Find Academic Supervisors

• Systems biology
• Artificial Intelligence
• Neural Networks
• +7 more

Professor Sandhya Samarasinghe is a leading researcher at Lincoln University's School of Landscape Architecture where she directs the Complex Systems, Big Data and Informatics Initiative (CSBII). With expertise spanning computational biology, AI, and complex systems modeling, she bridges theoretical frameworks with practical applications across biological, agricultural, and environmental domains. PhD, Virginia Tech, Blacksburg, United States MS, Virginia Tech, Blacksburg, United States MSc, International University of Moscow, Moscow, Russia Professor Samarasinghe's research centers on developing advanced computational methodologies for modeling complex systems. Her work explores how soft computing techniques—including neural networks, fuzzy systems, and machine learning—can unravel complexity in biological networks and environmental systems. She advances the view of living organisms as evolved information systems and develops conceptual frameworks for understanding biological complexity from molecular to organismal levels. Her research has significant applications in medical diagnostics, sustainable agriculture, and environmental management. Analysis of her recent publications reveals a strong emphasis on neural network applications across diverse domains, with particular focus on biological systems modeling, Alzheimer's disease research, agricultural technology, and environmental sustainability. Her work consistently integrates theoretical computational approaches with practical problem-solving. Fellow of Modelling and Simulation Society of Australia and New Zealand Senior Member of IEEE Visiting Fellow at Oxford University Visiting Fellow at Princeton University Visiting Fellow at Stanford University Visiting Fellow at CSIRO Professor Samarasinghe has supervised over 25 postgraduate students across diverse research areas including mastitis detection in dairy cattle, neural modeling of cell cycles, autonomous self-repair systems, and computational approaches to vaccine development. She leads the Complex Systems, Big Data and Informatics Initiative which develops cutting-edge computing solutions for complex biological and environmental challenges.

Hendra I NurdinView profile

Senior Lecturer

• Quantum Control
• Quantum Systems
• Systems Theory
• +10 more

Dr. Hendra I Nurdin is a Senior Lecturer in the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW), where he has been a faculty member since 2012. His academic background spans Electrical Engineering and Applied Mathematics with a focus on systems and control theory, with significant research intersections in quantum physics and power engineering. He serves as an Associate Editor for IEEE Control Systems Letters (IEEE L-CSS) starting from January 2024. Dr. Nurdin's research spans quantum systems and control, nonlinear control of microgrids, and neuromorphic computing for nonlinear systems. His work in quantum control intersects with quantum physics while his research on microgrid control intersects with power engineering. His research has resulted in numerous publications across prestigious journals including Nature Communications, Physical Review Research, IEEE Control Systems Letters, and IEEE Transactions on Automatic Control. His recent publications (2020-2025) predominantly focus on quantum reservoir computing, quantum parameter estimation, control of non-Markovian quantum systems, and applications of control theory to energy systems. The research demonstrates a clear trajectory toward practical implementations of quantum computing concepts and advanced control methodologies for both quantum and classical systems. Associate Editor for IEEE Control Systems Letters (IEEE L-CSS) from January 2024 Dr. Nurdin has supervised several PhD students to completion and currently mentors PhD candidate Wen Liu. His research group has produced significant work in quantum control systems, with former students including Wenxing Li, Yihuan Liao, Jiayin Chen, Jiacheng Li, Muhammad Ali, Zhan Shi, and Onvaree Techakesari. He actively seeks new students with strong academic backgrounds for research in his areas of interest, particularly in quantum systems and control. His laboratory work focuses on quantum systems control, quantum reservoir computing, and microgrid control systems. He has developed courses including ELEC9782 Special Topics in Electrical Engineering 2 (Quantum Control) and teaches ELEC4631 Continuous-Time Control System Design.

Pedro Alexandre Simões dos SantosView profile

Associate Professor

• Machine Learning
• Social Artificial Intelligence
• Game Design
• +3 more

Pedro Alexandre Simões dos Santos is an Associate Professor at Instituto Superior Técnico, with dual appointments in the Mathematics Department and Computer Science and Engineering Department. His work spans Machine Learning, Social Artificial Intelligence, and Game Design, focusing on convergence analysis of ML algorithms, Social AI, and AI for Games. He actively contributes to game design, both digital and analogic, with a special interest in historical themes. Affiliations: Mathematics Department, Instituto Superior Técnico Computer Science and Engineering Department, Instituto Superior Técnico INESC-ID Research Unit GAIPS - Group of AI for People and Society His research integrates theoretical and applied aspects, including reinforcement learning in non-stationary environments, neural network optimization, and socially intelligent agent authoring tools. He explores AI applications in games, mental health support systems, and legal domains. His recent publications highlight trends in NFT-based game ecosystems, dialogue systems for mental health, and multi-agent reinforcement learning frameworks. Pedro teaches subjects such as Mathematics for Machine Learning and the 1st Cycle Integrative Project in Applied Mathematics and Computing, bridging mathematical foundations with computational applications.

