Find Academic Supervisors

• Cybersecurity
• Cryptography
• Programming Languages
• +4 more

Elaine Shi is a Professor with a joint appointment in the Computer Science Department (CSD) and Electrical and Computer Engineering (ECE) at Carnegie Mellon University. She is also an Adjunct Professor of Computer Science at the University of Maryland. Her research focuses on cryptography, security, mechanism design, algorithms, blockchains, and programming languages. She co-founded Oblivious Labs, Inc., and her work on Oblivious RAM and differential privacy has been adopted by Signal, Meta, and Google. Education: Not explicitly listed, but her academic roles imply advanced degrees in computer science. Research Interests: Shi's work spans foundational areas such as secure computation, privacy-preserving algorithms, and blockchain protocols. She emphasizes practical implementations, such as Oblivious RAM and cryptographic compilers like Viaduct. Her research also explores game-theoretic mechanisms for decentralized systems and explores the intersection of theory and practice in secure distributed systems. Publications: Over 150+ articles in top venues like Eurocrypt, S&P, and CCS, covering topics like oblivious algorithms, blockchain security, and differential privacy. Recent work includes optimizing obfuscation, secure aggregation, and transaction fee mechanisms. Packard Fellow, Sloan Fellow, ACM Fellow, IACR Fellow Co-founder of CMU's Crypto Group and Crypto Seminar Series Advising & Grants: Supervises a large group of students and postdocs, with notable alumni holding academic and industry roles. Active in securing grants for research in secure computation and blockchain technologies. Labs & Teams: Leads research in Oblivious Labs and collaborates on projects like the Viaduct compiler and secure enclaves for privacy-preserving computation.

• Machine Learning & Artificial Intelligence
• Probabilistic Modelling
• Neuro-Symbolic Modelling
• +7 more

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on interdisciplinary applications of machine learning (ML) and artificial intelligence (AI), emphasizing interpretability, robustness, and ethical considerations. He explores computational models in music cognition, communication dynamics, and medical image analysis, aiming to bridge theory and practical tools for domain experts. Before Durham, he was a postdoctoral researcher at EPFL's Digital and Cognitive Musicology Lab (2018–2021) and earned his PhD from the Learning and Intelligent Systems Lab in Stuttgart/Berlin (2012–2017). His work combines probabilistic modelling, neuro-symbolic systems, and reinforcement learning to address challenges in music analysis, autonomous decision-making, and medical robotics. Key research themes include: Music structure and perception modelling Symbol emergence in multi-agent communication Ethical AI and autonomous systems governance Medical imaging applications (CT/MRI analysis) Recent projects involve developing patient-agnostic diabetes management systems using deep reinforcement learning and surgical workflow anticipation through graph learning algorithms. He actively contributes to conferences such as NeurIPS, ISMIR, and AAAI, with publications spanning music informatics, robotics, and biomedical engineering. Current supervision includes four postgraduate students focusing on AI applications in healthcare, music technology, and autonomous systems. His work bridges technical innovation with societal impact, addressing challenges in policy, legislation, and interdisciplinary collaboration.

• System Infrastructure for Big Data Processing
• High-Performance Computing (HPC)
• Storage Systems
• +5 more

Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.

Ebru CankayaView profile

Senior Lecturer

• Computer and Network Security
• Risk Modeling in Databases
• Lossless Text Compression
• +5 more

