جست‌وجوی استاد و پژوهشگر

Cameron Taylorمشاهده پروفایل

استادیار

• Rehabilitation Engineering
• Neuromuscular Sensing and Stimulation
• Electromagnetics
• +۳ مورد دیگر

Cameron Taylor is an Assistant Professor in the Lampe Joint Department of Biomedical Engineering at UNC Chapel Hill and NC State University . His research focuses on neuromuscular sensing and stimulation , electromagnetics , and computational science . He teaches BMME 385 - Bioinstrumentation and leads the Hi-PHI Lab , which develops transformative human interfacing technologies to restore ability in persons with movement disorders. His work integrates magnetoquasistatics , neural interfacing , and muscle physiology . Ph.D. in Media Arts and Sciences (Biomechatronics) MIT, 2020 M.S. in Media Arts and Sciences (Biomechatronics) MIT, 2016 B.S. in Electrical Engineering Brigham Young University, 2014 A.S. Mesa Community College, 2012 His research interests include novel electromagnetic strategies for sensing and imaging the human body, with applications in wearable technologies and clinical interventions. His lab’s innovations include magnetomicrometry —a first-of-its-kind technology for real-time muscle tissue tracking in humans. Awards : - 2023 Promising Investigator Award from the Rocky Mountain Muscle Symposium Lab and Team : The Hi-PHI Lab, launching Fall 2025, seeks graduate students and postdocs with expertise in electromagnetics , algorithm development , or signal processing . Current advisees include Mahavir Prasad (PhD candidate focused on affordable human interfacing technologies) and John Goebel (PhD candidate working on bioelectronic equipment for tissue measurements). His work has been featured in Physics World , MIT Technology Review , and Electronic Design .

Hafizul Asadمشاهده پروفایل

مدرس

• Formal Verification
• Hybrid Systems
• Intrusion Detection Systems
• +۵ مورد دیگر

Dr. Hafizul Asad serves as a Lecturer in Dependability at City St George's, University of London, leveraging his PhD in Electrical Engineering (City University of London, 2016) and MS in Aerospace Engineering (University of Belgrade, 2008) to advance cybersecurity and formal verification research. His expertise bridges critical infrastructure protection and cyber-physical systems security, with significant contributions to IoT/IIoT security frameworks. His educational journey includes: PhD in Electrical Engineering, City, University of London (2012-2016) MS in Aerospace Engineering, University of Belgrade, Serbia (2007-2008) BSc in Electrical and Electronics Engineering, University of Engineering and Technology Peshawar, Pakistan (1999-2003) Asad's research centers on formal verification of hybrid systems and verifiable intrusion detection mechanisms for interconnected environments. He pioneers provably robust security architectures for IoT/IIoT systems, emphasizing mathematical verification to ensure system resilience against cyber threats. His work integrates diversity principles to create defense-in-depth strategies for critical infrastructure, with recent focus on wind turbine cyber-safety and industrial control system protection. Analysis of his 15 most recent publications (2014-2025) reveals an evolution from aerospace applications and analog circuit verification toward cutting-edge cybersecurity for cyber-physical systems. His 2023-2025 work demonstrates increasing specialization in IoT security and formal methods, while maintaining foundational contributions to diversity-based security architectures established in his 2015-2018 research. No scientific awards or prizes are documented in the provided materials, though he maintains professional standing as a British Computer Society member and Higher Education Academy Associate Fellow. Details regarding doctoral student supervision or specific research grants are not disclosed in the source text. His professional trajectory indicates significant project involvement, including the D3S security project at City University of London (2015-2018) and Rolls-Royce-funded Future Systems Simulator development at Cranfield University (2018-2019), though current laboratory affiliations remain unspecified.

Ertem Esinerمشاهده پروفایل

پژوهشگر

• Cryptography
• Security
• Privacy
• +۱ مورد دیگر

Dr. Ertem Esiner is a Senior Research Scientist and Coordinator at the Illinois Advanced Research Center at Singapore (Illinois ARCS), affiliated with the University of Illinois Urbana-Champaign. He holds a Ph.D. in Computer Science and Engineering from Nanyang Technological University (2017), an M.Sc. in Computer Science and Engineering from Koç University (2013), and a B.Sc. in Computer Engineering from Koç University (2011). His academic career includes teaching assistantships at both NTU and KU. Cryptography Security Privacy Cyberphysical Systems His research focuses on security solutions for cyberphysical systems, including the development of a security engine for enforcing automated policies and provenance verification in industrial control systems. Current projects include the Plug & Play Security Engine (Principal Investigator, 2023-2026) and automated security policy generation tools for IIoT environments. His work spans applications in smart grids, building energy management, and communication-based train control systems. Recent publications demonstrate a trend toward real-time security enforcement and provenance tracking in time-critical systems, including patented solutions for message authentication. He has secured multiple research grants totaling 5+ years of funding and leads teams of 4-7 members across collaborative projects with NTU, RESYNC Technologies, and Singapore-ETH Centre. Notable researcher from CREATE (2019) SINGA PhD scholarship recipient WOWZAPP 2012 hackathon winner UPE/ACM Scholarship awardee Vehbi Koç Scholarship recipient He has contributed to ACM and IEEE journals, developed a Building Energy Management System (BEMS) testbed , and published foundational work on secure data storage, query integrity, and decentralized access control mechanisms. His teaching experience includes courses on cryptography, operating systems, and algorithm design.

