Find Academic Supervisors

Renuka MohanrajView profile

Assistant Professor

• Mobile Ad Hoc Networks
• Design and Analysis of Algorithms
• Software Engineering
• +2 more

Renuka Mohanraj is an Assistant Professor of Computer Science specializing in Mobile Ad Hoc Networks (MANET), with research interests in Design and Analysis of Algorithms, Software Engineering, Web Technology, and Wireless Application Protocol. She holds a PhD from Mother Teresa University and has contributed significantly to network security and optimization through her publications and a book. Education includes: BSc, Madras University MCA, Bharathidasan University MPhil, Periyar University PhD, Mother Teresa University Her research focuses on enhancing security and efficiency in MANET through innovative routing protocols and collision control mechanisms. Recent work extends to IoT and healthcare applications, addressing challenges like secure data transmission and energy efficiency. Earlier contributions centered on MANET’s reliability and multi-path data integrity. No scientific awards or grants are listed. No advisees or lab affiliations are mentioned in the provided information.

Nandini SidnalView profile

Senior Lecturer

• Artificial Intelligence
• Blockchain Technology
• Semantic Analytics
• +8 more

Nandini Sidnal serves as Senior Learning Facilitator and National Academic Course Coordinator for Torrens University's Master of Software Engineering program through the Centre for Artificial Intelligence Research and Optimisation (AIRO). With over 20 years of international teaching experience in Computer Science, Engineering, and Networking, she has established herself as a key academic figure in AI and blockchain applications. Her educational foundation includes: PhD in Computer Science and Engineering (Cognitive Computing using Intelligent Agents) from Visvesvaraya Technological University (2012) M.Tech in Computer Science and Engineering (Parallel and Distributed Computing using Intelligent Mobile Agents) (2003) Bachelor of Engineering (1993) Nandini's research spans Artificial Intelligence, Blockchain Security, and Cognitive Computing , with strong emphasis on practical implementations in agriculture and healthcare. Her work integrates intelligent agents with distributed systems to solve real-world problems like food supply chain security and medical diagnostics, demonstrating consistent innovation from her early best paper award-winning thesis to current cutting-edge applications. Recent publications reveal a pronounced trend toward AI-driven agricultural optimization (dairy quality, aeroponics, nut farming) and healthcare diagnostics (epilepsy detection), alongside critical work in edge security. These outputs consistently bridge theoretical frameworks with tangible industry solutions, particularly in blockchain-secured IoT systems and deep learning applications. Her scientific recognition includes: Best Paper Award at an international conference for distributed computing research Nandini actively mentors high-impact projects including 'Strengthening Mobile-Based Services for Agriculture' and 'Enhancing VANET Performance with Cloud and Edge Technology.' Her industry collaborations with Intel (Parallel Programming integration) and Nokia (Mobility Research Lab establishment in Finland) demonstrate exceptional academic-industry synergy. The AIRO Centre serves as her primary research hub where she guides PhD candidates in blockchain-secured agri-supply chains and semantic recommender systems. Her Mobility Research Lab in Finland remains a cornerstone of her practical innovation legacy, focusing on next-generation mobile application development that continues to influence current VANET and edge computing research directions.

• Blockchain Technology
• Distributed Systems
• Cryptography
• +3 more

Lucianna Kiffer is a researcher at ETH Zurich, Switzerland, specializing in blockchain technology, distributed systems, and cybersecurity. Her work focuses on peer-to-peer networks, consensus mechanisms, and cryptographic protocols within blockchain systems. Notably, she has contributed to studies on Ethereum's architecture, including analyses of Proposer-Builder Separation and the post-Merge PoW landscape. Her research also explores network stability, latency optimization, and adversarial scenarios in decentralized systems. Her publications span conferences like CCS, FC, and SIGMETRICS, addressing topics such as Nakamoto consensus under resource constraints and strategic behavior in P2P networks. Collaborations with experts like Roger Wattenhofer and Ari Juels highlight her engagement with cutting-edge challenges in distributed systems and cryptocurrency infrastructure.

