Find Academic Supervisors

Adnan SalihbegovicView profile

Lecturer

• Automation Engineering
• Instrumentation
• Embedded Systems
• +3 more

Adnan Salihbegovic is affiliated with the Department for Automation and Electronics, where he teaches advanced courses such as Special Measurements Techniques for undergraduate students. His curriculum emphasizes modern instrumentation methodologies, including LabVIEW programming, virtual instrumentation, and data acquisition system integration using DAQ modules and GPIB communication protocols. He oversees laboratory works involving HP and National Instruments equipment for signal analysis, logic analyzers, spectral analyzers, and oscilloscopes. His research interests focus on automation systems, embedded systems design, IoT applications, and control engineering. He has contributed to projects involving web-based SCADA systems, industrial automation workflows, and optimization of order-picking processes in logistics. His work also explores educational tools such as decompilable code editors and tiny OS kernels for teaching purposes. Salihbegovic’s publications span topics like enterprise GIS in power distribution, domain-specific modeling languages for IoT, and vibration control using fuzzy logic. He actively participates in international conferences related to automation and information technologies, demonstrating expertise in both theoretical and applied engineering solutions. His teaching integrates practical lab exercises with theoretical concepts, ensuring students gain hands-on experience with industry-standard tools like LabVIEW and DAQ systems. He also advocates for remote laboratory access and internet-based control systems to enhance educational accessibility and industrial efficiency.

• Deep Learning
• Machine Learning
• Software Engineering
• +7 more

Nemanja Milošević is an Assistant Professor at the Department of Mathematics and Informatics , Faculty of Sciences, University of Novi Sad, Serbia. He actively contributes to open-source communities as a Fedora Project Developer & Ambassador and maintains a personal blog related to academic topics. PhD in Computer Science Specializes in Deep Learning, Federated Learning, and Edge Computing Lectures courses: Deep Learning, Distributed Deep Learning, Business Software Development, Scientific Programming, and Mobile Application Development Research focuses on neural network architectures, system security, and toxicology applications His research spans multiple domains including: Deep Learning optimization techniques Industrial IoT security frameworks Big Data analytics across manufacturing and banking sectors Toxicology modeling with machine learning Edge computing resource management Software quality analysis methodologies Recent publications demonstrate expertise in federated learning implementations, negative feature classification, and deep learning applications in toxicology. He maintains active collaborations across Europe and contributes to IEEE and arXiv publications. As part of his academic practice, Nemanja: Develops open-source software solutions Participates in international research workshops Utilizes modern e-learning platforms (PMF Moodle) Engages in cross-disciplinary research projects Contact: nmilosev@dmi.rs | nmilosev@fedoraproject.org

• Cloud Computing
• Grid Computing
• Health Informatics
• +5 more

David Levine serves as an Associate Professor in the Computer Science and Engineering Department within the College of Engineering at The University of Texas at Arlington. His academic career spans decades with continuous teaching and research contributions, evidenced by his ongoing course instruction through Fall 2025 and active grant leadership. Levine's research spans cloud and grid computing, health informatics, bioinformatics, pervasive computing, and secure programming. His work integrates computational techniques with practical applications in healthcare (Smart Hospital for Elderly Care), high-energy physics (Fermilab collaborations), and accessibility infrastructure. He has pioneered curriculum development in emerging fields including Cloud Computing, Secure Programming, Mobile Computing, and Data Mining, with courses consistently reaching maximum enrollment capacity and establishing distance learning records within the engineering college. His publication portfolio demonstrates significant interdisciplinary impact across IEEE Supercomputing, health informatics, genetics, and pervasive computing. Recent work focuses on spatial data visualization for public health (2022), hardware simulation platforms (2017), and scalable notification frameworks for healthcare systems (2011). His research has secured over $5 million in funding from NSF, NIH, DoE, and industry partners including Luminant Power. Computer Science Department Teaching Award recipient Student teams won ATT University Challenge, Nokia Symbian Challenge, and Department of Energy Challenge ($200k+ total) Levine has mentored 4 PhD students as co-supervisor, chaired 6 Master's theses, and advised numerous undergraduate researchers. His laboratory work focuses on grid computing applications for high-energy physics and bioinformatics, with current projects including the GAANN Doctoral Fellowships in IoT and Smart Hospital infrastructure development. He serves as NTT Hiring Committee Chair and Faculty Senate Chair, demonstrating significant institutional leadership.