• Cybersecurity
• Privacy
• Networking for Big Data
• +4 more

Shui Yu is a Professor of the School of Computer Science in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where he also serves as the Deputy Chair of the UTS Research Committee. His academic career spans over 20 years in Australia and 7 years in China, with additional teaching experience in Hong Kong and Indonesia. He has developed more than 10 units in cybersecurity, computer science, data analytics, and computer games, serving as the Course Director for Computer Science undergraduate programs. Professor Yu's research interests center on cybersecurity, privacy, networking aspects of Big Data, and applied mathematics for computer science. He pioneered the field of 'networking for big data' in 2013 and edited the seminal book 'Networking for Big Data' published in 2015. His work has practical applications in industry, including Amazon Cloud's auto-scale strategy against distributed denial-of-service attacks. Current research focuses include privacy and security concerns associated with big data, security issues in smart grids, anonymous transactions on Blockchain, and anonymous communication for web browsing privacy. Analysis of his recent publications reveals a strong research trajectory spanning cybersecurity, privacy-preserving technologies, networking for big data, and applied mathematics. His work shows increasing focus on quantum-resistant cryptography, federated learning security, and adversarial robustness in AI systems. The interdisciplinary nature of his research bridges theoretical foundations with practical applications in IoT, blockchain, and cloud environments. Fellow of IEEE (2023) Distinguished Lecturer of IEEE Communications Society (2018-2021) Distinguished Visitor of IEEE Computer Society (2022-2024) Professor Yu has secured numerous research grants from the Australian Research Council, including current projects on privacy and fairness in high intelligence models (DP240100955), improved security and privacy for online platforms (LP220200808), and secure blockchain for financial applications (LP220100453). He has served on editorial boards of multiple IEEE journals including IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, and IEEE Internet of Things Journal. His service extends to organizing major conferences such as IEEE Globecom 2015 and IEEE INFOCOM 2016-2017.

• Composite Materials
• Neural Networks
• Design Methods
• +7 more

Mehran Koohgilani is a Principal Academic in Engineering at Bournemouth University, affiliated with the Department of Design, Engineering and Computing within the Faculty of Science and Technology. He holds a PhD from Bournemouth University and has been actively contributing to engineering education and research for decades. He is a Chartered Engineer (CEng) and a member of the Institution of Mechanical Engineers. PhD in Damage Accumulation in High Performance (Bournemouth University, 1998) MSc in Polymer Science & Engineering (University of North London, 1993) BEng (Hons) in Mechanical Engineering Design & Production (South Bank University, 1992) Mehran's research focuses on Composite Materials , Neural Networks in engineering applications , design methods , material selection , and applied technology . He has pioneered the integration of virtual reality in design education and has contributed significantly to curriculum development and accreditation of undergraduate programs in design engineering. His recent publications (2011–2025) reflect a strong trend in combining materials science with artificial intelligence for structural monitoring, particularly in marine composites. Other key themes include design education , surface engineering with nanomaterials , and corrosion protection . His work bridges theoretical modeling, experimental validation, and practical application in industry-relevant contexts. Mehran has been instrumental in supervising PhD students and is an active member of the Design Simulation Research Centre . He has led numerous teaching units in technological principles, materials & processing, and design management. His contributions extend to project supervision, particularly in final-year design engineering projects, and to co-creation in design education. His scientific leadership is evident in his role in developing and validating multiple undergraduate courses. He has also contributed to research grants and collaborative projects, though specific grant names are not listed in the provided text.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +12 more

