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Elin NymanView profile

Associate Professor

• Systems Biology
• Biomedical Engineering
• Drug Development
• +3 more

Elin Nyman is the Head of the Department of Biomedical Engineering (IMT) and an Associate Professor at Linköping University. She leads the department, fostering an environment of trust and collaboration. Her research focuses on systems biology and e-health, integrating mathematical models with experimental data to advance drug development and clinical tools. She supervises students in both the Faculty of Science and Engineering and the Faculty of Medicine and Health Sciences, examining courses like TMBI28 and 8BKG45. Her research interests include systems biology, particularly in drug development and AI applications in healthcare. Key projects involve robust metabolic measurements, AI-driven diagnostic tools (M4-health), and knowledge-driven drug development with AstraZeneca. Recent publications highlight liver steatosis dynamics, IL-10 feedback mechanisms, and insulin resistance modeling. Her work bridges interdisciplinary collaboration, such as a course combining medical and engineering students to develop digital health solutions. She contributed to a SEK 13 million grant for AI-based crime-solving using detailed analyses and AI. Current affiliations include the Division of Biomedical Engineering (MT) and IMT department.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +13 more

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

Murphy Yuezhen NiuView profile

Assistant Professor

• Quantum Computing
• Quantum Error Correction
• Machine Learning
• +3 more

Murphy Yuezhen Niu is an Assistant Professor and Stansbury Chair in Computer Science at the University of California, Santa Barbara (UCSB), since 2024. She holds an adjunct role as Adjunct Assistant Professor at the University of Maryland, College Park, and the University of Maryland Institute for Advanced Computer Studies. Niu earned her Ph.D. in theoretical and mathematical physics from MIT (2018) and a B.A. in Physics from Peking University. Her research focuses on quantum computing paradigms, including quantum control optimization, quantum error correction, quantum machine learning, and scalable quantum architectures. Her work applies deep reinforcement learning and generative models to quantum systems, with contributions to superconducting qubit-based processors, ion traps, photonic systems, and neutral atom qubits. Niu's research emphasizes reducing the computational cost of quantum digitization while achieving real-world impacts. Notable achievements include the Claude E. Shannon Research Assistantship for her work in photonic quantum computation and quantum cryptography. Niu’s publications (2021–2025) span topics like quantum error correction thresholds, hybrid analog-digital quantum simulators, and machine learning-driven quantum decoding. Her research bridges theoretical physics and applied quantum computing, with a focus on fault-tolerant architectures and scalable quantum control protocols. Education: Ph.D., Physics, MIT (2018) B.A., Physics, Peking University Awards: Claude E. Shannon Research Assistantship Labs/Teams: Google Quantum AI Team (former role) UCSB Quantum Computing Research Group

Herbert BuchnerView profile

Researcher

• Digital Signal Processing
• Acoustics
• Speech Recognition
• +9 more

Herbert Buchner is a researcher affiliated with the University of Cambridge in the Information Engineering Division , focusing on Machine Learning for Signal Processing and Human-Machine Interfaces . Research Interests : Acoustic scene analysis, biomedical interfaces, haptic systems, wave-domain adaptive filtering, and sensor networks. Applications : Speech recognition, wavefield synthesis, active noise control, and full-duplex communication systems. His work explores TRINICON (a framework for broadband adaptive MIMO filtering), blind source separation, and wave-domain filtering, emphasizing theoretical rigor and real-time implementation. Key Awards : Best Paper Award at ITG Conference on Speech Communication (2008) Best Student Paper Award at IEEE Intl. Workshop on Acoustic Echo and Noise Control (2001) Publications highlight 15 recent articles in areas like: Wave-Domain Adaptive Filtering Blind Source Separation for Convolutive Mixtures Robust Extended Multidelay Filters Multichannel Acoustic Echo Cancellation Active Room Compensation Biomedical Signal Processing

• Electrical Engineering
• Modeling and Simulation
• Machine Learning
• +5 more

Igor Simone Stievano is a Full Professor at the Polytechnic University of Turin , affiliated with the Department of Electronics and Telecommunications (DET) and the Interdepartmental Center Ec-L - Energy Center Lab . He holds a PhD in Electrical Engineering and has supervised numerous students in disciplines spanning electromagnetic compatibility, machine learning, and multi-energy networks. His research interests include: Modeling and simulation of integrated circuits Machine learning for signal integrity Multi-energy network resilience Stochastic analysis of electrical systems Electromagnetic compatibility Key projects include the EU-funded SHIMMER initiative on hydrogen injection in gas networks and commercial contracts for high-speed I/O macromodeling. He serves as a chair and committee member at major conferences like the IEEE Workshop on Signal and Power Integrity. Scientific recognitions : IEEE Senior Member Recipient of the 2013 Futuro in Ricerca grant Editorial Board member of ENERGIES (2020-) Stievano actively participates in PhD college evaluations for Mathematical Sciences and Metrology programs at Politecnico di Torino, while teaching courses in Electrical Engineering and Digital Technologies across biomedical, computer, and media engineering curricula.

