Find Academic Supervisors

Andreas HeinzView profile

Professor

• Psychiatry
• Mental Health
• Neuroimaging
• +3 more

Prof. Dr. Dr. Andreas Heinz serves as Director of the Department of Psychiatry and Psychotherapy CCM at Charité – Universitätsmedizin Berlin. His expertise spans global mental and somatic health, neuroimaging-based psychopathology frameworks, and international mental health policy. He leads the German Center for Mental Health (DZPG) and collaborates with global experts to address mental health disparities. His research focuses on brain-derived nosology, addiction neurobiology, and the impacts of climate change on mental health. He investigates mechanisms underlying psychiatric disorders using multimodal neuroimaging and machine learning, with emphasis on alcohol use disorder, eating disorders, and adolescent neurodevelopment. His work includes longitudinal studies on risk-taking behaviors, neural correlates of uncertainty processing, and ethical challenges in healthcare equity. Research interests include neuroimaging of brain networks, neurodevelopmental pathways, and translational approaches to mental health. He has pioneered studies on the Earth, Brain, Health Commission and the psychological effects of global environmental crises. His clinical work addresses refugee mental health through stepped care models and examines gender-specific alcohol use patterns during pandemics. He is committed to reducing coercion in psychiatric care and advocating for humane housing solutions for vulnerable populations.

Jacob FosterView profile

Professor

• Computational Sociology
• Collective Intelligence
• Cultural Evolution
• +3 more

Dr. Jacob Foster is a Professor of Sociology at the University of California, Los Angeles (UCLA). He is a founding co-Director of the Diverse Intelligences Summer Institute and an External Professor at the Santa Fe Institute. His research focuses on computational sociology, cultural evolution, and the intersection of cognition, culture, and computation. He holds a BS in Physics from Duke University and a PhD in Physics from the University of Calgary, with postdoctoral training at the University of Chicago. His work integrates computational methods with qualitative insights to study collective intelligence, the dynamics of ideas, and the co-construction of culture and cognition. Notable contributions include applying machine learning to analyze cultural meanings in text and modeling the evolution of scientific ideas. He has published in top journals like Science , American Sociological Review , and Proceedings of the National Academy of Sciences . Key awards include the NeurIPS 2021 Best Paper Award and the 2016 Star-Nelkin Paper Award. His research has been supported by the Institute for Advanced Study and the Santa Fe Institute. He is currently writing a book on knowledge as an emergent property of complex adaptive systems and leads initiatives to foster interdisciplinary collaboration in the study of intelligence.

Qingjin PengView profile

Professor

• Product Design and Manufacturing
• Digital Manufacturing
• Design Methodology
• +5 more

Qingjin Peng is a Professor in the Department of Mechanical Engineering at the University of Manitoba’s Price Faculty of Engineering. His work focuses on Product Design and Manufacturing, with expertise in Digital Manufacturing, Design Methodology, and Virtual Reality applications. He holds a PhD from the University of Birmingham (1998) and master’s and bachelor’s degrees from Xi’an Jiaotong University (1988, 1982). Education: PhD in Mechanical and Manufacturing Engineering, University of Birmingham (1998) M.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1988) B.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1982) Research Interests: His research spans Digital Manufacturing, Product Assembly/Disassembly Planning, CAD/CAM systems, and System Modeling. He pioneered methods integrating AI (e.g., Reinforcement Learning, Machine Learning) into product design and manufacturing processes. Notable contributions include optimizing Additive Manufacturing parameters using Taguchi methods and developing VR-based training systems for Coordinate Measuring Machines. Publications: Recent work emphasizes AI-driven innovation in manufacturing, with focus areas including sustainability in Additive Manufacturing, fault diagnosis of rotating machinery, and multi-agent systems for concept evaluation. His 2024 studies highlight advancements in product design through radical problem-solving frameworks, causal chain analysis, and genetic algorithm optimization. Advising & Grants: Currently no graduate student opportunities are listed, but his past collaborations include projects funded by national grants. Research teams often involve interdisciplinary partnerships, leveraging AI and data-driven approaches. Labs/Teams: His lab focuses on Digital Manufacturing and AI applications, though specific team names are not explicitly mentioned in the provided text.

