جست‌وجوی استاد و پژوهشگر

Dajiang Zhuمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Neuroscience
• Medical Data Analysis
• +۴ مورد دیگر

Dr. Dajiang Zhu is an Associate Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), part of the College of Engineering. He holds a PhD from the University of Georgia (2014) and a BA from Shanghai Jiao Tong University (2001). Prior to UTA, he was a Postdoctoral Scholar at the University of Southern California (2014–2017). His research focuses on Machine Learning , Computational Neuroscience , and Medical Data Analysis , with emphasis on brain imaging, computational modeling, and AI applications in healthcare. He leads the MEDICAL IMAGING AND NEUROSCIENTIFIC DISCOVERY (MIND) LAB , exploring brain structure-function relationships and AI-HI interactions. His work addresses neurodegenerative disorders like Alzheimer’s and depression. Key achievements include grants from NIH ($5+ million), 2 major awards (2024 Lawrence Stephens Award, 2017 UT System Rising STARs), and over 150 peer-reviewed publications. He advises over 20 students and serves as Associate Editor for Frontiers in Radiology and Frontiers in Human Neuroscience . Grants: IPAT-study (NIH, $1.4M), Alzheimer’s Progression Mapping (NIA, $2.7M), and Brain Connectome Development (NINDS, $2.8M) Labs: MIND Lab focuses on brain imaging AI, neurodegenerative disease modeling, and medical data analytics

Barry Trimmerمشاهده پروفایل

استاد

• Neuromechanics of Locomotion
• Modulation of Nociception
• Soft Robotics
• +۷ مورد دیگر

Barry Trimmer is the Henry Bromfield Pearson Professor of Natural Science in the Department of Biology at Tufts University, with additional appointments as Professor of Biomedical Engineering and Assistant Professor of Neuroscience. He leads the Neuromechanics and Biomimetic Devices Laboratory (BDL), where he studies the neurobiology of soft-bodied locomotion using the tobacco hornworm Manduca sexta as a model system. His research spans multiple interdisciplinary areas including neuromechanics of locomotion, nociception modulation, soft robotics development, and tissue engineering. Dr. Trimmer's work bridges fundamental biological research with engineering applications, particularly in creating bio-inspired soft robots and exploring cellular agriculture applications. The recent publications reflect a strong trend toward applying biological principles to robotics and sustainable food production. His work increasingly focuses on translating insights from insect neurobiology into soft robotics design, while expanding into cellular agriculture applications using insect cell cultures. This represents a growing interdisciplinary convergence between neurobiology, robotics, and sustainable food technology. Professional Distinctions: Distinguished Visiting Fellow at Scottish Informatics and Computer Science Alliance, University of Edinburgh (2018) Benjamin Meaker Visiting Professor at University of Bristol (2017) Nominated for IEEE EMBS Distinguished Lecturer (2019) Dr. Trimmer advises multiple PhD students including Gayathri Kondakath (studying nociception circuits) and Naya McCartney (investigating muscle development). His laboratory collaborates extensively with Professors Kaplan and Levin on tissue engineering projects. The lab maintains active grant funding from NSF, NIH, and DARPA for research on soft-bodied locomotion, biocomponent devices, and soft robotics. The Neuromechanics and Biomimetic Devices Laboratory focuses on three major research thrusts: understanding neuromechanical control systems in soft animals, developing SoftWorm robot platforms, and engineering novel biological devices through tissue engineering approaches. The lab combines neurobiological studies with advanced fabrication techniques including multi-material 3D printing.

