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Sergei KalininView profile

Professor

• Machine Learning in Microscopy
• Atom by Atom Fabrication
• Scanning Probe Microscopy
• +4 more

Sergei Kalinin is the Weston Fulton Professor at the Department of Materials Science and Engineering, University of Tennessee, Knoxville, and holds a Corporate Fellow position at Oak Ridge National Laboratory (ORNL). His academic affiliations include adjunct professorships at Sung Kyun Kwan University (South Korea) and Penn State University. He earned his M.S. summa cum laude from Moscow State University (1998) and Ph.D. from the University of Pennsylvania (2002), both in Materials Science. Kalinin's research focuses on transformative applications of machine learning and AI in materials science, particularly in: Atom-by-atom fabrication using electron beams Big data analytics in scanning probe/transmission electron microscopy Nanoscale electromechanical phenomena in functional oxides Development of novel microscopy techniques and autonomous experimentation frameworks His recent publications (2024-2025) demonstrate strong emphasis on AI-driven microscopy, autonomous labs, and computational materials design. Over 85% of his last 15 articles involve machine learning applications in experimental physics, with emerging focus on human-AI collaboration and real-time instrumentation control. Major scientific honors include: Blavatnik National Award (2018) Feynman Prize in Nanotechnology (2022) Presidential Early Career Award (2009) 4 R&D100 Awards (2008-2023) Fellowships in APS, MRS, IEEE, AVS He directs the ORNL Institute for Functional Imaging of Materials and leads research on autonomous experimentation systems integrating high-performance computing with microscopy platforms.

Bashir M. Al-HashimiView profile

Professor

• Energy-Efficient Computing
• Battery-Free Systems
• Internet of Things
• +2 more

Professor Sir Bashir M. Al-Hashimi is Vice President (Research & Innovation) at King’s College London and co-Director of the King's Institute for Artificial Intelligence. A renowned computer engineer, his research focuses on energy-constrained computing, semiconductor chips, and battery-free systems enabling IoT technologies. Significant contributions include founding the Arm-ECS industry-academia center, leading large-scale interdisciplinary research programs, and publishing over 350 technical papers. Honors include knighthood (2025), CBE (2018), and fellowships in Royal Society, IEEE, and Royal Academy of Engineering.

Matteo RinaldiView profile

Professor

• Micro/Nanoelectromechanical Systems (M/NEMS)
• Piezoelectric and Ferroelectric Materials
• Nanoscale Sensors
• +5 more

Matteo Rinaldi is a Professor in the Department of Electrical and Computer Engineering at Northeastern University's College of Engineering and serves as Director of the Institute for NanoSystems Innovation. His work focuses on advanced micro/nanoelectromechanical systems (M/NEMS) for sensing and communication applications. Ph.D. in Electrical and Systems Engineering, University of Pennsylvania (2010) M.Sc. and B.S. in Electronic Engineering, University of Rome Tor Vergata (2007, 2004) Research Interests: Rinaldi's research spans fundamental and applied aspects of piezoelectric nanomaterials, with emphasis on Aluminum Nitride (AlN) and Scandium Aluminum Nitride (ScAlN) based NEMS Plasmonically enhanced infrared and chemical sensors Low-power reconfigurable radio communication systems Integration of MEMS/NEMS with electronics Nanomaterials for harsh environments Publications & Research Trends: Rinaldi's recent work explores high-frequency resonators, infrared detection systems, and zero-power sensing technologies. Key trends include plasmonics for energy-efficient devices, ScAlN integration for improved performance, and machine learning applications in nanoscale device design. Scientific Recognition: 2025 IEEE IFCS Walter G. Cady Award 2024 Northeastern Global Network Accelerator Award Optica Fellow IEEE Sensors Council Early Career Award DARPA Young Faculty Award NSF CAREER Award Advising & Funding: Rinaldi advises graduate researchers like Antea Risso. His lab secures major grants from DARPA, NSF, DOE, and the Bill & Melinda Gates Foundation, focusing on next-generation sensing technologies and 6G communication systems. Research Infrastructure: Leads the Northeastern Sensors & Nano Systems Laboratory (NS&NS Lab) and co-directs the bicoastal Institute for NanoSystems Innovation with Boston and Oakland campuses, advancing nanoscale semiconductor technologies.

