Find Academic Supervisors

• 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.

• Artificial Intelligence
• Machine Learning
• Data Analytics
• +7 more

Leo Mrsic is a researcher with extensive contributions to Artificial Intelligence, Machine Learning, and Data Analytics. He has published numerous high-impact articles in journals like Artificial Intelligence Review and Quantum Machine Intelligence , covering topics such as quantum computing applications, blockchain in education, and neurotechnological frameworks. His work also extends to environmental engineering and biomedical research. Research Interests : AI, quantum computing, blockchain, sentiment analysis, educational technology, genomics Collaborators : Siddhartha Bhattacharyya, Mislav Balkovic, Mateo Sokac, Sovan Samanta Recent Articles (2025-2022) explore quantum-inspired metaheuristics, fog computing frameworks, and EEG-based image generation. No awards, students, or institutional affiliations are explicitly mentioned in the provided data.

• Neuroscience
• Computational Neuroscience
• Visual Processing
• +6 more

Tushar Chauhan is a Postdoctoral Fellow at the Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT). His research focuses on computational neuroscience, visual processing, and neural decoding, with a particular emphasis on understanding color perception, neural representations in the cortex, and biologically plausible learning mechanisms. He employs advanced techniques such as EEG signal analysis, spiking neural networks, and neuroimaging (fMRI/PET) to investigate how sensory information is encoded and processed in the brain. Key research areas include optic flow processing, developmental models of visual systems, and the neural correlates of spatial and color perception. His work bridges theoretical neuroscience with experimental validation, often using unsupervised learning approaches to model biological systems. Chauhan has contributed to understanding sub-optimality in early visual systems and the role of orientation information in face recognition. His publications span topics from neural geometry analysis to event-based robotics, demonstrating interdisciplinary applications of neuroscience principles. Despite no explicitly listed awards or grants, his prolific output (14+ articles from 2017–2024) indicates sustained academic engagement. No advising relationships or lab affiliations beyond the Picower Institute are documented in the provided materials.

• Tribology
• Biomechanics
• Materials Science
• +5 more

Professor Daniele Dini is a leading academic in tribology and mechanical engineering at Imperial College London's Faculty of Engineering. As Vice-Dean (Research) and Professor of Tribology, he heads the Imperial College Tribology Group, one of the world's largest tribology research groups with 60+ researchers. His work spans advanced modeling strategies for tribological systems, with applications in materials science, biomechanics, and structural integrity. Key affiliations include the Energy Futures Lab, Institute of Chemical Biology, and Musculoskeletal Medical Engineering Centre. Education: M.Eng from Politecnico di Bari (2000), D.Phil from University of Oxford (2004). Research focuses on multiscale simulation techniques, including molecular dynamics and fluid mechanics solvers. Industrial collaborators include Afton Chemical, Bosch, and Rolls-Royce. Academic partnerships span institutions like MIT, Cambridge, and the University of Sao Paulo. Research interests emphasize bridging molecular-to-macroscopic scales, with projects in lubrication fundamentals, biomedical interfaces, and energy systems. Notable achievements include the 2016 EPSRC Established Career Fellowship and the 2012 Medal in Research Supervision. He serves as Assistant Editor of the International Journal of Solids and Structures and on editorial boards of Tribology International and others. Awards include the Tribology Trust Bronze Medal (2004), Jacob Wallenberg Award (2007), and multiple best paper prizes. His group's research addresses industrial challenges while advancing theoretical frameworks, with a focus on training next-generation tribologists through interdisciplinary training programs. Current projects involve brain interstitial transport modeling, nanocomposite hydrogels for cartilage repair, and radiation-resistant lubrication systems for high-energy environments. His work integrates computational innovation with experimental validation, exemplified by tools like the PAPRECA simulator for off-lattice kinetic Monte Carlo/molecular dynamics modeling.

