Find Academic Supervisors

• Machine Learning
• Deep Learning
• Cybersecurity
• +4 more

Dr. Yi Wang is an Associate Professor and Department Chairperson of the Electrical and Computer Engineering Graduate Programs at Manhattan College, New York. He also serves as Director of the Electrical & Computer Engineering Graduate Program. His research focuses on machine learning, deep learning, cybersecurity, blockchain, and their applications in cyber-physical systems. Dr. Wang is an IEEE Senior Member (since 2021) and has secured NSF grants totaling over $149,000. Education: Ph.D. Computer Engineering, University of Alabama in Huntsville M.S. Computer Science, Wuhan University of Science and Technology B.S. Information Systems, Wuhan University of Science and Technology Research Interests: Machine learning and deep learning algorithms Cybersecurity for IoT and smart grids Blockchain applications in attribute-based access control Adversarial machine learning defense mechanisms Optical fiber communication systems Recent Publications Trends: His work spans blockchain platform comparisons, adversarial attack mitigation in power systems, and AI-driven smart home solutions. He frequently publishes in IEEE journals and conferences. Awards: Best Paper Award at 2017 IEEE UEMCON Best Paper Award at 2015 ICDIP Grants and Advising: Principal Investigator for NSF-funded UIRiSCS project (2022–2025). Co-Principal on NYC DOT Loading Zone Study. Recipient of Manhattan College Faculty Summer Grants (2021, 2017). Advises graduate students in cybersecurity and IoT research. Labs/Teams: Collaborates with the University of Zaragoza, Spain on smart systems research. Leads projects on plastic optical fiber networks and AI-driven smart home systems.

Mircea R. StanView profile

Professor

• AI Hardware
• Processing in Memory
• Low Power Design
• +7 more

Mircea R. Stan is a Professor of Electrical and Computer Engineering at the University of Virginia, serving as Director of Computer Engineering and Virginia Microelectronics Consortium (VMEC) Professor. He leads the High-Performance Low-Power (HPLP) lab and is an associate director of the Center for Automata Processing (CAP). His research focuses on AI hardware, Processing in Memory, Low Power Design, Cyber-Physical Systems, and Spintronics. Education: Ph.D. (1996) and M.S. (1994) from UMass Amherst; Diploma (1984) from Politehnica University, Bucharest. Research interests include energy-efficient computing architectures, IoT systems, and emerging technologies like magnetic skyrmions and memristors. He has pioneered work on asynchronous stochastic computing, thermal-aware microarchitecture, and microfluidic cooling for 3D-ICs. Key awards include the 2024 A. Richard Newton Technical Impact Award, 2018 ISCA Influential Paper Award, and IEEE Fellow (2014). He has held editorial roles at IEEE TVLSI, IEEE TNano, and IEEE Design & Test. Notable contributions include the HPLP lab’s advancements in low-power logic computing, the VCRFID framework for Industry 4.0, and thermal-aware design tools like Hot-LEGO and Cool-3D.

• Information Security and Reliability
• Cyber Security
• Artificial Intelligence
• +13 more

