Find Academic Supervisors

Stelios TsafarakisView profile

Associate Professor

• Consumer buying behavior
• Product line design and optimization
• Discrete choice models
• +6 more

Stelios Tsafarakis is an Associate Professor at the School of Production Engineering and Management of the Technical University of Crete . His academic journey includes a PhD in Scientific Marketing from the Technical University of Crete and Université Paris Dauphine (2010), a Postgraduate Diploma in Production Engineering and Management (2007), an MSc in Management Information Systems from the University of Southampton (2002), and a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (2000). PhD: Technical University of Crete & Université Paris Dauphine (2010) Postgraduate Diploma: School of Production Engineering and Management, TUC (2007) MSc: Management Information Systems, University of Southampton (2002) Diploma: Electrical & Computer Engineering, NTUA (2000) His research focuses on consumer buying behavior , product line design and optimization , discrete choice models , and the application of evolutionary algorithms to market structure analysis. His work bridges marketing theory with computational methods, emphasizing nature-inspired optimization techniques for practical business solutions. Recent publications highlight trends in digital platform analytics , ICT integration in education , and bio-inspired optimization algorithms . His studies explore gamification in tourism, automated learner grouping via particle swarm optimization, and fuzzy systems for product line design. Stelios contributes to the Organization & Administration Laboratory at TUC. His collaboration hours are available by arrangement , and he can be reached at office D5.009 (phone: +30 28210 37160).

Carlos PonceView profile

Assistant Professor

• Neurobiology
• Vision Science
• Computational Neuroscience
• +3 more

Carlos Ponce serves as an Assistant Professor of Neurobiology at Harvard Medical School within Harvard University, where he leads the Ponce Lab. His research program focuses on deciphering how interconnected cortical areas collectively generate visual perception of shape and motion in naturalistic environments, bridging experimental neurophysiology with computational approaches. Dr. Ponce's research spans Neurobiology , Vision Science , and Computational Neuroscience , with specific emphasis on sensation mechanisms , cortical circuit function , and cognitive processing . His laboratory employs advanced techniques including neuronal recordings, machine learning, and deep generative networks to investigate ventral stream organization, neuronal tuning landscapes, and object recognition principles. Current projects explore how neural populations encode visual features and how these representations evolve across cortical hierarchies. Analysis of Dr. Ponce's recent publications reveals dominant themes in ventral visual stream dynamics , neuronal encoding models , and AI-neuroscience integration . His work demonstrates how generative models can interpret neural responses, how feature maps progress through cortical areas, and how computational frameworks explain visual perception. This research trajectory shows increasing convergence between biological vision studies and artificial intelligence methodologies, particularly in using deep learning to decode neural activity patterns. The Ponce Lab actively recruits high school and college students through dedicated resources pages, fostering early-career development in neuroscience. While specific grant mechanisms aren't detailed in available materials, the lab's consistent output in high-impact journals ( Cell , Nature Communications ) indicates robust research funding supporting interdisciplinary investigations at the neuroscience-AI interface. Operating within Harvard Medical School's Department of Neurobiology, the Ponce Lab maintains a collaborative research environment integrating faculty, students, technicians, and alumni. The team specializes in combining in vivo neural recordings with computational modeling to unravel visual processing mechanisms, with particular expertise in ventral stream organization and machine learning applications for neural data interpretation.

Costin BadicaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Blockchain
• +13 more

Costin Badica is a University Professor at the University of Craiova, affiliated with the Faculty of Automatic Control, Computers and Electronics. His research focuses on Artificial Intelligence , Machine Learning , Multi-Agent Systems , and Explainable AI . Position: Professor Location: Office 303, Blvd. Decebal no. 107, Craiova, Romania Contact: costin.badica@ucv.ro Professor Badica's work spans Internet of Things , Blockchain , and Intelligent Distributed Systems , with recent publications on decentralized federated learning , agent-based simulations , and emotional recommender systems . He actively contributes to academic conferences like the Balkan Conference in Informatics . Key research trends include AI explainability in job markets, privacy-preserving algorithms , and optimization of industrial systems . He also explores quantum computing applications and bio-inspired approaches in digital image evolution.

