Find Academic Supervisors

• Pharmacology
• Toxicology
• Oxidative Stress
• +6 more

Ranieri Rossi serves as Associate Professor of Pharmacology (GSD: 05/BIOS-11, SSD: BIOS-11/A) in the Department of Biotechnology, Chemistry and Pharmacy at the University of Siena, Italy. His academic responsibilities include teaching Pharmacology for the Master's degree in Health Biology (2025/2026) and Toxicology for the 5-year Pharmaceutical Chemistry and Technology program. Based in room C_02_113 at Via Aldo Moro 2, Siena, he maintains regular office hours on Tuesdays from 09:00-10:00 by prior arrangement. Professor Rossi's research centers on oxidative stress mechanisms and redox biology, with particular emphasis on glutathione metabolism in disease pathways. His work investigates NRF2 activation in triple-negative breast cancer survival, melatonin's cytoprotective effects against cadmium-induced male infertility, and eryptosis mechanisms in erythrocytes. This pharmacological research extends to translational applications in veterinary science and food technology, examining dietary supplements in livestock models for improving meat quality, athletic performance in horses, and gut health in piglets. Analysis of his 2023-2025 publications reveals a strong interdisciplinary trajectory spanning human pharmacology, cancer biology, and agricultural science. His work demonstrates consistent focus on thiol-based redox regulation across diverse biological systems—from molecular studies of protein glutathionylation to whole-animal models of oxidative stress. The integration of basic biochemical mechanisms with practical applications in nutrition and toxicology represents a distinctive feature of his scholarly contributions.

Patrik LennartssonView profile

Senior Lecturer

• Biotechnology
• Resource Recovery
• Fungal Fermentation
• +3 more

Patrik Lennartsson is a Senior Lecturer at the University of Borås, Faculty of Textiles, Engineering and Business, Department of Resource Recycling and Community Development. His research focuses on biotechnology within resource recovery, with particular emphasis on fungal fermentation for ethanol production, protein extraction, and waste valorization. He has been employed at the university since 2012, after completing his PhD in Bioscience at Chalmers University of Technology in collaboration with University of Borås, and undergraduate studies at the same institution. Current affiliation: University of Borås (since 2012) PhD: Chalmers University of Technology (collaborative) Undergraduate: University of Borås His research spans multiple biorefinery approaches using filamentous fungi (e.g., Neurospora intermedia , Aspergillus oryzae ) to convert agro-industrial wastes like bread, grape pomace, and wheat straw into ethanol, protein, pigments, and biocomposites. Key methods include submerged and solid-state fermentation, lignocellulose pretreatment, and bioreactor design. He supervises doctoral students Aishwarya Thyagarajan and Sharmeen Mustafa. Recent publications highlight his work on fungal biotechnology for waste valorization, including grape marc conversion to food ingredients, oyster mushroom residue applications, and biocomposite development from pectin-cellulose matrices. His team explores the integration of first- and second-generation bioethanol processes, fungal enzyme production, and the role of second messengers like cAMP in fungal morphology.

Lyndon BrownView profile

Associate Professor

• Control Theory
• Welding Control
• Adaptive Control
• +4 more

Lyndon Brown is an Associate Professor in the Department of Electrical and Computer Engineering at Western University , specializing in control theory with applications to welding systems and bioinformatics. His groundbreaking work in welding control has influenced industrial practices, particularly in automotive assembly. PhD - University of Illinois @ Urbana/Champaign (1996) MS - University of Illinois @ Urbana/Champaign (1996) BASc - University of Waterloo (1988) Research Interests focus on intermittent cancellation control , welding control systems , and modeling cellular signal transduction . His work in periodic disturbance cancellation has implications for vibration control in semiconductors and precision disk drive operations. In bioinformatics, he develops tools for genomic data analysis related to cancer and metabolic processes. Control Laboratory (Lab #215) investigates: Adaptive algorithms for welding electrode monitoring Open/closed loop strategies for predictable disturbance cancellation Signal transduction pathway modeling for high-throughput biological data Collaborations include serving on the Board of Directors of IRW, Inc., the automotive industry's spot welding research consortium.

