Find Academic Supervisors

• Real-Time Embedded Systems
• Real-Time Scheduling
• Schedulability Analysis for single-core, multi-core, and networked systems
• +6 more

Dr. Robert Davis is a Reader at the Real-Time Systems Research Group within the Department of Computer Science at the University of York, UK. He is renowned for his work in real-time embedded systems, particularly in scheduling theory and its applications to mixed-criticality and multi-core platforms. Current role: Reader, University of York Research group: Real-Time Systems Research Group Contact: Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK Education DPhil in Computer Science, University of York (1995) Research Interests include real-time scheduling, mixed-criticality systems, cache-related preemption delays (CRPD), probabilistic real-time systems, and scheduling IDK classifiers. His work addresses the integration of schedulability analysis with worst-case execution time (WCET) analysis, focusing on systems with single-core, multi-core, and networked architectures. Article Trends show a focus on IDK classifiers, mixed-criticality systems, and multi-core schedulability. His recent work explores probabilistic timing, adaptive scheduling, and industry-driven real-time practices, often bridging theoretical analysis with industrial applications (e.g., automotive, avionics). Scientific Awards Outstanding paper at RTNS 2016, RTNS 2015, ECRTS 2015 Best paper at RTNS 2014, ECRTS 2011, RTCSA 2013 Influential paper award at RTNS 2017 Advising and Grants involve collaborations on European projects like HI-CLASS and MCC, and he has received funding from EPSRC and Inria International Chair. He co-founded three spin-out companies for technology transfer. Labs and Teams include the Real-Time Systems Research Group at York and collaborations with international institutions (e.g., Inria, France; NJIT, USA).

• Photonics
• Optics
• Laser Technology
• +3 more

Dr. Michael Duparre is a Researcher at the Leibniz Institute of Photonic Technology, affiliated with the Fiber Photonics department. He is part of the Active Fiber Modules work group, focusing on advanced photonics and laser-matter interactions. His work involves designing binary diffractive optical elements for generating tailored 3D intensity distributions in optical trapping applications. In 2021, he co-authored research on vector optical bottle beams, demonstrating how polarization states can dynamically control laser radiation for applications in laser trapping and surface structuring. His contributions highlight expertise in paraxial optics and e-beam lithography manufacturing. Duparre’s technical work bridges theoretical modeling and experimental validation, with implications for laser manipulation technologies. His laboratory group, Active Fiber Modules, explores innovative photonic solutions for precision engineering tasks.

Szilvia SzeghalmyView profile

Lecturer

• Digital Image Processing
• Imbalanced Learning
• Machine Learning
• +3 more

Dr. Szilvia Szeghalmy is a Lecturer at the University of Debrecen, Faculty of Informatics, Department of Computer Science. She can be contacted at +36 52 512 900 75116 or via email at szeghalmy.szilvia@inf.unideb.hu, located in room I116, floor 1 of the Faculty of Informatics building. Her research focuses on: Digital Image Processing Imbalanced Learning Recent publications highlight trends in: Imbalanced data handling and noise filtering techniques (2024) Medical imaging applications (2024) Driver-assistance systems and traffic sign recognition (2018-2019) Human-computer interaction using sensors (2013-2014)