• Neural Signal Processing
• Machine Learning for Neuroscience
• Brain Network Modeling
• +5 more

Dr. Quanying Liu serves as Associate Professor in the Department of Biomedical Engineering at Southern University of Science and Technology (SUSTech), where she leads the Neural Computing and Control Laboratory (NCC lab) as Principal Investigator and Doctoral Supervisor. Her academic journey includes a PhD from ETH Zurich in Biomedical Engineering, postdoctoral training at Caltech, and research positions at Huntington Medical Research Institute, KU Leuven, and Oxford University. PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Dr. Liu's research bridges neuroscience, machine learning, and control theory with focus on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for brain interpretation, and optimization frameworks for neuromodulation (tES, TMS). Her work develops high-density EEG source imaging algorithms, data-driven brain network modeling, and control-theoretic approaches for neural stimulation. The NCC lab specializes in machine learning algorithms, neural computation, multimodal data fusion, network control theory, and bidirectional brain-computer interfaces. Analysis of her recent publications reveals strong trends in EEG-based visual decoding, multimodal neural embeddings, and AI-driven brain network modeling. Her work increasingly integrates generative models for neural data synthesis, control theory for precise neuromodulation, and cross-modal approaches connecting neural signals with cognitive functions. The New Brain 30 (2023) AAIC travel award (2019) Estes Stars Award (2018) Shenzhen Peacock Talent Plan C Dr. Liu actively mentors students through SUSTech's doctoral program and leads multiple significant research initiatives including a National Natural Science Foundation Youth Project, a National Key R&D Program in Bio-Information Fusion, and several Shenzhen municipal projects. Her lab receives funding from Guangdong Provincial Basic Research Fund, Shenzhen Science and Technology Innovation Commission, and international fellowships including Boswell Postdoctoral Fellowship and Swiss National Science Foundation grants. The NCC lab maintains strong international collaborations with Caltech, ETH Zurich, and Oxford University while developing novel platforms like WheelCon for sensorimotor control studies. The Neural Computing and Control Laboratory operates as an interdisciplinary hub integrating computational neuroscience, machine learning, and control engineering. The lab develops specialized hardware-software systems for closed-loop neurostimulation, maintains the EEGdenoiseNET benchmark dataset, and pioneers methods like MOVEA for transcranial electrical stimulation optimization. Current research directions include generative models for neural decoding, network control theory applications, and AI-human cognitive interaction frameworks.

Evangelos TheodorouView profile

Associate Professor

• Control Theory
• Machine Learning
• Reinforcement Learning
• +9 more

Evangelos Theodorou is an Associate Professor at the Guggenheim School of Aerospace Engineering , Georgia Institute of Technology. His research bridges theoretical and applied domains in control theory, machine learning, and autonomous systems. Education: Diploma in Electronic and Computer Engineering (TUC, 2001), MSc in Production Engineering (TUC, 2003), MSc in Computer Science (University of Minnesota, 2007), MSc in Electrical Engineering (USC, 2010), PhD in Computer Science (USC, 2011). His research focuses on Nonlinear Stochastic Optimal Control, Reinforcement Learning, Statistical Mechanics, Information Theory, and Model Predictive Control , with applications in robotics, aerospace systems, and biomedical engineering. Recent work involves Schr" odinger bridges, covariance steering, and distributed optimization for multi-agent systems. Key trends in his publications include integration of deep learning with control theory , development of stochastic optimization frameworks , and safe control algorithms for autonomous vehicles and robotics. He also explores optimal transport methods and their applications in chemical reactions and urban air mobility. Contact: etheodorou3@mail.gatech.edu

• Biologically-Inspired Algorithms
• Cognitive Behaviour
• Decision Theory
• +5 more