Umar RazaView profile

Senior Lecturer

• Internet of Things
• Machine Learning
• Wireless Sensor Networks
• +9 more

Dr. Umar Raza serves as a Senior Lecturer in Networking, IoT and Smart Systems at Manchester Metropolitan University's Department of Engineering, where he concurrently manages the Cisco Network Academy. His academic career includes prior lecturing roles in Robotics and Computing at Staffordshire University, with research spanning industrial applications of emerging technologies. His educational credentials include a PhD in Wireless Sensor Networks from the University of Bradford (2014), Postgraduate Certificates in Research Methods and Professional Higher Education from Staffordshire University (2005, 2007), an MSc in Electronics and Computer Systems from Huddersfield University (1995), and a BSc in Engineering Electronics from DeMontfort University Leicester. Dr. Raza's research centers on Internet of Things applications integrated with Machine Learning across industrial, agricultural, and healthcare domains. Current projects address IoT/ML solutions for domicile care monitoring, cognitive impairment assistance, natural disaster early warning systems, and smart farming in Pakistan, with emphasis on practical implementations in real-world environments. His recent publications demonstrate a pronounced interdisciplinary trajectory, particularly at the IoT-Machine Learning nexus for healthcare innovation (cardiac diagnostics, autism support wearables) and agricultural technology. Significant contributions also appear in blockchain scalability, vehicular network security, and 5G propagation modeling, reflecting both theoretical rigor and industry-relevant problem solving. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) Dr. Raza actively supervises two MSc and two PhD candidates while leading multiple funded projects: Principal Investigator for KTP projects with Kindus Solutions (completed) and Trumeter Ltd Project Advisor for KTP with RAIT Ltd Lead researcher on NATO Science for Peace Security grant for natural disaster warning systems Principal Investigator for Ignite National Technology Fund smart farming initiative in Pakistan As Cisco Network Academy Manager, he drives industry-academia collaboration in networking education, developing curricula that integrate cutting-edge IoT and cybersecurity concepts while maintaining strong professional engagement through IEEE, IET, and BCS memberships.

Daniel Burbano LombanaView profile

Assistant Professor

• Dynamical Systems
• Control Theory
• Distributed Network Systems
• +6 more

Daniel Burbano Lombana serves as an Assistant Professor in the Department of Electrical Engineering at Rutgers University, specializing in dynamical systems, control theory, and distributed network systems with applications spanning swarm intelligence, collective animal behavior, and robot autonomy. Education: Ph.D. in Electrical Engineering, University of Naples Federico II, Italy (2015) M.S.c in Industrial Automation, National University of Colombia (2012) Research Interests: His work integrates theoretical control frameworks with real-world networked systems, focusing on data-driven behavioral modeling, network inference & learning mechanisms, and optimization of distributed control architectures. Key applications include bio-inspired swarm robotics and collective decision-making in multi-agent systems. Scientific Awards: No scientific awards were mentioned in the provided information. Advising and Grants: Information regarding current advisees, doctoral students, or research grants is not specified in the available text. Laboratory: He directs the Swarm Intelligence Lab at Rutgers University, which investigates emergent collective behavior in biological systems and develops scalable control algorithms for autonomous robotic swarms, as documented on the lab's official website.

• Artificial Intelligence
• Neural Networks
• Cellular Automata
• +3 more

Stefano Nichele is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, where he leads research in the Department of Computer Science with a focus on Artificial Intelligence. His laboratory investigates the intersection of biological and artificial intelligence through neural networks and cellular automata. Research Focus: Nichele's work bridges computational neuroscience, complex systems, and machine learning. Key themes include: Developing AI tools for neuroscience applications like dementia prediction Modeling neural network dynamics using biological and computational approaches Evolutionary algorithms and quantum computing hybrids Cellular automata frameworks for emergent intelligence Research Projects: AI-Mind: Developing AI-based dementia diagnostic tools FeLT: Human-machine-environment interactions in ecological contexts DeepCA: Biological-artificial intelligence integration SOCRATES: Efficient distributed data analysis Publications: His recent works (2024-2025) primarily explore neural network dynamics, cellular automata applications, and computational neuroscience models. The research demonstrates consistent focus on emergent behavior in complex systems and biological computation.