Neslihan Bayramoglu is a Senior Research Fellow at the Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Finland. She was awarded the title of Docent in Medical Imaging in 2022. Her research spans artificial intelligence, machine learning, and computer vision, with a focus on medical imaging and musculoskeletal diseases. Key areas include deep learning for histopathology image analysis, computer-assisted diagnosis, image processing, shape analysis, segmentation, classification, and 3D image retrieval. She has pioneered early applications of deep learning in breast cancer histopathology and is currently advancing machine learning approaches for osteoarthritis (OA) analysis using imaging and clinical data. Her recent publications emphasize deep learning techniques for OA progression prediction, radiographic segmentation, and lightweight CNN architectures for diagnostic efficiency. She has contributed to transfer learning in histopathology and GAN-based virtual tissue staining. Scientific awards include the Docent title in Medical Imaging (2022). She teaches graduate-level machine learning courses and supervises M.Sc. and doctoral students. Her work aligns with the Diagnostics of Osteoarthritis (DIOS) research group and involves collaboration on musculoskeletal health projects.

• Machine Learning
• Big Data
• Knowledge Discovery
• +7 more

Dr. Borja Sanz Urquijo serves as a Senior Lecturer at the Faculty of Engineering, University of Deusto, and has been a core researcher at DeustoTech-Computing since 2008, including a tenure as Head Researcher (2015-2018). He holds a cum laude PhD in Information Systems (2012) from the University of Deusto, specializing in Android malware detection. Education PhD in Information Systems, University of Deusto (2012, cum laude) His research spans machine learning, big data, and knowledge discovery, with critical expansion into AI ethics, fairness, accountability, and societal impact. He investigates AI applications in domestic violence intervention, health rights, law enforcement transparency, and Edge Computing optimization, consistently bridging technical innovation with social responsibility. His work demonstrates rigorous methodology in small-dataset machine learning and genomic sequence analysis. Dr. Sanz Urquijo's publication trajectory reveals evolving expertise from foundational cybersecurity (Android malware analysis, spam filtering) to contemporary societal challenges (feminist AI frameworks, quantum software security). Recent articles emphasize interdisciplinary collaboration, particularly in feminist technology studies and ethical AI governance, while maintaining technical depth in Edge Computing and genomic analytics. Advising and Projects He has supervised multiple theses including doctoral work on Edge Computing for digital twins and cybersecurity competency frameworks. As lead researcher in over 50 projects (H2020, national, private), he currently directs BEACON (industrial AI systems) and contributes to EU initiatives like IMPROVE (domestic violence response) and ELKARTEK (Industry 5.0 ethics). His collaborations span social organizations, enterprises, and research centers globally. Research Environment As a pillar of DeustoTech-Computing, he operates within a multidisciplinary unit advancing AI, cybersecurity, and Edge Computing applications. His leadership in projects like AI-Driven Cognitive Robotic Platforms and REal tiME control systems demonstrates integration of theoretical research with industrial implementation in smart manufacturing contexts.

• Medical Image Segmentation
• Motion Segmentation
• Visual Object Tracking
• +9 more

Dr. Sandeep Singh Sengar is a Senior Lecturer in Computer Science at Cardiff Metropolitan University, United Kingdom. Prior to his current role, he served as a Postdoctoral Research Fellow at the Machine Learning Section of the Computer Science Department at the University of Copenhagen, Denmark. His academic journey includes a Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India, and an M. Tech. in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India. Sengar actively engages in research collaboration and has organized special sessions and delivered keynote presentations at international conferences. Education: Dr. Sengar holds the following academic qualifications: Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India M. Tech. in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India Research Interests: Dr. Sengar's research focuses on advanced applications of machine/deep learning and computer vision, particularly in healthcare diagnostics and cybersecurity. His work combines theoretical methodologies with practical implementations, including: Medical Image Segmentation (e.g., brain tumors, functional connectivity) Motion Segmentation and Tracking for surveillance and activity recognition Visual Object Tracking and Recognition in dynamic environments Video Compression techniques leveraging adaptive optimization Cybersecurity frameworks targeting DDoS attack mitigation Exploring Metaverse applications in healthcare and AI ethics Publications Trends: His recent articles emphasize interdisciplinary innovation, with a strong focus on healthcare AI and cybersecurity. Key themes include hybrid neural networks for medical imaging, entropy-driven frameworks for neurological disorders, and proactive network threat detection using deep learning. He also investigates emerging technologies like the Metaverse's role in healthcare systems and generative AI's broader applications. Scientific Awards: No scientific awards were explicitly mentioned in the provided texts. Advising & Grants: While Dr. Sengar has not listed formal advisees or grant details, his research mentorship is evident through collaborative projects and conference contributions. He actively participates in organizing special sessions and peer-review processes, fostering academic-industry partnerships. Labs & Teams: Though no specific labs are named, his work aligns with Cardiff Met's research centers and innovation initiatives, such as the PDR International Centre for Design and Research and ZERO2FIVE Food Industry Centre.