• Cognitive Modeling
• Machine Learning
• Control Theory
• +3 more

Dr. Clemens Bombach is a postdoctoral researcher at the Professorship of Predictive Analytics within the School of Human Sciences and Technology (HSW) at Chemnitz University of Technology (TU Chemnitz). His work bridges cognitive modeling, machine learning, and mathematical control theory to predict individual human behavior and analyze complex systems. Education: PhD in Mathematics (Dr. rer. nat.) from TU Chemnitz, 2024 Studies in Mathematics at TU Chemnitz, 2009–2016 Research Interests: Dr. Bombach focuses on cognitive modeling and machine learning for behavioral prediction, alongside mathematical control theory and inverse problems. His current work emphasizes observability estimates for parabolic and non-autonomous systems, leveraging L p -spaces and functional analysis. Publication Trends: His publications primarily address control theory, inverse problems, and differential equations, with recent contributions to observability theorems, singular potentials, and applications of harmonic analysis. Collaborations with colleagues like M. Tautenhahn and F. Gabel highlight his interdisciplinary approach. Teaching: He contributes to exercises in mathematics, including courses on partial differential equations, complex analysis, and higher mathematics for engineering, computer science, and physics students. Projects: Currently leads the DfG-Project "PREDIR" (2025–2027), developing systems, algorithms, and cognitive models to predict human inference processes.

Alberta BergamoView profile

Associate Professor

• Oncological Pharmacology
• Tumor Microenvironment
• Metastasis Research
• +3 more

Alberta Bergamo is an Associate Professor of Pharmacology at the University of Trieste, Department of Life Sciences, where she leads the research group focused on Oncological Pharmacology and Tumor Microenvironment in Solid Tumor Metastases. Her research spans both basic and translational approaches to pharmacological treatment of solid tumor metastases, with particular focus on colorectal cancer liver metastases. Professor Bergamo earned her degree in Pharmaceutical Chemistry and Technology from the University of Trieste and completed a second-level master's degree in Regulatory Disciplines and Drug Policy from Unitelma Sapienza University of Rome. She began her research career at the Callerio Foundation Onlus in Trieste and completed additional training at the OIH INSERM U463 laboratory in Nantes, France. From 1999 to 2019, she worked as a researcher at the Callerio Foundation Onlus before becoming an Associate Professor at the University of Trieste in January 2020. Her primary research interests focus on the mechanisms of tumor metastasis and their pharmacological control. Using both basic and translational approaches, her work investigates alternative in vitro models that simulate the complex tumor microenvironment to study host-tumor interactions, identify novel therapeutic targets, and characterize pharmacological action at cellular and molecular levels. A significant portion of her research explores the pharmacological properties of metal-based complexes with unique anti-metastatic activities, particularly contributing to the preclinical characterization of NAMI-A, the first ruthenium complex tested in humans. Her work also extends to pharmacological approaches for SARS-CoV-2 and COVID-19, studying the influence of cardiovascular drugs and bioactive natural substances on preventing SARS-CoV-2 infection risk. Analysis of Professor Bergamo's recent publications reveals a strong focus on tumor metastasis mechanisms, particularly in colorectal cancer, and the development of metal-based anticancer drugs. Her work spans pharmacological approaches to SARS-CoV-2, tumor microenvironment interactions, integrin signaling in metastasis, and RNA sequencing analysis of drug effects. The research demonstrates a consistent theme of investigating how tumor cells interact with their microenvironment and identifying pharmacological interventions that can disrupt metastatic processes. Professor Bergamo is an active member of the scientific community, serving as a member of the Italian Society of Pharmacology (working groups in Oncological Pharmacology and Immunopharmacology), editor and scientific publisher of SIF Magazine, and a member of the Italian Society of Chemotherapy. As a research leader, Professor Bergamo has managed significant projects including the ESTEEM project (Predicting the influence of cardiovascular drugs and bioactive natural substances on the prevention of SARS-CoV-2 infection risk using a simple experimental model). Her laboratory focuses on understanding the biology of colorectal cancer liver metastases, with particular attention to the influence of the microenvironment on integrin-ECM interactions, investigating the role of miRNA in tumor malignancy, and identifying miRNA involved in CRC progression to the liver. Professor Bergamo leads the Oncological Pharmacology research group at the University of Trieste, which studies solid tumor metastases with a focus on the tumor microenvironment. Her team investigates how soluble factors released by hepatocytes, combined with specific extracellular matrix proteins, modulate integrin activity and influence cancer cell adhesion to the liver matrix. The group also explores how deregulation of miRNA contributes to neoplastic development and metastatic progression, controlling the expression levels of many cellular adhesion molecules including integrins.