Professor James A. R. Marshall serves as Director of the Centre for Machine Intelligence and Professor of Theoretical and Computational Biology at the University of Sheffield. He also divides his time with Opteran Technologies, where he is Founder Science Officer, bridging academic research with commercial applications in bio-inspired robotics and AI. Dr. Marshall leads the Behavioural and Evolutionary Theory (BET) Lab, which focuses on understanding biological decision-making systems and translating these principles into computational algorithms. Marshall's research spans multiple disciplines at the intersection of biology, computer science, and mathematics. His primary interests include biologically-inspired algorithms, cognitive behavior, decision theory, theoretical neuroscience, evolutionary theory, mathematical biology, social evolution, and social insect behavior. His work particularly emphasizes how collective intelligence emerges from simple individual behaviors in biological systems, with applications to robotics, AI, and distributed computing systems. A notable contribution is his book Social Evolution and Inclusive Fitness Theory: An Introduction published by Princeton University Press in 2015. Analysis of Marshall's recent publications reveals a strong focus on optimal decision-making processes across multiple scales - from neural circuits to social insect colonies. His work consistently explores the speed-accuracy tradeoffs in decision systems, confidence-weighted collective decisions, and bio-inspired approaches to robotics and AI. A unifying theme is the application of evolutionary theory to understand and engineer intelligent systems. EPSRC Programme Grant: "Brains on Board: Neuromorphic Control of Flying Robots" (Principal Investigator) ERC Consolidator Grant: "DiODe: Distributed Algorithms for Optimal Decision Making" (Principal Investigator) EPSRC/BBSRC Grant: "AIBACS: Social Insects, Simulated Evolution and Biologically Inspired Algorithms" (Co-Investigator) BBSRC Project: "Optimal Collective Decision-Making in Social Insect Colonies" (Principal Investigator) EPSRC Project: "Green Brain: Computational Modelling of the Honeybee Brain" (Principal Investigator) Marshall serves on multiple editorial boards including Swarm Intelligence (2017-present) and has been an EPSRC Peer Review College Member since 2010. His BET Lab has produced influential work connecting neuroscience, collective behavior, and computational algorithms, with applications in robotics and AI systems. Current research directions include neuromorphic computing inspired by insect brains and optimal decision-making frameworks for autonomous systems.

• Adaptive and robust control
• Non-linear control systems
• Consensus and formation control
• +3 more

Dr. Vu Phi Tran is a Researcher at the University of New South Wales (UNSW) Canberra, affiliated with the School of Engineering and Technology. He serves as a Research Assistant, Teaching Staff member, and Casual Professional staff within the Trusted Autonomy research group since 2020, contributing to high-impact publications in IEEE Transactions and Elsevier journals. His educational background includes a B.E. in Automation and Control Engineering from HCMC University of Technology and Education (2010), an M.S. in Mechatronics Engineering from Asian Institute of Technology (2015), and a Ph.D. in Aerospace Engineering from UNSW Canberra (2019). He previously served as a Lecturer at HCMC University from 2010-2012. Tran’s research spans adaptive and robust control , non-linear systems , UAV swarm coordination , and neural-fuzzy control architectures . His work focuses on real-world applications including gas source localization, resilient flight control for nano-drones, and formation control under disturbances, leveraging negative-imaginary systems theory and machine learning. Recent publications demonstrate a strong trend toward multi-robot environmental monitoring and adaptive learning-based controllers for uncertain environments. His scientific achievements include: Rockwell Automation Scholarship for Engineering Excellence THE HISAMATSU PRIZE for outstanding Mechatronics performance UNSW Tuition Fee Scholarship Dean’s Award for Outstanding PhD Theses T.F.C Lawrence Prize for Aeronautical Science Tran actively serves as a reviewer for IEEE Transactions on Robotics and Vehicular Technology, while leading projects funded by DST and AFOSR grants. His current work focuses on machine learning for obstacle avoidance in robot swarms and robust flight control systems. He operates from the Autonomous System Laboratory (Building 17, Room 131) at UNSW Canberra, collaborating with international researchers on UAV-UGV interaction systems.