• Dynamical systems
• Mathematical biology
• Computational topology
• +4 more

Konstantin Mischaikow is a Professor of Mathematics at Rutgers, The State University of New Jersey, affiliated with the Department of Mathematics. His research focuses on dynamical systems, computational topology, mathematical biology, and machine learning applications in complex systems analysis. He specializes in rigorously analyzing nonlinear dynamics using topological methods, with emphasis on robotics control, ecological modeling, and parameterized ordinary differential equations. His work combines theoretical rigor with computational techniques, addressing challenges in global dynamics characterization, data-driven modeling, and topological data analysis. Notable contributions include the development of the DSGRN (Dynamic Signatures Generated by Regulatory Networks) database for systematically analyzing regulatory networks. His research often bridges pure mathematics with applied fields like robotics, systems biology, and granular materials physics. Recent studies focus on applying persistent homology to chaotic systems (e.g., Rayleigh-Bénard convection), identifying attractors via machine learning, and quantifying dynamics in high-dimensional spaces. He has collaborated extensively on projects involving granular media, synthetic biology circuits, and spatiotemporal chaos analysis. His academic contributions span over three decades, with a focus on merging topological tools with computational methods to solve real-world problems. While no specific awards are listed, his work has significantly impacted computational dynamics and topological data science.

Juan Andrade CettoView profile

Researcher

• Mobile Robotics
• Computer Vision
• Neuromorphic Systems
• +3 more

Juan Andrade Cetto is a Researcher at the Institute of Robotics and Industrial Informatics (IRI), part of the Spanish National Research Council (CSIC). He previously served as Director of IRI from 2014 to 2020. His expertise spans mobile robotics, computer vision, and neuromorphic systems. He holds a BSEE from CETYS University (1993), an MSEE from Purdue University (1995), and a PhD in Robotics from the Universitat Politècnica de Catalunya (UPC, 2003), recognized with the EURON Georges Giralt Best PhD Award in 2005. Research Interests: - Mobile Robotics and Autonomous Systems - Event-based Vision and Neuromorphic Sensors - Real-time Sensor Fusion and SLAM - Control Systems for Energy Conversion (e.g., PEM Fuel Cells) - Probabilistic Methods for Robotics and Computer Vision Recent Research Trends: His work emphasizes neuromorphic computing and event-based systems for robotics applications, including optical flow estimation, ego-motion tracking, and real-time SLAM. Collaborations include EU projects like ALLIES (AI for Sustainable Development Goals) and RAMON LLULL (AIRA Postdoctoral Programme). He leads the BOMBUS project (Perception and Navigation for Agile Robotics, 2024–present). Scientific Awards: EURON Georges Giralt Best PhD Award 2005 Grants & Leadership: Principal Investigator in multiple national/EU projects including BOMBUS (CSIC) and ALLIES (European). Advising: - Current Students: José Agustín Aguilar Plazaola (Fuel Cell Control), Raquel De Frutos Arranz (Event Camera-based Collision Avoidance) - Past Students: Not explicitly listed in provided text. Labs & Teams: Active in IRI’s Robotics and Industrial Informatics research groups, focusing on applied robotics and sensor systems.

Shuminoski TomislavView profile

Researcher

• Telecommunications
• Wireless Networks
• Cybersecurity
• +6 more

Shuminoski Tomislav is a researcher at the Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, North Macedonia. He holds a PhD in Electrical Engineering and Information Technologies (2016) with a dissertation on 5G QoS mechanisms and vertical multi-homing. His academic background includes a Master’s (2010) and Bachelor’s (2008) in Telecommunication, both with perfect grades. He specializes in telecommunications, cybersecurity, and next-generation mobile networks, focusing on QoS optimization, 5G infrastructure, and secure communication frameworks. Education: PhD, 2016: Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies (Dissertation: “QoS Mechanisms and Vertical multi-homing for 5G Mobile and Wireless Networks”) M.Sc., 2010: Telecommunication, Module: Communication and Information Technologies B.Sc., 2008: Telecommunication Research Interests: His work spans 5G networks, mobile edge computing, cybersecurity, IoT security, QoS provisioning, and blockchain applications in cloud computing. He emphasizes practical implementations of theoretical models in real-world scenarios, such as emergency response systems and ultra-reliable low-latency communication. Publications: Over 30 peer-reviewed articles since 2009, including innovative papers on 5G frameworks, secure communication platforms, and steganographic techniques. Recent work focuses on AI-driven resource allocation and quantum-resistant cryptography for IoT. Awards/Grants: No specific awards mentioned, but active in collaborative industry projects on network security and cloud infrastructure. Labs/Teams: Associated with the Telecommunications Institute at FEIT, contributing to research on wireless networks and critical infrastructure systems.

