Find Academic Supervisors

• Adaptive structures
• Mechanical metamaterials
• Robotic materials
• +14 more

Andres Arrieta is an Associate Professor in the School of Mechanical Engineering at Purdue University. His research focuses on adaptive structures, mechanical metamaterials, and programmable systems. He holds a PhD from the University of Bristol and conducted postdoctoral research at ETH Zurich. Education: Mechanical Engineer, Universidad de los Andes, 2006 PhD in Mechanical Engineering, University of Bristol, 2010 Postdoctoral Research Fellow, ETH Zurich, 2012 Research Interests: Adaptive Structures Multistable Systems Structural Nonlinearity Robotics & Mechanosensing Origami Engineering Awards: 2019 ASME Best Paper Award 2018 Gary Anderson Early Achievement Award 2012 ETH Postdoctoral Fellowship Labs: Directs the Programmable Structures Lab , exploring smart materials and morphing systems.

• Acoustics
• Acoustofluidics
• Acoustic Metamaterials
• +8 more

Junfei Li is an Assistant Professor in the School of Mechanical Engineering at Purdue University. His research focuses on advanced acoustic technologies, including acoustic tweezers, acoustofluidics, metamaterials, and underwater communication systems. He specializes in multiphysics wave propagation, noise control, and energy harvesting. Li's work bridges fundamental science and engineering applications in biomedical devices, sustainable energy, and advanced materials. Research Interests: Acoustic tweezers for microscale manipulation Design of metamaterials for acoustic control Ultrasound and underwater communication systems Energy-efficient noise mitigation strategies His recent publications emphasize innovations in acoustic metasurfaces, nonreciprocal sound propagation, and biomedical acoustic applications. Li’s research has implications for improving medical imaging, energy sustainability, and next-generation acoustic devices. Awards & Recognition: None explicitly listed in the provided materials. Advising & Grants: No student advisees or grant information specified in the text.

Joseph A. CampbellView profile

Assistant Professor

• Explainable AI
• Robotics
• Machine Learning
• +3 more

Joseph A. Campbell is an Assistant Professor in the Department of Computer Science at Purdue University, leading the Collaborative AI for Machines and People (CAMP) Lab. He holds a Ph.D., M.S., and B.S. in Computer Science and Computer Engineering from Arizona State University. Before academia, he worked as a software engineer for five years. Prior to Purdue, he was a Postdoctoral Fellow at Carnegie Mellon University's Robotics Institute. His research focuses on explainable machine learning and robotics, particularly how agents use explanations for self-improvement and decision-making. Key areas include theory of mind in multi-agent systems, lifelong learning, and interpretable transfer learning. His work bridges robotics and AI, with applications in human-robot interaction and prosthetic control. Notable publications include advancements in reinforcement learning with language models, multi-agent collaboration frameworks, and methods for enhancing state estimation in robots. His research has been presented at top conferences like NeurIPS, EMNLP, and CoRL. Dr. Campbell maintains an active GitHub profile (joe-campbell) with repositories such as Interaction Primitives for robotics applications. His lab, CAMP, explores AI systems that collaborate effectively with humans and other machines.