Prof. Dr. Murat Dener is a full Professor in the Department of Information Security Engineering at the Institute of Science and Technology, Gazi University, where he also serves as the Head of Department since 2020. He has been continuously affiliated with Gazi University since 2005, progressing from Research Assistant to full Professor in 2023. He is also a Member of the Gazi University Rectorate Quality Commission and a Researcher at the High Performance Computational Neuroscience Laboratory at the Neuroscience and Neurotechnology Excellence Joint Application and Research Center. His educational background includes a BSc, MSc, and PhD, all from Gazi University, with his doctoral studies partially conducted at Georgia Tech University, USA. He completed English language training at Georgia State University and has participated in multiple EU-funded international projects across Greece, Portugal, Belgium, Germany, and Kazakhstan. His research focuses on cutting-edge areas such as cyber security, artificial intelligence, internet of things, blockchain, big data analytics, and computational neuroscience. His work bridges theoretical research with practical innovation, exemplified by the development of Turkey’s first domestically produced wireless sensor node, "WiSeN", through a company he founded in 2014. The analysis of his recent publications reveals a strong trend toward integrating AI and deep learning into cyber defense, secure IoT systems, blockchain applications, and computational neuroscience. His work emphasizes real-world applicability, security, scalability, and innovation in smart city and critical infrastructure technologies. Academic Achievement Award, Georgia State University (2011) Second Prize, Gazi University Business Idea Competition (2014) Turkish First Prize, Junior Chamber International 'Ten Successful Young People of Turkey' (2014) World Top 20 Finalist, Junior Chamber International (Japan, 2014) First Prize, Young Entrepreneurship Category, Liyakat Association (2015) First Prize, Smart Buildings and Environment, TET R&D Project Market (2018) Prof. Dener actively advises master’s and doctoral students and has led numerous national and international research projects, including EU-funded collaborations. His leadership extends to editorial and peer-review roles in multiple journals. He is deeply involved in academic quality improvement as a member of the Rectorate Quality Commission and leads advanced research in computational neuroscience using high-performance computing platforms. He founded a technology company in 2014 under the Technopreneurship Capital Support Program, producing the first Turkish-made wireless sensor node (WiSeN), which led to several national innovation awards. His lab work centers on the High Performance Computational Neuroscience Laboratory, where he contributes to neurotechnology research, combining AI and neuroscience for brain-computer interface applications.

Christopher R. JohnsonView profile

Academic

• Scientific Computing
• Scientific Visualization
• Image Analysis
• +3 more

Christopher R. Johnson is a Distinguished Professor of Computer Science and Founding Director of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. He holds additional appointments as Research Professor of Bioengineering and Adjunct Professor of Physics. His research focuses on scientific computing, visualization, and biomedical applications, with notable contributions to software tools like SCIRun, ShapeWorks, and FluoRender. He has led the SCI Institute since its founding in 1992, growing it to over 200 members. Johnson has pioneered advancements in cardiac electrophysiology modeling, medical imaging, and high-performance computing. Johnson’s academic journey includes a B.S., M.S., and Ph.D. in Computer Science, though specific institutions are not mentioned. His editorial roles include co-editing The Visualization Handbook and serving on multiple journal boards. He has advised numerous students, including Brian Zenger, Jake Bergquist, and Lindsay Rupp, who have received prestigious awards like the NSF Graduate Research Fellowship. His awards span decades and disciplines, including the NSF Presidential Faculty Fellow Award, IEEE Visualization Career Award, and Utah Cyber Pioneer Award. He is a Fellow of AIMBE, AAAS, SIAM, and IEEE. Johnson’s work emphasizes translating computational methods into clinical tools, such as deep brain stimulation for Parkinson’s and atrial fibrillation treatment. Johnson’s lab, the SCI Institute, collaborates widely with industry and academia, developing open-source software for biomedical computing. Key projects include SCIRun (a biomedical problem-solving environment) and ShapeWorks (statistical shape modeling). He has been instrumental in securing over $6 million in NIH grants for the Center for Integrative Biomedical Computing (CIBC).

