Find Academic Supervisors

Mohamed GharibView profile

Academic

• Vibration Control
• Robotics Systems Design
• Dynamic Systems
• +4 more

Dr. Mohamed Gharib is an Instructional Associate Professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University's College of Engineering. He also serves as Program Coordinator for Multidisciplinary Engineering Technology and holds affiliated faculty status in Multidisciplinary Engineering. His roles include teaching, research leadership, and academic program development. Educational Background: Ph.D., Mechanical Engineering, Southern Methodist University (2011) M.S., Aerospace Engineering, Cairo University (2007) B.S., Aerospace Engineering, Cairo University (2002) Research Interests: Dr. Gharib focuses on advanced engineering systems and education innovation. His technical work spans vibration control , robotics systems design , and impact mechanics , with notable contributions to drill string vibration suppression and novel damping technologies. In education, he pioneers STEM outreach programs and curriculum development, including initiatives like the Awesome STEM Lab and e-STEM programs . His COBOTICS Lab integrates robotics research, undergraduate training, and community engagement. Research Impact: Recent work addresses dexterous robotics (via NSF's HAND ERC), adaptive control algorithms for drilling systems, and advanced composite coatings for tribological applications. His publications emphasize real-world applications in oil well operations, structural acoustics, and educational technology. Awards: Francisco José de Caldas Award for Sustainable Excellence (2021) Excellence in STEM Implementation Award (ASME, 2020) Outstanding Graduate Student Awards (2010) Labs & Programs: Director of the COBOTICS Lab , which develops robotic systems, control technologies, and STEM education tools. Active in multidisciplinary projects like the NSF-funded Human Augmentation via Dexterity (HAND ERC) collaboration.

• Algorithms
• Data Structures
• Pattern Matching
• +3 more

Simon J. Puglisi is a Professor in the Department of Computer Science at the University of Helsinki. His research focuses on algorithms, data structures, pattern matching, and data compression, with applications in bioinformatics and genomics. He has collaborated extensively with researchers in the field, including Travis Gagie, Juha Kärkkäinen, and Andrew Turpin. His work spans theoretical computer science and practical applications in genomic data processing and efficient indexing techniques. Key research interests include genome sequencing algorithms, efficient compression methods (e.g., Lempel-Ziv and relative Lempel-Ziv), and the development of succinct data structures for handling large genomic datasets. His contributions to suffix arrays, wavelet trees, and de Bruijn graphs have advanced computational methods in bioinformatics and information retrieval. Puglisi's articles frequently address challenges in text indexing, error correction in short-read sequencing, and optimizing algorithms for scalability. He is known for his work on self-indexing techniques and the SHREC error correction method for genomic data. His research bridges theoretical algorithm design with real-world applications in high-throughput sequencing and large-scale data management.

Glenn GulakView profile

Professor

• Homomorphic Encryption
• Quantum Cryptography
• CMOS Biosensors
• +4 more

Glenn Gulak is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds the Canada Research Chair in Signal Processing Microsystems and the Edward S. Rogers Sr. Chair in Engineering. A Senior IEEE Member and Professional Engineer in Ontario, he received his Ph.D. from the University of Manitoba. His research spans: Digital Communication Systems: VLSI implementations of MIMO detectors, lattice reduction algorithms, and homomorphic encryption accelerators Lab-on-Chip Microsystems: CMOS biosensors for rapid pathogen detection and integrated fluorescence imaging Recent publications (2019-2025) demonstrate a dominant focus on privacy-enhancing technologies, with 73% concentrated in cryptographic hardware and homomorphic encryption. This reflects industry-aligned work on confidential computing and secure data processing. Awards & Honors: IEEE Millennium Medal (2001) Canada Research Chair in Signal Processing Systems (Tier 1, 2005-2012) Edward S. Rogers Sr. Chair (2005-2010) RBC Research Prize L. Lau Chair (1999-2004) Teaching Award (1999) He has supervised 44+ graduate students (PhD/MASc) with thesis topics spanning VLSI communication systems, CMOS biosensors, and cryptographic accelerators. Notable industry collaboration includes serving as CTO of a semiconductor startup (2001-2003). His lab develops hardware for quantum cryptography and secure medical computation.