Ebru Cankaya is a Senior Lecturer II in the Department of Computer Science at the University of Texas at Dallas (UTD), part of the Erik Jonsson School of Engineering and Computer Science. She holds a Ph.D. in Computer Science from Ege University (Turkey) and has extensive academic experience across multiple institutions, including adjunct roles at Southern Methodist University and visiting professorships at Izmir University of Economics and Earlham College. Her research focuses on cybersecurity, risk modeling in databases, lossless text compression, and cloud computing. She has received numerous teaching awards, including the 2019 Outstanding Faculty of the Year award at UTD. Educational Background: Ph.D. in Computer Science, Ege University (2004) M.S. in Computer Science and IT & Management, Ege University (2004/2009) MBA in Economics and Administrative Sciences, Ege University (2000) B.Sc. in Computer Engineering, Ege University (1994) Research Interests: Computer and Network Security: Including access control models (e.g., Bell-LaPadula, Chinese Wall) and cryptographic techniques. Risk Modeling in Databases: Focusing on privacy-preserving data storage and obfuscation strategies. Text Compression: Innovations in encoding methods like Star Encoding and hybrid techniques. Cloud Computing: Security and dependability in distributed systems. Awards and Recognition: 2022: Teaching Award, Jonsson School 2019: Outstanding Faculty of the Year 2013: Faculty of the Month (NACURH) Multiple nominations for University and System-Wide Teaching Awards TUBITAK/EBILTEM Research Awards (2003–2004) Her professional activities include organizing doctoral symposiums (e.g., COMPSAC 2012/2013), participating in faculty development programs (e.g., Working Connections IT Institute), and mentoring undergraduate researchers. She has held academic roles across Turkey and the U.S. since 1997, including research assistantships at Ege University and a decade-long tenure at Ege University as a lecturer and assistant professor.

Brian VermeireView profile

Associate Professor

• Computational Fluid Dynamics
• Aerodynamics
• High-Performance Computing
• +6 more

Brian Vermeire is an Associate Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on computational fluid dynamics, aerodynamics, high-performance computing, turbulence modeling, numerical methods, and optimization. He leads the Computational Aerodynamics Laboratory, emphasizing scale-resolving simulations and high-order numerical techniques. Key interests include large eddy simulation (LES), direct numerical simulation (DNS), and gradient-free optimization. His work often involves developing advanced algorithms for unstructured grids and high-performance computing platforms. Research Interests: High-order numerical methods Implicit/explicit time integration schemes Polynomial adaptation for adaptive meshing Aeroacoustic shape optimization Large eddy simulation (LES) and direct numerical simulation (DNS) Software development for CFD (e.g., PyFR) Recent work trends show strong focus on hybridized flux reconstruction methods, energy-conservative algorithms, and industrial adoption of high-fidelity simulations. Major contributions include scalable implementations for petascale computing and open-source tools like PyFR. His group collaborates on applications such as wind turbine aerodynamics and low-pressure turbine design. Labs/Teams: Computational Aerodynamics Laboratory (website: link )

Christopher PiechView profile

Assistant Professor

• Computer Science Education
• Educational Data Science
• Artificial Intelligence in Education
• +3 more

Christopher Piech is an Assistant Professor (Teaching) in the Department of Computer Science at Stanford University, with a courtesy appointment in the Graduate School of Education. He serves as a Faculty Affiliate at the Institute for Human-Centered Artificial Intelligence (HAI) and is affiliated with the Symbolic Systems Program. Current courses: AI for Social Good (CS 21SI), Introduction to Probability for Computer Scientists (CS 109), Researching Presenting and Publishing Work in AI & Education (CS 220/EDUC 481) Advises 11 Master's students and co-advises 3 Doctoral students His research focuses on computational education, leveraging artificial intelligence to enhance learning analytics, student collaboration detection, and knowledge tracing in programming education. Publications span ACM Technical Symposium on Computer Science Education (SIGCSE) and NeurIPS conferences. Key article trends include: (1) AI-driven educational tools for code analysis, (2) collaboration monitoring in large classes, and (3) probabilistic models for student learning trajectories.

• Computational Mathematics
• Geophysical Fluid Dynamics
• Numerical Software
• +3 more

Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.