• Optics
• Photonics
• Wireless Optical Communication
• +3 more

Serap Altay Arpali is an Associate Professor at Başkent University's Department of Electrical and Electronics Engineering. Her research focuses on optics, photonics, and biomedical imaging with structured illumination techniques. Education: PhD (2007) from Gazi University, MS (2003) from Çankaya University, BS (1999) from Dumlupınar University Research interests include laser beam propagation through turbulent media, optical wireless communication, and on-chip fluorescence imaging for medical diagnostics. Her recent work has emphasized underwater optical communication systems and biological tissue imaging. Scientific contributions span atmospheric optics, beam shaping, and biomedical device development. Key article trends examine beam propagation in turbulent environments, fluorescent imaging for high-throughput blood screening, and adaptive optics algorithms. Publications range from 2003 to 2022, with recent emphasis on practical biomedical applications. Current affiliations include: Department of Electrical and Electronics Engineering, Başkent University Postdoctoral Researcher at University of California Los Angeles (2012)

Stanislao GraziosoView profile

Assistant Professor

• Soft Robotics
• Nonlinear Modeling
• Bio-inspired Design
• +4 more

Stanislao Grazioso serves as Assistant Professor in the Department of Industrial Engineering at the University of Naples Federico II (UNINA), where he leads the BIOIC (Bioinspired soft robotic systems for cognitive production) project. His work focuses on transforming manufacturing through soft robotic technologies, with expertise spanning design, modeling, and control of compliant robotic systems for industrial and biomedical applications. Education: Bachelor of Science in Mechanical Engineering, University of Naples Federico II Master of Science in Mechanical Engineering, University of Naples Federico II Ph.D. in Industrial Engineering, University of Naples Federico II (2018), specializing in soft robotics Research Focus: Dr. Grazioso investigates the entire soft robot development lifecycle, emphasizing innovative design concepts for variable-stiffness actuators, advanced modeling using Cosserat rod theory and finite element methods, and human-robot interaction for safe collaboration. His research platforms include soft grippers for agrifood automation, continuum manipulators for confined spaces, and exoskeletons for industrial assistance. Key applications target nuclear remote handling (DTT fusion reactor), biomechanical analysis, and cognitive manufacturing systems. Publication Trends (2022-2024): Recent work demonstrates accelerating innovation in soft growing robots for remote sensing, bioinspired grippers with embedded adhesion mechanisms, and wearable biomechanics for athlete monitoring. His publications bridge theoretical modeling (e.g., screw theory for continuum robots) with industrial implementation, particularly in fusion reactor maintenance and agrifood automation, reflecting strong technology transfer orientation. Project Leadership: As BIOIC project lead, he drives integration of biological principles into manufacturing robotics. His involvement in DTT remote handling and NEFERTARI projects highlights expertise in nuclear robotics, while collaborations with clinicians demonstrate commitment to medical applications like soft exoskeletons for rehabilitation. Current efforts focus on virtual reality integration for ergonomic workplace assessment and EMG-based wearable systems.

• Spiking Neural Networks
• Machine Learning
• Computational Neuroscience
• +3 more

Nico Reeb is a Researcher at the Technical University of Munich , affiliated with the Chair for Robotics, Artificial Intelligence and Real-time Systems (I6). He joined the group in December 2020 and focuses on spiking neural networks and neuromorphic computing applications. Education : B.Sc. in Physics (2018) from Ludwig-Maximilians University M.Sc. in Physics (2020) from Ludwig-Maximilians University His research bridges machine learning and computational neuroscience , with applications in automotive radar processing , 3D point cloud reconstruction , and neuromorphic hardware . He has contributed to the KI-ASIC project for energy-efficient AI in autonomous vehicles. Recent publications highlight his work on spiking network optimization, phase-encoded signal processing, and sensor fusion techniques. His educational background includes a semester at the University of Queensland and research experience at the Max Planck Institute for Astrophysics . Key Projects : KI-ASIC (Neuromorphic AI for autonomous vehicles) ANTARES Neutrino Telescope (bioluminescence and neutrino detection) He teaches courses like the SoSe 2021 Seminar on Bio-inspired Data Processing , and his work integrates physics-based approaches with computer science challenges.