Adín Ramírez Riveraمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Representation Learning
• +۶ مورد دیگر

Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.

Hao Zhangمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Computer Vision
• +۶ مورد دیگر

Hao Zhang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He directs the Human-Centered Robotics Laboratory (HCRLab), focusing on lifelong collaborative autonomy, robot adaptation, and human-robot teaming. His research integrates robotics, AI, and machine learning to develop algorithms for real-world applications like manufacturing, autonomous driving, and environmental monitoring. He holds an NSF CAREER Award and DARPA Young Faculty Award, among other recognitions. Dr. Zhang earned a PhD from the University of Tennessee, Knoxville (2014) and an MS from the Chinese Academy of Sciences (2009). His work addresses challenges in unstructured environments through innovations like self-reflective terrain adaptation and graph-based perception systems. He actively promotes equity in robotics through his PROGRESS outreach program. His research sponsors include NSF, DARPA, and industry partners such as Toyota. Publications span conferences like RSS, ICRA, and IROS, with best paper awards. He serves on editorial and program committees for top-tier journals/conferences including RA-L, NeurIPS, and AAAI.

• Artificial Intelligence
• Biomedical Informatics
• Drug Design
• +۳ مورد دیگر

Marinka Zitnik is an Associate Professor of Biomedical Informatics at Harvard Medical School, Associate Faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence, and Associate Member at the Broad Institute of MIT and Harvard. Her research focuses on advancing artificial intelligence for medicine and science, including AI-driven drug design, genomic medicine, and multimodal data fusion. She leads initiatives such as the Therapeutics Data Commons and the International AI4Science initiative, emphasizing AI's role in scientific discovery and personalized healthcare. Research Interests: Dr. Zitnik's work integrates AI with biomedical challenges, developing geometric deep learning and multi-scale models to address therapeutic design, disease prediction, and precision medicine. Her lab pioneers approaches for contextualized AI systems and large pretrained models to analyze genetic, cellular, and molecular data for clinical insights. Labs/Teams: Zitnik Lab (focusing on AI in Biomedicine) and collaborations across institutions like the Broad Institute and Harvard Data Science Initiative. She also contributes to global initiatives like the AI4Science network.

Cristiana Bolchiniمشاهده پروفایل

استاد

• Dependability
• Fault Tolerance
• Reliability Analysis
• +۸ مورد دیگر

Cristiana Bolchini is a Professor at the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. She holds a PhD in Automation and Computer Science Engineering (1997) and a Laurea in Electronic Engineering (1993), both from Politecnico di Milano. Her research focuses on dependable systems, fault tolerance, and embedded systems design, with recent work on ICT solutions for smart buildings and energy efficiency. She coordinates projects such as the FP7 SAVE initiative and serves on technical committees for conferences like DATE and DAC. Education: PhD in Automation & Computer Science (1997), Laurea in Electronic Engineering (1993), both from Politecnico di Milano. Research interests span dependability (fault modeling, diagnosis), heterogeneous architectures, and sustainable smart environments. She collaborates with Prof. Giuliana Iannaccone on foresight for sustainable built environments and has led EU-funded projects like the SAVE initiative. Publications include over 150 refereed papers on dependability and context-awareness. She holds editorial roles for journals such as IEEE Transactions on Computer-Aided Design and ACM Transactions on Embedded Computing Systems. Awards include IEEE Senior Member status and two Cisco University Research Program Fund gifts (2012, 2014). Academic roles include Rector’s delegate for Southeast Asia relations and leadership of Technology Foresight workgroups. She teaches courses on computer science fundamentals and dependable systems, emphasizing problem-solving and programming in Python and C.