Ann Majewicz FeyView profile

Associate Professor

• Robotics
• Surgical Robotics
• Medical Robotics
• +4 more

Ann Majewicz Fey is an Associate Professor at the University of Texas at Austin's Department of Mechanical Engineering, holding the Robert and Francis Stark Centennial Fellowship in Engineering. She directs the Human-Enabled Robotic Technology (HER) Laboratory and holds courtesy appointments at Dell Medical School's Department of Surgery and Perioperative Care, as well as UT Southwestern Medical Center's Department of Surgery. She is also a Core Faculty member of Texas Robotics. Dr. Fey earned her B.S. degrees in Mechanical and Electrical Engineering from the University of St. Thomas, an M.S.E. in Mechanical Engineering from Johns Hopkins University, and a Ph.D. in Mechanical Engineering from Stanford University. Prior to UT Austin, she was an Assistant Professor at the University of Texas at Dallas. Her research focuses on human-robotic systems, particularly in surgical and interventional care. Key areas include haptic feedback systems, robotic surgical tools, and stress detection during medical procedures. She pioneers technologies like bio-inspired continuum robots for fetal medicine and novel gripping systems for soft tissues. Her work also emphasizes surgical training through haptic simulators and project-based mechatronic education. Notable achievements include the NSF CAREER Award (2019) and CRII Award (2014). She serves as an associate editor for IEEE Robotics and Automation Letters and has contributed to advancing skill assessment and teleoperation in robotic surgery. Dr. Fey’s research bridges clinical needs with engineering innovation, aiming to enhance both patient outcomes and provider efficiency. Her lab actively develops prototypes for minimally invasive surgery, training tools, and adaptive robotic systems.

Mingbo CaiView profile

Assistant Professor

• Cognitive Neuroscience
• Brain-Inspired AI
• Reinforcement Learning
• +5 more

Mingbo Cai is an Assistant Professor in the Department of Psychology within the College of Arts and Sciences at the University of Miami. His research focuses on the intersection of human cognition and machine intelligence, leveraging computational models and experiments to study learning, decision making, and spontaneous thoughts. He develops tools like the Brain Imaging Analysis Kit (BrainIAK) to advance neuroimaging research. Key interests include reinforcement learning, Bayesian inference, and computational psychiatry. His lab explores how the brain constructs cognitive maps and processes spatial navigation through fMRI studies, emphasizing the role of predictive learning principles. Recent work includes studies on 3D object perception, unsupervised learning inspired by infant cognition, and decoding spontaneous thoughts via neuroimaging. Cai has published extensively on neural representation analysis, fMRI simulation, and transfer learning, with contributions to both theoretical neuroscience and practical machine learning applications. Current opportunities include PhD admissions (fall 2025) and a postdoc position focusing on fMRI studies of spontaneous thoughts. Collaborations leverage interdisciplinary approaches to bridge cognitive models with AI advancements, aiming to understand how neural systems achieve adaptive behavior through structured computations.

Christos PapadimitriouView profile

Professor

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +8 more

Christos Papadimitriou is the Donovan Family Professor of Computer Science and Provost's Senior Faculty Teaching Scholar at Columbia University. He previously held the C. Lester Hogan Professorship at UC Berkeley (1996–2017) and taught at Harvard, MIT, Stanford, UC San Diego, and the National Technical University of Athens. His research bridges theory and practice, focusing on algorithms, complexity, computational biology, AI/ML, game theory, and neuroscience. He pioneered algorithmic game theory and explores brain-mind interfaces using neuronal assembly models. Education: BS in EE (Athens Polytechnic, 1972), PhD in EECS (Princeton, 1976) Awards: IEEE John von Neumann Medal (2016), Gödel Prize (2012), National Academy of Sciences/Engineering membership, 9 honorary doctorates Books: Elements of the Theory of Computation , Computational Complexity , Algorithms , and novels like Logicomix His work applies computational lenses to biology, economics, and language, emphasizing formal models for emergent cognition. Recent research includes neuronal assembly-based computation, fairness in ML, and game-theoretic dynamics.

• Computational Neuroscience
• Artificial Intelligence
• Cognitive Science
• +3 more

Jascha Achterberg serves as a Career Development Research Fellow at the University of Oxford, holding dual affiliations with the Department of Physiology, Anatomy and Genetics (DPAG) and St John's College. He actively contributes to the Costa Group research collective, focusing on the critical intersection of neuroscience and artificial intelligence to decode the principles of intelligence. Academic Background: PhD in Cognitive Neuroscience from the University of Cambridge (MRC Cognition and Brain Sciences Unit), supervised by John Duncan and Matthew Botvinick with collaborative projects at Google DeepMind and Intel. Research Vision: Dr. Achterberg pioneers investigations into how the brain's system-level architecture enables flexible cognition, with particular emphasis on specialized submodules, circuit patterns, and communication topologies. His work bridges biological intelligence and artificial systems through brain-inspired computing, targeting scalable distributed architectures that replicate cognitive flexibility. This transdisciplinary approach integrates computational neuroscience, AI engineering, and cognitive theory to establish general principles applicable to both neuroscientific modeling and next-generation computing systems. Publication Trajectory: Recent publications (2023-2025) demonstrate a cohesive research program advancing brain-inspired AI through three converging fronts: (1) modeling neural dynamics for complex problem-solving (frontal lobe networks), (2) developing heterogeneous expert architectures that mimic biological processing pathways, and (3) establishing spatially embedded neural networks that reconcile structural and functional neuroscience data. These works consistently reveal how biological constraints can drive efficiency in artificial systems, particularly in domain-general cognition and scalable learning. Research Collective: As a core member of the Costa Group at Oxford, Achterberg operates within a dynamic ecosystem focused on systems neuroscience and computational modeling of brain function, leveraging institutional resources from DPAG and cross-sector collaborations with industry partners like Intel and Google DeepMind.