Nasreen AnjumView profile

Lecturer

• Cybersecurity
• Cyber-Biosecurity
• Artificial Intelligence
• +7 more

Dr. Nasreen Anjum is a Lecturer in Computer Science at the University of Portsmouth's Faculty of Technology, with a dual appointment at the Institute of Life Sciences and Healthcare. Her work bridges cybersecurity, artificial intelligence, and biomedical data protection, focusing on securing emerging technologies like IoT, digital twins, and next-generation sequencing systems. PhD in Computer Science, King's College London (2014-2019) Specialist in cyber-biosecurity and AI-driven network security Principal Investigator for UK-Saudi Challenge Fund (2024-2025) Her research spans four key areas: IoT Security : Lightweight cryptography, edge computing integration, and AI-based anomaly detection Cyber-Biosecurity : Genomic data protection, AI-bio convergence security, and NGS threat mitigation Digital Twins : Resilient architectures for healthcare and smart manufacturing applications Edge/Cloud Security : Adaptive access control and hybrid ecosystem protection Selected publications demonstrate interdisciplinary breadth: 2025: Foundational work on cyber-biosecurity in genomic sequencing 2024: Malware awareness education through gamification 2023: Cross-national cybercrime deterrence strategies 2022: IoT applications in pandemic monitoring Scientific recognition includes: Schlumberger Faculty for the Future Fellowship Best Paper Award nomination at IEEE WINCOM Fellow of the Higher Education Academy Actively supervising postgraduate research, Dr. Anjum welcomes PhD candidates in cybersecurity, AI, and technology ethics domains.

• Speech Recognition
• Voice Signal Processing
• Artificial Intelligence
• +4 more

Rita Singh is a Research Professor at the Language Technologies Institute within the School of Computer Science . Her research focuses on automated discovery, measurement, and learning of information from voice signals for voice intelligence. Contact information includes a phone number at 412-268-9859 . Developed generalizable speech processing algorithms (language-agnostic) and techniques for voice-based profiling of human parameters. Current work involves AI systems for genetic discovery, biomarker identification, and exploration of human physical state and psyche through voice. Research emphasizes scalable learning algorithms, voice signal analysis, and noise-resistant speech processing. She advises students such as Hira Dhamyal . Her methodologies integrate physics and bio-mechanics of voice production, enabling quantitative analysis of voice signals independent of semantic content.

Erik MeijeringView profile

Professor

• Biomedical Image Computing
• Bioimage Analysis
• Neuron Reconstruction
• +3 more