Christian WeinertView profile

Lecturer

• Privacy-Preserving Technologies
• Cryptography
• Secure Communication
• +3 more

Christian Weinert is a Lecturer in Information Security at the Department of Information Security, Royal Holloway, University of London. He previously held postdoctoral and doctoral research positions at TU Darmstadt's ENCRYPTO group. His research focuses on privacy-preserving protocols, secure communication, and large-scale data protection. He is a Fellow of the Higher Education Academy (FHEA) and has contributed to ISO/IEC standards through his BSI committee involvement. Education: PhD in Computer Science (TU Darmstadt, 2021), M.Sc. (TU Darmstadt, 2016), B.Sc. (TU Darmstadt, 2013). Research interests include secure long-term archiving, federated learning privacy, and cryptographic protocols. He teaches modules like Advanced Cryptography and Digital Forensics, and leads the Cryptography Group at Royal Holloway. Notable awards include the German IT-Security Award (2020) for ContactGuard, a Runner-up Distinguished Paper Award (IEEE SaTML'24), and a Noteworthy Reviewer Award (USENIX Security 2023). He advises PhD students Sophie Hawkes, Katy Lambert, Tabitha Ogilvie, and Simon Pohmann. His work spans 31 publications, including top-tier venues like USENIX Security, EuroCrypt, and ACM CCS.

• Machine Learning
• Cybersecurity
• Biometric Authentication
• +3 more

Dr Wencheng Yang is a Senior Lecturer in Computing at the School of Mathematics, Physics and Computing , University of Southern Queensland. He holds a PhD in Computing from UNSW, an MSc from Korea, and a BMgmt from Wuhan University of Technology. His research spans multiple domains including machine learning security , biometric authentication , privacy-preserving systems , and IoT applications . Education : BMgmt (Wuhan University of Technology), MSc (Korea), PhD (UNSW) Yang’s work emphasizes privacy and security in AI systems, particularly in biometric authentication (e.g., fingerprint, face, ECG) and IoT security. His recent publications focus on model inversion attacks , homomorphic encryption , and federated learning for healthcare applications. Key trends include secure face-swapping , Alzheimer’s disease prediction , and anti-forensic detection . His research has been cited 3378 times, with 1458 total downloads and 31 monthly views. While no explicit scientific awards are listed, his interdisciplinary work bridges computer science , healthcare , and cryptography . Current projects include privacy-preserving frameworks for implantable medical devices and military health systems .

• Operations Research
• Scheduling
• Machine Learning
• +6 more

BEZOUI Madani is a Researcher-Lecturer at CESI, affiliated with the 'Engineering and Numerical Tools' research team. He holds a PhD in Operational Research from the University of Science and Technology Houari Boumediène (2019), with a focus on multi-objective programming in portfolio optimization. His academic roles include serving as a pedagogical tutor for FISA training courses and heading the 'Data Sciences' program for 5th-year Computer Science Engineers at CESI. His research interests center on Industry 4.0/5.0, optimization of complex systems, machine learning, IoT/BIM technologies, and scheduling. Notable work includes integrating human-centricity and sustainability into digital twin models and advancing hybrid metaheuristics for multi-objective manufacturing optimization. Recent publications (2021–2024) address preference-driven optimization methods, tabu search algorithms, and IoT network vulnerability detection. He has authored a book on Euclidean graph boundaries and contributed to frameworks for flexible job shop scheduling. His ongoing research focuses on decision-maker preference integration in dynamic scheduling under Industry 5.0 contexts. Advising and grants: No specific advising roles or grants mentioned in the CV. His educational activities emphasize pedagogical leadership in data science and operational research. Labs/teams: Active member of CESI’s Engineering and Numerical Tools group, collaborating on IoT, digital twins, and optimization projects.

Yangming LiView profile

Research Fellow

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +7 more

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Minxuan ZhouView profile

Assistant Professor

• Systems
• High Performance Computing
• Parallel Computing
• +5 more

Minxuan Zhou is an Assistant Professor of Computer Science at Illinois Institute of Technology. His research focuses on systems, high-performance computing, and parallel computing with an emphasis on processing-in-memory (PIM) acceleration, fully homomorphic encryption (FHE), and hyperdimensional computing. He holds a Ph.D. and M.S. in Computer Science from the University of California San Diego, and a B.S. in Computer Science and Technology from Beihang University. His work spans hardware-software co-design, cryptographic acceleration, and energy-efficient computing systems. Research Interests Processing-in-Memory (PIM) architectures for accelerating machine learning and graph processing Hardware acceleration for secure computation (FHE) Hyperdimensional computing frameworks for edge AI Thermal-aware design and memory optimization in 3D systems Recent Research Trends Recent publications emphasize co-optimization techniques for PIM architectures, FHE hardware acceleration, and privacy-preserving machine learning. His work often bridges theoretical algorithm design with practical hardware implementations using novel memory technologies like ReRAM and 3D-stacked memory. Grants & Labs Engaged in cutting-edge research through collaborations on projects involving PIM accelerators and encrypted computation systems. His lab focuses on developing full-stack solutions for next-generation computing architectures.