• Artificial Intelligence
• Neuroscience
• Robotics
• +3 more

Madeleine Bartlett is a Research Fellow at the University of Waterloo, affiliated with the Post Doctoral Scholars and Visitors groups. Her work bridges neuroscience and artificial intelligence, focusing on biologically plausible algorithms and their applications in robotics and human-robot interaction. Her research explores how neural mechanisms inspire computational models for reinforcement learning, action representation, and social signal processing. Key areas include integrating biological constraints into machine learning systems and developing robots capable of interpreting human social cues, particularly for therapeutic applications in autism spectrum disorder (ASD). Her publications highlight contributions to continuous action representations in basal ganglia models, biologically-based memory systems for reinforcement learning, and statistical rigor in human-robot interaction studies. She has also pioneered methods for estimating engagement in social HRI using novel neural architectures like Legendre memory units. While no formal awards or grants are listed, her work demonstrates significant innovation at the intersection of neuroscience-inspired AI and socially assistive robotics. She contributes to advancing robots as therapeutic tools through validated autonomous systems that mimic human guidance patterns observed in ASD therapy contexts.

• Microbiology
• Neurosurgery
• Oncology
• +5 more

Joseph N Paulson is an Adjunct Assistant Professor in the Department of Neurosurgery at the Yale School of Medicine. He holds a PhD in Applied Mathematics, Statistics, and Scientific Computation from the University of Maryland, College Park (2015). His research focuses on integrating computational methods with clinical and microbial genomic studies to address complex medical challenges. Key areas of investigation include microbial pathogenesis (e.g., Paenibacillus infections in pediatric hydrocephalus), cancer genomics (e.g., lymphoma prognosis and treatment response), and the development of bioinformatics tools for microbiome and transcriptomic analysis. Dr. Paulson’s work bridges disciplines such as computational biology, infectious diseases, and oncology. He leads collaborative projects analyzing microbial communities in clinical settings, including vaginal microbiome dynamics and bacterial virulence mechanisms. His computational tools, like mbQTL and the Network Zoo package, enable advanced microbiome QTL analysis and gene regulatory network modeling. He also contributes to clinical trials evaluating targeted therapies in breast and blood cancers, leveraging genomic signatures for personalized treatment strategies. Notable contributions include identifying Paenibacillus as a novel pathogen in Ugandan infants with post-infectious hydrocephalus and developing prognostic models for diffuse large B-cell lymphoma using imaging and genomic data. His research emphasizes global health applications, particularly in pediatric neurosurgery and infectious disease epidemiology.

Vladimir MironovView profile

Researcher

• Semantic Web Technologies
• Systems Biology
• Bioinformatics
• +3 more

Vladimir Mironov is a Senior Scientist at the Norwegian University of Science and Technology (NTNU), specializing in Semantic Web technologies and their application in systems biology and bioinformatics. His work focuses on knowledge graph integration, biological data modeling, and semantic network construction. Email: vladimir.mironov@ntnu.no Email: vladimir.n.moronov@gmail.com His research interests include: Development of Semantic Web tools for gene regulatory network analysis Ontology engineering for biological knowledge management Integration of chromosome location data with functional genomics Triple store evaluation for biological datasets Recent publications highlight his contributions to: Knowledge graph frameworks for gene regulation Network analysis tools in bioinformatics Ammonia-tolerant microbial communities in biotechnology Orthology prediction algorithms Semantic data modeling for biological datasets He has actively contributed to projects like DrugLogics and Crossover Research since 2008, developing semantic tools for biomedical research, including the Cytoscape BioGateway App and Cell Cycle Ontology (CCO).