Andrew NereView profile

Assistant Professor

• Machine Learning
• Computer Architecture
• Embedded Systems
• +3 more

Andrew Nere serves as Assistant Professor of Computer Science in the Math & Computer Science Department at Western Colorado University, teaching courses including Introduction to Web Design, Computer Science I, and Software Entrepreneurship since joining the faculty in Fall 2022. His industry background includes co-founding Thalchemy (2013), a startup specializing in embedded machine learning for wearable and environmental sensor applications, alongside prior internships at IBM and Qualcomm. His educational credentials feature: PhD in Electrical Engineering from University of Wisconsin-Madison (2013) MS in Electrical Engineering from University of Wisconsin-Madison (2010) B.S. in Computer Engineering from St. Cloud State University (2007) Nere's research centers on hardware-software co-design for efficient AI deployment, with dual focus on neuromorphic computing architectures and practical embedded systems implementation. He bridges theoretical neuroscience with engineering solutions, particularly for resource-constrained environments like fitness trackers and environmental sensors where computational efficiency is paramount. His work emphasizes translating academic research into real-world applications rather than pure theoretical exploration. Analysis of his 2010-2013 publications reveals consistent innovation in brain-inspired computing hardware, with recurring themes of GPU-accelerated neural simulations, specialized cache architectures for AI workloads, and energy-efficient neuromorphic designs. The research demonstrates strong interdisciplinary collaboration across computer architecture, neuroscience, and machine learning communities, frequently targeting hardware acceleration for cognitive computing tasks. Scientific recognition includes: Best Paper Nomination at IEEE International Symposium on Workload Characterization (2012) Best Paper in Track at International Parallel and Distributed Processing Symposium (2011) Nere brings substantial industry experience to academia, having served as university collaborator on the DARPA/IBM SyNAPSE project modeling brain functionality in computing systems. His startup Thalchemy exemplifies his commitment to applied research translation, while his current Software Entrepreneurship course provides students with practical business development frameworks for technology ventures. He actively leverages Gunnison Valley's natural environment for both recreation and potential research applications, noting particular interest in environmental sensing opportunities afforded by the region's wilderness proximity and diverse ecosystems.

Habib HamamView profile

Professor

• Optics
• Biomedical Engineering
• Wireless Communication
• +7 more

Habib Hamam is a Professor in the Department of Electrical Engineering at the University of Moncton's Faculty of Engineering. His research spans optics, biomedical engineering, wireless communication, and AI applications. Specializations: Diffractive elements, optical interconnections, biomedical engineering, hybrid fiber/wireless systems, and AI-driven optimization. Contact: Email habib.hamam@umoncton.ca | Phone (506) 858-4762 Recent publications highlight his focus on AI integration across healthcare, energy systems, and education, combined with blockchain for security and transparent data management. Key subdomains include medical imaging, renewable energy optimization, and IoT scalability. Notable research trends involve deep learning for diagnostics (e.g., brain tumors, diabetic retinopathy), blockchain applications in smart grids and fisheries, and quantum-inspired algorithms for energy forecasting.

