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Reshma KarView profile

Assistant Professor

• Health Informatics
• Machine Learning
• Data Analysis
• +3 more

Dr. Reshma Kar is an Assistant Professor at the Data Science Institute, Saint Peter’s University. She holds a Ph.D. in Engineering from Jadavpur University, India, where she previously served as a project fellow, senior project fellow, and guest faculty. Her research focuses on health informatics and machine learning, with applications in healthcare analytics and predictive modeling. Dr. Kar teaches graduate-level courses including Data Analysis and Decision Modeling, Python in Data Science, and Machine Learning. Her professional achievements include a Regional i-Corps Grant from the NSF for diabetes prediction research (2024) and multiple Faculty Research Associate Awards at Saint Peter’s University. Notable research contributions include developing interpretable machine learning algorithms for ICU length-of-stay prediction and analyzing brain signals for motor activity detection. She has also contributed to natural language processing studies, such as classifying social media data related to the pandemic. Dr. Kar has over 20 peer-reviewed publications and actively mentors students in research projects, emphasizing programming and data science methodologies. Her work bridges computational techniques with real-world healthcare challenges, reflecting her commitment to advancing both academic and applied data science.

Seyedali MirjaliliView profile

Professor

• Optimization
• Swarm Intelligence
• Evolutionary Algorithms
• +6 more

Seyedali Mirjalili is a Professor of Artificial Intelligence and Director of the Centre for Artificial Intelligence Research and Optimization (AIRO) at Torrens University Australia. He holds distinguished professorships at VSB Technical University of Ostrava, Óbuda University, and De La Salle University, and is an adjunct researcher at Griffith University and Yonsei University. His research focuses on optimization algorithms, swarm intelligence, evolutionary computation, and machine learning. Education: PhD in Computer Science (Griffith University, 2016). Research interests include optimization techniques, swarm intelligence algorithms (e.g., Grey Wolf Optimizer, Whale Optimization Algorithm), machine learning applications, and robust optimization frameworks. He has published over 600 papers with an H-index of 110 and is among the most cited researchers in optimization fields globally. Awards include the Pro-Vice Chancellor Research Special Award (2019) and recognition as a top 1% highly cited researcher (since 2019). He serves as an associate editor for journals like Neurocomputing and Applied Soft Computing. Labs/Teams: Leads the AIRO Centre at Torrens University, focusing on AI-driven solutions for complex optimization problems.

• Health Disparities
• Cancer Care
• Health Equity
• +3 more

Do Lee is a Researcher at the COPPER Center within the Yale School of Medicine at Yale University. She holds a B.S. in Elementary Education from the University of Maryland, College Park, and an MPH in Biostatistics from George Washington University. Her research focuses on addressing racial and socio-economic disparities in cancer care to advance equitable healthcare. She contributes to interdisciplinary efforts in health equity, biostatistics, and public health, leveraging her expertise to improve patient outcomes through data-driven strategies. Affiliated with both the COPPER Center and the Department of Internal Medicine, her work integrates statistical methodologies with clinical and translational research. While no awards are explicitly noted, her contributions to health disparities research reflect a commitment to impactful translational science. Her scholarly publications span neuromorphic computing, artificial synapse electronics, and efficient machine learning techniques, demonstrating a blend of computational innovation and applied health research. Collaborations likely bridge engineering and medical domains to address complex healthcare challenges.

• Biomedical Engineering
• Cardiovascular Systems
• Fluid Dynamics
• +3 more

Arian Aghilinejad is a Postdoctoral Scholar Research Associate in Aerospace at the California Institute of Technology, affiliated with the Graduate Aerospace Laboratories (GALCIT) within the Division of Engineering and Applied Science. His work focuses on biomedical engineering, cardiovascular systems, fluid dynamics, and machine learning applications in healthcare. Research interests include non-invasive cardiovascular monitoring, hemodynamic modeling of the circulatory system, and microfluidic technologies for circulating tumor cell (CTC) separation. He investigates mechanisms like aortic stretch/recoil dynamics, wave-pumping effects, and age-related changes in heart-aorta-brain coupling. His methods combine machine learning with traditional engineering approaches, such as Fourier-based analysis and fluid-structure interaction modeling. Recent work emphasizes translating non-invasive pressure measurements into clinical insights via spectral regression and hybrid machine learning techniques. He also develops patient-specific models for aortic dissection using MRI validation and deep learning. His studies explore longitudinal wave dynamics in compliant tubes and bio-inspired pumping mechanisms. Publications span cardiovascular disease prediction, CTC separation via microfluidics, and the role of vessel wall mechanics in energy transmission. He collaborates on lab-fabricated phantoms and in-vitro hemodynamic simulators to bridge engineering and clinical challenges.