Erik Meijering is a Professor of Biomedical Image Computing at the School of Computer Science and Engineering of the University of New South Wales in Sydney, Australia. With a PhD and status as an IEEE Fellow (FIEEE), he leads research in bioimage analysis, neuron reconstruction, and cell tracking. His work bridges computer science, biomedical engineering, and neuroscience, with applications across multiple medical disciplines. Meijering's research focuses on developing computational methods for analyzing biological images, particularly in the areas of neuron tracing, cell tracking, and medical image segmentation. His work employs advanced techniques including deep learning, algorithm benchmarking, and large-scale image analysis. He has pioneered community efforts like the Cell Tracking Challenge and BigNeuron initiative, which have established standardized benchmarks for evaluating bioimage analysis algorithms. His research has significant implications for neuroscience, cancer diagnostics, and personalized medicine. His publication record shows a clear evolution from foundational work in image processing to cutting-edge applications of deep learning in bioimage analysis. Recent work emphasizes cross-domain generalizability of medical AI models, high-plexity microscopy techniques, and large-scale neuron reconstruction. Meijering's research consistently addresses the challenge of translating computational methods into practical biomedical applications. Distinguished Lecturer, IEEE Signal Processing Society (2022-2023) Guest Editor, Special Issue on Deep Learning in Biological Image and Signal Processing (2022) Chair, IEEE Technical Committee on Bio Imaging and Signal Processing (2018-2019) Associate Editor, IEEE Transactions on Medical Imaging (2004-2024) Meijering has secured substantial research funding from multiple sources including NHMRC, EU, NWO, and UNSW. His current projects include Revolutionizing Immunotherapy Response Prediction in Non-Small Cell Lung Cancer (2022-2025) and the Academic Start-Up Fund for Biomedical Image Computing (2019-2024). He has successfully led international collaborations through initiatives like BigNeuron and the Cell Tracking Challenge, which have brought together researchers from Australia, Europe, Asia, and the USA. His research group maintains active collaborations with numerous institutions worldwide, spanning neuroscience, medical imaging, and computational biology. Through his leadership of the BigNeuron project and Cell Tracking Challenge, Meijering has established international communities focused on advancing bioimage analysis. These initiatives have created standardized benchmarks and resources that accelerate progress in neuron reconstruction and cell tracking. His work has helped establish best practices for algorithm evaluation in bioimage analysis, significantly impacting how researchers develop and validate computational methods for biological imaging.

Gian Luca BarbruniView profile

Researcher

• Wireless Power Transfer
• Mixed-signal CMOS Design
• Low-power ICs
• +5 more

Gian Luca Barbruni is a Postdoctoral Researcher at the Bio/CMOS Interfaces Laboratory at École polytechnique fédérale de Lausanne (EPFL), Switzerland, where he focuses on designing novel circuital architectures for in-memory sensing and computing, and on drinkable µm-sized bioelectronics for enhanced brain imaging and precise diagnostics. He also served as a Doctoral Assistant at the Integrated Systems Laboratory (LSI1) at EPFL. Dr. Barbruni earned his Ph.D. in Microsystems and Microelectronics from EPFL in 2023, focusing on the design and development of innovative cortical visual prosthesis to revert blindness. Prior to that, he received his M.Sc. in Biomedical Instrumentation (2019) and B.Sc. in Biomedical Engineering (2017) from Politecnico di Torino, Italy. His research primarily centers on the intersection of low-power mixed-signal IC design, wireless power transfer, and microfabrication techniques for biomedical applications. His work spans brain-computer interfaces, vision restoration technologies, cancer diagnostics, and electrochemical sensing systems. Dr. Barbruni's approach integrates circuit design with electrode-tissue interface engineering to create miniaturized, wireless neural interfaces that overcome limitations of traditional neurostimulation systems. His research on frequency-switching inductive links has demonstrated significant improvements in efficiency and power delivery for large-scale neural interfaces. His publication record shows a clear progression toward increasingly sophisticated and miniaturized neural interface systems, with recent work focusing on in-memory sensing for cancer diagnostics and advanced microfabrication techniques for electrode integration. The consistent theme across his publications is the development of wireless, miniaturized systems that can operate within safety constraints while delivering sufficient power for neural stimulation and sensing applications. Dr. Barbruni has received several notable recognitions for his work: Best Student Paper Award in Electronics at IEEE MOCAST, 2022 in Bremen, Germany As a Principal Investigator, he has secured multiple competitive grants including MINT-CMOS (Enable 2022) and WIMOS-RES (Enable 2022). He serves as a reviewer for prestigious journals including IEEE Transactions of Biomedical Circuits and Systems and IEEE Sensors Journal, and as a TPC member for major conferences such as IEEE BioCAS, IEEE Sensors, IEEE MeMeA, and IEEE ICECS. Dr. Barbruni is actively involved with the Bio/CMOS Interfaces Laboratory in Neuchâtel, part of EPFL's School of Engineering, where he leads research on novel circuital architectures for in-memory sensing and computing. His work on 'Neural Dot' represents a significant advancement in fully integrated monolithic chips for neural interfaces, featuring wafer-level CMOS-compatible post-processing techniques for electrode integration that address traditional challenges in miniaturized neural implant design.