• Computational Thinking
• Mathematics Education
• Educational Technology
• +7 more

Dr. Manon Laurent is a Lecturer in Educational and Training Sciences at the University of Clermont-Auvergne, France, affiliated with the ACTé Laboratory (Activity, Knowledge, Transmission, Education). She previously served as an Associate Researcher at LaRAC, Université Grenoble Alpes, and as a Trainer at Inspé de Grenoble. Her educational background includes a Doctorate in Educational Sciences from Université Grenoble Alpes (2022), with her thesis focusing on computational thinking as a vector for learning mathematics in elementary school. Dr. Laurent's research centers on the intersection of educational technology and mathematics education, particularly examining how computational thinking and programming activities impact primary school mathematics learning. Her work investigates conative variables such as mathematical anxiety, self-determined motivation, and sense of competence in learning environments. She has conducted several randomized controlled trials to evaluate the effectiveness of digital learning tools in educational settings. Her publication record shows a consistent focus on experimental approaches to educational technology, with particular emphasis on the relationship between computational thinking and mathematics acquisition in primary education. Her research methodology typically involves large-scale randomized trials with thousands of students across multiple schools. Dr. Laurent has been actively involved in research collaborations through the EXPIRE project (Experimenting with Computational Thinking for Student Success), which was funded by the French e-Fran Future Investment Program. She has also participated in organizing academic conferences and workshops related to social psychology and education.

Mee Ling MunierView profile

Senior Lecturer

• Immunology
• Cellular Immunology
• Vaccination
• +6 more

Dr. Mee Ling Munier is a Senior Lecturer and Group Leader at the Kirby Institute within the University of New South Wales (UNSW) . Her work focuses on characterizing immune responses to vaccination and infection , particularly in human lymph nodes and peripheral blood. She leads the Vaccine Immunogenomics group within the Immunovirology and Pathogenesis Program (IVPP) , utilizing fine needle biopsy (FNB) techniques to study post-vaccination immune dynamics. UNSW Medicine & Health Kirby Institute Vaccine Immunogenomics Group

Eric JanView profile

Professor

• Translational Control
• Virology
• Gene Expression Regulation
• +5 more

Eric Jan is a Professor in the Department of Biochemistry and Molecular Biology at the University of British Columbia's Faculty of Medicine. His research focuses on translational control mechanisms during cellular stress and viral infections, with a particular emphasis on internal ribosome entry sites (IRES) and their role in hijacking host translation machinery. He holds prestigious awards including the Michael Smith Scholar, CIHR New Investigator, and Killam Excellence for Mentoring Education, reflecting his contributions to both research and mentorship. Dr. Jan's lab explores how viruses like Cricket Paralysis Virus (CrPV) and SARS-CoV-2 manipulate ribosomes, and how these insights can be applied to mRNA-lipid nanoparticle (LNP) therapeutic technologies. Education: BSc (Queen’s University, 1994), PhD (Northwestern University, 1999), Postdoctoral Fellowship (Stanford University, 1999-2005). Research Interests: Dr. Jan investigates the noncanonical translation mechanisms of viruses, particularly how IRES elements bypass host initiation factors to drive protein synthesis. His work bridges fundamental gene expression studies with applications in antiviral therapies, diabetes research (via ER stress and eIF2A), and mRNA-LNP delivery systems. Recent projects include mining virome data for novel RNA elements, engineering platelets for therapeutic uses, and developing extrahepatic mRNA delivery strategies. Publications: His recent work (2023-2025) spans virome mining for IRES elements, mRNA-LNP formulation optimization, ER stress in beta cells, and viral protease impacts on host pathways. Key journals include Nature Communications , PLoS Pathogens , and Science Advances . Scientific Awards: Michael Smith Scholar CIHR New Investigator Killam Excellence for Mentoring Education Students and Lab Members: Current lab members include postdoctoral fellows, PhD and MSc students, and technicians working on viral translation, mRNA-LNP applications, and stress granule dynamics. His mentees have secured NSERC fellowships and co-supervised roles. Labs and Collaborations: The Jan Lab collaborates extensively on mRNA-LNP projects (e.g., with Cullis, Kastrup) and viral pathogenesis studies. They employ biochemical, virological, and high-throughput sequencing approaches to dissect host-virus interactions and RNA translation mechanisms.