Simha SethumadhavanView profile

Professor

• Computer Architecture
• Computer Security
• Hardware Security
• +3 more

Simha Sethumadhavan is a Professor of Computer Science at Columbia University and an Alfred P. Sloan Research Fellow. His research focuses on computer architecture and security, particularly on securing hardware first and systematically hardening systems stacks. Hardware Security Microarchitectural Side Channels Memory Safety Analog Accelerators His recent work explores mechanism design for security, memory safety, reliability co-design, and cloud security. Publications span journals like Communications of the ACM and conferences including IEEE Security and Privacy, CCS, ISCA, and MICRO. Distinguished Paper Awards IEEE Micro Top Picks Best Student Paper Awards Best of CAL 2016 He advises PhD students such as Adam Hastings, Evgeny Manzhosov, and Miguel Arroyo, and has taught courses like Computer Architecture (CSEE 4824) and Hardware Security (COMS 6424). He has served on editorial boards and program committees for ISCA, IEEE Security and Privacy, and MICRO.

Andreas AndreouView profile

Professor

• Neuromorphic Engineering
• CMOS Microsystems
• Sensory Information Processing
• +6 more

Andreas Andreou is a Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the JHU Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab, focusing on brain-inspired microsystems, neuromorphic engineering, and biomedical sensors. His research spans CMOS-based neuromorphic processors, event-based vision systems, and wearable health monitoring devices like the StethoVest. Key contributions include silicon retinas, polarization-sensitive imagers, and algorithms for pattern analysis. Research Interests: Neuromorphic Computing: Designing energy-efficient brain-inspired chips using 3D CMOS, FETs, and memristive technologies. Biomedical Microsystems: Wearable acoustic sensors for cardiac monitoring and vestibular prosthetics. AI Hardware: Neuromorphic accelerators for edge computing, leveraging LLMs for automated circuit design. Notable Achievements: IEEE Fellow (since 2020) Recipient of the 3rd Best Paper Award at IEEE BioCAS 2018 $2M DARPA grant for bio-inspired event cameras Labs/Teams: The Andreou Lab collaborates with the Kavli Neuroscience Discovery Institute and NSF-funded neuromorphic projects. Ongoing work includes neuromorphic Ising machines, LLM-driven chip design, and quantum sensing for medical applications.

Andrea CiniView profile

Researcher

• Machine Learning
• Graph Deep Learning
• Time Series Forecasting
• +3 more

Andrea Cini is a postdoc researcher affiliated with the Graph Machine Learning Group and the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) at the University of Lugano (USI). He also holds a position as a SNSF postdoc fellow at the University of Oxford under Prof. Michael Bronstein, focusing on machine learning for time series forecasting and graph processing. His research integrates graph deep learning methodologies with spatiotemporal dynamics, emphasizing applications in healthcare, energy systems, and intelligent systems. Education: PhD in Computer Science and Engineering (USI, 2020), supervised by Prof. Cesare Alippi MSc and BSc in Computer Science and Engineering (Politecnico di Milano) Visiting researcher at Imperial College London (Prof. Danilo Mandic) Research interests span graph neural networks , time series forecasting , and spatiotemporal data processing . His work has introduced influential methods such as the Torch Spatiotemporal library, and has been recognized with a best paper award. Recent publications emphasize applications in relational conformal prediction, hierarchical forecasting, and energy grid optimization. Awards include the Best Paper Award for contributions to graph-based forecasting methodologies. Current projects are funded by the Swiss National Science Foundation, exploring graph-based reinforcement learning and spatiotemporal modeling at the University of Oxford. Collaborations include affiliations with the Northernmost Graph Machine Learning group at UiT the Arctic University of Norway. His research bridges theoretical advancements and industrial applications in fields like healthcare dynamics prediction and smart grid optimization.

• Materials Science
• Photovoltaics
• Semiconductor Physics
• +3 more

Bruno Ehrler is a Professor at the University of Groningen (honorary) and Group Leader of the Hybrid Solar Cells Group at AMOLF in Amsterdam since 2014. His research focuses on perovskite materials science, including fundamental studies and device applications like solar cells. He holds significant grants (ERC Starting, NWO Vidi) and is a WIN Rising Star award recipient. He previously worked at the University of Cambridge, where he earned his PhD in Physics under Prof. Neil Greenham. His expertise spans optoelectronics, quantum dots, and singlet fission photovoltaics. Ehrler contributes to national energy initiatives and serves on advisory boards for the Dutch Chemistry Council and nanoGe conferences. Education: PhD in Physics, University of Cambridge (2009–2012) MSci in Physics, University of London (Queen Mary College, 2005–2009) Studies at RWTH Aachen and University of London Research Interests: Perovskite materials, solar cell efficiency, ion migration dynamics, photonic materials, and sustainable energy technologies. His work bridges fundamental material science with applied device engineering, addressing challenges in scalability, stability, and energy conversion efficiency. Grants & Awards: ERC Starting Grant (2020): Artificial synapses from halide perovskites NWO Vidi Grant (2017): Metal halide perovskites WIN Rising Star Award (2018) Advising & Labs: Leads the Hybrid Solar Cells Group at AMOLF, focusing on perovskite innovation. Collaborates on national energy projects like the Netherlands Energy Research Alliance (NERA). Active in mentoring early-career researchers through advisory roles and grant programs.