• IoT Security
• Edge Computing
• Context-Aware Systems
• +2 more

Dr. Abayomi Otebolaku is a Senior Lecturer in Computing at Sheffield Hallam University, specializing in IoT-driven service personalization and edge intelligence. His research intersects mobile computing, trust management, and AI, with applications in healthcare and energy systems. Current projects include: 1) OHL network safety solutions for preventing overhead line strikes, 2) WiseIoT (EU-S.Korea collaboration) for federated IoT security, and 3) Underwater autonomy in the SWARMs project. His group develops hybrid deep learning models for context-aware systems. Publications demonstrate consistent focus on IoT trust frameworks (2018-present), evolving toward security vulnerability detection (2025). Recent work applies neural networks to critical infrastructure protection. Teaches distributed programming and machine learning courses. Supervises PhD candidates in ambient intelligence applications. Serves on technical committees for IEEE IoT conferences and as journal reviewer for IEEE Access.

Sebastian SternView profile

Researcher

• Wireless Communications
• MIMO Systems
• Lattice Coding
• +3 more

Dr.-Ing. Sebastian Stern serves as a Senior Researcher at the Institute of Communications Engineering, Ulm University, Germany. His research focuses on advanced communication systems with particular expertise in MIMO transmission, lattice-based coding, and optical communications. Stern maintains active collaborations with researchers including Robert F.H. Fischer and Johannes K. Fischer across multiple publications. His educational background includes a dissertation titled Advanced Equalization and Coded-Modulation Strategies for Multiple-Input/Multiple-Output Systems (2019) from Ulm University, building upon his Master's thesis Comparison of the peak value behavior of single- and multi-carrier methods (2012) which earned the VDE Award for Outstanding Master Thesis. Stern's research interests span wireless communications, optical transmission systems, and advanced signal processing techniques. His work demonstrates significant contributions to 4D constellation design using Hurwitz and Eisenstein integers, lattice reduction methods for MIMO systems, and non-binary coding schemes for high-throughput fiber-optic communication. Recent publications show expanding work into quaternionic lattices and AI applications for network security. His publication record shows consistent output since 2013 with 25+ journal and conference papers, including high-impact publications in IEEE Transactions on Information Theory and Journal of Lightwave Technology. Recent work (2023-2025) demonstrates continued innovation in integer-forcing techniques, quaternionic Minkowski reduction, and 4D constellation optimization. VDE Award for Outstanding Master Thesis (2013) Stern has taught multiple courses at Ulm University including Communications Engineering lab courses, Multiuser Communications and MIMO Systems exercises, and Signale und Systeme tutorials between 2013-2017. His teaching experience complements his research in practical implementation of advanced communication techniques.

Rejwan Bin SulaimanView profile

Lecturer

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

Rejwan Bin Sulaiman is a Lecturer and Module Leader at Northumbria University London, combining academic teaching with research leadership at STEMResearch.ai. His expertise spans Cybersecurity, Artificial Intelligence, and Machine Learning, with a focus on Federated Learning, Image Processing, and Computational Intelligence. He actively supervises BSc and MSc students and engages in industry consultancy to bridge academic and professional practices. His research emphasizes cutting-edge applications such as AI-driven cybersecurity solutions, healthcare diagnostics (e.g., Alzheimer's and cancer detection), and blockchain-based frameworks for railway ticketing and medical devices. Recent work includes decentralized vulnerability assessment, smart grid stability forecasting, and Bangla emotion corpus analysis using transformers. Rejwan’s publications reflect a trend toward interdisciplinary innovation, merging AI with healthcare, agriculture, and IoT security. His contributions to conferences and journals highlight advancements in both theoretical and practical applications of machine learning, emphasizing explainability and privacy preservation. He collaborates in scientific meetups and leads teams at STEMResearch.ai, driving projects like credit card fraud detection frameworks and blockchain-enhanced supply chain solutions. His work underscores a commitment to advancing technology for societal benefit, particularly in healthcare equity and smart infrastructure.