Peter Behrensdorff PoulsenView profile

Researcher

• Solar Cell Engineering
• Photovoltaics
• Drone Engineering
• +5 more

Peter Behrensdorff Poulsen serves as Solar Photovoltaic Systems Group Leader at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU). His work spans photovoltaic research, solar-powered lighting systems, and drone-based inspection technologies within DTU's College of Engineering. His research focuses on Solar Cell Engineering , Photovoltaic Modules , and Drone Applications for renewable energy systems. Key areas include bifacial photovoltaics, electroluminescence imaging diagnostics, building-integrated photovoltaics, and ultra-efficient solar-powered lighting solutions. His work significantly contributes to UN Sustainable Development Goals related to affordable clean energy and sustainable cities. Recent publications reveal strong trends in AI-driven PV diagnostics , bifacial module performance in northern climates, and dark-sky compatible lighting systems . His research bridges fundamental photovoltaic science with practical applications in urban infrastructure and environmental sustainability. Best Poster Award at 44th IEEE Photovoltaic Specialists Conference Best Poster Award at 48th IEEE Photovoltaic Specialists Conference Characterizing the Performance of Daylight Filters for Electroluminescence Imaging Poster Award at 35th European Photovoltaic Solar Energy Conference Poster Prize at Sustain 2017 Poulsen leads multiple research grants including the Ultra-efficient Dark Sky-compatible Solar-powered Outdoor Lighting project (2024-2026) and previously managed the DronEL project (2017-2019) for drone-based PV inspection. His work connects photovoltaic engineering with practical lighting applications through the Lighting Color and Radiation Laboratory. He actively collaborates with industry partners on building-integrated photovoltaics and solar-powered infrastructure solutions, with notable projects including the Plateau Sun Hub public charging stations and Black Si BIPV panel development.

Agostino CortesiView profile

Professor

• Software Engineering
• Static Analysis
• Cybersecurity
• +5 more

Agostino Cortesi is a Full Professor at Ca' Foscari University of Venice , affiliated with the Department of Environmental Sciences, Informatics and Statistics. He serves as Rector's Delegate for Research Quality Assessment and Deputy Coordinator of the Scientific Committee for the Innovation Ecosystem Project. His academic career includes a PhD in Applied Mathematics and Informatics from the University of Padova (1992), a postdoctoral fellowship at Brown University, and visiting professor roles at institutions such as the University of Illinois and École Normale Supérieure Paris. Research interests focus on software engineering , static analysis , security applications , and abstract interpretation . He has pioneered techniques for formal verification of software systems and explored cybersecurity in e-Government and robotics. His work spans over 200 publications in top journals and conferences (e.g., ACM TOPLAS, IEEE TSE, POPL, PLDI). Key contributions include advancements in abstract domains for behavioral property verification and security-oriented analysis frameworks. He has held leadership roles including Vice-Rector at Ca' Foscari, Dean of Computer Science programs, and Chair of the Department of Computer Science. Cortesi coordinates EU Horizon 2020 projects (e.g., Families_Share €1.6M) and regional initiatives like CEVID (€360K). He founded Factors , a university spin-off focused on robotic systems verification, which won the 2020 Veneto SmartCup ICT Prize. Education: PhD in Applied Mathematics and Informatics (1992, University of Padova) Editorial Roles: Co-Editor-in-Chief of Springer’s 'Services and Business Process Reengineering', and member of editorial boards for 'Computer Languages' and others Grants: Over €3M in EU and regional funding for projects in cybersecurity, Industry 4.0, and digital innovation Teaching includes courses on Software Correctness , Data Programming , and Computer Networks across Computer Science and Management programs. His research lab actively engages in industrial partnerships with Cisco, Leonardo, and AGID (Italy’s Digital Agency).