Carla P. GomesView profile

Professor

• Artificial Intelligence
• Constraint Reasoning
• Optimization
• +3 more

Carla P. Gomes is a Professor of Computer Science at Cornell University with joint appointments in the Department of Computer Science and the Dyson School of Applied Economics and Management. She holds a PhD in computer science from the University of Edinburgh and an M.Sc. in applied mathematics from the University of Lisbon. Her research focuses on artificial intelligence, constraint reasoning, optimization, and computational sustainability. As Director of the Institute for Computational Sustainability (ICS) and co-director of the Cornell University AI for Science Institute, she leads efforts to integrate AI with sustainability challenges. Her research themes include the integration of constraint reasoning, machine learning, and operations research to solve large-scale problems. She pioneered the field of Computational Sustainability, addressing environmental, economic, and societal challenges through AI. Gomes directed two NSF Expeditions in Computing awards and established CompSustNet, a large-scale sustainability research network. Key awards include the 2021 ACM–AAAI Allen Newell Award, AAAI Feigenbaum Prize, and fellowships from AAAI, ACM, and AAAS. Her work spans over 200 publications, with contributions to AI, sustainability, and materials discovery. She advises numerous PhD students and oversees postdocs in AI, sustainability, and interdisciplinary projects. Gomes' lab focuses on AI for scientific discovery, including autonomous materials synthesis and crystal-structure phase mapping. She collaborates with institutions like JCAP and the Materials Project, advancing AI-driven solutions for energy and environmental challenges. Current projects include Schmidt AI in Science postdoc initiatives and AI-driven materials discovery platforms like DRNets and SARA.

Ryan CarneyView profile

Associate Professor

• Paleontology
• Epidemiology
• Functional Morphology
• +5 more

Dr. Ryan Carney is an Associate Professor of Digital Science at the University of South Florida's Department of Integrative Biology (College of Arts and Sciences). His research bridges paleontology and epidemiology, using cutting-edge technologies like 3D imaging, AI, and VR/AR. He studies dinosaur biomechanics (e.g., Archaeopteryx flight mechanics) and mosquito-borne diseases (e.g., malaria, Zika) through NSF/NIH-funded collaborations with agencies like NASA and the CDC. His work integrates citizen science platforms (iNaturalist, Mosquito Alert) for disease surveillance and develops AI tools for mosquito classification. Education: PhD (Brown), MPH/MBA (Yale), B.A.'s in Biology & Art (UC Berkeley) Lab: SCA 107 (focuses on digital paleontology and disease modeling) Research interests span dinosaur functional morphology, epidemiological modeling using GIS/remote sensing, and translating science through immersive technologies. He received awards like the National Geographic Emerging Explorer and multiple USF teaching/research accolades. Awards: CAS Liberal Arts Teaching Award, Outstanding Research Achievement, etc. Grants include NSF funding for AI-driven mosquito surveillance and NIH support for disease control. His lab's innovations include the Global Mosquito Observations Dashboard (GMOD) and VR reconstructions of prehistoric species.

Marina BlantonView profile

Associate Professor

• Applied Cryptography
• Information Security
• Privacy-Preserving Computation
• +3 more

Marina Blanton is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, and Faculty Director of Women in Science and Engineering within the School of Engineering and Applied Sciences. She holds a PhD in Computer Science from Purdue University (2007), along with multiple advanced degrees in Computer Science and Electrical Engineering from prestigious institutions in the US and Russia. Her research focuses on applied cryptography, information security, and privacy-preserving computation and outsourcing. She has pioneered work on secure multi-party computation protocols, privacy-preserving biometric authentication, and secure data analytics across distributed systems. Her contributions include foundational frameworks like PICCO, a compiler for private distributed computation, and advancements in protocols for genomic data analysis and floating-point secure computation. Blanton has been recognized with numerous awards, including IEEE and ACM Senior Membership (2016/2015), the ACM CCS Test of Time Award (2015), and the AFOSR Young Investigator Award (2013). Her research has been supported by grants such as NSF SaTC awards and AFOSR funding. Her work emphasizes practical implementations of secure computation, with applications in healthcare, biometrics, and distributed data systems. She has advised numerous students and contributed to educational initiatives promoting women in STEM through her leadership roles.

• Defects in Semiconductors
• Porous AlGaN Materials
• Cathodoluminescence Spectroscopy
• +4 more

Gunnar Kusch is a Senior Research Associate at the Department of Materials Science & Metallurgy, University of Cambridge. His research focuses on defects in semiconductors, porous AlGaN materials, and advanced characterization techniques like cathodoluminescence (CL) and atom probe tomography (APT). He holds a PhD from the University of Strathclyde and leads projects on UV-B LED optimization, nanoscale defect behavior analysis, and semiconductor device design. His work bridges materials synthesis, characterization, and device performance, with applications in energy-efficient lighting and solar cell technology. Key research areas include: Defect engineering in III-nitride semiconductors Porous AlGaN templates for high-efficiency UV emitters Correlative microscopy techniques (CL, EBSD, APT) Composition-structure-property relationships in photovoltaic materials Notable contributions include developing CL-based methods for nanoscale defect analysis and demonstrating improved Cu(In,Ga)S₂ solar cell efficiencies through compositional engineering. His laboratory focuses on translating microscopic insights into macroscopic device improvements.