Mahesh SuryawanshiView profile

Lecturer

• Photovoltaics
• Renewable Energy Engineering
• Materials Science
• +3 more

Mahesh Suryawanshi is a Senior Lecturer and ARC DECRA Fellow at the School of Photovoltaic and Renewable Energy Engineering (SPREE), UNSW Sydney. He holds a Ph.D. in Physics (Materials Science) from Shivaji University, India (2015), and completed postdoctoral work at Chonnam National University, South Korea (2015-2019) under the Brain Korea (BK)-21 Fellowship. Research Interests: Development of earth-abundant nanocrystals/quantum dots for solar energy conversion, surface ligand engineering, solution-processed thin films for photovoltaics and water splitting, and electrocatalytic systems for hydrogen production. His work emphasizes scalable synthesis methods and in-situ operando characterization. Grants & Awards: ARC DECRA Fellowship (2021-2024), Early Career Researcher Award (European Commission, 2019-2021), and multiple accolades including the Key Scientific Article Award (REGI, Canada) and Best Presentation Award at AFORE 2018. Scientific Awards: ARC DECRA Fellowship Early Career Researcher Award Best Presentation Award (AFORE 2018) Key Scientific Article Award (REGI) Supervision: Mentors HDR students including Xinyao Guo and Yiming Xia. Established the Multifunctional NanoMaterials and Devices subgroup at SPREE, fostering international collaborations. Labs & Teams: Affiliated with SPREE at UNSW, previously worked with Prof. Jin Hyeok Kim at Chonnam National University and Prof. Xiaojing Hao/Martin Green at UNSW. Focuses on scalable nanocrystal synthesis and device integration.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +14 more

Evren Dağlarlı is an Associate Professor in the Department of Computer Engineering at the Faculty of Computer and Informatics, Istanbul Technical University. He holds a Ph.D. in Control and Automation Engineering from ITU and has extensive experience in robotics, artificial intelligence, and intelligent systems. He is actively involved in research projects and has published in top-tier IEEE conferences and journals. Ph.D., Control and Automation Engineering, Istanbul Technical University (2008–2019) M.Sc., Mechatronics Engineering (Intelligent Systems and Robotics), Istanbul Technical University (2005–2007) B.Sc., Electrical Education, Marmara University (2000–2004) B.Sc., Electrical-Electronics Engineering, Ege University (2021–2023) His research focuses on robotics, artificial intelligence, human-robot interaction, cognitive neuroscience, and machine learning . He develops brain-inspired cognitive architectures and applies them to humanoid robots and autonomous systems. His work integrates deep learning, personality modeling, and explainable AI to enhance intelligent behavior in machines. Recent publications highlight a strong trend in generative AI, personality-integrated language models, UAV design, and computational cognitive modeling . His work bridges neuroscience and AI, aiming to create more adaptive, self-aware, and explainable robotic systems. He frequently collaborates with E. Aribas on topics ranging from UAVs to AI-driven personality modeling. Yenilikçilik Ödülü (Innovation Award), Istanbul Technical University, 2024 He has supervised students and co-authored multiple papers with advisees. He has participated in national and international research projects, including the Gökbörü Alemdar Savaşan İHA Projesi (2024–2025). His technical expertise includes ROS, embedded systems, real-time operating systems, and AI frameworks. He is a member of IEEE, ACM, and ASME and actively contributes to the advancement of intelligent systems through research, development, and academic mentorship.

• Neural Algorithms
• Computational Neuroscience
• Machine Learning
• +7 more

Dr James Bennett is an Assistant Professor in Computer Science & AI (Informatics) at the School of Engineering and Informatics, University of Sussex, where he has been a faculty member since 2023. Prior to this, he held postdoctoral research positions at the University of Sheffield (2022–2023), University of Sussex (2017–2022), and the University of Oxford (2014–2017). His academic background spans physics, neuroscience, and AI, reflecting his interdisciplinary research approach. MPhys in Physics, University of Warwick (2004–2008) MSc in Neuroscience, University of Oxford (2008–2009) DPhil in Neuroscience, University of Oxford (2009–2014) His research lies at the intersection of biological and artificial neural systems, focusing on how insect brains—especially the Drosophila mushroom body—implement learning algorithms akin to those in machine learning. He models reinforcement learning mechanisms in biological circuits and applies neural principles to improve artificial intelligence. His work is highly relevant to sustainable development, particularly biodiversity and life on land. The recent publications show a consistent focus on modeling neural learning in insect brains, particularly reinforcement and reward prediction errors, pattern formation, and plasticity. These studies bridge computational neuroscience and AI, using biologically plausible models to inform machine learning. Dr Bennett teaches advanced courses in machine learning and neural networks at both undergraduate and postgraduate levels, contributing to the education of future AI and computer science specialists. He has been active in collaborative research, with co-authors including Thomas Nowotny, Andrew Philippides, and Wyeth Bair. While no formal awards are listed, his work has been cited over 100 times and disseminated through major preprint and open-access platforms. Dr Bennett supervises no listed students yet, but his research group likely involves collaborative projects within the informatics and neuroscience communities. His ORCID is 0000-0002-9474-426.