Ahmed FarooqView profile

Lecturer

• Haptics
• Multimodal Interaction
• Human-Computer Interaction
• +7 more

Ahmed Farooq is a University Lecturer and Senior Researcher at the Tampere Unit for Computer-Human Interaction (TAUCHI), Tampere University. He holds a PhD in Computer Science and has over 20 years of experience in haptic and multimodal interaction systems. His current role combines academic teaching (e.g., HI 520 Haptic Interaction course) with advanced research in haptic mediation, wearable technologies, and AI-driven interaction systems. Affiliations: TAUCHI Research Group, Tampere Institute of Advanced Studies (TIAS) Positions: Permanent Lecturer (2024–present), TIAS Postdoctoral Fellow (2022–2024), Visiting Researcher at McGill University (2020) and Purdue University (2019–2020) Research Interests: Haptic signal mediation, 3D-printed haptic waveguides, magnetorheological fluid actuators, AI integration in multimodal systems (e.g., TAUCHI-GPT), and applications in automotive UI, VR/XR, and food interaction systems. His work focuses on reducing driver distraction, enhancing tactile feedback in virtual environments, and developing ethical AI tools. Key Projects: Origo Steering Wheel (German Design Award, 2021), Augmented Eating Experiences (AEE), Huawei Haptic Mediation, and TAUCHI-GPT open-source AI framework. These projects involve collaboration with industry partners like Huawei and Bentley University. Publications: Over 60 peer-reviewed articles in top venues (ACM, IEEE, EuroHaptics) focusing on haptic actuation, multimodal interaction, and AI ethics. Recent work explores prompt injection attacks in LLMs and implantable haptic actuators. Awards: TIAS Postdoctoral Fellowship (2022–2024), Finnish Cultural Foundation Mobility Grant (2018). His research has led to 9 patents (e.g., multifunctional haptic actuator, 2024) and 6 granted US/Japan patents.

Vanessa FreiView profile

Researcher

• Cognitive Neuroscience
• Auditory Perception
• Neural Speech Processing
• +5 more

Vanessa Frei is a Postdoctoral Researcher at the Department of Computational Linguistics at the University of Zurich. She works within the Computational Neuroscience of Speech & Hearing group, focusing on auditory perception, cognitive capacity in older adults with hearing loss, and immersive training via virtual reality (VR) and electroencephalography (EEG). Education: Bachelor's and Master's in Psychology with a focus on Cognitive Neuroscience from the University of Zurich Research Interests: Her work explores how immersive auditory-cognitive training modifies the relationship between auditory perception and cognitive capacity in aging populations. She investigates neural speech processing using VR and EEG, aiming to enhance speech-in-noise comprehension through multisensory integration and motivational factors. Publication Trends: Her research spans neuroscience, cognitive science, and speech processing, emphasizing VR-based training, neural speech tracking, and aging. Key subfields include sensory-cognitive interactions, EEG analysis, and the role of working memory in auditory perception among hearing-impaired older adults. Labs & Teams: Vanessa Frei is affiliated with the Computational Neuroscience of Speech & Hearing group at the Phonetics and Speech Sciences unit, Department of Computational Linguistics, University of Zurich.

• Sustainable Manufacturing
• Production Systems
• Virtual Reality
• +7 more

Björn Johansson is a Professor at Chalmers University of Technology, specializing in Production Systems. His research focuses on sustainability aspects of manufacturing through virtual tools, aiming to minimize environmental, social, and economic impacts. Key methodologies include flow simulation, dynamic environmental assessments, 3D visualization, and layout optimization. Primary research areas: Sustainable Manufacturing, Digital Twins, Environmental Impact Assessment Collaborations: Mélanie Despeisse, Henrik Söderlund, and others in automotive, battery production, and maritime industries His work emphasizes integrating digital technologies (e.g., VR, IoT) with sustainable practices, addressing challenges in supply chain resilience, human-robot collaboration, and circular economy models. Recent studies explore VR training environments, 5G-enabled manufacturing, and extended reality (XR) frameworks. Current projects involve 44 initiatives across battery systems, servitization, and digitalization for sustainability. Publications span 179 articles, including topics like digital twin implementation, ergonomic VR assessments, and hybrid simulation models for environmental analysis.