Barbara CutlerView profile

Associate Professor

• Computer Graphics
• Geometry Processing
• Visualization
• +3 more

Barbara Cutler is an Associate Professor in the Computer Science Department at Rensselaer Polytechnic Institute (RPI), with an affiliation to EMPAC. She holds a Ph.D. and served as a Post-Doctoral Lecturer at MIT in Electrical Engineering and Computer Science, focusing on computer graphics and visualization within the CSAIL lab. Her research spans computer graphics, geometry processing, architectural design tools, and educational technology, particularly in autograding systems like Submitty and plagiarism detection (Lichen). She has contributed to open-source platforms, container-based grading solutions, and policies to reduce academic stress and misconduct. Education Background: Ph.D. and Postdoc at MIT (EECS). Research Interests: Combining computational techniques with real-world applications in architecture, visualization, and education. Key areas include erosion modeling, tangible user interfaces, and collaborative infrastructure restoration simulations. Her work bridges theory and practice through tools like Submitty for scalable programming assignment evaluation. Grants & Projects: Developed Submitty, a widely used autograding platform. Authored over 50 publications on autograding systems, plagiarism detection, and computer graphics. Collaborated across institutions on H/FOSS curriculum integration and late-day policy studies. Labs & Teams: Affiliated with RPI’s Computer Science Department and EMPAC. Active in the Submitty development community and interdisciplinary projects involving architecture and engineering.

Lei YuView profile

Assistant Professor

• Data Privacy
• AI Security
• Machine Learning Systems
• +3 more

Lei Yu is a Tenure-Track Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). Previously, he was a Research Staff Member at IBM Research's Thomas J. Watson Research Center. He holds a Ph.D. in Computer Science from Georgia Institute of Technology and earlier studied at Harbin Institute of Technology. His research focuses on data privacy, AI security, machine learning systems, and cloud/mobile computing, with notable contributions to federated learning, adversarial attacks, and blockchain security. Yu has been awarded multiple IBM accolades, including the 2022 IBM Research Outstanding Accomplishment and the IBM Outstanding Innovation Award. He has authored numerous patents and serves on technical program committees for top conferences like ACM ASIACCS and IEEE ICDCS. Education: Ph.D. in Computer Science (Georgia Tech), M.S. and B.S. from Harbin Institute of Technology (China). His work spans theoretical and applied research, including privacy-preserving techniques, secure AI systems, and distributed computing. He leads the RPI lab focused on trustworthy AI and has advised over 15 students across Ph.D., Master’s, and undergraduate programs. Research interests emphasize threats in data lifecycle, machine learning robustness, and system security. His lab’s projects include membership inference attacks, model deduplication, and blockchain-based solutions for data integrity. He has secured grants such as the RPI-IBM AIRC research award and maintains active collaborations with industry and academia. Awards: IBM Research Awards (2020–2022), NSF Travel Grants, Best Paper Awards. Professional Activities: Guest Editor for Wireless Communications and Mobile Computing , TPC roles in ICDCS, AsiaCCS, and ICDE. Teaching: Courses on database systems, network programming, and machine learning security at RPI.

Ahmed SenouciView profile

Professor

• Civil Engineering
• Construction Management
• Sustainable Construction Materials
• +3 more

Ahmed Senouci is a Professor in the Construction Management Program at the University of Houston, specializing in civil engineering and sustainable construction technologies. His affiliation spans both academic and practical domains, with roles in curriculum development and resource-constrained scheduling research. Ph.D., Civil Engineering, University of Wisconsin-Madison M.S., Construction Management, University of Wisconsin-Madison M.S., Civil Engineering, University of Wisconsin-Madison B.S., Civil Engineering, National Polytechnic School of Algiers His research focuses on integrating artificial intelligence and optimization methods in construction, advancing sustainable concrete technologies with recycled materials, and exploring 3D printing applications in green building. Key subfields include graphene nanoplatelet enhancement, nano-fly ash utilization, and cybersecurity in connected autonomous vehicle systems. Recent publications analyze AI-driven BIM education, eco-friendly asphalt mixes, and mineral waste valorization in road construction. His work bridges technical innovation with environmental responsibility, particularly in material science and infrastructure sustainability. Professor Senouci teaches courses such as CNST 3355 (Strength of Construction Materials), CNST 6396 (Master’s Project), and CNST 6307 (Statistical and Optimization Methods in Construction). As Program Coordinator, he leads undergraduate construction management initiatives. Current projects include experimental studies on self-healing concrete microcapsules, sustainable material synthesis, and fatigue monitoring in construction workers using wearable sensors.