• Database Systems
• Data Analytics
• Machine Learning in Databases
• +3 more

Jignesh M. Patel is a Professor at the University of Wisconsin, Madison, WI, USA , with over 25 years of contributions to database systems, data analytics, and hardware-aware query processing. His work bridges theoretical advancements with practical systems engineering. Research Interests span: Database systems optimization (query processing, transaction management) Hardware acceleration for analytics (eBPF, PIM, GPUs) Machine learning integration in databases (feature selection, model optimization) Efficient data structures (hashing, encoding, indexing) Multi-tenant and cloud database management Recent Work focuses on kernel-embedded databases (BPF-DB, 2025), memory-efficient dataframe processing (SplitDF, 2024), and algorithmic-hardware co-design for dense retrieval (DReX, 2025). He has pioneered techniques for adapting to data skew (VIP Hashing, 2022), leveraging static analysis in R optimization (ROSA, 2017), and rethinking benchmarking paradigms. Collaborations include key partnerships with: Systems researchers (Andrew Pavlo, José F. Martínez) Machine learning experts (Arun Kumar, Kevin Skadron) Education-focused colleagues (Adalbert Gerald Soosai Raj, Richard Halverson) Industry leaders (David J. DeWitt, Microsoft Research)

Pierrick LottonView profile

Research Professor

• Electroacoustics
• Thermoacoustics
• Audio Transducers
• +4 more

Pierrick Lotton serves as a CNRS Research Director at Le Mans University's Institute of Acoustics (LAUM), a joint research unit between CNRS and the university. He leads critical work within LAUM's Transducers team, focusing on fundamental and applied research in electroacoustics and thermoacoustics. His institutional affiliation places him at France's premier acoustics research laboratory, which maintains extensive facilities for acoustic measurements, ultrasonic experimentation, and transducer development across multiple specialized domains including materials science, opto-acoustics, and bioacoustics. Lotton's research program centers on two interconnected pillars: electroacoustics and thermoacoustics. His electroacoustic investigations pioneer advanced modeling, development, and characterization of audio transducers with particular emphasis on nonlinear behaviors in loudspeakers and electric guitar pickups. Simultaneously, his thermoacoustic research explores acoustic refrigeration systems, complex couplings between acoustic and thermal energy fields, and transient nonlinear phenomena. This dual focus enables innovative cross-pollination between audio engineering and thermal physics, driving advancements in both fundamental understanding and practical applications of acoustic energy conversion. Analysis of his 2019-2024 publications reveals a consistent trajectory in transducer physics, particularly MEMS-based piezoelectric speakers, voice coil dynamics in magnetic environments, and digital acoustic projection systems. His work demonstrates exceptional methodological diversity spanning analytical modeling, experimental validation, and educational innovation. Notable contributions include the ASKNOWN project's open-access acoustics courseware and breakthroughs in understanding transducer nonlinearities for both consumer audio and specialized applications like fish sound localization. No major scientific awards were documented in the available institutional materials, though his sustained publication record in high-impact journals and presentations at European Acoustics Association forums indicate significant peer recognition. His collaborative research network spans France, Germany, Italy, and the Czech Republic, reflecting strong international engagement. Lotton's academic supervision activities aren't explicitly detailed, but his educational initiatives like the ASKNOWN project demonstrate commitment to pedagogy. His research is supported through LAUM's institutional framework and collaborative projects including European initiatives and ANR-funded programs. Current work appears concentrated on advancing MEMS transducer technology, refining thermoacoustic cooling systems, and developing next-generation educational resources for acoustics. As a core contributor to LAUM's Transducers team, Lotton operates within one of Europe's leading acoustics laboratories. His current projects align with LAUM's strategic focus on transducer innovation and thermoacoustic applications, positioning him at the forefront of both theoretical acoustics research and practical engineering solutions. The laboratory's comprehensive infrastructure supports his work from fundamental wave propagation studies to applied device development.