• High-Performance Computing
• Parallel Computing
• Data Compression
• +4 more

Kento Sato is a researcher specializing in High-Performance Computing (HPC), with a focus on checkpointing systems, data compression, and parallel computing optimization. His work addresses challenges in fault tolerance, algorithmic efficiency, and resource management in large-scale computing environments. Collaborating with institutions like the Joint-Laboratory of Extreme Scale Computing, he explores trade-offs between compression efficiency and hardware costs, as well as automatic variable identification for checkpointing. His contributions span theoretical advancements and practical implementations, with notable publications in venues such as SC, IPDPS, and the International Journal of High Performance Computing Applications. Key Research Themes: Checkpointing protocols, lossy compression for scientific data, HPC workflow optimization. Long-term Collaboration: Regular co-authorship with experts like Satoshi Matsuoka and Martin Schulz. Publications consistently highlight innovations in fault tolerance mechanisms, adaptive hardware design, and scalable algorithms for machine learning and distributed systems.

Keith UlmerView profile

Associate Professor

• Experimental Elementary Particle Physics
• Supersymmetry
• Dark Matter Models
• +3 more

Keith Ulmer is an Associate Professor in the Department of Physics at the University of Colorado Boulder. His research focuses on experimental particle physics, particularly using the CMS experiment at CERN's LHC. He leads projects for the CMS Global Track Trigger and US-CMS Trigger/DAQ upgrades. His work includes searches for physics beyond the Standard Model, such as supersymmetry and dark matter, and detector R&D leveraging FPGA-based machine learning. Education: Ph.D. in Physics (2007, University of Colorado Boulder), B.A. from Amherst College. Awards include the 2024 LPC Distinguished Researcher, CU Teaching Excellence, and CERN Scientific Associate roles. Research interests span high-energy collider physics, detector innovation, and advanced computing. He advises graduate students and collaborates internationally on SUSY searches, trigger system upgrades, and Higgs physics. Notable contributions include the first observation of B_s→μμ decay and leadership in CMS Phase-2 upgrades.

Adnan Siraj RakinView profile

Assistant Professor

• AI Security
• Deep Learning
• Computer Vision
• +3 more

Adnan Siraj Rakin is an Assistant Professor at Binghamton University's School of Computing. He holds a PhD and MS in Computer Engineering from Arizona State University (2022 and 2021) and a BS in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2016). His research focuses on AI security, including adversarial attacks on deep learning systems, model stealing, and hardware vulnerabilities. Notable contributions include defenses against bit-flip attacks, weight duplication frameworks, and RowHammer exploits. Research Interests: Adversarial Attacks (Input/Weight/Model Stealing) Deep Learning Security Hardware Vulnerabilities (e.g., FPGA/DRAM) Robust Neural Network Design Publications highlight advancements in detecting and mitigating adversarial perturbations, with work featured in CVPR, ICCV, and IEEE Security & Privacy. His recent efforts address LLM vulnerabilities and edge computing efficiency. He received the 2022-2023 Educator of the Year award from Binghamton's CS department. Current projects include developing full-stack obfuscation frameworks, secure domain adaptation, and exploring adversarial impacts on robotics systems.

Davide MottinView profile

Associate Professor

• Graph Theory
• Machine Learning
• Data Mining
• +4 more

Davide Mottin is an Associate Professor at the Department of Computer Science, Aarhus University. His primary research focuses on graph theory, machine learning, and data mining, with significant contributions to knowledge graphs, algorithm design, and interdisciplinary applications in drug discovery and material science. He holds a leadership role in large international conferences such as CIKM 2024 as a Program Chair. His research explores scalable graph algorithms (e.g., subgraph matching, alignment), robust knowledge graph cleaning, and leveraging large language models for scientific tasks. Mottin has pioneered work on spectral methods for graph analysis (e.g., NetLSD, VERSE embeddings) and developed frameworks for interactive data exploration (e.g., X2Q, MetaExp systems). Key contributions include FUGAL for graph alignment and Ucode for community detection Active in reproducibility efforts, as seen in retraction notices and algorithmic redesigns Focus on practical applications in drug discovery via evolution-based models (EvolMPNN) He has authored over 60 peer-reviewed publications and holds grants supporting interdisciplinary research at the intersection of computer science and life sciences. Mottin is affiliated with the university's AI and data science initiatives, contributing to both theoretical advancements and real-world system implementations.