Guillermo GallegoView profile

Professor

• Robotics
• Computer Vision
• Event-based Vision
• +5 more

Guillermo Gallego is a Professor of Robotic Interactive Perception at the Faculty of Electrical Engineering and Computer Science , Technische Universität Berlin , holding the Einstein Center Digital Future (ECDF) Professorship since 2019. His research bridges robotics , computer vision , and applied mathematics , focusing on optimization methods for interdisciplinary imaging and control problems. Education : PhD in Electrical and Computer Engineering (Georgia Tech, 2011), MS in Mathematics (Georgia Tech, 2009), MS in Electrical Engineering (Georgia Tech, 2007), MS in Mathematical Engineering (Universidad Complutense de Madrid, 2005). Gallego's work explores event-based vision to enhance robot perception through low-latency sensing and real-time 3D reconstruction . He previously held postdoctoral positions at the Institute of Neuroinformatics (University of Zurich/ETH Zurich) and Technical University of Madrid (Marie Curie Experienced Researcher). His interdisciplinary projects span applications in ocean remote sensing , autonomous driving , and space exploration . Key scientific awards include the Fulbright Fellowship (2005-2010) and Marie Curie Experienced Researcher (2011-2014). His recent publications focus on event camera algorithms for optical flow , SLAM , and noise estimation , reflecting his leadership in event-based vision research. Collaborations include institutions like University of Zurich , Georgia Tech , and University of Pennsylvania . Research Grants : Funded through ECDF and Marie Curie programs. Labs : Affiliated with the Einstein Center Digital Future and Institute of Neuroinformatics (Zurich/ETH Zurich).

• AI Hardware
• Digital Chip Design
• Efficient AI Algorithms
• +6 more

Deliang Fan is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ. His research focuses on AI hardware, in-memory computing, and neuromorphic systems. He received his MS and PhD from Purdue University under Prof. Kaushik Roy. Education: PhD, Purdue University (2015) Research Interests: AI Hardware, In-Memory Computing, Adversarial AI, Neuromorphic Computing His work spans cross-layer co-design for AI applications, including deep learning, bioinformatics, and graph processing. He has authored 170+ peer-reviewed papers and developed hardware solutions for spintronic and memristor-based systems. Recent publications emphasize efficient architectures for transformers, federated learning, and robust neural networks. Awards include the NSF Career Award and multiple best paper recognitions. He serves in editorial and organizational roles for leading conferences like DAC, ISQED, and GLSVLSI.

• Geotechnical Engineering
• Environmental Monitoring
• Fiber Optic Sensing
• +2 more

Dante Fratta is a Professor in the Department of Civil & Environmental Engineering at the University of Wisconsin-Madison, where he has been actively involved in research and teaching since 2000. His work focuses on geotechnical engineering, environmental monitoring, and fiber optic sensing technologies. Education: PhD (1999) – Georgia Institute of Technology M.A.Sc. (1995) – University of Waterloo Diploma (1993) – Universidad Nacional de Córdoba Research Interests: Geomaterial process evaluation using elastic and electromagnetic waves, fundamental physical behavior of soils and rocks, geophysical assessment of near-surface environments, and distributed fiber optic sensing methods. Recent Publication Trends: He has pioneered applications of Distributed Acoustic Sensing (DAS) and fiber optic technologies for geotechnical, environmental, and energy infrastructure monitoring, including wind turbines, geothermal systems, and mining operations. Scientific Awards: Benjamin Smith Reynolds Award for Excellence in Teaching (2012) Chi Epsilon Excellence in Teaching Award (2008) Best Paper Award, GeoCongress Sensing Methods and Devices Track (2006) Distinguished Alum, Universidad Nacional de Córdoba (2013) Multiple keynote and invited speaking engagements Teaching: Fratta teaches graduate and undergraduate courses in geotechnical engineering, including Foundations, Applied Geophysics, and Pre-Dissertator Research, with active involvement in Spring 2025 classes.