Steven H. H. DingView profile

Assistant Professor

• Cybersecurity
• Machine Learning
• Data Mining
• +7 more

Dr. Steven H. H. Ding is an Assistant Professor at McGill University's School of Information Studies, specializing in cybersecurity, machine learning, and data mining. His research focuses on AI-driven solutions for malware detection, software vulnerability analysis, and reverse engineering. He holds a PhD from McGill University and has been supported by BlackBerry Cylance and DRDC. His work bridges theoretical advancements with practical applications in military systems and avionics cybersecurity. Dr. Ding earned his PhD in 2019 with notable awards including the FRQNT Doctoral Research Scholarship and McGill's Dean’s Graduate Award. His educational background includes degrees from McGill, Concordia University, and the University of Shanghai for Science and Technology. His research interests span cybersecurity domains such as zero-day malware identification, code obfuscation countermeasures, authorship verification for digital forensics, and AI applications in avionics anomaly detection. He actively contributes to open-source tools like the Kam1n0 MapReduce-based assembly clone search system. Recent work emphasizes adversarial machine learning for evasive malware generation, transformer-based anomaly detection in avionics, and automated SBOM generation for firmware analysis. His publications reflect a focus on real-world cybersecurity challenges in both civilian and defense sectors. Dr. Ding leads the L1NNA Lab and collaborates with industry partners on cutting-edge projects. His contributions include novel techniques for phishing detection leveraging large language models and innovative approaches to reverse engineering software composition in JavaScript applications.

• Electrical and Electronic Engineering
• Computer Hardware
• Computer Software
• +7 more

Professor Peter Y. K. Cheung is a Professor of Digital Systems at Imperial College London, holding dual affiliations within the Department of Electrical and Electronic Engineering and the Dyson School of Design Engineering. His work focuses on reconfigurable systems, FPGA architectures, and high-level synthesis tools. He co-founded one of the UK's leading FPGA research groups with Professor Wayne Luk, addressing challenges in variability mitigation, reliability, and application-specific FPGA deployments. His research spans Field-Programmable Gate Arrays (FPGAs) Reconfigurable computing Neural network acceleration Cryptographic protocols Embedded systems He has pioneered techniques such as logic shrinkage for FPGA-based neural networks and developed frameworks like LUTNet for efficient inference. His contributions also include fault-tolerant FPGA designs and methodologies for distributed computation protocols. Key collaborations include work with the Department of Computing on FPGA-based AI acceleration and cybersecurity applications. His recent work explores edge computing, secure decentralized systems, and pandemic modeling using adaptive control strategies. Notable projects include the DSCS protocol for secure distributed computation, acceleration of gravitational wave detection algorithms, and energy-efficient CNN implementations. His research bridges hardware-software co-design with real-world applications in healthcare, finance, and aerospace.

Yuankai TaoView profile

Associate Professor

• Biomedical Engineering
• Medical Imaging Technologies
• Optical Imaging Systems
• +9 more