Soha Hassounمشاهده پروفایل

استاد

• Machine Learning
• Systems Biology
• Metabolic Engineering
• +۵ مورد دیگر

Soha Hassoun is a Professor at Tufts University with primary appointment in the Department of Computer Science and secondary appointments in the Department of Electrical and Computer Engineering and the Department of Chemical and Biological Engineering. Her interdisciplinary work bridges computer science, engineering, and biology to develop computational tools for biological systems analysis and design. Education: Ph.D. from University of Washington (1997) M.S. from Massachusetts Institute of Technology (1988) B.S. from South Dakota State University (1986) Soha Hassoun's research focuses on the intersection of Machine Learning and Biology, with particular emphasis on developing ANALYSIS + DESIGN tools to advance (RE-)DESIGNING BIOLOGY. Her work spans Systems Biology, Metabolic Engineering, and computer-aided design for integrated circuits. She develops MACHINE LEARNING models specifically tailored for biological data, drawing inspiration from her prior experience in circuit design and design automation for electronic systems where she contributed seminal ideas for FinFET devices and power grids for 3D integrated circuits. Her recent publications demonstrate a strong trend toward applying advanced machine learning techniques, particularly graph neural networks and transformer models, to biological problems including metabolite annotation, enzyme-substrate interaction prediction, and metabolic network analysis. Her work increasingly focuses on creating standardized benchmarks and frameworks for evaluating computational methods in metabolomics. Soha leads the Hassoun Lab, which is dedicated to developing computational tools that provide insight into complex biological systems and enable building novel biological components to produce useful chemicals and therapeutics. The lab's research spans from fundamental machine learning algorithm development to practical applications in biotechnology.

• Neuroscience
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Prof. Jochen Triesch is a Senior Fellow and Johanna Quandt Research Professor at the Frankfurt Institute for Advanced Studies (FIAS), with concurrent professorships in Physics and Computer Science at Goethe University Frankfurt. His research bridges theoretical neuroscience, machine learning, and medical applications, focusing on cognitive processes, spiking neural networks, and developmental AI. He leads a lab investigating how intelligent systems emerge through self-organization and learning, with applications in healthcare and robotics. Education and affiliations include a physics background and roles at UC San Diego before joining FIAS. His lab offers open thesis topics in machine learning, neuroscience, and medical diagnostics, with notable work on epilepsy prediction and brain tumor detection via AI. Research interests span neural coding, unsupervised learning, and embodied vision. Key awards include the EU’s Marie Curie Excellence Center Award (2006). His lab collaborates extensively, with projects on neural dynamics, computational pathology, and virtual reality applications for vision disorders. Teaching includes courses on theoretical neuroscience, reinforcement learning, and complex systems. Over 30 students have graduated under his mentorship, many contributing to AI, healthcare, and neuroscience fields. Labs/Teams: Triesch Lab (FIAS) focuses on interdisciplinary approaches to brain-inspired AI, combining experimental and computational methods to understand learning and perception.

Thomas Wennekersمشاهده پروفایل

دانشیار

• Neural Networks
• Cognitive Modeling
• Computational Neuroscience
• +۲ مورد دیگر

Thomas Wennekers is an Honorary Associate Professor at the University of Plymouth's School of Engineering, Computing and Mathematics. His research focuses on developing biologically constrained neural network models to understand cognitive functions such as language processing, memory formation, and sensory integration. He specializes in hierarchical temporal memory systems and spiking neural networks. Wennekers investigates neural mechanisms underlying semantic processing, cognitive impairments like dementia, and cross-modal plasticity in sensory systems. His work combines computational neuroscience with robotics to create bio-inspired cognitive systems, examining how neural constraints shape learning and perception. His publications explore neurobiological models of language, visual processing, and memory, with applications in neuromorphic computing and cognitive robotics. Recent work simulates neural reorganization in blind individuals and models cognitive disorders using brain-constrained architectures.

Christian O'Reillyمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neurodevelopmental Disorders
• EEG Analysis
• +۴ مورد دیگر

Dr. Christian O'Reilly is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, with joint appointments at the Artificial Intelligence Institute (AIISC) and Carolina Autism and Neurodevelopmental (CAN) Research Center. He holds a B.Ing in Electrical Engineering (2008), M.Sc.A in Biomedical Engineering (2011), and Ph.D. in Biomedical Engineering (2012) from Polytechnique Montréal. His interdisciplinary research focuses on understanding the brain across spatial and temporal scales to address neurodevelopmental disorders like autism. Research approaches include: Analytical techniques: EEG source reconstruction and functional connectivity Computational modeling: Point neurons, morphologically-detailed neurons, neural masses Bayesian model-driven analyses to infer latent biological variables Novel applications of artificial intelligence in neuroscience Development of biologically-inspired neural networks His extensive publication record spans computational neuroscience, neurodevelopmental disorders, EEG analysis methods, and AI applications in neuroscience, with recent work focusing on multimodal data integration, neurobiological modeling, and translational applications for autism spectrum disorder. Dr. O'Reilly leads the Laboratory for Integrative Neuroscience Analysis (LINA) and has received the Penn Criminology Thesis Award for his work on defense counsel impacts on sentencing outcomes.