• Software Engineering
• Search-Based Software Engineering
• Automated Refactoring
• +4 more

Dr. Mel Ó Cinnéide is an Associate Professor at the School of Computer Science, University College Dublin. He holds a PhD from Trinity College Dublin (2001) and has over three decades of experience in academia and industry. His research focuses on automated refactoring, search-based software engineering, design patterns, and energy-efficient software development. He leads the Masters in Advanced Software Engineering program at UCD and has received multiple research grants and best paper awards. Prior to academia, he worked as a software engineer at Philips (Netherlands) and Motorola (Cork). Education: BSc in Computer Science, University College Cork MSc in Computer Science, University College Cork PhD in Computer Science, Trinity College Dublin Diploma in Gaeilge Fheidhmeach (Computing Irish), UCD Research interests emphasize practical applications of refactoring techniques to improve software quality and energy efficiency. He pioneers tools like Code-Imp and RefDetect for automated refactoring, integrating multi-objective optimization and interactive systems. His work bridges theoretical software engineering principles with real-world industry challenges. Awards and Grants: - Best Paper Awards in peer-reviewed conferences - Competitive research grants supporting software engineering projects Advising & Leadership: - Director of UCD's Masters in Advanced Software Engineering - Supervisor of numerous academic and industrial projects Labs & Projects: - Co-lead of the CodeImp Project (automated search-based refactoring) - Involved in international workshops on refactoring and software engineering

Bin HuView profile

Assistant Professor

• Resilient Cyber-Physical Systems (CPS)
• AI-Human Collaboration Safety & Security
• Big Data & Machine Learning-Based Control
• +5 more

Dr. Bin Hu is an Assistant Professor in the Department of Engineering Technology at the University of Houston (2022–present) and previously held the same position at Old Dominion University (2019–2022). He earned a Ph.D. in Electrical Engineering from the University of Notre Dame (2016), an M.S. in Control Science from Zhejiang University (2010), and a B.S. in Electrical Engineering from Hefei University of Technology (2007). His research focuses on resilient Cyber-Physical Systems (CPS), AI-human collaboration safety, machine learning-driven control, and vehicular networks. Key projects include NASA-funded work on adaptive human-autonomy responsibility allocation (2023–2026) and NSF-supported AI-human collaboration in autonomous vehicles (2020–2023). He has secured over $1.2 million in grants, including a $700k NASA grant (pending) and an ONR Rapid Solutions grant ($42k). Dr. Hu’s work bridges control theory, machine learning, and human factors. Recent contributions include safer leader-follower robotic systems, fault-tolerant UAV control, and studies on driver trust in AI-driven vehicles. His research has been recognized through awards like the 2024–2025 APeX Excellence Speaker Series. Teaching experience includes courses on C++ programming and electrical engineering fundamentals at both the University of Houston and Old Dominion University. He advises multiple undergraduate and graduate students in projects ranging from LiDAR systems to cyber-physical security. Key Labs/Teams : NAIL Lab (Networked Autonomous Intelligence & Learning), part of the University of Houston’s Cybersecurity and Robotics initiatives. Grants : NASA TTT Grant ($700k), NSF CHS Grant ($500k), ONR RSLP Project ($42k).

• Theoretical Computer Science
• Algorithm Engineering
• Parallel Computing
• +3 more

Prof. Dr. Peter Sanders is a full professor in Theoretical Computer Science at the Karlsruhe Institute of Technology (KIT), leading the Algorithm Engineering group. His academic career includes a doctoral degree from Karlsruhe University and research stints at institutions like the Max Planck Institute for Informatics. He specializes in algorithm theory and engineering, focusing on parallel computing, large-scale data processing, and graph partitioning. His research bridges theoretical foundations with practical implementations, emphasizing real-world applications in optimization, route planning, and distributed systems. Education: Ph.D. in Computer Science, Karlsruhe University (1996) Bachelor/Master studies at Karlsruhe University (1988-1996) Research Interests: Algorithm design and analysis Parallel and distributed algorithms Graph algorithms and partitioning Algorithm engineering for big data High-performance computing Publications: Over 250 papers, emphasizing parallel algorithms, distributed systems, and graph theory. Recent work includes scalable SAT solving, hypergraph partitioning, and distributed string sorting. His contributions have advanced practical applications in route planning, load balancing, and large dataset processing. Awards: Recipient of the prestigious Leibniz Prize (DFG) and Baden-Württemberg State Research Prize. He coordinated the DFG Priority Program on Algorithm Engineering and is an active reviewer for major funding bodies. Consulting: Engages with companies like SAP and Google, focusing on optimization, route planning, and database algorithms. Leads projects on algorithm scalability and real-world problem-solving. Labs/Teams: Heads the Algorithm Engineering group at KIT, fostering collaborations in distributed computing and algorithmic research.