Yuankai (Kenny) Tao is an Associate Professor of Biomedical Engineering at Vanderbilt University's School of Engineering and an SPIE Faculty Fellow. He directs the Graduate Studies program in Biomedical Engineering and leads research in optical imaging systems for clinical diagnostics and therapeutic monitoring in ophthalmology, gastroenterology, and oncology. His lab develops technologies like intraoperative OCT and SECTR, focusing on noninvasive subcellular visualization and biomarker monitoring. Collaborations span engineering, basic sciences, and medicine to translate innovations into clinical tools. Education: Ph.D., Biomedical Engineering, Duke University M.S., Biomedical Engineering, Duke University B.S.E., Biomedical Engineering and Electrical Engineering, Duke University Research Interests: Biomedical optics, optical coherence tomography (OCT), image-guided surgery, therapeutic monitoring, big data analytics, and high-throughput imaging for drug discovery. His work bridges engineering and medicine, emphasizing real-time feedback systems and interdisciplinary innovation. Grants & Labs: Director of the Vanderbilt Institute for Surgery and Engineering (VISE)-affiliated lab, focusing on surgical imaging and translational research. Projects include automated instrument tracking, SECTR systems, and AI-driven imaging analysis. Collaborations involve clinicians and researchers across disciplines.

B. Aditya PrakashView profile

Associate Professor

• Data Science
• Machine Learning
• Artificial Intelligence
• +6 more

Aditya Prakash is an Associate Professor and Associate Chair for Academic Affairs in the School of Computational Science and Engineering at Georgia Tech. He holds a PhD from Carnegie Mellon University (2012) and a B.Tech from IIT Bombay (2007). His research focuses on data science, machine learning, and AI applied to epidemiology, healthcare, security, and urban computing. His work has led to impactful tools used by organizations like CDC, ORNL, and Walmart. Notable awards include the NSF CAREER Award (2018) and IEEE's 'AI Ten to Watch' (2017). Education: PhD (Computer Science, CMU, 2012), B.Tech (IIT Bombay, 2007) Research Interests: Epidemic forecasting, network analysis, healthcare informatics, and large-scale data-driven solutions for societal challenges. He has authored over 80 papers and holds two patents. His lab develops methods for disease modeling, urban infrastructure analysis, and cybersecurity. Key projects include the NSF-funded BEHIVE initiative for pandemic prediction and collaborations with MIDAS network for infectious disease modeling. Awards: NSF CAREER, Facebook Faculty Award, IEEE AI Recognition, and multiple best-paper awards. His group advises students across PhD, MS, and undergraduate levels, with notable alumni in academia and industry. Labs & Affiliations: Core faculty at ML@GT (Machine Learning Center) and IDEaS (Institute for Data Engineering and Science). Active in organizing conferences like AAAI, KDD, and SIGMOD.

Juan GarayView profile

Professor

• Cryptography
• Information Security
• Cryptographic Protocols
• +7 more

Juan Garay is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on cryptography, information security, and distributed systems, with notable contributions to cryptographic protocols, blockchain technologies, and consensus mechanisms. He holds a leadership role in advancing theoretical and applied aspects of secure computation and network security. Research Interests: Cryptography and Information Security Secure Multiparty Computation Cryptocurrencies and Blockchain Protocols Consensus Algorithms Distributed Computing Game Theory in Cryptography Publications highlight his work on the Bitcoin Backbone Protocol, secure multiparty computation, and post-quantum cryptographic systems. He actively contributes to conferences and workshops in cryptography and distributed systems. He advises graduate students in computer science and engineering, though specific advisee names are not listed. His work is supported by grants from the National Science Foundation (NSF) and other institutions, focusing on secure protocols and distributed systems. Office: Peterson Building (PETR 429) Contact: garay@cse.tamu.edu

• Biomedical Signal and Image Processing
• Cardiovascular and Neural Modeling
• Physiologically-interpretable Artificial Intelligence systems
• +5 more