Matteo SaverianoView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +2 more

Matteo Saveriano is an Assistant Professor at the University of Trento, focusing on integrating cognitive robotics into industrial and social environments through AI solutions inspired by human behavior. His research bridges robotics, AI, and human-robot interaction to enhance productivity in smart factories and assistive technologies. Research Focus His work centers on developing robotic systems capable of intuitive learning and safe interaction. Key areas include: Robotic skill acquisition via imitation learning Safe human-robot collaboration frameworks Variable impedance control systems Cognitive architectures for industrial automation Publication Trends Recent publications demonstrate interdisciplinary focus spanning clinical applications (dysphagia diagnostics, Parkinson's therapies), surgical robotics (vocal fold reconstruction), and fundamental biomechanics (tissue elasticity modeling). This reflects a translational research approach connecting engineering innovation with medical practice. Projects & Leadership He coordinates major EU initiatives including: INVERSE (Horizon Europe): Interactive robots learning tasks through reasoning MAGICIAN : Human-robot collaboration for defect detection ARIEL : Assistive robotics for elderly care

Alberto SalleoView profile

Professor

• Organic bioelectronics
• Organic semiconductors
• Structure-property relations
• +5 more

Alberto Salleo serves as Professor of Materials Science and Chair of the Department of Materials Science and Engineering at Stanford University's School of Engineering since 2019. He joined Stanford in 2005 as Assistant Professor, progressing to Associate Professor (2013) and Full Professor (2019) with a visiting professorship at École des Mines de Saint-Etienne in 2016. His research focuses on structure-property relationships in organic semiconductors , specializing in: Operando characterization techniques (X-ray/electron microscopy) Organic bioelectronics for biosensors and neuromorphic computing Development of artificial synapses and wearable diagnostic devices Electronic transport mechanisms in conjugated polymers His group's work bridges fundamental materials science with applications in healthcare and energy-efficient computing. Salleo's publication record includes multiple Clarivate Highly Cited Papers in Science , Nature Materials , and Journal of the American Chemical Society , demonstrating significant impact across organic electronics and neuromorphic engineering. His research trends show increasing focus on bioelectronic interfaces and energy-efficient computing architectures. Award highlights: Elected Fellow of Materials Research Society (2022) and European Academy of Sciences (2021) Cavaliere Ordine al Merito della Repubblica Italiana (2020) Walter J. Gores Award (Stanford's highest teaching honor, 2016) NSF Career Award and SPIE Early Career Award Salleo directs an active research group developing novel characterization methods and devices, serving as Principal Editor of MRS Communications and board member for the Materials Research Society. His leadership extends to international scientific collaboration through the Italian Scientists and Scholars in North America Foundation.

Rajit ManoharView profile

Professor

• Asynchronous VLSI Design
• Concurrency
• Formal Methods
• +5 more

Rajit Manohar is the John C. Malone Professor of Electrical & Computer Engineering at Yale University, with appointments in Applied & Computational Mathematics and Computer Science. He is a core member of the interdisciplinary Computer Systems Lab (CSL), which bridges ECE and CS departments. His research focuses on asynchronous VLSI design, neuromorphic computing, and hardware-software co-design. Education: Manohar holds a B.S., M.S., and Ph.D. from the California Institute of Technology. His academic career spans over two decades, with notable contributions to asynchronous circuit theory and neuromorphic engineering. Research Interests: Manohar's work emphasizes energy-efficient asynchronous architectures, concurrency control, and biologically inspired computing. He explores topics like formal methods for circuit verification, cognitive systems, and dynamic sensor networks. His lab develops tools like Fluid (asynchronous synthesis) and Neurobench (neuromorphic benchmarking). Publications: Recent work includes advancements in asynchronous logic synthesis (Maelstrom), neuromorphic frameworks (Neurobench), and scalable brain-computer interfaces (SCALO). His research often intersects NSF-funded projects in energy-aware computing and neuromorphic systems. Awards: Inaugural Misha Mahowald Prize (2025), MIT TR35 (2000s), IBM Goldberg Award (2023) Grants & Labs: Manohar leads NSF-supported initiatives in carbon-aware networking and neuromorphic hardware. The Computer Systems Lab collaborates across disciplines to advance sustainable computing and neuro-inspired architectures.