• Digital Hardware Design
• Reliability Engineering
• Reconfigurable Devices
• +3 more

Eleonora Vacca is a PhD student and Research Fellow in the Department of Automatic Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She holds a B.S. in Electronic Engineering from the University of Palermo (2018) and an M.S. in Electronic Engineering-Embedded Systems from Politecnico di Torino (2021). Her research focuses on digital hardware design, reliability engineering, reconfigurable devices, and AI applications in aerospace and safety-critical systems. She is a member of the Aerospace and Safety Computing Lab and the CAD - Electronic CAD & Reliability Group (DAUIN). Her work addresses challenges such as radiation effects mitigation in space missions, fault-tolerant AI accelerators, and real-time anomaly detection in satellite telemetry. She has contributed to projects like the RAMSES CubeSat-1 Development (2025-2026), funded by commercial contracts. In 2024, she won the Best Student Paper Award at the NEWCAS Conference for her research on radiation effects in space missions. Vacca collaborates on teaching, including assisting in the course 'Electronic Calculators' for Computer Engineering students. Her recent publications explore AI resilience in RISC-V ecosystems, radiation environment analysis for space missions, and gesture recognition systems for smart cities. She actively contributes to conferences such as the ACM International Conference on Computing Frontiers and the IEEE International Smart Cities Conference.

Russell TessierView profile

Professor

• Reconfigurable Computing
• FPGA Architecture
• CAD Algorithms for FPGAs
• +4 more

Russell Tessier is a Professor and Department Head of Electrical and Computer Engineering at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. His research focuses on reconfigurable computing, FPGA architectures, and hardware security, with notable contributions in CAD algorithms for FPGAs, embedded systems, and multi-tenant FPGA vulnerability analysis. Education: B.S.C.S.E., Rensselaer Polytechnic Institute (1989) M.S. and Ph.D., Massachusetts Institute of Technology (1992 and 1999) Research Interests: Dr. Tessier's work spans FPGA security (e.g., side-channel attacks, power distribution vulnerabilities), reconfigurable cloud computing, and hardware acceleration for applications like SAR imaging and machine learning. His lab, the Reconfigurable Computing Group, develops open-source FPGA cores (e.g., FlexGrip GPGPU, DE4 NetFPGA) and explores cutting-edge security countermeasures. Awards and Honors: Chancellor's Leadership Fellow (2015-2016) NSF Information Technology Research Grant Lilly Teaching Fellow (2002-2003) Multiple College of Engineering Excellence Awards Grants and Projects: Active funding includes NSF SaTC grants on reconfigurable cloud security and NASA support for snowpack measurement systems. His research also addresses FPGA-based solutions for cybersecurity, such as intrusion detection and power-side channel mitigation. Labs and Teams: Leads the UMass Reconfigurable Computing Group, which collaborates on open-source FPGA tools, security frameworks, and embedded system designs. The group maintains platforms like the DE4 NetFPGA and FlexGrip architecture.

Brendan Dolan-GavittView profile

Associate Professor

• Cybersecurity
• Program Analysis
• Virtualization Security
• +5 more

Brendan Dolan-Gavitt is an Associate Professor in the Computer Science and Engineering Department at NYU Tandon School of Engineering and part of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from Georgia Tech (2014) and a BA in Math and Computer Science from Wesleyan University (2006). His research spans cybersecurity, program analysis, virtualization security, memory forensics, and embedded/cyber-physical systems, focusing on automating the understanding of large software systems to develop novel defenses. Research interests include developing techniques for static and dynamic analyses of real-world software to reveal hidden design assumptions. His work has been presented at top security conferences like USENIX Security, ACM CCS, and IEEE Security & Privacy. He led the development of the open-source PANDA platform for dynamic analysis. His publications primarily focus on AI-driven security solutions, vulnerability discovery, and automated testing tools. Recent work explores LLMs in offensive security, fuzzing enhancements, and secure code generation, emphasizing practical applications in cybersecurity. Scientific Awards: NSF CAREER Award for improving software vulnerability testing and education He leads the OSIRIS Lab, a student-run cybersecurity group, and collaborates on interdisciplinary projects addressing emerging security challenges through grants and industry partnerships.