Georgios PriniotakisView profile

Professor

• Smart Textiles
• Textile Sensors
• Sustainable Technologies
• +5 more

Prof. Georgios Priniotakis is a Full Professor at the University of West Attica (UNIWA), specializing in Innovative Textile Technologies for Multifunctional Clothing Products . He serves as Head of the Department of Industrial Design and Production Engineering and represents Greece in international research networks like AUTEX and COST Action. His work bridges smart textiles, environmental sustainability, and occupational safety. Education: M.Sc. in Fiber Science and Technology (1992), University of Leeds Ph.D. in Conductive Textiles (2005), Ghent University Research Interests span smart clothing for medical and military applications, conductive textile sensors, sustainable materials from waste, and ergonomic design. He focuses on systems modeling, wearable health monitoring, and integrating AI into textiles. Article Trends highlight advancements in smart textile sensors , sustainable design , machine learning for disaster response, and biomimetic approaches to eco-design. His work intersects textile engineering with environmental science and medical technology . Scientific Leadership: AUTEX Lifetime Achievement Award 2025 Coordinator for EU-funded projects (e.g., DISOLEA, SECOVE, REWAFT) Grants & Projects include EU programs like Erasmus+, COST, and Horizon 2020, focusing on smart textiles, education innovation, and disaster management. He has over 350 citations and an h-index of 12 (Google Scholar).

Douglas RichardsonView profile

Lecturer

• Light Microscopy
• Super-resolution Microscopy
• Tissue Clearing
• +3 more

Douglas Richardson serves as Director of Imaging at the Harvard Center for Biological Imaging (HCBI) since 2013 and Lecturer in Molecular and Cellular Biology within Harvard's Faculty of Arts and Sciences since 2016. He leads a state-of-the-art microscopy core facility serving Harvard and Greater Boston researchers through confocal, light sheet, super-resolution, and slide scanning systems. His educational background includes: PhD in Cancer Cell Biology from Queen's University (Canada), Department of Pathology and Molecular Medicine Alexander von Humboldt Postdoctoral Fellowship at Max Planck Institute for Biophysical Chemistry under Nobel Laureate Stefan Hell Richardson's research expertise spans super-resolution microscopy , light sheet imaging , tissue clearing techniques , and image processing methodologies . He actively develops and evaluates advanced microscopy approaches for biological applications in cancer, neuroscience, and ophthalmology. His work emphasizes practical implementation of cutting-edge imaging technologies for diverse research questions. Analysis of his recent publications reveals dominant trends in 3D tissue imaging applied to neurodegenerative diseases (Alzheimer's), cancer biology (melanoma, breast cancer), and vascular ophthalmology. His contributions to tissue clearing standardization and light sheet microscopy optimization enable high-resolution whole-organ analysis, while his technical tutorials address critical microscopy artifacts. His scientific recognition includes: Alexander von Humboldt Postdoctoral Fellowship Richardson teaches MCB 68 (Cell Biology through the Microscope) and MCB 352 (Microscopy), directs the HCBI Lunch and Learn Lecture Series, and organizes tissue clearing workshops. The HCBI facility he leads provides critical infrastructure for Harvard researchers, with recent expansions including high-content screening (2024) and Zeiss Lightfield 4D integration (2025). Under Richardson's leadership, the Harvard Center for Biological Imaging serves as a national resource for advanced microscopy training and consultation, maintaining active YouTube educational content and developing protocols for challenging biological specimens through its tissue clearing initiatives.