Charlotte FrenkelView profile

Assistant Professor

• Neuromorphic Engineering
• AI Processor Design
• Digital and Mixed-Signal IC Design
• +5 more

Charlotte Frenkel is a Tenure-Track Assistant Professor in the Microelectronics Department at Delft University of Technology (TU Delft), where she leads research in neuromorphic engineering and low-power AI hardware. Her work bridges the gap between biological intelligence and artificial neural networks, focusing on energy-efficient computing at the edge. Dr. Frenkel's research spans digital and mixed-signal IC design, computer architecture, learning algorithms, and neuroscience. She directs the Cognitive Sensor Nodes and Systems (CogSys) lab, which develops neuromorphic processors like ODIN, MorphIC, SPOON, and ReckOn that demonstrate competitive advantages over conventional neural network accelerators. Her publications reveal a strong focus on spiking neural networks, event-based processing, and on-chip learning. Key trends include developing hardware that leverages sparsity for energy efficiency, creating bio-inspired learning algorithms that solve weight transport and update locking problems, and establishing frameworks for benchmarking neuromorphic systems through initiatives like NeuroBench. Scientific Awards: IBM Innovation Award 2021 Nokia Bell Labs Scientific Award 2021 IEEE ISCAS 2020 Best Paper Award NEUROTECH/NICE Best Early Researcher Presentation 2021 AiNed Fellowship Grant Dr. Frenkel is actively expanding her research group through PhD and postdoc positions. She serves as Associate Editor for IEEE Transactions on Biomedical Circuits and Systems and Frontiers in Neuroscience, and has held numerous leadership roles in conference organization including Program Chair for tinyML Research Symposium 2024 and Neuro-Inspired Computational Elements conference 2023-2024. Her service includes extensive reviewing activities for top IEEE journals and conferences in her field.

Babak ShadganView profile

Assistant Professor

• Bio-sensing
• Clinical Biophotonics
• Musculoskeletal and Sports Medicine
• +3 more

Dr. Babak Shadgan is an Assistant Professor in the Department of Orthopaedics, Faculty of Medicine, and Lecturer at the School of Biomedical Engineering, all at the University of British Columbia. He also holds research roles at the Vancouver Prostate Centre and Vancouver General Hospital, with affiliations to ICORD, the Peter Wall Institute, and the Canadian National Transplant Research Program. Education: M.D., Tehran Azad University M.Sc. in Sports Medicine, University of London Ph.D. in Muscle Biophysics, University of British Columbia Fellowship in Diffused Optical Tomography, MIT/Harvard Postdoctoral Fellowship in Clinical Biophotonics, University of British Columbia Dr. Shadgan’s research focuses on clinical biophotonics and translational applications of near-infrared spectroscopy (NIRS) for monitoring tissue oxygenation and hemodynamics in acute spinal cord injury, reconstructive surgery, and sports medicine. His work spans neuroprotection, musculoskeletal health, and non-invasive diagnostics. Recent articles highlight NIRS for spinal cord monitoring, muscle spasm analysis, and cardiac arrest detection. His lab has also explored blood flow restriction exercise for spinal cord injury rehabilitation and wearable sensor design. Scientific Awards: MSFHR Scholar Award (2018) Peter Wall Institute Wall Solutions Award (2020) US DoD Clinical Trial Award (2021) MITACS Postdoctoral Fellowship (2020) Dr. Shadgan supervises a multidisciplinary team in his clinical biophotonics lab, including PhD and Master’s students. His grants include funding from the Canadian Institutes of Health Research, the Michael Smith Foundation, and the US Department of Defense.