Carlos Bonillaمشاهده پروفایل

استاد

• Soil Physics
• Soil Hydrology
• Soil Health
• +۷ مورد دیگر

Dr. Carlos Bonilla is a Professor of Crop and Soil Science at Oregon State University and serves as the Director of the Hermiston Agricultural Research and Extension Center (HAREC). He is affiliated with the College of Agricultural Sciences and the Department of Crop and Soil Science at Oregon State University. Dr. Bonilla earned his PhD in Soil Science from the University of Wisconsin-Madison in 2006, an MS in Engineering (Hydraulic and Environmental Engineering) from Pontificia Universidad Católica de Chile in 1996, and a BS in Agronomy from the same institution in 1993. His research focuses on soil physics and hydrology, soil health and carbon sequestration in natural and managed landscapes, irrigation improvement, soil aggregates, soil erosion, and mathematical models to assess the impact of management practices on nutrient fate and transport in soil and water. His work spans both theoretical modeling and practical field applications, with particular emphasis on wildfire-affected soils, soil hydraulic properties, and sustainable land management practices. Recent publications demonstrate a strong focus on soil physical properties, particularly how they're affected by environmental stressors like heating and wetting/drying cycles, with increasing incorporation of machine learning techniques for prediction and analysis. Associate Editor for the Soil Science Society of America Journal Member of the editorial Advisory Board in Geoderma Soil Science Society of America (2003-) American Society of Agronomy (2005-) Crop Science Society of America (2005-) Soil and Water Conservation Society (2012-) American Society of Agricultural and Biological Engineers (2017-) Dr. Bonilla has received numerous teaching awards throughout his career, particularly during his time at Pontificia Universidad Católica de Chile, where he was recognized multiple times as an inspiring professor and for teaching excellence. His research has been supported by various grants and fellowships, including the President of the Republic Graduate Scholarship from the Government of Chile and the Cochrane Fellowship from the USDA-ARS Fellowship Program. His laboratory and research team focus on soil physical properties measurement and modeling, with recent work emphasizing the impacts of wildfires on soil properties and the development of innovative methods for soil hydraulic characterization.

Dimitrios Makrisمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Video Analysis
• +۳ مورد دیگر

Dimitrios Makris is a Professor in Computer Vision and Machine Learning at Kingston University's Department of Computer Science, within the Faculty of Engineering, Computing and the Environment. He holds a PhD in Computer Vision from City University London and an MEng in Electrical and Computer Engineering from Aristotle University of Thessaloniki. As Faculty Director of PGR Studies, he oversees postgraduate research programs and leads investigations into video analysis, motion tracking, and neuromorphic vision systems. His research focuses on: Multi-dimensional time-series analysis for human motion tracking Neuromorphic vision sensors for robotic grasping applications Medical video analysis using contrast-enhanced ultrasound Scene semantic modeling and multi-camera systems He has secured multiple research grants from national and international organizations to advance these areas. Publication analysis reveals consistent focus on computer vision fundamentals with growing emphasis on healthcare applications and neuromorphic computing. Recent works demonstrate increased integration of machine learning with sensor technologies for real-time robotic and medical systems. Awards and leadership roles include: Deputy Editor-in-Chief for Special Issues, IET Image Processing Journal Member of Steering Committee, IET Vision and Imaging Network He actively contributes to academic communities through editorial responsibilities and steering committee membership for major vision and imaging networks.

Chinwe Ekennaمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Computational Biology
• +۴ مورد دیگر

Chinwe Ekenna is an Associate Professor in the Department of Computer Science at the University at Albany, State University of New York (SUNY) and directs the Robotics, Algorithm and Computable Systems (RACS) Laboratory . She serves as a faculty advisor for UAlbany's ACM-W chapter , promoting women in robotics and STEM. PhD in Computer Science from Texas A&M University (2016) MSc in Computer Science from Covenant University, Nigeria (2008) BSc in Computer Science from Covenant University, Nigeria (2006) Her research focuses on intelligent motion planning for robotics and proteins, integrating machine learning to enhance planning efficiency and accuracy. Key areas include computational biology , robotic navigation , and topological modeling . Her recent publications explore transformer-based motion planning , attention mechanisms for molecular analysis , and discrete Morse theory for robotics . These works bridge artificial intelligence , robotics , and biomedical applications . As a leader, she organized the Meet the Women in Robotics Workshop at RSS 2019, funded by the National Science Foundation. Her teaching includes courses in theory of computation and algorithms . At the RACS Laboratory , her team investigates path planning , adversarial robustness , and robotics in dynamic environments , emphasizing interdisciplinary collaboration and innovation.