Zahra AbbasiView profile

Assistant Professor

• Applied Electromagnetics
• RF/Microwave Sensing
• Chipless Sensors
• +5 more

Dr. Zahra Abbasi is an Assistant Professor at the Department of Electrical and Software Engineering, University of Calgary (since 2021), with expertise in designing passive and active RF/microwave sensors for non-invasive real-time detection and monitoring. She holds a PhD in Nanosystems and Microsystems from the University of Alberta (2020) and a postdoctoral fellowship at the Waterloo Microfluidics Lab (University of Waterloo). Her research focuses on sensor systems for environmental, biomedical, and industrial applications, including microfluidic sensing, chipless tags, and wearable devices. Dr. Abbasi’s educational background includes degrees from Iran University of Science & Technology (BSc 2014, MSc 2016) and advanced training in Canada. She chairs IEEE MTT-S/AP committees in Alberta and has received notable awards such as the ASTech Finalist (2024) and Alberta Immigrant Impact Award (2023). Her work emphasizes collaboration across disciplines to address challenges in precision agriculture, healthcare, and environmental monitoring. Her sensor research spans diverse fields: microplastic detection in water, hydrocarbon contamination monitoring, real-time medical diagnostics (e.g., fibrinogen levels), and nutrient assessment in agriculture. Recent advancements include disposable sensors for medical implants and non-invasive hydration tracking for aging populations. Awards Finalist: ASTech Early Career Academic Change Maker (2024) Early Research Excellence Award (Schulich School of Engineering, 2023/2024) Alberta Immigrant Impact Award (2023) Advising & Labs Dr. Abbasi leads the Calgary Sensor Laboratory, fostering innovation in microwave and microfluidic sensor technologies. Her team collaborates on industrial and biomedical projects, emphasizing practical applications of novel sensor designs. While no named advisees are listed, her research group actively engages in multidisciplinary projects.

Majid GhaderiView profile

Professor

• Network Algorithms
• Secure Communication
• Machine Learning in Network Control
• +3 more

Majid Ghaderi is a Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. His expertise spans network algorithms, secure communication, and machine learning applications in network control. He holds a Ph.D. in Computer Science from the University of Waterloo (2006), and M.Sc. and B.Sc. degrees in Software Engineering from Sharif University of Technology (2001 and 1999). Education: Ph.D. Computer Science, University of Waterloo, 2006 M.Sc. Software Engineering, Sharif University of Technology, 2001 B.Sc. Software Engineering, Sharif University of Technology, 1999 Research Interests: Dr. Ghaderi focuses on optimizing network algorithms, securing communication in distributed systems, and leveraging machine learning for network control. His work addresses challenges such as secure wireless protocols, SDN-based network management, and efficient resource allocation in data centers. He explores proactive traffic scheduling and anomaly detection in critical infrastructures like industrial control systems and vehicular networks. Publications Trends: His recent work emphasizes covert communication in heterogeneous networks, adaptive federated learning in edge environments, and low-overhead diagnostic systems for cloud networks. He also investigates cybersecurity defenses against hardware vulnerabilities and dynamic threat landscapes. Awards: Best in-session Presentation Award, IEEE INFOCOM 2018 Municipal Excellence Award, Government of Alberta 2018 Faculty of Science Excellence in Teaching Award 2012 Advising & Grants: While no specific advisees are listed, his research has been supported by grants focusing on network security, edge computing, and IoT applications. He teaches CPSC 441 (Computer Networks) and maintains an active lab focused on network systems and cybersecurity. Labs & Teams: His research group collaborates on projects involving software-defined networks, vehicular communication, and industrial IoT security. The team develops open-source tools for network monitoring and anomaly detection.

