Sapienza University of Rome (2014) Berlin Science Award (2009) His team's recent articles highlight breakthroughs in 3D spatial transcriptomics , circRNA degradation mechanisms , and mitochondrial disease modeling using human brain organoids. The lab actively collaborates with clinical partners across Charité and European institutions, driving the LifeTime initiative for cell-based interceptive medicine.

• Structural Engineering
• Structural Health Monitoring
• Civil Engineering
• +3 more

Dr. Andy Nguyen is a Senior Lecturer in the School of Engineering at the University of Southern Queensland. He holds a PhD from Queensland University of Technology (QUT), an MEng from the National University of Civil Engineering (NUCE), and a BEng from NUCE. His research focuses on structural health monitoring, integrating machine learning and deep learning techniques to assess infrastructure integrity. Key areas include damage detection in bridges, pavements, and buildings, as well as sustainable construction materials like bamboo. Nguyen leads projects such as the 'Next Generation Living Laboratory for Engineering Education and Engagement,' emphasizing real-world applications of technology in civil infrastructure. His work spans crack detection algorithms, finite element model updating, and vibration-based structural analysis. He collaborates on AI-driven solutions for autonomous vehicle object detection and smart maintenance planning. Nguyen’s contributions include over 50 peer-reviewed publications and active supervision of postgraduate research in composite materials and transport infrastructure. His research outputs highlight advancements in computational mechanics, sensor technologies, and data-driven methods for infrastructure resilience. Nguyen’s expertise bridges civil engineering challenges with cutting-edge machine learning, advancing both theoretical and applied solutions for sustainable and safe structures.

• Machine Learning
• Medical Image Analysis
• Deep Learning
• +5 more

Erik Bjørnager Dam is a Professor in the Machine Learning section at the Department of Computer Science, University of Copenhagen (UCPH). His research spans theoretical foundations of machine learning to practical applications in medical data analysis, sustainability, and materials science. His key research interests include: Small-scale and resource-efficient deep learning Medical image analysis and segmentation Sustainable and environmentally conscious AI development Graph neural networks for materials science Resource-constrained AI systems Professor Dam's recent publications demonstrate a strong focus on making AI more accessible and sustainable while maintaining high performance standards. His work on 'Performance Per Resource Unit' metrics addresses critical challenges in deploying AI in resource-limited environments, particularly in healthcare applications. His research bridges theoretical machine learning with practical implementations across multiple domains. His notable professional activities include: Co-founding Cerebriu A/S (since 2018) Co-founding Biomediq A/S (since 2008) Delivering lectures on AI's role in green transition (April 24, 2023) Media contributions on deep learning applications in plant research (September 13, 2018) With 74 documented research outputs, Professor Dam maintains an active research profile with significant contributions in 2023-2025 across medical imaging, sustainable AI, and materials science applications.

Long NguyenView profile

Professor

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +4 more

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

Ooi Beng ChinView profile

Professor

• Database Systems
• Distributed Systems
• Blockchain
• +6 more

Ooi Beng Chin is a Professor at the School of Computing , National University of Singapore (NUS). He holds concurrent roles as an adjunct Chang Jiang Professor at Zhejiang University, Visiting Distinguished Professor at Tsinghua University, and Director of NUS AI Innovation and Commercialization Centre in Suzhou, China. He earned his B.Sc. (1st Class Honours, 1985) and Ph.D. (1989) from Monash University, Australia. His research spans database systems, blockchain, machine learning, and large-scale analytics , focusing on system architectures, security, and cross-domain applications. Notable contributions include initiating the Apache SINGA distributed deep learning platform and developing Blockbench, the first blockchain benchmarking system. He also co-founded MZH Technologies (2018) for healthcare analytics. Key publications highlight his work in blockchain-database integration, AI for healthcare/finance, and 5G-enabled data systems. Awards include the ACM SIGMOD EF Codd Innovation Award (2020), Singapore President's Science Award (2011), and fellowships from SNAS, IEEE, ACM , and SAEng (2023). He leads the Singapore Blockchain Innovation Programme (SBIP) and contributes to industry collaborations with healthcare institutions and financial organizations. Fellow, Singapore National Academy of Science (SNAS) Fellow, IEEE Fellow, ACM Singapore President's Science Award, 2011 IEEE Kanai Award, 2012 NUS Outstanding Researcher Award, 2013 ACM SIGMOD EF Codd Innovation Award, 2020 Foreign Member, Chinese Academy of Sciences, 2023