Jesús Requena CarriónView profile

Professor

• Data Science
• Machine Learning
• Computer Modelling
• +6 more

Jesús Requena Carrión is the Executive Vice Dean and Director of Queen Mary School Hainan (QMSH), and a Professor of Data Science and Engineering at Queen Mary University of London's School of Physical and Chemical Sciences. His research focuses on statistical data analysis, machine learning applications in healthcare and robotics, computer modeling of complex systems, and ethical approaches to data science. He advocates for rigorous and ethical use of digital technology and authored the open-access book Principles of Machine Learning: A Deployment-First Perspective . His work spans interdisciplinary areas, including healthcare technology, serious games for emotion and decision-making studies, and sensor systems for infrastructure maintenance. He leads the Data Science and Engineering Research Group, emphasizing practical deployment and societal impact. Recent research trends include machine learning-driven solutions for medical diagnostics, autonomous systems, and educational tools. Publications reflect innovation in robotics trajectory planning, neurophysiological risk assessment, and stress recognition via wearable sensors. He emphasizes collaboration in global education through QMSH and contributes to neuromorphic sensor networks and bio-inspired engineering applications. No scientific awards are explicitly listed, but his work impacts diverse fields from biomedical engineering to urban planning.

Hussam AmrouchView profile

Professor

• AI Hardware Acceleration
• Emerging Nanoelectronics
• Cryogenic Circuits
• +4 more

Prof. Hussam Amrouch is a Full Professor of AI Processor Design at the Technical University of Munich (TUM), leading the TUM School of Computation, Information and Technology. His research focuses on ultra-efficient embodied AI, reliable designs in emerging technologies, and cryogenic circuits for quantum computing. He holds a Dr.-Ing. from Karlsruhe Institute of Technology (2015, Summa cum Laude) and previously led the "Dependable Hardware" group at KIT and the Chair of Semiconductor Test and Reliability at University of Stuttgart. He is affiliated with Munich Quantum Valley (MQV) and Munich Institute of Robotics and Machine Intelligence (MIRMI). Research interests include ferroelectric FETs, in-memory computing, cryogenic electronics, and neuromorphic systems. Key achievements include 10× HiPEAC Paper Awards, 3× DAC/DATE best paper nominations, and pioneering work on FeFET-based AI accelerators. His work bridges nanoelectronics with AI, addressing challenges in energy efficiency, reliability, and quantum integration. Publications span cutting-edge topics like cryogenic FinFETs, hyperdimensional computing, and monolithic 3D integration. He has developed novel testing methodologies, self-aware silicon systems, and energy-efficient architectures for edge-AI. Current projects explore cryogenic circuit design, radiation-resistant FeFETs, and carbon-efficient 3D neural networks. His awards reflect contributions to high-performance and embedded architectures. Research groups under his leadership focus on device-level innovations and system-level integration of emerging technologies. He actively contributes to interdisciplinary initiatives at MQV and MIRMI, advancing quantum computing and AI hardware frontiers.

• Cognitive Linguistics
• Language Evolution
• Grammaticalization
• +3 more

Dr. Andrew Smith is a Lecturer in English Studies at the University of Stirling, co-directing the MSc in English Language and Linguistics. He teaches courses on Foundations of Language, Language and the Brain, and the History of the English Language. His research focuses on the cognitive and cultural foundations of language evolution, exploring mechanisms like grammaticalization and metaphor-driven complexity. He also investigates cross-situational learning and language transmission dynamics. As an External Examiner for the University of Edinburgh's MSc in Evolution of Language and Cognition, he contributes to academic governance. Awards include the 2012 Most Inspiring Lecturer Award from RATE Teaching Awards. Research Interests: - Grammaticalization and language complexity - Metaphor as a cognitive driver of linguistic evolution - Cross-situational word-learning processes - Cultural transmission of linguistic systems - Interactions between learning and language structure Articles Trends: His work bridges theoretical linguistics with experimental methods, emphasizing evolutionary models and cognitive mechanisms. Key themes include metaphor's role in grammar emergence, computational modeling of lexicon acquisition, and the interplay between systematicity biases and language design. Later publications (2010s) shift toward interdisciplinary approaches combining psychology and computational linguistics. Professional Roles: - External Examiner, University of Edinburgh - Co-Director of the MSc in English Language and Linguistics Awards: - Most Inspiring Lecturer: RATE Teaching Awards 2012