Pankaj MehraView profile

Adjunct Professor

• Computer Architecture
• Memory Systems
• Storage Systems
• +13 more

Pankaj Mehra is an Adjunct Professor in the Department of Computer Science and Engineering at the Jack Baskin School of Engineering, University of California, Santa Cruz (UCSC). He is affiliated with the Storage Systems Research Center (SSRC), now succeeded by the Center for Research in Storage Systems (CRSS). Alongside his academic role, he is the Founder and CEO of Elephance Memory, Inc., and serves as Workstream Lead for Computational x Programming (x = Memory or Storage) at the OpenCompute Project. Ph.D. in Computer Science, University of Illinois at Urbana-Champaign Former Faculty, IIT Delhi Adjunct Faculty, University of California, Wright State University Computer Scientist, NASA Ames Research Center Founder, HP Labs Russia Executive Roles: SVP & WW CTO at Fusion-io; VP at Samsung, SanDisk, Western Digital Pankaj Mehra's research centers on next-generation memory and storage systems. His work explores disaggregated memory architectures, computational storage, non-volatile memory (NVM), and data-centric operating systems. He investigates how to optimize data placement, improve system performance through intelligent caching, and restructure computing models around emerging memory technologies like CXL and persistent memory. His recent publications focus on far memory, tiered memory systems, and offloading computation to storage devices. His recent publications demonstrate a strong focus on memory disaggregation, computational storage, and non-volatile memory systems. Themes include rethinking data and pointer management in distributed memory environments, resource allocation in tiered systems, and designing operating systems that treat data as a first-class citizen. His work bridges academic research and industrial innovation, often involving collaboration with leading researchers at UCSC. Samsung R&D Award (2019) CES R&D Innovation Award (2021) Terabyte Sort Trophy by Jim Gray (1998) TPC-C Cluster Performance Records (1997) Pankaj Mehra has advised and collaborated with numerous researchers and engineers across academia and industry. His work has been supported by affiliations with HP Labs, Fusion-io, Samsung, and now Elephance Memory, Inc. He has led major research and development initiatives, including SmartSSD at Samsung and foundational work on persistent memory at HP. He has also contributed to standards efforts through the InfiniBand Trade Association. He has been a key contributor to the UCSC Storage Systems Research Center (SSRC/CRSS), collaborating with faculty and researchers including Ethan L. Miller, Heiner Litz, and Daniel Bittman. His industry leadership at Elephance Memory, Inc. and involvement in the OpenCompute Project further extend his influence in shaping future data infrastructure technologies.

Igor S. PandžićView profile

Professor

• Computer Vision
• Virtual Humans
• Facial Animation
• +7 more

Prof. Dr. Igor S. Pandžić is a Full Professor at the Department of Telecommunications , Faculty of Electrical Engineering and Computing (FER) , University of Zagreb . He is also affiliated with the Center of Excellence for Computer Vision , actively contributing to research in computer graphics, virtual humans, and interactive systems. Education: While specific degrees are not listed in the provided text, his title "prof. dr. sc." (Croatian for Full Professor with a Doctor of Science degree) indicates he has completed doctoral studies and achieved the highest academic rank. Research Interests: Virtual Humans & Avatars: Creation, animation, and behavioral modeling of lifelike virtual characters for interactive applications. Facial Animation & Biometrics: Real-time speech-driven facial animation, facial expression recognition, age/gender estimation, and biometric data filtering. 3D Graphics & Virtual Environments: Real-time rendering, networked collaborative virtual environments (e.g., VLNET), and 3D visualization on mobile/web platforms. Human-Computer Interaction: Multimodal interfaces combining speech, gesture, and facial cues for embodied conversational agents (ECAs). Computer Vision: Face alignment, landmark detection, pattern recognition with decision trees, and efficient algorithms for mobile deployment. Research Trends from Publications: His recent work (2022-2023) emphasizes efficiency in computer vision (e.g., fast face alignment, memory-efficient models), unsupervised learning for biometric data quality improvement, and real-time applications on mobile devices. Earlier foundational work spans MPEG-4 facial animation standards, networked virtual environments, and virtual human frameworks. Scientific Awards: No specific awards are mentioned in the provided text. Advising & Grants: While individual students are not named, his extensive publication record and leadership in research centers imply active supervision of PhD and Master's students. Grant details are not specified. Labs & Teams: He leads or heavily contributes to the Center of Excellence for Computer Vision at FER, fostering interdisciplinary collaboration in visual computing and AI.