Albert H. TitusView profile

Professor

• Wearable Sensors
• Biosensors
• Bioinstrumentation
• +5 more

Albert H. Titus is a Professor in the Department of Biomedical Engineering and an Adjunct Professor in the Department of Electrical Engineering at the University at Buffalo, State University of New York. He serves as Associate Vice President for Regulatory Support in the Office of the Vice President for Research and Economic Development. His research focuses on analog VLSI design for neuromorphic visual processing, biosensors, wearable devices, optoelectronic systems, and neural networks. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology (1997) MS in Electrical Engineering, University at Buffalo (1991) BS in Electrical Engineering, University at Buffalo (1989) Research Interests: His work spans wearable and implantable sensors, bioinstrumentation, neural network-based visual processing, analog VLSI implementations, optoelectronics, and electronic packaging. He pioneered CMOS-based neuromorphic systems and developed patented technologies for glare sensing and RF power calorimetry. Publication Trends: His recent articles emphasize CMOS-integrated sensors, machine learning for bioimpedance analysis, implantable medical devices, and xerogel-based optical biosensors. These works bridge biomedical engineering and microelectronics. Scientific Recognition: He is a Fellow of the National Academy of Inventors and has received the SUNY Chancellor’s Award for Excellence in Service (2017), NSF CAREER award, and Western New York Inventor of the Year (2010). His inventions include a patented low-power glare sensor (U.S. Patent 7,586,079) featured in Popular Science’s 2011 Top Ten Inventions. Academic Leadership: As a faculty member, he has supervised nearly 20 PhD and over 40 MS students, while teaching courses in circuits, IC design, sensors, and signal processing across electrical and biomedical engineering disciplines.

Andrea FumagalliView profile

Professor

• Optical Networking
• Photonic Slot Routing
• Wavelength Routing and Protection
• +7 more

Andrea Fumagalli is a Professor in the Department of Electrical Engineering at the Erik Jonsson School of Engineering and Computer Science , The University of Texas at Dallas. He earned his Ph.D. (1992) and Laurea (1987) in Electrical Engineering from Politecnico di Torino, Italy. Research Interests: All-Optical Network Architectures, Photonic Slot Routing, Wavelength Routing and Protection, Sensor Networks, Cooperative Wireless Networks, Network Optimization, Next Generation Internet (NGI), and Multi-hop Optical Networks. Education: Ph.D., Electrical Engineering, Politecnico di Torino (1992) Laurea, Electrical Engineering, Politecnico di Torino (1987) Key Research Trends: His recent publications focus on 5G networking, optical network automation, elastic optical networks, network reliability, and cross-layer optimization. He explores FPGA acceleration in 5G Low-PHY functions, live migration of containerized network components, and spectral fragmentation mitigation in EONs. Scientific Awards: Best Teaching Award, Electrical Engineering, UTD (2002) Best Thesis Award for Ph.D. Advisee Isabella Cerutti (2002) IEEE ComSoc Distinguished Lecturer Tour (2000) Best Paper Award (1999): 'An Optimal Design Algorithm for Photonic Slot Routing Networks Migrating to Optical Packet Switching' Advising and Grants: He advised Ph.D. student Isabella Cerutti. In 2001, he secured a $300,000 grant from FUNDACAO CPqD for optical network reliability research. He leads the Optical Networking Advanced Research (OpNeAR) Lab at UTD, collaborating on international projects like the Italian government-funded grid computing initiative (2002) and the OMEGA Test-bed for differentiated reliability. Laboratories and Teams: He directs the OpNeAR Lab , which develops tools for optical network emulation and reliability testing. His projects involve partnerships with institutions in Brazil (Unicamp), Sweden (KTH), Italy (Politecnico di Torino, Scuola Superiore Sant'Anna), and CNR/CNIT.

Dong LiView profile

Assistant Professor

• Wireless Sensing
• Mobile Computing
• Wearable Sensing
• +4 more

Dong Li is an Assistant Professor in the Department of Computer Science and Electrical Engineering (CSEE) at the University of Maryland, Baltimore County (UMBC). His research focuses on wireless sensing, mobile computing, wearable sensing, multi-modal sensing, and smart health, aiming to develop affordable and accessible technologies to address healthcare equity and environmental sustainability challenges. He holds a PhD from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, an M.Eng. in Software Engineering from Shanghai Jiao Tong University, and a B.S. in Computer Science from the University of Electronic Science and Technology of China. His work has been published in prestigious venues such as MobiCom, SenSys, IPSN, UbiComp, and HotNets. Key research themes include anomaly detection via knowledge graphs, privacy prediction models for social networks, and interactive recommendation systems. His interdisciplinary approach integrates machine learning, data mining, and cybersecurity to tackle real-world problems in health and environmental sustainability. Dr. Li’s contributions span theoretical advancements and practical system development, with a focus on bridging the gap between cutting-edge research and societal impact. His recent publications highlight innovations in data stream processing, weak supervision frameworks, and privacy behavior analysis in online platforms.