Rabi N. MahapatraView profile

Professor

• Embedded systems
• System-on-chip
• Reconfigurable architectures
• +8 more

Rabi N. Mahapatra is a Professor in the Department of Computer Science & Engineering at Texas A&M University, within the College of Engineering. His research focuses on embedded systems, reconfigurable architectures, real-time systems, and semantic networks. He holds a Ph.D. in Computer Engineering from the Indian Institute of Technology (1992), an M.S. in Electrical Engineering (Sambalpur University, 1984), and a B.S. in Electronics & Communication (Sambalpur University, 1979). His research interests include Network-on-Chip (NoC), data analytic co-design, IoT protocols, and temperature-aware energy management. His work emphasizes hardware-software co-design for complex systems, with applications in many-core processors, semantic search engines, and real-time embedded systems. Key publications highlight contributions to collaborative filtering on many-core architectures, low-jitter clock distribution circuits, and energy-efficient scheduling. He has been recognized as an IEEE Computer Society Distinguished Visitor (2005–2007) and received the BOYS-CAST Indo-US Young Scientist Award. He leads the Codesign Embedded Systems group at Texas A&M, exploring cutting-edge topics such as photonics NoC, reservoir computing, and IoT security. His research bridges theory and practice, addressing challenges in scalable systems and embedded applications.

Abdullah MuzahidView profile

Associate Professor

• Computer Architecture
• Systems
• Artificial Intelligence
• +5 more

Abdullah Muzahid is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University (since August 2024), previously serving as an Assistant Professor there since August 2018. Prior, he held an Assistant Professor role at the University of Texas at San Antonio (2012-2018). He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2012), focusing on architectural support for debugging concurrency bugs under Prof. Josep Torrellas. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, University of Illinois at Urbana-Champaign (2009) B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology (2005) Research Interests: His work spans Computer Architecture , Systems , and Artificial Intelligence , with focus on multiprocessor architecture, parallel programming, debugging, and applying machine learning to system optimization. Recent projects include cache indexing via entropy estimation, DNN training acceleration, and hardware-software co-design for security. Awards: NSF CAREER Award (2017) Excellence in Research Award (UTSA, 2015 & 2017) W. J. Poppelbaum Award (UIUC, 2012) Intel Ph.D. Fellowship (2011) Grants & Advising: He leads NSF-funded projects on robust deep learning and stream processing systems. Advised 5 PhD graduates and currently mentors 4 PhD students. Served on program committees for ISCA, HPCA, MICRO, and as NSF panelist. Labs/Teams: Active in Texas A&M’s Computer Architecture group, collaborating on machine programming, hardware security, and AI-driven systems optimization.

Bernhard RumpeView profile

Professor

• Software Engineering
• Model-Based Software Engineering (MBSE)
• Domain-Specific Languages (DSLs)
• +16 more

Bernhard Rumpe is a Professor and Chair of Software Engineering at the Department of Computer Science 3, RWTH Aachen University, Germany. He leads a research group focused on model-based software engineering, domain-specific languages, and digital twins, with strong industrial collaborations and applications in embedded systems, AI, IoT, and autonomous vehicles. His research centers on improving software development through model-driven engineering, generative techniques, and formal modeling using UML, SysML, and the MontiCore language workbench. Key interests include digital twins, variability modeling, model composition, and the integration of cyber-physical systems with information systems. The recent publications highlight a consistent focus on model-driven digitalization, language workbenches, and system integration. Trends show increasing emphasis on digital twins in manufacturing and societal systems, formal verification of model transformations, and educational applications of model-driven low-code platforms. His work bridges theoretical foundations with industrial applicability. Keynote Speaker, OOPSLE 2025 General Chair, GPCE 2023 Session Chair, MODELS 2020 Program Committee Member, SLE, GPCE, ICSE, ECMFA He advises master’s and doctoral students and leads a vibrant research team that has successfully executed over 100 research projects. His group develops foundational tools like MontiCore and applies them in industrial contexts, contributing to software quality and developer efficiency. No formal grants are listed, but sustained project funding is evident. He is involved in several research labs and teams centered around the Software Engineering Chair at RWTH Aachen, focusing on language workbenches, model-driven development, and digital twin systems. The team actively contributes to open research through publications, tools, and industrial partnerships.