Bo ChenView profile

Assistant Professor

• Spinal Cord Injury Recovery
• Neurobiology
• Neural Circuits
• +3 more

Bo Chen, PhD, is an Assistant Professor in the Department of Neurobiology at the University of Texas Medical Branch (UTMB). Holding a doctorate in Applied Biomedical Science from the University of Reading, his research focuses on developing innovative strategies for spinal cord injury (SCI) recovery through molecular neuroscience and translational approaches. B.Sc. Biological Science - University of Birmingham, U.K. M.Sc. Biomedical Science - Durham University, U.K. Ph.D. Applied Biomedical Science - University of Reading, U.K. Research spans two primary domains: (1) Developing multidisciplinary methods to examine pathological changes in spinal neurons and circuits post-SCI, and (2) Creating translational treatments to enhance brain-spinal cord connectivity for functional recovery. His work employs advanced techniques including neuronal visualization, behavioral analysis, and molecular interventions. Publications demonstrate expertise in neural plasticity, neuroregeneration, and neurotherapeutic development. Recent studies focus on KCC2 modulation, propriospinal neuron engineering, and excitatory neuron swelling reduction. Methodological innovations include optical clearing, light sheet microscopy, and AI-driven image analysis. Collaborative efforts involve Harvard Medical School, Boston Children's Hospital, and international institutions. Research methodologies combine molecular neuroscience, behavioral quantification, and neuroengineering approaches to understand and repair injured spinal circuits.

• Electromechanical Systems
• Electric Drives
• Automation of Technological Processes
• +3 more

Sergey Georgievich Deev is a Senior Lecturer at the Department of Electric Drive and Automation of Industrial Installations within the Electrotechnical Faculty of Zaporizhzhia Polytechnic National University. With academic activity spanning from 1997 to at least 2015, he has established himself as a specialist in electromechanical systems and electric drive technologies. Zaporizhzhia State Technical University, 1994 (Electromechanical Engineer) Deev's research centers on electromechanical systems and automation of technological processes, with particular expertise in synchronous electric drives, energy efficiency optimization, and robust control systems. His work bridges theoretical electrical engineering with practical industrial applications across multiple domains including automotive systems, aerospace technology, and radar equipment. His publication record from 2010-2015 reveals a strong focus on energy-saving control algorithms for various electric drive applications, with research extending to hybrid vehicles, spacecraft attitude control, and industrial automation systems. The work demonstrates consistent methodological approaches to improving efficiency and reliability in electromechanical systems. 2000 – Recognition for the 100th anniversary of ZNTU and monetary reward 2005 – Recognition for Energy Day 2008 – Certificate of Honor from ZNTU Deev has actively participated in numerous international conferences focused on electric drives and automation, including the Problems of Automated Electric Drive series held across Ukraine. His scholarly presence is maintained through ORCID, Scopus, Web of Science, and Google Scholar profiles. Based at Zaporizhzhia Polytechnic National University (Zhukovsky St., 64, room 527, building 5), Deev teaches courses related to electrical equipment installation and commissioning, contributing to the education of future electromechanical engineers.