Jialie ShenView profile

Professor

• Computer Vision
• Deep Learning
• Data Science
• +3 more

Professor Jialie Shen is a leading academic in computer vision and machine learning at City St George's, University of London . With a PhD from UNSW Sydney (2006) , he transitioned from a Reader at Queen's University Belfast (2018–2022) to his current role as a full Professor since 2022. His research spans artificial intelligence , focusing on computer vision , deep learning , and data science , with over 100 publications in top journals like IEEE Transactions on Multimedia , Pattern Recognition , and ACM TOMM . Education: PhD in Computer Science, UNSW Sydney, 2006 Research Focus: Computer Vision Deep Learning Data Science Machine Learning Scientific Awards: Lee Foundation Fellowship for Research Excellence Singapore Microsoft Mobile Plus Cloud Computing Theme Research Program Award Best Reviewer Awards (IP&M 2019, ACM Multimedia 2020) Test of Time Reviewer Award (IP&M 2022) Associate Editor with Honourable Mention (Pattern Recognition 2023/24) Editorial Roles: Associate Editor, IEEE Transactions on Big Data (2025–present) Associate Editor, IEEE Transactions on Circuits and Systems for Video Technology (2022–present) Editorial Board Member, ACM TOMM and others Publications: 15 most recent articles (2025–2021) across biomedical engineering , AI , and graph representation learning

Mark DickmanView profile

Professor

• Nucleic Acid Therapeutics
• Oligonucleotide Therapeutics
• mRNA Vaccines/Therapeutics
• +7 more

Professor Mark Dickman, affiliated with the School of Chemical, Materials and Biological Engineering at the University of Sheffield, is a leading expert in Bioanalytical Science and Engineering . His research focuses on developing advanced analytical techniques for characterizing nucleic acid therapeutics, particularly oligonucleotide and mRNA-based drugs. Industry collaborations include ThermoFisher, AstraZeneca, Syngenta, and RedShift Bio. Academic Background : First Class BSc (Biochemistry/Chemistry), PhD at the Krebs Institute (University of Sheffield). Professional History : Research Scientist at Transgenomic Ltd (1993-2003), then academic staff at the University of Sheffield. Key research areas include: Bioseparations (HPLC, 2D-LC, ion-pair chromatography) Biological Mass Spectrometry for RNA and protein analysis Nucleic Acid Therapeutics (mRNA vaccines, oligonucleotide drugs) CRISPR-Cas Systems and RNA interference His publications (over 40) demonstrate expertise in chromatographic methods, bacterial cell wall analysis, and epigenetic studies. Recent work explores: mRNA critical quality attribute monitoring RNA biocontrol purification Peptidoglycan structural characterization Mass spectrometry-based RNA sequence mapping Professional memberships include the Royal Society of Chemistry and American Society for Mass Spectrometry . He contributes to teaching through the CPE 240 Engineering From Living Systems course.

• Synthetic Biology
• Microbiome Engineering
• Drug Delivery Systems
• +6 more

Nikhil Aggarwal is a Senior Research Fellow at the Yong Loo Lin School of Medicine, National University of Singapore, affiliated with the Dean-Med Synthetic Biology Translational Research Programme. His work focuses on synthetic biology applications for drug delivery, microbiome engineering, and sustainable bioproduction. University: National University of Singapore School: Yong Loo Lin School of Medicine Rank: Senior Research Fellow Email: nikhil01@nus.edu.sg His research spans multiple domains, including: Development of engineered commensal bacteria for nose-to-brain drug delivery and obesity treatment Design of tumor-targeting probiotics using prodrug conjugation Advancements in waste feedstock utilization for bio-based economies Innovations in organ-on-chip technology for biomedical research CRISPR-based nucleic acid detection systems (FELICX) Microbiome-host interactions in metabolic and inflammatory diseases Publications highlight significant contributions to synthetic biology, with 13 years of continuous research output. His work bridges basic science and translational applications, particularly in microbial engineering for therapeutic and diagnostic purposes.