Gaetano Valenza is an Associate Professor of Bioengineering at the University of Pisa, Italy, where he leads the Neuro-Cardiovascular Intelligence Lab at the Enrico Piaggio Research Centre. He holds affiliations with the Neuroscience Statistics Research Laboratory at MIT and has served as a Research Fellow at Harvard Medical School and Massachusetts General Hospital. His academic work spans bioengineering, computational physiology, and affective computing. His research focuses on statistical and nonlinear biomedical signal and image processing , cardiovascular and neural modeling , and physiologically interpretable artificial intelligence . He develops wearable systems for physiological monitoring, with applications in autonomic nervous system assessment, brain-heart interactions, and mental health. His work has led to novel metrics such as the Sympathetic and Parasympathetic activity indices derived from ECG. The 15 most recent publications reflect a consistent trend in brain-heart interplay , complexity analysis of physiological signals , explainable AI in healthcare , and virtual reality applications in mental health . His work integrates advanced signal processing, nonlinear dynamics, and machine learning to decode emotional and cognitive states from physiological data. Dr. Valenza is a Senior Member of IEEE and serves on several technical committees. He is an active editorial leader, currently serving as Associate Editor for IEEE-EMBC , Plos One , Complexity , and Scientific Reports , and has guest-edited special issues in Philosophical Transactions of the Royal Society A and IEEE Journal of Biomedical and Health Informatics . He has led or participated in numerous international research projects, including FP7 and H2020 initiatives such as NEVERMIND and EXPERIENCE. He teaches courses in Biostatistics, Probability & Biostatistics, and Advanced Image Processing at the University of Pisa. As lab head and project coordinator, he leads a multidisciplinary team working on neuro-cardiovascular intelligence, wearable systems, and AI-driven mental health interventions.

• Space Propulsion
• Plasma Physics
• Fluid Dynamics
• +3 more

Pablo Fajardo Peña is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid (UC3M). He leads the Plasmas and Space Propulsion Team (EP2) and co-directs the Aerospace Engineering Research Group. His research focuses on advanced propulsion technologies for space applications, including plasma thrusters, electric propulsion, and fluid-thermal systems. Professor Peña's work spans computational modeling of plasma dynamics, experimental characterization of propulsion systems, and development of novel thrusters like Hall effect thrusters and electrospray systems. His recent publications analyze plasma discharge mechanisms, thruster plume behavior, magnetic nozzle effects, and propellant interactions. He leads multiple EU and Spanish-funded projects including HIPATIA (Helicon Plasma Thruster), CHEOPS (Hall Effect Orbital Propulsion), and ADAPT (Advanced Plasma Propulsion). These initiatives focus on developing efficient propulsion systems for spacecraft and addressing challenges in space debris removal. Professor Peña supervises doctoral research on plasma diagnostics and thruster simulation, and has developed simulation tools like HYPHEN (Hybrid Plasma Thruster Holistic Environment). His team collaborates with ESA, Airbus, and SENER Aeroespacial on propulsion technology validation.

Niels van OortView profile

Associate Professor

• Public Transport Planning
• Data-Driven Design
• Transport Sustainability
• +5 more

Niels van Oort is an Associate Professor of Public Transport at Delft University of Technology (TU Delft), co-director of the Smart Public Transport Lab, and affiliated with the Department of Transport & Planning within the Faculty of Civil Engineering and Geosciences. His work focuses on public transport planning, data-driven design, and societal impacts including inclusiveness, land-use, and sustainability. He holds a dual role as a part-time consultant via his company Van Oort Mobility Consultancy since 2020. Education: MSc in Public Transport from TU Delft (2003), PhD in Service Reliability in Public Transport (2011). Professional experience includes roles at HTM (public transport operator), MIT (2009), and Goudappel Coffeng consultants (2007–2012). Transitioned to academia as a part-time assistant professor at TU Delft in 2012, becoming full-time in 2018. Research emphasizes network optimization, timetable reliability, micromobility, and first/last mile solutions. Key areas include pandemic impacts on transport behavior, digital inequality in mobility access, and sustainable transport policies. Supervises PhD students on integrated networks and passenger behavior. Teaching includes courses on public transport operations, advanced modelling, and transport networks. Active in academic publishing and conferences, with over 20 peer-reviewed articles since 2010. Engaged in interdisciplinary labs like SUM Lab (Smart Urban Mobility) and contributes to public discourse through media and consultancy. Labs/Teams: Smart Public Transport Lab (co-director), SUM Lab, and Delft Transport Modelling Group. Grants and projects focus on data-driven solutions for public transport efficiency and equity.