Katsushi ArisakaView profile

Professor

• Physics
• Astronomy
• Dark Matter
• +9 more

Katsushi Arisaka is a Distinguished Professor in the Department of Physics and Astronomy at the University of California, Los Angeles (UCLA), within the College of Physical Sciences. His research spans multiple disciplines including particle physics, cosmology, biophysics, and neurophysics. Dr. Arisaka began his academic journey at the University of Tokyo in 1979 as a graduate student under Professor Masatoshi Koshiba, working on the development of the world's largest 20-inch photomultiplier for the Kamiokande Experiment. He moved to the United States in 1985 and established his research group at UCLA in 1988. His educational background includes a Ph.D. from the University of Tokyo, though specific dates are not provided in the available materials. Professor Arisaka's research interests center around answering fundamental questions about the universe and life itself. His work explores three primary areas: the origin of the universe through dark matter research and cosmic ray studies; the origin of life through biophysics and molecular tracking; and the origin of consciousness through neurophysics. His approach consistently leverages advanced photon detection technologies across these diverse fields. Early in his career, he focused on rare decay processes of kaons to understand CP-violation at BNL and Fermilab, then shifted to cosmology in 1998, participating in the Pierre-Auger Cosmic Ray Observatory and CMS Endcap Muon Chambers for LHC at CERN. Since 2007, his main focus has been dark matter experiments including XENON100 at Gran Sasso in Italy and its successor XENON 1Ton, while also collaborating with DarkSide and MAX projects. His recent publications (2020-2023) reveal a strong trend toward interdisciplinary research, particularly at the intersection of physics, neuroscience, and consciousness studies. The 2022-2023 publications show a significant focus on visual perception, neural holographic tomography, and the grand unified theory of mind and brain. Earlier works (2017-2020) demonstrate continued activity in dark matter detection with experiments like XENON and DarkSide, as well as applications of advanced photon detectors to biological imaging. Grand Unified Theory of Mind and Brain (2022 series) Visual Perception of 3D Space and Shape (2022 series) Transverse sheet illumination microscopy (2023) DarkSide direct dark matter search (2017) Dr. Arisaka has been actively involved in major international collaborations including the CMS experiment at CERN's Large Hadron Collider, the XENON dark matter project at Gran Sasso in Italy, and the DarkSide experiment. His laboratory has developed innovative imaging techniques such as the Spatio-Temporal Multiplexing (STEM) microscope for multiple plane imaging and high-speed confocal microscopy systems capable of capturing 1,000 frames per second. The STEM microscope, developed with Adrian Cheng, allows simultaneous scanning of multiple planes using time differences between beams. At UCLA, Professor Arisaka has established productive collaborations across campus, particularly with the Medical School, where his advanced photon detection technologies have been applied to neuroscience research. His laboratory has contributed to significant discoveries in hair cell oscillation measurements and neural development studies. He teaches several physics courses including Physics 6B, 6C, 89 for 6B, 89 for 6C, and Physics 19, and regularly seeks graduate and undergraduate students interested in his research directions. His group has developed virtual reality systems for rats to study spatial recognition in the hippocampus in collaboration with Prof. Mayank Mehta's group. The Arisaka Lab maintains state-of-the-art facilities including a Photon Detector Lab and collaborates with multiple research groups on campus. Current research directions include the development of Transverse Sheet Illumination Microscopy (TransIM) and continued work on dark matter detection with next-generation XENON experiments. His lab's philosophy centers on using physics principles to answer the fundamental questions: 'Where do we come from? What are we? Where are we going?' through experimental approaches rather than philosophical speculation.

Juan Jesús García DomínguezView profile

Professor

• Electronics Engineering
• Sensor Technology
• Biomedical Engineering
• +3 more

Juan Jesús García Domínguez is a Professor in the Department of Electronics at the Universidad de Alcalá. His primary research focuses on sensor systems, wearable technology, and their applications in healthcare and smart environments. He leads the GEINTRA research group, dedicated to Electronic Engineering Applied to Intelligent Spaces and Transport. Dr. García Domínguez holds a PhD from Universidad de Alcalá (2006) with a thesis on infrared obstacle detection in railway environments. His research interests include biomedical sensors, inertial measurement units (IMUs), indoor localization systems (BLE/UWB), and elderly care technology. He has pioneered work on non-invasive behavioral monitoring, gait analysis, and wearable devices for vulnerable populations. Recent projects involve developing systems for activity recognition, fall detection, and telemedicine applications. Key contributions include the FrailWear wearable IoT device, BLE-based behavioral analytics frameworks, and multi-sensory systems for long-term patient monitoring. His work bridges electronic engineering with healthcare, emphasizing practical solutions for aging-in-place and smart transportation safety. Teaching includes digital electronics and active learning methodologies in engineering education. Dr. García Domínguez has published extensively on topics like acoustic positioning systems, IMU calibration algorithms, and NILM techniques for energy management. His lab focuses on translating sensor data into actionable insights for medical and environmental applications.