Lorenzo MaccioniView profile

Researcher

• Mechanical Engineering
• Computational Fluid Dynamics (CFD)
• Tribology
• +5 more

Lorenzo Maccioni is a researcher in the Faculty of Engineering at the University of Bolzano . His work focuses on computational fluid dynamics (CFD), lubrication, and mechanical design, particularly for gear systems and bearings. He collaborates with Professor Franco Concli on advanced simulations and experimental validations. His research explores: Meshing strategies for CFD simulations Load-independent power losses in lubricated systems Innovations in gear design and cycloidal reducers Sustainable mechanical engineering practices Recent work trends include multi-phase flow modeling, thermal stability analysis, and additive manufacturing applications in mechanical systems. Scientific Awards : Best Paper Award at INGEGRAF International Conference (2025) STLE Captain Alfred E. Hunt Memorial Award (2025) Maccioni contributes to teaching courses like Machine Elements and participates in experimental studies, such as the Flying Egg Contest, to engage students in engineering challenges.

• Cognitive Systems
• Artificial Intelligence
• Autonomous Driving
• +4 more

Henrik Svensson is a Senior Lecturer and Head of School at the School of Informatics, University of Skövde. He holds a PhD in Cognitive Systems and has been with the university since at least 2003, progressing from PhD student to his current position as Senior Lecturer in Informatics since 2017. His research interests span cognitive systems, artificial intelligence, autonomous driving, mental simulation, and educational technology. Dr. Svensson's work focuses on developing bio-inspired approaches for autonomous vehicles, particularly exploring how dream-like mechanisms and mental simulation can improve AI systems. He has also made significant contributions to educational research, particularly in international student mobility and industry-academia collaboration. His publication record shows a consistent focus on cognitive approaches to autonomous systems, with recent work examining human-inspired autonomous driving, curriculum learning for self-driving vehicles, and the utility of dreaming-like processes in artificial agents. His research demonstrates a unique interdisciplinary approach bridging cognitive science, AI, and practical applications in transportation. Dr. Svensson is involved in several significant research projects including WISER (2021-2026) focusing on skills development in future technologies, Virtual Engineering, and WISE (2020-2022) supporting the automotive industry's transition to electric vehicles. His educational background includes a PhD in Cognitive Systems (2013), Licentiate in Cognitive Systems (2007), Master of Arts in Computer Science (2002), and Bachelor of Arts in Cognitive Science (2001), demonstrating a strong foundation in both cognitive science and computing disciplines.

Sicong ShaoView profile

Assistant Professor

• Cybersecurity
• Machine Learning
• Artificial Intelligence
• +7 more

Dr. Sicong Shao is an Assistant Professor in the School of Electrical Engineering and Computer Science (SEECS) at the University of North Dakota (UND) , where he joined as a Tenure-Track Assistant Professor in August 2023. He previously held a Research Assistant Professor position at the University of Arizona, where he earned his PhD. His research focuses on cybersecurity , machine learning , AI , and software engineering , particularly in intrusion detection, system security, and AI-assisted cybersecurity techniques. Lead of the Intelligent Software and System Security Lab (iSSSL) Active in conference organization (Co-Chair, IEEE CARS 2024; Committee Member, IEEE HAST 2024, IEEE CEC 2025, etc.) Editorial roles: Editorial Board Member (Cluster Computing), Guest Editor (Electronics special issues) Dr. Shao’s recent publications highlight trends in cyber-physical systems security , transformer-based anomaly detection , edge computing resilience , and AI applications for intrusion detection . His work spans academic and industrial domains, including biomedical signal processing and IoT security. Early Career Scholars Program award (UND, 2024) SARJ award through NSF RII Track 2 FEC (2024) He mentors PhD students in cybersecurity and software engineering and teaches courses like Secure Software Engineering (CSCI 387/567) and Cyber Physical Systems Security (CSCI 552) . His lab welcomes motivated graduate students seeking interdisciplinary research opportunities.