Sebastián FerradaView profile

Assistant Professor

• Knowledge Graphs
• Graph Databases
• Multimedia Databases
• +3 more

Sebastián Ferrada is an Assistant Professor at the Data & Artificial Intelligence Initiative of Universidad de Chile. He also serves as Young Researcher at the Institute for Foundational Research on Data (IMFD) and Collaborating Researcher at the National Center for Artificial Intelligence Research (CENIA). His research focuses on Knowledge Graphs, with special emphasis on extraction, management, and applications for querying, browsing, and AI systems. His academic background includes: PhD in Computer Science (2021), Universidad de Chile MSc in Computer Science (2017), Universidad de Chile BEng in Computer Science (2017), Universidad de Chile Sebastián's research explores several key areas: Multimedia Databases with applications to Wikimedia Commons images Graph Databases and Knowledge Graphs construction Federated Data Management across heterogeneous RDF sources SPARQL query extensions for similarity-based operations Graph data management and compression techniques His recent publications demonstrate strong trends in knowledge graph construction, similarity-based querying, and efficient graph data management. These works combine theoretical advancements with practical implementations in real-world systems like IMGpedia and MillenniumDB. Scientific achievements include: Best Paper Award at CoopIS 2023 Best Demonstration Award runner-up at SIGMOD/PODS 2024 Best Student Paper (Resources Track) and Best Poster at ISWC 2017 First prize in CLEI 2017 for his Master's thesis Sebastián currently leads the Fondecyt project on graph data management and contributes to the U-Inicia project on AI processes in graph databases. He serves on the editorial board of Transactions on Graph Data and Knowledge.

Sara GoeringView profile

Professor

• Bioethics
• Neuroethics
• Feminist Philosophy
• +6 more

Sara Goering is a Professor and Chair of the Department of Philosophy at the University of Washington, affiliated with the College of Arts & Sciences. She holds additional roles as Core Faculty in the Program on Ethics and the Disability Studies Program, and is an Adjunct Professor in the Department of Bioethics and Humanities. Her research focuses on bioethics, neuroethics, feminist philosophy, and the ethical implications of neurotechnology. Goering co-leads the ethics thrust at the UW Center for Neurotechnology and directs the UW Center for Philosophy for Children. She has held leadership positions such as Graduate Program Coordinator and has contributed to interdisciplinary initiatives in ethics and disability studies. Her education includes a Ph.D. in Philosophy from the University of Colorado, Boulder (1998) and a B.S. in Psychology from the University of Illinois, Urbana-Champaign (1989). Her work bridges philosophy with practical applications, including collaborations with neuroscientists, engineers, and clinicians to address ethical challenges in emerging technologies. Key research areas include neurotechnology ethics—particularly deep brain stimulation (DBS) and brain-computer interfaces (BCI)—disability justice, feminist approaches to science, and philosophy for children. Recent publications explore agency in neurotechnology users, ethical frameworks for emerging technologies, and justice in healthcare access. Goering has secured significant grants (e.g., NIH R01, NSF ER2) to advance neuroethics research and equitable science practices. Awards include the Gerler Faculty Fellowship (2016-2018) supporting her work on philosophy for children. She advises numerous graduate students in philosophy, bioethics, and interdisciplinary fields. Goering’s service includes leadership roles in academic governance, diversity initiatives, and national ethics committees, reflecting her commitment to ethical, inclusive scholarship.

Ben Ward-CherrierView profile

Senior Lecturer

• Robotics
• Computational Neuroscience
• Prosthetics
• +2 more

Ben Ward-Cherrier is a Senior Lecturer in Robotics at the University of Bristol's School of Engineering Mathematics and Technology. His research focuses on biomimetic tactile sensing, neuromorphic systems for robotics, and haptic interfaces. He develops artificial tactile systems inspired by biological sensory mechanisms for applications in prosthetics, robotic manipulation, and human-robot interaction. Key research areas include neuromorphic tactile sensors that mimic biological afferents, real-time texture and edge classification algorithms, incipient slip detection for stable grasping, and vibrotactile feedback systems. Recent work integrates spiking neural networks with tactile hardware for efficient sensory processing. Publications demonstrate advancement in tactile sensing capabilities, including braille recognition in noisy environments, psychophysics-inspired benchmarking, multi-modal texture/velocity classification, and industrial applications like composite defect detection. Research bridges computational neuroscience with practical robotic systems.