• Explainable AI
• Interpretable Machine Learning
• Deep Learning
• +13 more

Wojciech Samek is a Professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and Head of the AI Department at Fraunhofer Heinrich Hertz Institute (HHI), Germany. He holds a joint appointment, bridging academic research and industrial application in artificial intelligence. He is a Fellow at BIFOLD and ELLIS Unit Berlin, and a Principal Investigator in several DFG projects including DeSBi and BIOQIC. PhD (Dr. rer. nat.) with distinction, Technical University of Berlin, 2014 Studies in Computer Science, Humboldt University, Heriot-Watt University, University of Edinburgh His research centers on Explainable AI (XAI) , Trustworthy Deep Learning , and Efficient AI . He pioneered Layer-wise Relevance Propagation (LRP), a foundational method for interpreting deep neural networks. His work spans model interpretation, robustness against adversarial attacks, neural network compression, federated learning, and applications in healthcare, communications, and multimedia. The recent articles highlight a strong trend toward extending explainability beyond deep models , with research on concept-level explanations, unsupervised learning interpretability, and XAI-driven model improvement. There is a consistent focus on robustness, privacy, and efficiency in distributed learning settings, particularly for federated and edge AI. Applications in medical AI and neuroscience are prominent, emphasizing safety and interpretability in high-stakes domains. Best Paper Award, Pattern Recognition (2020) Digital Signal Processing Best Paper Prize (2022) Fellow, BIFOLD - Berlin Institute for the Foundation of Learning and Data Member, Germany's Platform for Artificial Intelligence Senior Editor, IEEE TNNLS; Associate Editor, Pattern Recognition, Digital Signal Processing, PLoS ONE Area Chair, NeurIPS, ICML, NAACL Program Chair, IEEE MLSP 2023 Contributor to ISO/IEC MPEG-17 NNC standard Prof. Samek advises a large group of PhD students and leads a vibrant research team at Fraunhofer HHI and TU Berlin. His group has secured significant funding through DFG, BIFOLD, and industrial collaborations. He has co-authored over 200 peer-reviewed papers, many of which are ESI Hot or Highly Cited. He is deeply involved in organizing workshops and tutorials on XAI, federated learning, and neural compression at top venues like NeurIPS, ICML, CVPR, and IEEE conferences. His lab develops open-source tools such as the LRP Toolbox , Keras Explanation Toolbox , DeepCABAC , and Quantus , promoting reproducibility and adoption of interpretable and efficient AI methods. The team is actively working on next-generation AI that is not only accurate but also transparent, robust, and trustworthy.

Yakun Sophia ShaoView profile

Associate Professor

• Computer Architecture
• Specialized Accelerators
• Heterogeneous Architecture
• +4 more

Yakun Sophia Shao is an Associate Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She holds a Ph.D. (2016) and M.S. (2014) in Computer Science from Harvard University, alongside a B.E. in Electrical Engineering from Zhejiang University, China. Her research focuses on computer architecture , particularly domain-specific accelerators , heterogeneous systems , and agile VLSI design methodologies . Key research centers: Agile Design of Efficient Processing Technologies (ADEPT), Berkeley Emerging Technologies Research (BETR), Berkeley Wireless Research Center (BWRC), SpeciaLIzed Computing Ecosystems (SLICE) Her work explores hardware-software co-design for efficiency in AI and robotics, including projects like Simba (chiplet-based AI accelerators) and Virgo (GPU matrix units). Notable tools developed include WIICA (workload characterization) and Chipyard (SoC frameworks). Selected honors include: 2024 CRA-WP Anita Borg Early Career Award 2023 NSF CAREER Award 2022 IEEE TCCA Young Computer Architect Award 2022 Intel Rising Star Faculty Award She teaches courses including EECS 151 (Digital Design & ICs) and EECS 251A (Advanced Digital Design).