Martin KelloggView profile

Assistant Professor

• Software Verification
• Computer Security
• Programming Languages
• +3 more

Martin Kellogg is an Assistant Professor in the Department of Computer Science at NJIT. His research focuses on making software verification practical for developers by integrating it into standard development workflows. He emphasizes enhancing software reliability and security through methods that prevent critical errors like security vulnerabilities, resource leaks, and array bounds issues. Education: Ph.D., Computer Science, University of Washington-Seattle Campus (2022) B.S., Computer Science, University of Virginia-Main Campus (2016) Research Interests: Kellogg’s work spans software verification techniques, static analysis, and their application to security and reliability. He develops tools like specialized typecheckers and pluggable type inference systems to address challenges such as resource management and nullability analysis. His research has led to tools deployed at companies like Amazon Web Services. Key Article Trends: His recent work explores static program reduction, nullability inference, and modular resource leak verification. These studies emphasize scalability and practicality, often leveraging machine learning and pluggable type systems to improve verification outcomes. Scientific Awards: None explicitly mentioned in the provided text. Advising & Grants: Kellogg has been involved in NSF grants such as “SHF: Small: Verification-guided Assessment and Reduction of Code Complexity” (2024) and “SHF: MEDIUM: General and Scalable Pluggable Type Inference” (2023). No formal advisees are listed in the text. Labs/Teams: His research aligns with NJIT’s broader efforts in computer science and software engineering. A personal website ( https://web.njit.edu/~mjk76/ ) provides further details on his work.

Shuai MuView profile

Assistant Professor

• Distributed Systems
• Concurrency Control
• Large-Scale Storage Systems
• +4 more

Shuai Mu is an Assistant Professor in the Department of Computer Science at Stony Brook University. Previously, they held a post-doctoral researcher and lecturer position at New York University. They earned a Ph.D. in computer science from Tsinghua University in 2015. Shuai's research focuses on distributed systems, particularly concurrency control in large-scale storage systems. They aim to enhance system efficiency without compromising correctness guarantees like serializability. Key areas include database systems, transactional memory, wide-area replication, and hardware/software optimizations. Their work addresses challenges in distributed/multicore architectures and fault-tolerant replication. Recent publications emphasize sustainability in data centers, concurrency control algorithms, and hardware acceleration techniques. They have explored innovative approaches such as the Causalmesh causal cache for serverless computing and NCC for natural concurrency control in datastores. Teaching responsibilities include CSE 535. While no awards are explicitly listed, their research contributions reflect a strong commitment to advancing distributed systems theory and practice.

• Clinical Decision Support Systems
• Knowledge Management
• Personalized Medicine
• +3 more

Dr. Kensaku Kawamoto is a Professor and Vice Chair of Clinical Informatics at the University of Utah Department of Biomedical Informatics, and Associate Chief Medical Information Officer at University of Utah Health. He leads the ReImagine EHR initiative, developing award-winning EHR add-ons like neonatal bilirubin management and lung cancer screening tools. He chairs the Clinical Decision Support committee and co-leads the HL7 Clinical Decision Support Work Group. His research focuses on clinical decision support systems, personalized medicine, and health IT standards. Education: B.A. in Biochemical Sciences, Harvard University M.D., Ph.D. in Biomedical Engineering (focusing on informatics), Duke University M.H.S. in Clinical Research, Duke University School of Medicine Key Roles: HL7 Interoperability Standards Priorities Task Force Co-Chair Past Board Member of HL7 Fellow of American College of Medical Informatics & AMIA His research interests emphasize clinical decision support , standards development , and healthcare equity . Recent work addresses disparities in genetic testing access via chatbot platforms (BRIDGE trial) and lung cancer screening optimization. He has secured $55M+ in grants from NCI, NIDDK, AHRQ, and industry. Notable achievements include 100+ peer-reviewed articles, OpenCDS initiative leadership, and recognition as a Top 25 Healthcare Innovator (2019). Current projects include the MAINTAIN PRIME weight management trial and lung cancer screening tools integrated into EHR workflows. Dr. Kawamoto's labs focus on interoperable clinical decision support, with collaborations spanning Huntsman Cancer Institute and national standards bodies. He advocates for precision medicine enabled by scalable informatics solutions.