David ChoppView profile

Professor

• Numerical Methods
• Scientific Computing
• Interface Motion
• +4 more

David Chopp is a Professor of Engineering Sciences and Applied Mathematics at Northwestern University's McCormick School of Engineering. His research focuses on numerical methods, scientific computing, and interface motion, with applications in bacterial biofilms, neurophysiology, and materials science. He holds the Charles Deering McCormick Professor of Teaching Excellence award. Chopp earned his Ph.D. in Mathematics from UC Berkeley and a B.S. in Mathematics and Applied Mathematics from the University of Washington. Research interests include level set methods, computational neuroscience, fracture mechanics, and biofilm modeling. Notable contributions span phase field modeling, microbial fuel cell simulations, and adaptive algorithms for neural systems. His work bridges computational tools with real-world applications in engineering and biology. Key Projects: Phase field modeling with large driving forces (2023), biofilm potassium signaling (2021), non-planar crack tracking (2016). Awards: Charles Deering McCormick Professor of Teaching Excellence. Chopp advises students in biofilm dynamics, computational materials, and fracture mechanics. His lab develops algorithms for interface tracking and neural simulations, collaborating on biofilm bioclogging and material failure analysis. Active in teaching and publishing, he authored textbooks on high-performance computing and contributed to over 60 peer-reviewed articles.

Valerie VenturaView profile

Professor

• Computational Neuroscience
• Time Series Forecasting
• Graphical Models
• +6 more

Valerie Ventura is a Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. She serves as Co-Director of the Ph.D. Program and maintains dual affiliations as affiliated faculty in the Machine Learning Department and the Center for the Neural Basis of Cognition (CNBC). As a graduate advisor for the Program in Neural Computation (PNC), she bridges statistical methodology with neuroscience applications. Her research focuses on computational neuroscience, particularly time series forecasting, networks, algorithms, and nonparametric methods. Key research areas include computational neuroscience, biostatistics & epidemiology, graphical models & networks, and DELPHI lab projects. Her work integrates advanced statistical techniques to solve complex problems in neural data analysis, motor control, and neural decoding. Analysis of her publication record reveals consistent contributions to neural decoding methodologies, spike train analysis, and cortical covariance modeling. Her recent work emphasizes practical applications in brain-machine interfaces and neural prosthetics, with a strong focus on improving decoding accuracy through novel statistical approaches to spike sorting and waveform analysis. The integration of machine learning with traditional statistical methods characterizes her interdisciplinary approach. Scientific Awards: 2006 Canadian Journal of Statistics Best Paper Award As an educator, Ventura serves as Co-Director of the Ph.D. Program in Statistics and Data Science while advising students through the CNBC's Program in Neural Computation. Her collaborations span multiple departments and research centers, particularly through the DELPHI Lab Group which focuses on computational neuroscience applications. Current research directions suggest continued innovation in neural decoding algorithms and applications to motor control systems.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +5 more

Dr. Kit Yan Chan is a Senior Lecturer at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University. His research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with applications in wireless communications, signal processing, and power systems. He has held editorial roles in journals such as Neurocomputing, Sensors, and the International Journal of Ad Hoc and Ubiquitous Computing. His teaching spans courses like Transmission and Interface Design, Advanced Research in AI, and Mobile Cloud Computing. Dr. Chan's work emphasizes interdisciplinary approaches, combining computational intelligence with engineering challenges. He has contributed to over 100 publications in areas such as resource allocation in heterogeneous networks, deep learning for load forecasting, and underwater acoustic communication systems. His research bridges theoretical advancements and practical implementations, addressing real-world problems in telecommunications, energy systems, and smart technologies. His recent projects include optimizing energy efficiency in 5G networks, developing robust power control strategies, and advancing neural network architectures for real-time applications. Collaborations with industry and global institutions underscore his commitment to impactful research.

Zhen LiView profile

Assistant Professor

• Multiscale/Multiphysics modeling
• Soft matter and Smart materials
• Biophysics and Collective dynamics
• +7 more