Grace Li ZhangView profile

Assistant Professor

• Hardware-Software Co-design
• AI Accelerators
• Emerging Memory Technologies (RRAM, FeFET)
• +3 more

Grace Li Zhang is an Assistant Professor (Tenure Track) in Hardware for Artificial Intelligence at TU Darmstadt since 2022. Previously, she served as Group Leader on Heterogeneous Computing at TU Munich (2018–2022) and earned her Dr.-Ing. in Electrical and Computer Engineering (summa cum laude) from TU Munich (2014–2018). Her research focuses on AI hardware-software co-design, including hardware accelerators for AI algorithms, neuromorphic computing, and emerging memory technologies like RRAM and FeFET. She explores circuit design methodologies, explainability of AI systems, and energy-efficient architectures. Recent work emphasizes leveraging large language models (LLMs) for automated hardware design, verification, and code generation. Her projects address challenges in optical neural networks, in-memory computing, and robustness against hardware non-idealities. Zhang’s contributions span 60+ peer-reviewed articles, with a strong focus on practical implementations for real-world applications. She leads the TU Darmstadt Hardware for AI group, collaborating with industry partners on next-generation computing systems. Her work bridges theoretical innovations with tangible hardware solutions, targeting efficiency, scalability, and security in AI infrastructure.

Bruce DraperView profile

Professor

• Artificial Intelligence
• Machine Learning
• Visual Learning
• +7 more

Bruce Draper is a Professor and Chair of the Department of Computer Science in the College of Natural Sciences at Colorado State University. His work bridges artificial intelligence, machine learning, and computer vision, with a strong emphasis on real-world applications involving visual data and intelligent systems. Research Interests: Draper's research centers on machine learning with a focus on visual learning, adversarial AI, and visual agents. He investigates how AI systems can perceive, interpret, and interact with visual environments through technologies like facial recognition, object tracking, augmented reality, and automated visual communication. His work addresses both the capabilities and vulnerabilities of modern AI, particularly in defending systems against adversarial attacks. Publication Trends: His recent scholarly output reflects a consistent trajectory in advancing computer vision and AI robustness. The articles span topics from adversarial defense mechanisms and visual agent autonomy to scalable learning frameworks and real-time video analysis. Collectively, they emphasize secure, efficient, and context-aware visual intelligence systems grounded in deep learning and representation learning. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no students or grants are explicitly listed, his leadership role as department chair and prior experience as a DARPA program manager suggest extensive involvement in research funding, mentorship, and high-impact project direction. His background indicates likely supervision of graduate students and management of federally funded research initiatives in AI and computer vision. Labs and Teams: Although no specific lab or research group is named, his research scope implies leadership or affiliation with interdisciplinary teams working on AI security, computer vision, and augmented reality systems within the Department of Computer Science at CSU.

• Machine Learning
• Computer Vision
• Intelligent Systems
• +7 more

Evi Zouganeli is a Professor at the Department of Mechanical, Electrical and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet). She leads the Automation, Robotics, and Intelligent Systems (ARIS) research group and serves as a board member of the Norwegian Artificial Intelligence Society (NAIS). Her work bridges academic research and industrial applications in intelligent systems. Research Focus: Zouganeli specializes in Machine Learning and Computer Vision applications for Cognitive Robotics, with emphasis on assistive technologies, smart cities, and smart industry. Her research explores neuromorphic computing, sensor network optimization, and multi-modal AI integration. Publications Trends: Recent works focus on AI-enabled robotics, sensor placement optimization in smart homes, and neuromorphic reservoir networks. Earlier contributions include European collaborative research in photonic technologies and broadband networks. Additional Contributions: She actively participates in public discourse about AI implementation challenges and Norway’s AI development potential, engaging with industry leaders and policymakers.

Luciano LavagnoView profile

Professor

• Asynchronous Circuit Design
• Embedded Systems
• Logic Synthesis
• +4 more

Luciano Lavagno is a prominent academic in computer science and engineering, specializing in asynchronous circuit design, logic synthesis, and embedded systems. He has collaborated extensively with researchers like Alberto Sangiovanni-Vincentelli, Joaquim Cortadella, and Alex Yakovlev. Key contributions: Hazard-free asynchronous circuits, STG manipulation, FPGA synthesis frameworks Research focus: Formal verification, design methodologies, hardware-software co-design His publications from 1991–2025 demonstrate sustained innovation in asynchronous control logic, embedded system validation, and machine learning hardware acceleration. Notable works include SIS (sequential synthesis) and Petrify (asynchronous controller tool). While his 2018 DiracDeltaNet work extended into neural network optimization, his core expertise remains rooted in formal methods for circuit design.