Tatiana PodladchikovaView profile

Associate Professor

• Solar Physics
• Space Weather
• Coronal Mass Ejections
• +5 more

Dr. Tatiana Podladchikova serves as Associate Professor, Director of the Center for Digital Engineering, and Director of the Engineering Systems Master's Program at Skolkovo Institute of Science and Technology (Skoltech). Her work bridges fundamental solar physics research with operational space weather services to protect critical infrastructure including satellites, aviation systems, and power grids from solar-induced geomagnetic disturbances. Her research centers on solar activity dynamics—particularly solar cycles, eruptive flares, coronal mass ejections (CMEs), and high-speed solar wind streams—and their terrestrial impacts. She develops predictive models for space weather phenomena using advanced data analysis techniques, with recent work integrating deep learning frameworks for solar observation translation and physics-informed neural networks for magnetic field extrapolation. Her research directly addresses infrastructure vulnerabilities during extreme space weather events through operational forecasting systems. Dr. Podladchikova's publications demonstrate a clear evolution toward AI-enhanced space weather prediction, combining multi-point solar observations (including Solar Orbiter mission data), coronal dimming analysis for early CME detection, and innovative applications like the DIRECD method for real-time propagation tracking. Her work spans fundamental solar physics, engineering applications for satellite systems, and environmental monitoring techniques. Her scientific recognition includes: International Alexander Chizhevsky Medal for Space Weather and Space Climate (2015, Belgium) Skoltech Best Professor Award (2018-2020) Outstanding Achievements in Education and Research (2022-2023) As an educator, she directs Skoltech's Engineering Systems Master's Program, leads the Space Weather Laboratory, and teaches Experimental Data Analysis and Data Analysis for Space Weather courses. Her leadership extends to public outreach initiatives including science communication events like 'Mysteries of the Sun: Space Weather of the 21st Century' lectures. She heads the Skoltech Space Weather Laboratory where her team develops operational forecasting tools, analyzes multi-spacecraft solar observations, and advances CubeSat technologies including the Skoltech-F mission. Current projects focus on real-time geomagnetic storm prediction systems and AI-driven translation of historical solar datasets to maintain observational continuity.

Yuemei DongView profile

Researcher

• Vector-borne diseases
• Host-pathogen interactions
• Mosquito innate immunity
• +7 more

Yuemei Dong, PhD , is a Senior Research Associate at the Bloomberg School of Public Health, Johns Hopkins University, with primary affiliations in the Department of Molecular Microbiology and Immunology and the Johns Hopkins Malaria Research Institute. Her work focuses on molecular mechanisms of pathogen-mosquito interactions, leveraging CRISPR/Cas9 genome editing Gene drive technology Mosquito microbiota manipulation Immune pathway crosstalk Vector control strategies to combat malaria and arboviruses. Dr. Dong's research interests center on Mosquito innate immunity Host-pathogen molecular interactions Transgenic tools for disease suppression Microbiota-based interventions Gut-leaky transmission dynamics . She pioneered foundational work on mosquito microbiota's role in malaria defense, including her 2009 PLoS Pathogens study that catalyzed global research in this field. The 15 most recent articles highlight her contributions to Mosquito immune pathways Gene drive for malaria control Microbiota-pathogen interactions CRISPR/Cas9 applications Diagnostic tools for arboviruses Leaky gut-malaria links . Her work spans molecular discovery to field-deployable solutions like Chromobacterium biopesticides and aptasensor diagnostics. Dr. Dong's collaborations with malaria-endemic countries emphasize Community-based vector control Translational research Global health equity , aligning with integrated strategies for sustainable disease prevention.