Dr. Zhen Li is an Assistant Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined Clemson in August 2019 after serving as a research associate professor at Brown University and a postdoctoral research associate at University of California, Merced. Education: Ph.D. in Fluid Mechanics, Shanghai University, 2012 MS in Fluid Mechanics, Shanghai University, 2008 BS in Engineering Mechanics, Wuhan University, 2005 Dr. Li's research focuses on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics using both bottom-up (coarse-grained molecular modeling) and top-down (from continuum descriptions to fluctuating hydrodynamics) approaches, along with high-performance computing. His work spans mathematical theory for coarse-graining and model reduction, statistical methods and machine-learning approaches applied to multiscale modeling, memory effects in complex fluids, and concurrent coupling of heterogeneous solvers for scale-bridging. Analysis of Dr. Li's recent publications reveals a strong trend toward integrating machine learning with traditional computational methods, particularly neural operators for multiscale problems. His work spans diverse applications from bubble dynamics and blood flow to materials science and bioprinting, demonstrating the versatility of his computational approaches across multiple disciplines in engineering and physics. Awards and Recognition: CECAS Dean's Professor Award (2024) Award of Excellence - Junior Faculty (2021-2022) Best Research Poster Award at SC19 (2019) 2nd Place Award of Best Poster Presentation at DOE/EFRC AIM for Composites meeting (2024) Dr. Li actively mentors PhD students including Miles Lu, Ryan Wan, Haizhou Wen, and Ali Mohammadi, who have published significant research in computational mechanics. His research is supported by multiple grants including an NSF Elements grant as PI for 'SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms', an NSF CDS&E grant as co-PI for 'HAM3R: Heterogeneous Automated Management of Multiscale Methods and Resources', a DOE/EFRC grant as Thrust lead co-PI for 'AIM for Composites', and a NASA EPSCoR grant as Science-PI. Dr. Li leads the MuthComp (Multiscale theory and Computation) research group, which focuses on developing interfaces between Engineering, Applied Mathematics, Physics-based Machine Learning, and High Performance Scientific Computing. The group has active collaborations with institutions including Idaho National Laboratory, University of Tokyo, and Brown University, and has developed open-source software including USERMESO for GPU-accelerated DPD simulations.

Max GaragnaniView profile

Senior Lecturer

• Computational Neuroscience
• Neural Networks
• Cognitive Science
• +7 more

Dr. Max Garagnani is a Senior Lecturer in Computer Science at Goldsmiths, University of London, and co-director of the MSc in Computational Cognitive Neuroscience. He holds dual PhDs from the University of Cambridge (Computational Cognitive Neuroscience, 2009) and the University of Durham (Artificial Intelligence, 1999). His primary research focuses on developing biologically realistic neural networks to model cognitive functions like language acquisition, decision-making, and perception-action integration. Garagnani's work emphasizes neurocomputational models constrained by brain anatomy and physiology, particularly exploring how language emerges from neural activity in the cortex. He has collaborated with institutions like the Free University of Berlin’s Brain Language Lab and the MRC Cognition and Brain Sciences Unit. His research combines computational modeling with neuroimaging (MEG/fMRI) and robotics (e.g., iCub). Key achievements include modeling semantic dementia breakdown, explaining visual cortex recruitment in blind individuals, and simulating prediction-error signals in cortical hierarchies. Awards include the EU Marie Curie Fellowship and EPSRC grants. He teaches courses in computational neuroscience and supervises projects in neural network modeling and cognitive systems.

Prashanth Thattai RavikumarView profile

Lecturer

• Human-AI Co-creativity
• Generative Audio Technologies
• Real-Time Music Creation
• +5 more

Prashanth Thattai Ravikumar is a Lecturer in the Computing Department at Goldsmiths, University of London. His research focuses on enhancing human-AI co-creativity through generative audio and music systems, with applications in VR, gaming, and EdTech. He leads courses in MOOCs, Digital Signal Processing (DSP), and computer graphics. Education: PhD in Human-AI Co-creativity (National University of Singapore, 2020); MA in Communications and New Media (NUS, 2014); BTech in Computer Science and Engineering (National Institute of Technology, Trichy, 2012). Research interests include developing efficient software for generative audio, real-time music creation, and deep neural networks. His work emphasizes human-AI collaboration to produce novel creative experiences. Key projects include Harmonaise (algorithmic music generation from gestures) and Syntex (audio texture datasets for neural networks). Recent work trends focus on parametric audio systems, AI-driven music interfaces, and scalable audio datasets. His 2022 Arts X Tech Grant supported a pioneering music technology project in Singapore. Awards: Arts X Tech Grant (2022) Grants: $30k National Arts Council award for music tech innovation Lab/Team affiliations include projects like COVID-Life (cellular automaton game) and Rhythm Partner Study , exploring interactive AI-driven music systems.