• Intelligent Transportation Systems
• Connected and Autonomous Vehicles
• Vehicular Networks
• +6 more

Daxin Tian is a prominent professor at Beihang University's School of Transportation Science and Engineering, specializing in intelligent transportation systems and vehicular networks. With over 170 publications spanning from 2006 to 2025, his research has significantly contributed to the advancement of connected and autonomous vehicle technologies. His work appears consistently in top-tier IEEE journals including IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, and IEEE Internet of Things Journal, establishing him as a leading authority in the field. Professor Tian's research interests encompass several critical areas in modern transportation technology: Connected and Autonomous Vehicle Systems Vehicular Networking and Communication Protocols Vehicle Platooning and Cooperative Driving Algorithms Edge Computing Applications for Transportation Computer Vision for Autonomous Driving Perception Traffic Flow Optimization and Prediction Models Resource Allocation in Vehicular Networks His recent publications demonstrate an increasing sophistication in addressing complex multi-vehicle scenarios while maintaining practical considerations like communication reliability, energy efficiency, and safety constraints. The research trajectory shows a clear evolution from foundational networking and control problems toward more integrated AI-driven solutions that combine computer vision, natural language processing, and advanced control theory for next-generation transportation systems. Professor Tian maintains extensive international collaborations, particularly with researchers at Canadian institutions including Victor C. M. Leung's group, while leading a substantial research team at Beihang University. His work frequently bridges theoretical advances with practical transportation challenges, resulting in numerous high-impact publications that address real-world implementation barriers in intelligent transportation systems.

• Computer Architecture
• High-Performance Computing
• Multicore and Manycore Systems
• +7 more

Miquel Moreto Planas is a Senior Lecturer in the Department of Computer Architecture at the Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading institution in high-performance computing. His academic profile is deeply rooted in computer architecture and high-performance computing, with a strong emphasis on practical and theoretical advancements in multicore systems, memory management, and hardware acceleration. His research interests span a wide range of topics including computer architecture, high-performance computing, multicore and manycore systems, cache and memory management, hardware acceleration for genomics and AI, RISC-V processor design, processing-in-memory, interconnection networks, and real-time systems. These interests are reflected in his extensive publication record and collaborative projects. The most recent articles highlight a significant trend toward interdisciplinary research, particularly the application of advanced computer architecture techniques to bioinformatics and healthcare. Key themes include the acceleration of genomic sequence alignment using novel hardware such as processing-in-memory, the development of benchmarks for ARM-based HPC systems in genomics, and the creation of AI-based 3D decision support tools for neurosurgical applications. His work also continues to advance core computer architecture topics like cache management, power-aware resource allocation in heterogeneous systems, and the design of secure, post-quantum cryptographic hardware based on RISC-V. Fulbright Award 2011 HiPEAC Paper Award HiPEAC Paper Award 2024 HiPEAC Paper Award Moreto has been a principal investigator or key contributor to multiple competitive R&D+i projects, such as the STRATUM project for neurosurgical tools, REDIOH for open hardware, and the Laboratorio Zettaescala de Barcelona. He has advised several doctoral students, including López, G., Kostalampros, I., and Haghi, A., and is a core member of the CAP (High Performance Computing) research group at UPC. His work is characterized by strong collaborations with leading researchers like Mateo Valero, Eduard Ayguadé, and Jesús Labarta, often bridging the gap between UPC and BSC-CNS. His laboratory and team affiliations are centered around the CAP group and the Barcelona Supercomputing Center, where he contributes to cutting-edge research in high-performance and embedded computer architectures. His recent work on the BIMSA accelerator and the STRATUM project demonstrates a clear future direction toward applying high-performance computing solutions to critical problems in genomics and medicine.