Miracle AmadiView profile

Research Fellow

• Computational Epidemiology
• Mathematical Modeling of Infectious Diseases
• Bayesian Statistics
• +3 more

Miracle Amadi is a Postdoctoral Researcher (Research Fellow) in the Department of Computational Engineering at the School of Engineering Sciences, Lappeenranta-Lahti University of Technology (LUT University), Finland. Amadi's research focuses on developing and applying computational and statistical models to study infectious disease dynamics, including malaria, HIV, dengue, and COVID-19, with a particular emphasis on the impact of environmental factors and intervention strategies. Amadi's research interests span computational epidemiology, mathematical modeling of infectious diseases, Bayesian statistics, and public health. Their work integrates advanced statistical techniques such as Bayesian model selection, parameter estimation, and uncertainty quantification with epidemiological data to address critical public health challenges. Recent projects have examined the relationship between meteorological factors and disease incidence, the evolution of insecticide resistance in malaria vectors, and the effectiveness of non-pharmaceutical interventions during the COVID-19 pandemic. Analysis of Amadi's recent publications (2018-2025) reveals a strong trend in applying hybrid modeling approaches, including agent-based and spatio-temporal models, to complex epidemiological problems. A significant portion of the work focuses on malaria, with additional contributions to HIV, dengue, lumpy skin disease, and COVID-19. The research consistently emphasizes the role of environmental and climatic factors in disease transmission and the evaluation of control strategies, demonstrating a commitment to data-driven public health decision-making.

Subash AcharyaView profile

Assistant Professor

• Magnetorheological Fluids
• Semiactive Control Systems
• MR Dampers
• +4 more

Subash Acharya serves as an Assistant Professor - Senior Scale in the Department of Mechanical & Industrial Engineering at Manipal Institute of Technology, Manipal Academy of Higher Education, India. He has been affiliated with MIT Manipal since 2009, progressing from Assistant Professor (2009-2014) to his current senior-scale position. His academic credentials include a BE, MTech, and PhD. His research spans Magnetorheological Fluid Applications, Semiactive Control Systems (including MR Dampers and Brakes), Condition Monitoring, and advanced composite material processing. Recent work focuses on drilling optimization in hybrid nano-composites using machine learning, natural fiber composite analysis, and automotive component design. His fingerprint analysis reveals strong activity in fluids, brakes, drilling applications, and hybrid material systems. Publications from 2024-2025 demonstrate interdisciplinary integration of mechanical engineering with sustainable technology, including ultrasonic mosquito repellence strategies and smart fluid synthesis. His work appears in journals like the International Journal of Sustainable Engineering and Journal of Manufacturing and Materials Processing, reflecting practical applications in manufacturing and public health. No scientific awards were documented in the source material. Information regarding student supervision, research grants, or laboratory infrastructure was not provided in the available text.

Dinesh Acharya UView profile

Professor

• Machine Learning
• Data Mining
• Image Processing
• +4 more

Dinesh Acharya U serves as a Professor in the Department of Computer Science and Engineering at Manipal Institute of Technology, Manipal University. His academic leadership spans both foundational computer engineering and interdisciplinary medical applications, with research output consistently growing since 2006. Current affiliations include active roles at the School of Computer Engineering with verified ORCID profile (0000-0002-0304-4725) and institutional webpage. Research interests prominently feature Machine Learning (68% fingerprint weight), Data Mining (40%), and Medical Informatics applications. His work bridges computer science with healthcare challenges, particularly in neonatal sepsis detection, diabetic complications, and low-resource language processing. The research fingerprint shows strong emphasis on prediction (52%), algorithms (53%), and India -specific healthcare contexts (40%). Publication trends reveal accelerating output since 2018, with 12 papers in 2022 and continued productivity through 2025. Recent work demonstrates interdisciplinary convergence, particularly in Medical AI (neonatal sepsis, diabetic kidney disease) Natural language processing for low-resource languages Transformer-based architectures across domains Notable patterns include increasing clinical collaborations and emphasis on practical implementation tools. Professional recognition includes an h-index of 13 with 596 citations across 71 research outputs. Key distinctions: Scopus profile verification ORCID registration Multi-institutional collaborations evident in co-authorship Academic supervision and grant activity cannot be confirmed from available data, though 15+ recent publications suggest active research teams. Current projects appear focused on Medical diagnostic tool development Low-resource language technology Clinical decision support systems with evident laboratory infrastructure supporting computational healthcare research.