Olga KurasovaView profile

Professor

• Machine Learning
• Data Visualization
• Cybersecurity
• +3 more

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.

Seshadri Sravan Kumar V.View profile

Associate Professor

• Power Engineering
• Applied Mathematics
• Time and Frequency Domain Modeling
• +8 more

Dr. V. Seshadri Sravan Kumar is an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Technology Hyderabad. His research spans Power Engineering and Applied Mathematics, with a focus on Time and Frequency Domain Modeling, Electromagnetic Transients, Phasor and Frequency Estimation, and Machine Learning applications in power systems. Ph.D. and M.Sc.(Engg.) from IISc Bangalore Current research trends include Battery-Ultracapacitor Integration, Electric Vehicle Power Systems, and Non-ideal Component Modeling His group has contributed to 15 recent publications in journals like IEEE Transactions on Vehicular Technology, Renewable Energy (Elsevier), and conferences including IEEE PES General Meeting and PEDES. Key awards include the 2021 IIT Hyderabad Teaching Excellence Award and 2016 POSOCO Power System Award. Students mentored include Ph.D. candidates Sai Vinay Kishore N and alumni such as Anirudh C V S and Naresh Palla. He teaches graduate-level courses like Matrix Theory, Electrical Machine Analysis, and Modeling of Electromagnetic Transients.

Stuart GibsonView profile

Senior Lecturer

• Digital Image Processing
• Machine Learning
• Forensic Science
• +5 more

Dr. Stuart Gibson is a Senior Lecturer in Physics and Astronomy at the School of Physics and Astronomy, University of Kent. He is the co-inventor of the EFIT-V facial composite system, widely adopted by UK police constabularies and international agencies. His academic contributions span interdisciplinary research bridging forensic science, computational methods, and machine learning. Research Interests: Forensic applications of digital image processing Machine learning in natural sciences Facial composites for criminal investigations Medical image analysis Computer vision with security applications Teaching: Stuart teaches numerical and computational methods, mathematical techniques for physical sciences, and digital forensics. His pedagogical focus integrates theoretical frameworks with practical forensic and computational tools. Publications & Collaborations: Over his career, Dr. Gibson has published extensively in journals such as Pattern Recognition Letters , ACS Nano , and Utilities Policy . His work includes innovations in evolutionary algorithms, facial composite systems, and applications of machine learning to muon spectroscopy and Raman spectroscopy.

• Communications Theory
• Wireless Communications
• Information Theory
• +1 more

Dr. Ajit Kumar Chaturvedi is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), specializing in Communications Theory and Systems. With decades of experience at IIT Kanpur, he has established himself as a leading researcher and educator in wireless communications and information theory. Dr. Chaturvedi earned his B.Tech (1986), M.Tech (1988), and PhD (1995) all in Electrical Engineering from IIT Kanpur, demonstrating his deep institutional commitment. His educational background reflects his dedication to academic excellence at his alma mater. His research focuses on Communications Theory and Systems, with specific expertise in Wireless Communications, Information Theory, and Spread Spectrum Systems. Dr. Chaturvedi's work bridges theoretical foundations with practical applications in modern communication technologies. His contributions have significantly advanced the understanding of signal processing techniques, multiuser detection methods, and error analysis in wireless systems. His research has direct implications for improving the reliability and efficiency of contemporary wireless communication standards. Dr. Chaturvedi's publication record demonstrates consistent contributions to communications engineering, spanning from fundamental signal processing techniques to complex system-level analyses. His work on ISI mitigation, orthogonal sequence design, and error rate analysis shows both theoretical rigor and practical relevance for real-world communication challenges. Distinguished Teacher award of IIT Kanpur (2007) Tan Chin Tuan Fellowship of NTU, Singapore (2008) Supervisor of Best B. Tech Project groups (2002, 2006, 2012) Plenary speaker at the 15th National Conference on Communications (2009) Mentor of IEEE Computer Society International Design Contest winning team (2002) As an educator, Dr. Chaturvedi has supervised numerous award-winning student projects, including three Best B.Tech Project awards. He has mentored teams to international recognition, such as the Microsoft Award for Innovation at the IEEE Computer Society International Design Contest World Finals in 2002. His office is located in ACES 201B at IIT Kanpur, where he continues to contribute to the academic and research mission of the institution.