Benjamin Hayesمشاهده پروفایل

پژوهشگر

• Neural Audio Synthesis
• Generative Models
• Probabilistic Modelling
• +۵ مورد دیگر

Benjamin Hayes is a research scientist at Sony Computer Science Laboratories Paris and a PhD candidate in Artificial Intelligence and Music at Queen Mary University of London's Centre for Digital Music (C4DM). His research focuses on neural audio synthesis, generative models, and perceptual aspects of sound design. He has held internships at Spotify, Sony CSL, and Bytedance, and previously worked as a music producer and lecturer in Electronic & Produced Music at the Guildhall School of Music & Drama. His work bridges digital signal processing, deep learning, and creative sound exploration. Education: PhD in AI and Music (Queen Mary University of London), Master’s/Undergraduate qualifications not explicitly stated but inferred from professional roles. Research Interests: Neural audio synthesis, differentiable digital signal processing, timbre perception, generative models, and psychoacoustics. Current project explores perceptually motivated deep learning approaches for sound synthesis, emphasizing semantic associations in timbre. Professional Experience: Over 10 years in music production, internships at leading tech firms, and academic collaborations across institutions like IRCAM, CNRS, and KTH. Key Contributions: Developed frameworks for end-to-end sound synthesis, addressed challenges in unordered neural network targets, and pioneered gamified systems for crowdsourcing timbre semantics (e.g., timbre.fun). Active in conferences such as ICLR, ICASSP, and DMRN workshops.

• Linked Data
• Data Management
• Ontology Engineering
• +۴ مورد دیگر

Dr. Ronald Maria Siebes serves as Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, with affiliations to the Network Institute and Business Web and Media department. His research focuses on knowledge organization systems, semantic web technologies, and FAIR data principles. He leads projects involving interoperability frameworks for restricted data access, IoT-enabled smart buildings, and historical chronicle analysis. Current roles include managing Open Data Infrastructure initiatives and guiding PhD research in data governance. Education details are not explicitly provided in the text. Research interests emphasize ontology engineering, knowledge graph applications, and data management standards. Recent work explores FAIR Implementation Profiles for research data, IoT sensor integration in office environments, and dynamic knowledge graph embeddings. Active in 6 collaborative projects including smart grid integration and historical source analysis. Labs/Teams: Involved in Network Institute initiatives and Open PHACTS Foundation projects. Supervised 2 PhD theses (names not specified in text). Grant activities span €2.1M in EU Horizon and national funding for data infrastructure and knowledge engineering research.

Masum Billahمشاهده پروفایل

مدرس ارشد

• Data Analytics
• Telecommunications
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Masum Billah is a Senior Lecturer at Staffordshire University, affiliated with the Digital, Tech, Innovation & Business School. He joined the university in 2022 after teaching full-time at other universities since 2019. His academic journey includes a B.Sc. in Engineering from Bangladesh, an M.Sc. in Computer & Network Engineering from the University of Greenwich (UK), and a PhD in Electrical and Electronic Engineering from Staffordshire University. He also holds postgraduate certificates in Higher Education and Research Methods. As a course leader for the B.Sc. Embedded Electronic Systems Design and Development Engineer program, he teaches both undergraduate and postgraduate courses such as IoT Devices, Artificial Intelligence, and Communications. His research focuses on data analytics, telecommunications, IoT security, and healthcare research, with a particular interest in wireless sensor networks and machine learning applications. He actively promotes STEM education as a STEM Ambassador and contributes to initiatives like university competitions and school outreach. Masum’s professional memberships include Fellow of The Higher Education Academy (FHEA), IEEE, and roles in academic leadership. His work integrates flipped learning methodologies and spans engineering consultancy, short courses, and enterprise-focused projects. Recent recognitions include Whatuni Student Choice Awards 2023 for Facilities and Social Inclusion, reflecting his commitment to educational excellence.

Thomas Heldtمشاهده پروفایل

دانشیار

• Computational Physiology
• Neuromonitoring
• Critical Care Informatics
• +۲ مورد دیگر

Thomas Heldt is Associate Professor of Electrical and Biomedical Engineering in the Department of Electrical Engineering and Computer Science at MIT, and a Principal Investigator at MIT's Research Laboratory of Electronics. He leads the Integrative Neuromonitoring and Critical Care Informatics Group and serves as Associate Director of the Institute for Medical Engineering and Science. Research focuses on: Noninvasive intracranial pressure monitoring Computational models of cerebrovascular dynamics Sepsis detection algorithms Wearable physiological monitoring Clinical decision support systems Recent publications (2022-2024) demonstrate advances in hemodynamic modeling, diagnostic algorithms for critical care, and AI applications for physiological monitoring. Key innovations include open cranium models for intracranial hypertension studies, deep learning frameworks for fatigue assessment, and mobile-based neurocognitive tracking. Professor Heldt collaborates with Boston Children's Hospital, Beth Israel Deaconess Medical Center, and Boston Medical Center to translate research into clinical practice. His work has been recognized through the W.M. Keck Career Development Professorship and IEEE EMBS Distinguished Lectureship.