Manuel EisnerView profile

Professor

• Interpersonal Violence Prevention
• Sociology of Violence
• Child Development
• +3 more

Prof. Manuel Eisner is the Wolfson Professor of Criminology and Director of the Institute of Criminology at the University of Cambridge, and a retired Professor of Sociology at the University of Zurich. His research focuses on explaining societal and historical patterns of interpersonal violence, psychological and social mechanisms influencing violent behavior, and evidence-based prevention strategies. He leads the Violence Research Center and founded the Zurich Project on Social Development (z-proso). Eisner has authored 15 books and over 100 journal articles, with work spanning English, German, Spanish, and French. His advisory roles include collaborations with the WHO, UNICEF, and World Bank. Notable awards include the Sellin-Glueck Award from the American Society of Criminology. Education: Studied history at the University of Zurich, earned a doctorate in sociology. His research integrates criminology, sociology, and public health, examining topics like adolescent victimization impacts, historical homicide patterns, and global violence reduction frameworks. He co-organized the 2014 WHO global violence reduction conference at Cambridge. Research interests emphasize transdisciplinary approaches to violence prevention, combining longitudinal cohort studies with innovative methods like ecological momentary assessment and machine learning. His work bridges academic theory with practical policy, influencing international initiatives such as the WHO INSPIRE framework. Eisner's team includes researchers like Denis Ribeaud and Margit Averdijk, advancing understanding of violence dynamics through large-scale data analysis.

Wilfred G. van der WielView profile

Professor

• Nanoelectronics
• Quantum Devices
• Brain-Inspired Computing
• +12 more

Wilfred G. van der Wiel is a Full Professor in the Nano Electronics department at the MESA+ Institute for Nanotechnology, University of Twente. His work bridges nanotechnology, quantum physics, and unconventional computing, focusing on novel hardware for machine learning and brain-inspired systems. Full Professor, Nano Electronics, MESA+ Institute, University of Twente Research Interests: His research lies at the intersection of nanoelectronics and cognitive computing. He investigates disordered dopant-atom networks in silicon , gold nanoparticle assemblies , and in-materio computing to develop energy-efficient, adaptive hardware. His work explores how physical systems can inherently perform computation, bypassing traditional von Neumann architectures. Publication Trends: Recent articles (2023–2025) highlight a shift toward practical implementations of neuromorphic computing, including speech recognition in physical systems and systematic reviews of brain-inspired architectures. Earlier works focus on fundamental transport mechanisms in nanostructures. Collectively, his publications reveal a trajectory from basic physics of nanodevices to applied neuromorphic engineering and software frameworks like Brains-PY. Scientific Contributions: While specific awards are not listed, his high-impact publications in journals like Nature and Physical Review Applied , along with continuous funding and supervision of research, indicate significant recognition in the field. Advising and Grants: He has supervised at least 29 research projects, including PhD and postdoctoral work, as indicated by 'Supervised Work (29)'. He leads or participates in externally funded projects related to nanotechnology and neuromorphic computing, evidenced by collaborative publications and datasets. Labs and Teams: He is a key member of the MESA+ Institute for Nanotechnology at the University of Twente, working within the Nano Electronics group. He collaborates extensively with researchers in physics, materials science, and computer science, particularly on projects involving unconventional computing and nanofabrication.