André BauerView profile

Assistant Professor

• Performance Engineering
• Data Science
• Systems Engineering
• +7 more

Dr. André Bauer is an Assistant Professor of Computer Science at Illinois Institute of Technology. He holds a Dr. rer. nat., M.S., and B.S. in Computer Science from the University of Würzburg. His research focuses on performance engineering and systems optimization for data science applications, addressing challenges in resource management, adaptive systems, and sustainable computing. Bauer leads the SPEC RG Predictive Data Analytics Working Group and has contributed to benchmarks like Libra for time series forecasting. His work spans cloud computing, container orchestration, and security protocols such as homomorphic encryption. Education: Dr. rer. nat., Computer Science, University of Würzburg M.S., Computer Science, University of Würzburg B.S., Computer Science, University of Würzburg Research Interests: Bauer’s research integrates interdisciplinary expertise to optimize scientific computing infrastructures. Key areas include resource allocation in heterogeneous environments, adaptive distributed systems, and privacy-preserving technologies. Recent contributions include studies on serverless cold starts, microservice benchmarking, and federated learning frameworks. Articles Trends: Bauer’s publications emphasize performance analysis in cloud-native systems, security for IoT/CPS, and machine learning interpretability. Recent work explores hybrid compression methods for time series and the environmental impact of HPC workloads. Awards: 2023 SPECtacular Award (SPEC) 2023 Leadership Academy Fellowship (German Scholars Organization) 2023 SPEC Kaivalya Dixit Distinguished Dissertation Award Advising & Leadership: Bauer chairs the SPEC RG Predictive Data Analytics group and co-authored influential tools like Libra. His research is supported by grants in cloud benchmarking and secure group communication. Labs/Teams: Active in Illinois Tech’s Computer Science department and collaborates with institutions like Argonne National Laboratory on distributed computing initiatives.

• Privacy-Preserving Techniques
• Cryptography
• Data Security
• +4 more

Shantanu Sharma is an Assistant Professor in the Department of Computer Science at the New Jersey Institute of Technology (NJIT). His research focuses on privacy-preserving techniques, secure data management, and cryptographic methods. He leads a project funded by the National Science Foundation (NSF) titled 'Compliance as a Service (CaSe): A Reflective Approach to Enforcing User Privacy Regulations.' Education: Not explicitly stated in the text. His research interests include secure computation over outsourced databases, privacy-preserving algorithms, and cryptographic protocols for distributed systems. He has contributed to topics like secret sharing, secure query processing, and mitigating cryptographic leakages in encrypted databases. Recent publications highlight advancements in private key security, privacy-preserving smart spaces, and secure multi-party computation. His work often bridges theoretical cryptography with practical applications in distributed systems and data privacy. Grants: NSF grant (2023–2026) on reflective approaches to privacy regulations. No labs or teams are explicitly mentioned in the provided text.

Peppino FazioView profile

Associate Professor

• Telecommunications
• Wireless Networks
• Vehicular Networks
• +9 more

Peppino Fazio is an Associate Professor at the Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice. He holds a degree in Computer Engineering (2004) and a PhD from the University of Calabria (2008). His research focuses on telecommunications, wireless networks, and quantum key distribution, with contributions to vehicular networks, cybersecurity, and IoT systems. Key career milestones include post-doctoral research in Spain (2008), involvement in EU projects (e.g., ORACOLO), and leadership in StartCup-winning ventures like SPITCH (2014). He has authored over 130 publications, with recent work addressing quantum networks, deep learning for intrusion detection, and multi-layer robotic swarm management. Received StartCup Calabria 2012 and Lamarck Award 2014. Co-founder of SPITCH and developer of the Intelligent Remote Controller (IRC). His research spans theoretical and applied domains, including network emulation, mobility prediction in 5G/6G systems, and medical neuroscience modeling through network operators.

Darko ZibarView profile

Professor

• Machine Learning in Photonic Systems
• Optical Communication
• Quantum Communication
• +3 more

Darko Zibar is a Professor at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), and leads the Machine Learning in Photonics Systems (MLiPS) group. He holds a M.Sc. in Telecommunication and a Ph.D. in Optical Communications from DTU (2004, 2007). As a Visiting Professor, he has contributed to research at Politecnico di Torino, Friedrich Alexander University of Erlangen, University of California Santa Barbara, and University of Colorado, Boulder. Research Interests: Machine learning applications in optical communication systems Digital signal processing for classical and quantum photonics Optimization of Raman amplifiers and frequency combs Nonlinear fiber optic transmission modeling Photonic reservoir computing with silicon microring resonators Scientific Contributions: His work includes record-breaking achievements in optical phase noise measurement (approaching quantum limits) and programmable gain Raman amplifier design (S+C+L band). He has received prestigious awards such as the ERC Consolidator Grant (2017), Humboldt Bessel Research Award (2021), and Villum Investigator Award (2023).