• Neural Modeling
• Computational Neuroscience
• Neuroscience
• +3 more

Michael Hines is a Senior Research Scientist in the Department of Neuroscience at the Yale School of Medicine. His work focuses on developing and optimizing computational tools for neural modeling, notably the NEURON simulation environment. This software enables researchers to model cellular and network mechanisms involved in neurological disorders and therapeutic interventions. Hines holds a PhD from the University of Chicago (1975) and has contributed extensively to computational neuroscience through software development and interdisciplinary collaboration. Education: PhD, University of Chicago, 1975 MS, Physics, University of Chicago, 1972 BS, Physics, Michigan State University, 1970 His research interests revolve around advancing computational methods for simulating neural systems, including the development of dynamic clamp techniques and real-time interfacing with living cells. He has collaborated with institutions globally to enhance the scalability and accuracy of NEURON for supercomputing environments. Hines' work bridges neuroscience, mathematics, and computer science, addressing challenges in modeling complex neural networks and their applications to disease mechanisms. Publications highlight his contributions to neural simulation software, error analysis in computational models, and interdisciplinary tools like ModelDB and SenseLab. His research underscores the integration of experimental and computational approaches to advance understanding of neurological disorders and therapeutic strategies. Labs/Teams: Hines leads the development of the NEURON Simulation Environment and contributes to collaborative projects in computational neuroscience at Yale.

Guowei WeiView profile

Professor

• Machine Learning
• Topological Data Analysis
• Biochemistry
• +4 more

Guowei Wei is a MSU Research Foundation Professor in the Department of Mathematics and the Department of Biochemistry & Molecular Biology at Michigan State University. He holds a dual appointment in the BioMolecular Science Gateway and is affiliated with the Wei Lab. His research focuses on integrating topological data analysis, machine learning, and computational methods to address complex problems in biochemistry, biophysics, and materials science. Dr. Wei’s work spans multiple disciplines, including topological machine learning for protein-ligand interactions, persistent homology applications in molecular flexibility analysis, and AI-driven drug discovery. He develops novel algorithms combining algebraic topology with deep learning to model biological systems and material properties. His recent contributions include advancements in predicting protein binding affinities, analyzing viral mutations, and optimizing drug candidates for opioid use disorder and addiction treatments. He teaches advanced graduate courses (MTH 890/990) and maintains an active research lab. His methodologies bridge mathematical theory with practical applications in computational biology and healthcare, leveraging tools like the Poisson-Boltzmann equation and transformer networks for molecular analysis. Dr. Wei’s work emphasizes interdisciplinary approaches to solve challenges in drug design, pathogen evolution, and systems biology. Key contributions include topological deep learning frameworks for biomedical data, persistent Laplacian techniques for scarcely labeled data classification, and AI models for predicting mutation impacts on protein function. His research has significant implications for personalized medicine, pandemic preparedness, and understanding complex biological networks.

Samuel IsaacsonView profile

Professor

• Numerical Analysis
• Mathematical Biology
• Mathematical Physics
• +4 more

Samuel Isaacson is a Professor at Boston University's Department of Mathematics and Statistics, specializing in numerical analysis, mathematical biology, and mathematical physics. His research focuses on developing and analyzing numerical methods for stochastic reaction-diffusion models in cellular biology, with applications to cell signaling, T cell activation, and antibody-antigen interactions. He emphasizes rigorous coarse-grained modeling, unstructured mesh methods, and parameter inference from experimental data. Recent work includes advancements in reactive Langevin dynamics models, mean-field limits of particle systems, and molecular mechanisms underlying antibody efficacy. His interdisciplinary approach combines computational modeling, experimental collaboration, and mathematical theory to address biophysical questions. Notable contributions include the Catalyst software for reaction network modeling and studies on spatial effects in genetic circuits and chromatin structure. Isaacson's grants and collaborations span computational methods for stochastic systems, parameter estimation in biochemical networks, and the influence of cellular geometry on signaling. His lab focuses on bridging microscopic particle-level models with macroscopic biological observations, with applications in immunology and synthetic biology. Current projects explore the role of molecular 'reach' in antibody-virus interactions and the development of efficient simulation tools for complex biological systems.