• Theoretical Neuroscience
• Complex Systems
• Statistical Physics
• +12 more

Viola Priesemann is a Professor of Theoretical Neural Systems at the University of Göttingen and leads a research group at the Max Planck Institute for Dynamics and Self-Organization. She is also an External Faculty member of the Complexity Science Hub and a board member of the Campus Institute for Data Science. Her work bridges physics, neuroscience, and societal dynamics. Education: PhD in Physics, University of Frankfurt (2013) Diploma (Master) in Physics, Technical University Darmstadt (2008) Research at Caltech, MPI for Brain Research, FIAS Summer School, Marine Biological Laboratory, Woods Hole Her research focuses on collective information processing in neural and social networks, aiming to uncover universal principles of self-organization and learning. She applies statistical physics and information theory to understand how intelligence emerges in adaptive systems, from brains to societies. During the COVID-19 pandemic, she became a leading voice in modeling infection dynamics and advising public policy, serving on the German government’s Corona Expert Council. Her recent work explores predictive coding, curiosity, and the neural basis of cognition. Her publications reveal a strong trend toward interdisciplinary modeling, combining epidemiology, neuroscience, and social dynamics. She frequently employs Bayesian inference, dynamical systems theory, and computational simulations to analyze complex phenomena across scales. Scientific Awards: Communitas Award, Max-Planck-Society Wissenschaftspreis Niedersachsen Medaille für naturwissenschaftliche Publizistik, DPG Dannie-Heineman-Award Arthur-Burkhardt-Preis Lise-Meitner-Lecture Young Scientist Award for Socio- and Econophysics She actively mentors students and leads a vibrant research group investigating curiosity, neural dynamics, and societal information spread. She has secured significant research funding, including a German-Israel Foundation Young Investigator Grant and leadership in the Physics to Medicine Initiative. Her lab collaborates internationally and is involved in major centers such as the Max Planck–University of Toronto Centre for Neural Science and Technology. She leads the Collective Information Processing group at MPI-DS, which investigates self-organization in living systems, with projects spanning neural networks, AI, and social dynamics. The lab fosters interdisciplinary collaboration and open science, with code and models publicly available.

• Nanotechnology
• Materials Science
• Neuromorphic Computing
• +3 more

Gisya Abdi , an Adjunct Assistant Professor at the Department of Photophysics and Electrochemistry of Semiconductors within the Academic Centre for Materials and Nanotechnology at AGH University of Science and Technology, focuses on advanced materials for neuromorphic computing and environmental applications. Her work spans memristor development, nanomaterials for greenhouse gas adsorption, and artificial photosynthesis systems. University: AGH University of Science and Technology School: Academic Centre for Materials and Nanotechnology Department: Department of Photophysics and Electrochemistry of Semiconductors Email: agisya@agh.edu.pl Research Interests: Design of memristors for neuromorphic data computing and synaptic plasticity Development of nanomaterials for reservoir computing and sensor technologies Study of multiferroic ceramics and unconventional resistive switching mechanisms Application of graphynes and graphene oxide derivatives in environmental science Article Trends: Recent publications highlight memristor-based neuromorphic systems, nanomaterials for energy/environmental applications, and hybrid technologies integrating electrochemical and optical properties. Key areas include physical reservoir computing, diffusive memristors, and adsorbent materials for pollution mitigation.

• Machine Learning
• Deep Learning
• Network Security
• +5 more

Ayman Yafoz is an active Associate Professor in the Department of Computer Science at Alexandria University's College of Engineering. His research bridges computer science and engineering with a focus on practical security and optimization applications. His scholarly output includes 60 publications with an h-index of 12, demonstrating consistent contributions to his fields of expertise. Dr. Yafoz's research interests center on the intersection of optimization algorithms and machine learning/deep learning techniques. His work spans network security, intrusion detection systems, medical image analysis, Arabic language processing, and IoT security applications. He frequently develops novel hybrid models that combine metaheuristic optimization approaches with neural network architectures to solve complex real-world problems. His publication portfolio reveals strong trends in security applications, with approximately 60% of his recent work focused on various aspects of cybersecurity including intrusion detection, botnet detection, and encryption. Another significant portion addresses medical imaging and accessibility technology, demonstrating his commitment to applying computational methods to healthcare challenges. Dr. Yafoz maintains active research collaborations with scholars across the Middle East, particularly with co-authors Raed Alsini, Omar Alghushairy, and Khaled Tarmissi. His work appears primarily in engineering journals with notable publications in Alexandria Engineering Journal, IEEE Access, and Sustainability.