Samaneh Berenjianمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Network Security
• Peer-to-Peer Networks
• +۴ مورد دیگر

Samaneh Berenjian is a Senior Lecturer in the Department of Computer Science at New Jersey Institute of Technology (NJIT). Her academic journey includes a Ph.D. from Stevens Institute of Technology (2022), and M.S. degrees from Stevens (2018) and Amirkabir University of Technology (2013), alongside a B.S. from the University of Isfahan (2011). Her research focuses on cybersecurity, network security, and peer-to-peer systems, with contributions to secure protocol design, intrusion response mechanisms, and privacy-preserving technologies. She has also explored navigation models in AI and wireless sensor networks. Her research interests span cybersecurity frameworks, P2P network anonymity, and intelligent systems. Notable work includes encryption-based protocols, incentive models for secure P2P fairness, and fuzzy logic-based intrusion detection systems. Her 2020 GridCell navigational model highlights interdisciplinary AI contributions. Her publications reflect expertise in secure communication, network architecture, and optical fiber error control. While no awards are listed, her work demonstrates consistent engagement with cutting-edge topics in computer science and information security.

Andrew Vardyمشاهده پروفایل

استاد

• Swarm Robotics
• Biologically Inspired Robotics
• Autonomous Systems
• +۱ مورد دیگر

Andrew Vardy is a Professor jointly appointed to the Faculty of Engineering and Applied Science and the Department of Computer Science at Memorial University of Newfoundland. He holds a B.Eng. in Electrical Engineering (Memorial), M.Sc. in Evolutionary and Adaptive Systems (University of Sussex), and PhD in Computer Science (Carleton University). His research explores swarm robotics, biologically inspired algorithms, autonomous navigation, and multi-robot systems. Key areas include visual homing, distributed construction, environmental sensing, and robotic simulation tools. He leads the Memorial University Bots Lab, developing applications for marine and terrestrial environments. Vardy teaches courses in autonomous robotics, computer programming, software design, and control systems. His publications emphasize computational complexity in swarm algorithms, reinforcement learning for coordination, and novel applications like drum accompaniment AI and marine urchin detection. Recent work integrates attention mechanisms, transformer networks, and edge computing paradigms.

• Artificial Intelligence
• Neuroscience
• Cognitive Architecture
• +۳ مورد دیگر

Prof Chris Huyck is a Professor of Artificial Intelligence at Middlesex University's Faculty of Science and Technology, Department of Computer Science, with 25 years of academic experience. His career spans institutions including the University of Sheffield, Microsoft, and the American University in Cairo, culminating in a PhD from the University of Michigan (1994). Dr. Huyck's research focuses on Cell Assemblies (CAs) grounded in mammalian neural circuitry, extending to cognitive architectures , neuromorphic hardware , and NLP applications . Key projects include modeling associative memory , category learning , and robotic control systems using spiking neural networks. Recent Publications : 2025 firefly algorithm tuning studies; 2024 emotion-integrated cognitive models; 2023 UAV path planning with coronavirus-inspired optimization Awards : No specific honors mentioned Students : Supervising Geethu Joy (PhD Enrolled, 2022-) and Babak Mohajer Ashjaei (PhD Enrolled, 2025-) Labs : Active in Middlesex's AI research group, integrating neuroscience with robotics and NLP

Xin-She Yangمشاهده پروفایل

دانشیار

• Nature-inspired computing
• Mathematical modelling
• Engineering simulation
• +۳ مورد دیگر