Jean AuriolView profile

Researcher

• PDEs
• Control Theory
• Robustness
• +2 more

Jean Auriol is a researcher affiliated with the Signals and Systems Laboratory , focusing on advanced control theory and partial differential equations (PDEs). His work spans robustness, hyperbolic systems, and infinite-dimensional systems, often addressing real-world applications in engineering and physics. Research Interests: PDEs and Control Theory Robustness in Dynamical Systems Hyperbolic Systems and Networked Control Stochastic and Delayed Systems Recent Publications: Stability analysis of triangular hyperbolic systems Smart traffic management for autonomous vehicles Stochastic hyperbolic PDE stabilization Minimum time control of coupled PDEs Neural network-based parameter estimation for drilling systems

Rasha KarakchiView profile

Lecturer

• High Performance Computing
• Reconfigurable Computing
• Embedded Systems
• +3 more

Rasha Karakchi serves as a Lecturer in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, where she teaches diverse courses while maintaining an active research program in hardware acceleration and embedded systems. Her academic foundation includes: Ph.D. in Computer Science and Engineering, University of South Carolina (2020) M.E. in Computer Engineering, University of South Carolina (2016) Dr. Karakchi's research centers on high-performance reconfigurable embedded systems, with particular focus on hardware acceleration for automata processing, spiking neural networks, and genomic sequence alignment. Her work bridges theoretical computer science with practical hardware implementation, emphasizing energy efficiency and real-time performance in security-critical applications. Analysis of her 2023-2025 publications reveals a dominant research trajectory applying machine learning to optimize hardware configurations for domain-specific tasks. Key thematic clusters include ML-enhanced automata processors for pattern matching, lightweight encryption engines for embedded security, and specialized architectures for spiking neural network acceleration—all demonstrating consistent innovation in hardware-software co-design for computationally intensive workloads. Her research excellence has been recognized through: SPARC Award (South Carolina's Program to Advance Research and Creativity)

Adel AbusittaView profile

Assistant Professor

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

Dr. Adel Abusitta is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he conducts research at the intersection of artificial intelligence and cybersecurity. With expertise in secure and resilient AI systems, IoT security, and malware analysis, Dr. Abusitta has established himself as a significant contributor to the field of AI-powered cybersecurity solutions. Education: PhD in Computer Engineering from Polytechnique Montréal Postdoctoral Fellow at University of Montréal Postdoctoral Fellow at McGill University Dr. Abusitta's research focuses on developing secure and trustworthy artificial intelligence systems with applications in cybersecurity. His work spans several critical areas including explainable AI for security applications, AI-powered malware analysis, intrusion detection systems, and IoT security. He has made significant contributions to the understanding of how AI can be both secured against attacks and used to enhance security systems. His research addresses the dual challenge of making AI systems resilient to adversarial manipulation while leveraging AI's capabilities to detect and prevent cyber threats in complex environments like cloud computing and IoT networks. Analysis of Dr. Abusitta's recent publications reveals a strong focus on the intersection of AI and cybersecurity, particularly in developing robust anomaly detection systems, explainable security solutions, and resilient architectures for IoT environments. His work demonstrates a consistent trajectory toward making AI systems both more secure and more useful for security applications, with increasing emphasis on practical implementations that can withstand real-world challenges. Dr. Abusitta has collaborated extensively with Defence Research and Development Canada (DRDC) on projects related to AI-powered data analytics for discerning malware intent. He has also worked with industrial partners through the Institute for Data Valorization (IVADO) to develop privacy-preserving machine learning techniques that maintain accuracy while protecting sensitive information. His research has practical applications in critical infrastructure protection and secure AI deployment.