• Mobile and Wireless Networking
• Information and Communication Technologies for Development (ICTD)
• Network Performance Analysis
• +3 more

Elizabeth M. Belding is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB) and Associate Director of the Center for Information Technology and Society. She holds a Ph.D. (2000) and M.S. (1997) in Electrical and Computer Engineering from UCSB. Co-developer of AODV routing protocol (basis for IEEE 802.11s and Zigbee) Director of the Mobility Management and Networking (MOMENT) Laboratory Associate Dean and Faculty Equity Advisor, College of Engineering Her research focuses on mobile and wireless communication networks , including network performance analysis , digital equity , and information and communication technologies for development (ICTD) . She specializes in improving internet access for marginalized communities including Native American reservations, refugee camps, and rural areas in Zambia, South Africa, and Mongolia. Recent research trends include: Accurate broadband measurement in the U.S. using crowdsourced data Development of wireless solutions for underserved regions Analysis of 5G performance variability and GEO satellite latency Mapping cellular network evolution and infrastructure criticality Evaluating federal broadband funding programs (CAF, BEAD, NSF) Quantifying video streaming quality of experience (QoE) Scientific honors include: ACM Fellow (2018) IEEE Fellow (2014) AAAS Fellow ACM SIGMOBILE Test of Time Award (2018) NCWIT Harrold and Notkin Award (2015) UCSB Outstanding Graduate Mentor Award (2012) IRTF Applied Networking Research Prize (2024) She has advised 22 Ph.D. graduates from the MOMENT Lab, including Jiamo Liu (2024) and Udit Paul (2023). Current research receives funding from NSF, Bill & Melinda Gates Foundation, and industry partners like ViaSat, with emphasis on broadband deployment analysis and network solutions for challenged environments.

• Cybersecurity
• Machine Learning
• Data Mining
• +4 more

Dong Kyoo Shin is a Professor at Sejong University's Department of Computer Science and Engineering, where he has been employed since 1998. He holds a Ph.D. from Texas A&M University (1997), an M.S. from Illinois Institute of Technology (1992), and a B.S. from Seoul National University (1986). His professional background includes roles as a Researcher at the Korea Institute of Defense Analyses (1986-1991) and Senior Researcher at Hyundai Electronics (1997-1998). Shin leads research in cybersecurity, machine learning, and ubiquitous systems , with specialized interests in intrusion detection, data mining, cyber warfare frameworks, and adversarial ML defense. His recent publications focus on AI-driven security solutions, ransomware analysis, and resilience quantification in critical infrastructure. He directs the Cyber Warfare Research Institute (established 2017) and the Multimedia & Internet Lab , focusing on defense technologies and smart systems. His team has executed projects for the Ministry of National Defense, ADD, and ETRI, including cyber threat response systems and military security frameworks. Service includes advisory roles for the Ministry of National Defense, Defense Acquisition Program Administration, and editorial duties for defense journals. He holds patents in malware detection, data encryption, and sensor-based interfaces.

• Image Analysis
• Artificial Intelligence
• Deep Learning
• +5 more

Joakim Lindblad is a Professor at the Department of Information Technology, Uppsala University , and holds affiliated roles as Senior Research Associate at the Mathematical Institute of the Serbian Academy of Sciences and Arts, and Head of Research at Topgolf Sweden AB. With over two decades of expertise in image analysis and machine learning , his work bridges computational methods with biomedical applications. Key affiliations: Uppsala University, Serbian Academy of Sciences, Topgolf Sweden Specializations: Deep Learning, Multimodal Image Registration, Quantitative Microscopy His research focuses on reliable image processing frameworks that integrate intensity and spatial information , particularly for biomedical applications . Recent publications highlight innovations in autofluorescence-based cancer detection , self-supervised one-class learning for sparse instance identification, and rotation-equivariant CNNs for robust analysis of cytology images. Recent article trends demonstrate expertise in multimodal image analysis (2024: 3 papers), oral cancer detection (2025: 2 papers), and multiscale biomedical imaging . His 2025 work on the Uppsala Storytelling Dataset introduces novel frameworks for multimodal dataset creation in AI research. While no scientific awards are explicitly mentioned, his extensive publication record (2000-2025) across top venues like Pattern Recognition , PLOS ONE , and IEEE Transactions indicates significant academic impact. His methodological contributions span stochastic distance transforms , fuzzy set defuzzification , and multimodal image registration techniques. Collaborative work with researchers like Nataša Sladoje and interdisciplinary teams has produced innovations in automated cytology analysis , TEM image enhancement , and AI-driven medical diagnostics . His 2021-2022 projects introduced contrastive learning approaches for multimodal image registration and explainable AI frameworks for infant engagement analysis.