Xin-She Yang is an Associate Professor in Simulation & Modelling at Middlesex University, London. He holds a DPhil in Applied Mathematics from the University of Oxford and has over 25 years of experience in research and teaching. His academic roles include serving as an Editor-in-Chief, editorial board member for multiple journals, and chair of IEEE CIS Task Force on BIKM (2015-2020). He is a Fellow of the Institute of Mathematics and its Applications (2021), Asian Computational Intelligence Society (2023), and Soft Computing Research Society (2025). Research interests span nature-inspired computing, mathematical modelling, engineering simulation, AI, algorithms, and optimization. Notable achievements include being listed as a top 0.1% scientist in Web of Science since 2016 and receiving multiple Leader Awards in Computer Science (2023–2025). He supervises PhD students in areas like parameter tuning, nature-inspired computing, and optimization. Teaching includes courses on engineering simulation, mathematical optimization, and partial differential equations. He has authored books like Optimization Techniques and Applications with Examples and contributed to Springer's Nature-Inspired Computing and Optimization series. Education: DPhil in Applied Mathematics, University of Oxford (1998) Affiliations: IEEE, IMA, Asian CIS Grants: UKRI Doctoral Training Centres, National Research Councils (Iceland, Ireland, etc.)

Ali A. Minaiمشاهده پروفایل

استاد

• Neural Networks
• Artificial Intelligence
• Complex Systems
• +۷ مورد دیگر

Ali A. Minai is a Professor of Electrical & Computer Engineering at the University of Cincinnati, with a secondary appointment in Computer Science and affiliation in the Neuroscience Graduate Program. He holds a PhD in Electrical Engineering from the University of Virginia (1991) and postdoctoral training in neuroscience. His research spans neural networks, complex systems, computational neuroscience, and text analysis, with a focus on creativity dynamics and brain networks. He has led major grants including an NSF INSPIRE award (2012) and served as President of the International Neural Network Society (2015-16). His work bridges AI, cognitive models, and biological systems. Education: PhD in Electrical Engineering, University of Virginia, 1991 MS in Electrical Engineering, University of Virginia, 1987 Research Interests: His work emphasizes computational models of cognition, neural networks, and social networks. Current projects include analyzing text corpora to study creativity, hippocampal modeling for spatial processing, and applying deep learning to clinical medicine. His models explore how neural systems generate and adapt ideas, with applications to robotics and biomedical research. Grants & Awards: Over $2M in NSF grants, including the INSPIRE award (2012) for creativity studies. Collaborative projects with federal agencies (Air Force, Army) focus on machine learning for verification processes and innovation diffusion. Service & Editorial Work: Served on editorial boards of Neural Networks and IEEE Transactions on Neural Networks , and organized major conferences like IJCNN 2011. Active in professional societies (IEEE, INNS).

• Computational Complexity
• Algorithm Design
• Cognitive Science
• +۴ مورد دیگر

Dr. Harold Wareham is a tenured Professor in the Department of Computer Science at Memorial University of Newfoundland (MUN), Faculty of Science. He holds a Ph.D. from the University of Victoria and has been at MUN since 1999, advancing through roles including Assistant Professor, Associate Professor, and Full Professor. His research focuses on computational complexity theory applied to cognitive science, robotics, and computational biology. He has served on numerous academic committees and international conference program committees. Education: B.Sc. (Computer Science) and B.A. (Linguistics/Mathematics) from MUN, followed by an M.Sc. in Computational Biology and a Ph.D. in Parameterized Complexity in Computational Phonology from the University of Victoria. Research Interests Computational complexity of cognitive processes Algorithm design for robotics and swarm control Parameterized complexity in biological systems Machine learning interpretability Recent Work Trends Recent publications emphasize applying complexity theory to AI interpretability (e.g., neural network reverse-engineering) and cognitive science (segmentation processes). His robotics research explores algorithmic foundations for swarm construction tasks. He also investigates computational challenges in software reconfiguration and modularization. Awards 2017 Best Professor Award (MUN CS Graduate Society) Teaching Fellow at Radboud University (2014–) F. A. Aldrich Alumni Graduate Scholarship (1989) Grants & Advising Has supervised numerous courses across undergraduate and graduate levels, including AI topics, computational biology, and theoretical computer science. Active in grant review roles for NSERC and international funding bodies. Served on MUN committees for promotion, tenure, and graduate studies. Labs & Collaborations Associated with the MUN CS Theory Group and the Bio-inspired Robotics (BOTS) Lab. Collaborates internationally on projects like the Dagstuhl Complexity Workshops and cognitive science initiatives.