Ke Chenمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Artificial Intelligence
• Computational Cognitive Science
• +۹ مورد دیگر

Dr. Ke Chen is a Senior Lecturer in the School of Computer Science at The University of Manchester, leading the Machine Learning and Perception (MLP@UoM) Lab. His research focuses on machine learning, deep learning, reinforcement learning, and their applications in intelligent systems, computer vision, and audio/speech processing. He has supervised over 50 PhD students and holds editorial roles in journals like Neural Networks and IEEE Transactions on Neural Networks . Dr. Chen has received awards such as the NSFC Distinguished Principal Young Investigator Award (2001) and JSPS Research Award (1993). Education: PhD in Computer Science (1990), with academic positions at institutions including Peking University, The Ohio State University, and Kyushu Institute of Technology. His professional activities include roles in IEEE Computational Intelligence Society committees and external examiner roles at universities like Essex. Research interests span computational cognitive systems, biometric authentication, and video game AI. Key contributions include advancements in deep architectures, reinforcement learning, and speaker-specific feature extraction. His lab, MLP@UoM, explores topics like explainable AI and transfer learning. Recent publications (2024) include work on goal-conditioned reinforcement learning and bias-resilient algorithms. He is actively involved in international conferences, serving as a keynote speaker and program committee member.

Vivienne Szeمشاهده پروفایل

استاد

• Energy-Efficient Machine Learning
• Computer Vision
• Video Compression/Processing
• +۷ مورد دیگر

Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices. Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies. Research Interests: Energy-Efficient Machine Learning Accelerators Video Coding and Compression Standards Embedded Systems and Mobile Computing Processing-in-Memory (PIM) Architectures AI for Health Monitoring and Digital Health Sustainability in AI Infrastructure Publications highlight trends in: Optimizing DNNs for edge devices Innovations in entropy coding and CABAC Memory-efficient Gaussian-based algorithms Energy-aware design for photonic computing Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.

Mark Hooverمشاهده پروفایل

مدرس

• Mathematics Education
• Social Justice in Education
• Teacher Professionalization
• +۵ مورد دیگر

Mark Hoover is an Associate Research Scientist and Intermittent Lecturer at the University of Michigan's Marsal Family School of Education, Department of Mathematics Education. His work focuses on disrupting systemic educational inequities through mathematics teaching and teacher professionalization, emphasizing social justice and ethical pedagogy. He draws inspiration from civil rights activist Robert Moses and education researcher Deborah Ball, whose collaborations are highlighted in his research. Hoover has held roles in both research and teaching, contributing to the development of practice-based approaches to studying teaching demands and advocating for curricula that promote civic participation. His research bridges mathematics content and pedagogy, exploring how teaching practices can address historical injustices embedded in educational systems. Research Interests: Equity in STEM education, mathematical knowledge for teaching (MKT), teacher professionalization, curriculum development, assessment design, and the intersection of mathematics education with social justice movements. He critiques oppressive educational standards and accountability systems while investigating how teaching can be reimagined to center justice. His work also explores the role of mathematicians in shaping mathematics education and the importance of tasks for assessing teaching proficiency. His scholarly output reflects a sustained engagement with equity and justice, analyzing classroom practices and teacher education through critical frameworks. Recent publications emphasize collaborative research initiatives (e.g., TWG19) and the moral dimensions of pedagogy. Grants: Recipient of the Communicating Mathematically Across Difference in the Work of Teaching Award (2018-2025). Advising: No formal advisees listed in provided materials, but his work supports professional learning communities across disciplines. Hoover is affiliated with the Connected Mathematics Project, contributing to curriculum development and its impact assessment. His contributions span both research and practical teaching, aiming to create inclusive and just educational environments.

Edoardo Serraمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۵ مورد دیگر

Edoardo Serra is an Associate Professor in the Department of Computer Science at Boise State University (BSU), a role he has held since July 2021. He previously served as an Assistant Professor at BSU from 2015 to 2021 and holds a joint appointment as a Senior Researcher at Pacific Northwest National Laboratory (PNNL) since June 2021. Since January 2023, he has co-directed the Computing Ph.D. Program at BSU and serves as General Chair of the 2024 ACM CIKM Conference. His academic journey includes a Ph.D. in Computer Science Engineering from the University of Calabria, Italy (2012), followed by postdoctoral positions at the University of Calabria and the University of Maryland. He also served as a Visiting Researcher at UCLA (2010–2011). His research focuses on AI/ML applications in cybersecurity, graph representation learning, generative AI, and robust AI systems. Notable projects include: NSF-funded cybersecurity curriculum integration Department of Defense-funded analysis of terrorist networks Idaho Department of Commerce precision agriculture initiatives Key research areas include graph neural networks, adversarial robustness, and ML-driven security solutions. His work has been recognized with awards such as Best Application Paper (2021) and Best Paper Award (2018). He actively contributes to professional service roles, including program chairs and editorial boards. Current projects emphasize AI ethics, generative models, and scalable graph algorithms. He advises on applied AI consulting for industry and government, focusing on model interpretability and cybersecurity implications.