Han Mao KiahView profile

Assistant Professor

• Coding Theory
• Combinatorics
• Information Theory
• +1 more

Han Mao Kiah is an Assistant Professor at Nanyang Technological University , specializing in Coding Theory and Combinatorics . He earned his Ph.D. in Mathematics at NTU under Yeow Meng Chee and held a postdoctoral position at the Coordinated Science Lab, University of Illinois at Urbana-Champagne with Olgica Milenkovic . Current Role: Assistant Professor, NTU, Department of Mathematics Education: Ph.D. in Mathematics (NTU), Postdoc (University of Illinois) His research focuses on Coding Theory for applications in DNA-based data storage , Reed-Solomon codes , and combinatorial designs . Recent work includes Private Information Retrieval , Sequence Reconstruction , and Error Correction in distributed systems. Key trends in his publications involve Reed-Solomon codes , Private Information Retrieval , and DNA sequence profiling with a focus on Efficient Algorithms and Constrained Coding for error control in emerging storage systems. ISITA Early Career Researcher Paper Award (2022, Researcher: D. T. Dao) Memorable Paper Award Finalist (2021, Student: J. Chrisnata) Best Student Paper Award (2020, Student: J. Chrisnata) Student Paper Award Finalist (2012)

Benjamin AzizView profile

Associate Professor

• Cyber Security
• Formal Methods
• Software Engineering
• +4 more

Benjamin Aziz is an Associate Professor at Buckinghamshire New University, where he leads courses in Software Engineering, Cyber Resilience, and Computer Science. Previously, he was a Senior Lecturer at the University of Portsmouth's School of Computing (2010-2023) and worked as a Senior Research Scientist at Rutherford Appleton Laboratory. He holds a Ph.D. in Computer Science from Dublin City University (2003) and an M.Sc. in Networks and Distributed Systems from Trinity College Dublin (1999). His research focuses on formal methods, cybersecurity, IoT systems, and data-driven approaches to systems security. Key affiliations include Fellow of Advance HE and memberships with IEEE, IET, BCS, and ERCIM. He has contributed to EU projects like GridTrust and Consequence, and serves as Associate Editor for Wiley's Security and Communication Networks journal. His work spans over 160 peer-reviewed publications and two books, emphasizing formal analysis of protocols, steganography detection, and incident response metrics. Research interests include: Cybersecurity frameworks for IoT and cloud systems Formal specification and verification of security protocols Data-driven approaches for anomaly detection Attribute-based access control models Recent articles explore requirements engineering matrices, healthcare monitoring logic models, and Korean police dataset analysis. He has advised on cyber resilience strategies and contributed to tools like Trusty for social network data sharing.

Thu D. NguyenView profile

Professor

• Datacenters
• Sustainability
• Computer Systems
• +5 more

Thu D. Nguyen is a distinguished Professor and currently serves as the Dean of Mathematical and Physical Sciences at the School of Arts and Sciences, Rutgers University. She holds a faculty position in the Department of Computer Science where she has made significant contributions to research and education. Her research interests span multiple domains within computer science, with a particular focus on Datacenters , Sustainability , Computer Systems , Approximate Computing , and Computer Science Education . Dr. Nguyen leads the Computer and Networked Systems research group, where her team investigates innovative approaches to energy-efficient computing and data management. Analysis of her recent publications reveals a strong trend toward addressing challenges in big data management, missing data imputation, blockchain applications in education, and machine learning for educational technology. Her work bridges theoretical computer science with practical applications in educational systems and healthcare data management. Dr. Nguyen has received numerous prestigious awards recognizing her contributions to the field: Rutgers Big Ten Academic Alliance Department Executive Officers Fellow (2018) Best paper award at FAST 2016 for research on datacenter reliability IEEE Micro Top Picks selection (2014) Multiple best paper awards including at IGCC 2010 NSF CAREER award (2005-2010) Faculty of the Year Award for Excellence in Teaching (awarded in 2000 and 2002) Her research has been supported by substantial grants including a $4 million DIBBS grant, NSF CSforAll grant, Microsoft SEIF grant, and multiple NSF CRI grants. Dr. Nguyen has played a leadership role in establishing Rutgers' new Data Science major, minor, and certificate programs, reflecting her commitment to advancing computer science education.