Anna TzanakakiView profile

Associate Professor

• Optical Networking
• 5G/6G Systems
• Cloud-RAN
• +2 more

Anna Tzanakaki is an Associate Professor at the National and Kapodistrian University of Athens (Physics Department) and Visiting Research Fellow at the University of Bristol. Her research focuses on optical networks supporting 5G/6G systems, converged infrastructures, and network slicing. Key research areas include: Optical-wireless convergence for 5G/6G transport Intent-based network management using AI/ML Techno-economic analysis of network deployments Her extensive publication record (200+ papers) shows consistent focus on optical network design, cloud-RAN architectures, and multi-access edge computing, with recent work emphasizing AI-driven network optimization and integrated sensing-communication systems. As technical coordinator of the EU 5G-PPP project 5G-VICTORI, she drives innovation in railway communications and vertical industry applications.

Yongge WangView profile

Professor

• Cryptography
• Blockchain
• Post-Quantum Cryptography
• +6 more

Yongge Wang is a Professor at the Department of Software and Information Systems within the College of Computing and Informatics at the University of North Carolina at Charlotte. His research spans cryptography, blockchain, post-quantum cryptography, deep learning, and secure communications. He has contributed to industry standards such as IEEE 1363.2 (SRP5) and IEEE 1363.3 (WANG-KE), as well as IETF RFC 4050 for ECDSA with XML-Signature syntax. Research Interests : Cryptography: Focused on post-quantum schemes (e.g., RLCE, polarRLCE), garbled computation, and secure authentication protocols. Blockchain: Led work on consensus protocols (XP for asynchronous networks), DeFi mechanisms, and censorship-resistant systems. Algorithmic Randomness: Pioneered LIL testing techniques for pseudorandom generators, analyzing vulnerabilities in Debian OpenSSL. Publications : His recent articles (2016-2024) address blockchain security, code-based cryptography, garbled circuits, and quantum-resistant algorithms, with trends emphasizing decentralized systems and secure cloud computation. Grants and Funding : Secured multiple NSF and UNC Charlotte grants for cybersecurity research, including projects on privacy-preserving databases and secure storage systems. Education : Holds a PhD in Mathematics from Heidelberg University (Germany), focusing on randomness and computational complexity. Teaching : Courses include Applied Cryptography, Cloud Data Storage, and Network Security, with a strong emphasis on academic integrity and hands-on projects. Software Tools : Developed the LIL statistical testing package for pseudorandomness, available as a Java application.

• Cryptography
• Security
• Privacy
• +3 more

Elena Pagnin serves as an Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology, where she specializes in cryptography and security research. Her academic profile centers on developing advanced cryptographic protocols that address contemporary security challenges in digital communications and data protection. Her research interests span multiple dimensions of modern cryptography including pairing-based systems, privacy-preserving protocols, and secure communication frameworks. Dr. Pagnin's work particularly focuses on optimizing cryptographic efficiency while maintaining robust security properties, with significant contributions to digital signature schemes, anonymity protocols, and verifiable delegation mechanisms. Her research bridges theoretical cryptography with practical implementations for real-world security applications. Analysis of her recent publications reveals a strong trajectory in developing innovative cryptographic solutions that address emerging security threats. Her work shows particular expertise in balancing efficiency with security guarantees, especially in pairing-based cryptography and privacy-enhancing technologies. The research demonstrates consistent advancement in cryptographic protocol design, with increasing sophistication in handling complex security requirements while maintaining practical implementation considerations. Dr. Pagnin actively contributes to the cryptographic research community through her publications in top security venues, though specific awards or honors were not mentioned in the available materials. Her research program appears well-established with consistent output of high-quality cryptographic research addressing both theoretical foundations and practical applications.