Shyam GollakotaView profile

Professor

• Artificial Intelligence
• Computing + Biology
• Human-Centered Computing
• +6 more

Shyam Gollakota is a Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His research spans Artificial Intelligence, Human-Centered Computing, and Wireless Systems , with interdisciplinary collaborations in Biology and Medicine. He leads the Mobile Intelligence Lab , co-founded Sound Life Sciences (acquired by Google), and developed battery-free computing technologies. Education: Ph.D. from MIT (2013), B.Tech from IIT Madras Awards: Infosys Prize 2024, Moore Inventor Fellow, NSF CAREER, ACM Grace Murray Hopper Award His research focuses on machine learning for mobile systems, battery-free sensing, and human auditory augmentation . Recent work includes proactive in-ear assistants (ACL 2025), spatial speech translation (CHI 2025), and sound bubbles for personalized audio (Nature Electronics 2024) . Publications span Human-Computer Interaction, Wireless Networking, and Medical AI . Scientific achievements include: Best Paper Awards: MOBICOM, SIGCOMM, UbiComp, SenSys, NSDI, CHI Breakthroughs: Battery-free phones, ambient backscatter, smartphone-based medical diagnostics His team co-developed underwater GPS (SIGCOMM 2023), AI-enabled ClearBuds (MobiSys 2022), and wireless blood clot testing (Nature Communications 2022) . Collaborations include the Washington Department of Agriculture for monitoring invasive hornets.

• Biomedical Engineering
• Wireless Power Transfer
• Bio-inspired Electronics
• +5 more

Paolo Motto Ros is an Assistant Professor at the Department of Electronics and Telecommunications at Politecnico di Torino, with the MiNES (Micro&Nano Electronic Systems) group. He holds a Ph.D. in electronic engineering from Politecnico di Torino (2009), following an M.Sc. (2005). His career spans roles at Neuronica Laboratory (2009-2012), Istituto Italiano di Tecnologia (2012-2019), and since 2019 at Politecnico di Torino as Senior Post-Doctoral Researcher and Adjunct Professor. He is an IEEE member and has organized conferences like BioCAS, ICECS, and ISCAS satellite events. Currently, he supervises 6 PhD students in projects related to wireless power systems and biomedical devices. Education: M.Sc. and Ph.D. in Electronic Engineering (Politecnico di Torino, 2005/2009) Research Interests focus on: Event-driven digital integrated circuits and systems Low-power smart sensor networks Bio-inspired electronics for robotics and medicine Wireless power transfer for implants and wearables Human motion applications and agrifood electronics Publication Trends show expertise in biomedical device design, neural interfaces, wireless power systems, and bio-inspired wearables. He leads the NerveRepack EU-funded project (2023-2027) for neural exoprosthetics. His lab affiliations include the VLSILAB group, and he serves on editorial boards for Frontiers in Bioengineering and Biotechnology . No scientific awards are mentioned in the provided texts.

Scott HuxtableView profile

Associate Professor

• Nanotechnology
• Thermal Transport
• Thermoelectric Devices
• +5 more

Scott Huxtable is an Associate Professor and Associate Department Head of Undergraduate Studies in the Department of Mechanical Engineering at Virginia Tech (College of Engineering). He also holds an affiliate membership in the Department of Engineering Science and Mechanics. His research focuses on micro-nano-scale thermal transport, sensors/actuators, nanoscale energy conversion (e.g., thermoelectric devices), and thermal management of electronic systems. He has been recognized with awards including the 2010 Virginia Tech Dean’s Award for Excellence in Teaching and the 2006 NSF CAREER Award. His work bridges fundamental materials science with applications in clean energy, electric vehicles, and energy harvesting. Education: Ph.D. (2002) and M.S. (1999) in Mechanical Engineering from UC Berkeley, and B.S. (1997) in Mechanical Engineering from Bucknell University. Research interests emphasize nanotechnology-driven solutions for energy systems, including thermoelectric materials, thermal management in electronics, and bio-inspired multifunctional materials. His pedagogical work explores innovative educational tools to enhance engineering concept comprehension through virtual and hands-on learning environments. His publications span over two decades, with recent trends focusing on advanced materials characterization, automotive energy efficiency, and thermoelectric waste heat recovery systems. He has collaborated on projects funded by NSF and DOE, advancing scalable thermoelectric technologies for vehicle applications. Awards also include Virginia Tech College of Engineering’s Outstanding New Assistant Professor and Certificate of Teaching Excellence, reflecting his dual commitment to research and education.