Find Academic Supervisors

• Microeconomic Theory
• Game Theory
• Mechanism Design
• +4 more

Shurojit Chatterji is a Professor of Economics at the School of Economics, Singapore Management University, where he conducts research at the intersection of microeconomic theory and social choice. His academic foundation includes a Ph.D. in Economics from SUNY at Stony Brook (1993) and a B.A. (Honours in Economics) from the University of Delhi (1988). His educational qualifications are: Ph.D. in Economics, SUNY at Stony Brook, 1993 B.A. (Honours in Economics), University of Delhi, 1988 Professor Chatterji's research centers on Mechanism Design , Social Choice Theory , and Game Theory , with deep investigations into strategy-proofness, preference domain structures, and efficient allocation mechanisms. His work rigorously examines single-peakedness in voting systems, multidimensional choice environments, and the welfare implications of redundant assets under heterogeneous forecasts. This theoretical framework extends to dynamic economic models involving Radner equilibria and imperfect foresight, bridging foundational microeconomic principles with practical design applications. Analysis of his 15 most recent publications (2020-2025) reveals consistent focus on strategy-proof mechanisms across unidimensional and multidimensional domains, probabilistic social choice, and the decentralizability of efficient allocations under uncertainty. Key trends include the taxonomy of non-dictatorial domains, decomposability properties in fractional allocation, and the role of redundant assets in welfare economics—demonstrating how theoretical insights address real-world market imperfections and institutional design challenges. As an academic advisor, he has mentored PhD student Paulo Daniel Salles Ramos. His extensive publication record across top economic journals indicates sustained research activity and scholarly influence, though specific grant details are not documented in available sources.

• Artificial Intelligence
• Computational Social Choice
• Computational Complexity
• +5 more

Edith Hemaspaandra is a Professor in the Department of Computer Science at the Rochester Institute of Technology (RIT), located in the Golisano College of Computing and Information Sciences. She holds a BS, MS, and Ph.D. in Computer Science from the University of Amsterdam (the Netherlands). Her research focuses on computational social choice, computational complexity theory, logic complexity, and formal methods. She teaches courses such as CSCI-262/263 (Introduction to Computer Science Theory) and CSCI-664 (Computational Complexity). Her work explores the algorithmic aspects of voting systems, including election manipulation, control, and bribery, with a focus on their computational complexity. She has also contributed to formal methods for automata theory, educational tools like JFLAP extensions, and the study of complexity classes such as LWPP and WPP. Her research bridges theoretical computer science with practical applications in social choice theory and algorithm design. Her grants include an NSF-funded project on computationally protecting elections from manipulation (2011). She actively publishes in top venues like STACS and ISAAC, addressing topics ranging from graph reconstruction to hybrid election models. Though no awards are explicitly listed, her extensive publication record highlights her contributions to theoretical computer science. Her advising and grant activities include collaborative research projects and educational tool development. She is affiliated with RIT’s Department of Computer Science and maintains a personal website and ORCID profile.

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +3 more

Günter Rote is a Professor in the Department of Computer Science at Freie Universität Berlin, specifically within the Theoretical Computer Science group (Arbeitsgruppe Theoretische Informatik). He holds a formal academic title of Professor Dr. and is affiliated with the Faculty of Mathematics and Computer Science. His research focuses on theoretical computer science, computational geometry, algorithms, and discrete mathematics. Key research interests include geometric algorithms, optimization problems (e.g., shortest paths, traveling salesman problems), and algorithm design for parallel computing systems. His work spans topics such as systolic arrays, convex hulls, and combinatorial optimization. Rote’s contributions include foundational studies on computational geometry problems, algorithmic complexity, and practical applications in energy equity and infrastructure design. Publications highlight contributions to solving extremal equations, polygon transformations, and the quadratic assignment problem. He has been active in academic leadership, mentoring students, and contributing to computational science communities. His email is rote@inf.fu-berlin.de, and his office is located at Takustraße 9 in Berlin.

Laura ToniView profile

Associate Professor

• Large-scale signal processing for machine learning
• Graph signal processing
• Reinforcement learning
• +9 more

Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.

Jordi Perello MuntanView profile

Associate Professor

• Telecommunications Networks
• Optical Fiber Networks
• Optical Networks
• +13 more

Jordi Perelló Muntan is an Associate Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, where he is also affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He is a member of the Broadband Communications Systems and Architectures (CBA) and IDEAI-UPC research groups, focusing on advanced optical and future internet networking technologies. Research Interests: His research spans telecommunications networks, optical fiber and optical networking, resource optimization, network architectures, and the Future Internet. He investigates performance optimization in 5G transport networks, Spatial Division Multiplexing (SDM), Recursive Inter-Network Architecture (RINA), elastic optical networks, and cognitive networking. His work integrates SDN, network virtualization, and green networking principles for scalable and efficient infrastructures. Publication Trends: His recent publications focus on probabilistic constellation shaping in multicore fiber networks, cognitive strategies for optical margin reduction, RINA-based QoS assurance, and migration planning toward spectrally-spatially flexible optical networks. These reflect a strong trend toward intelligent, adaptive, and energy-efficient network design for future communication systems. Scientific Awards: Co-recipient of the 2020 Fabio Neri Best Paper Award Runner-up (Elsevier Journal of Optical Switching and Networking) Co-recipient of the ONDM 2021 Best Paper Award Co-recipient of the 2019 IEEE Communications Society Charles Kao Award Co-recipient of the ONDM 2012 Best Student Paper Award Advising and Grants: He has advised multiple PhD students on topics including RINA, optical network planning, and virtual provisioning. He has led or participated in major European (H2020, FP7) and national (PID, TEC) research projects such as SLICENET, PRISTINE, TRAINER, and ALLIANCE, focusing on 5G, RINA, and sustainable network infrastructures. Labs and Teams: He is an active member of the CBA research group at UPC, contributing to experimental and theoretical advancements in optical and programmable networks. His team collaborates internationally on testbed development and standardization efforts in next-generation networking.

Albert AtseriasView profile

Professor

• Computational Complexity
• Logic in Computer Science
• Finite Model Theory
• +7 more

Albert Atserias is a Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the ALBCOM research group (Algorithms, Bioinformatics, Complexity, and Formal Methods). He is also associated with the Institut de Matemàtiques de la UPC-BarcelonaTech. His research is central to theoretical computer science, with a strong emphasis on logic and complexity. Atserias's research interests span Computational Complexity, Logic in Computer Science, Finite Model Theory, Proof Complexity, and Constraint Satisfaction Problems . His work explores the fundamental limits of computation, the expressive power of logical languages over finite structures, and the complexity of proving mathematical statements. He investigates the algebraic and combinatorial properties of proof systems, the limits of efficient algorithms for constraint solving, and the theoretical foundations of databases. His research often bridges logic, algebra, and combinatorics to provide deep insights into computational phenomena. The trends in his recent publications show a sustained focus on the logical and algebraic underpinnings of computational problems. Key themes include the consistency and complexity of database queries , the power and limitations of proof systems (like resolution and sum-of-squares), and the expressive power of homomorphism counts in graph theory. His work on the hardness of automating resolution and the development of circular proof systems are particularly significant contributions to proof complexity. The 2024 PODS Best Paper Award for work on relational consistency underscores the impact and timeliness of his research. Among his notable scientific awards are the prestigious ICREA Acadèmia , the PODS 2024 Best Paper Award , the Premi Extraordinari de Doctorat (Extraordinary Doctoral Prize), and the Kleene Award for Best Student Paper . These accolades reflect both the excellence of his early work and his continued leadership in the field. Atserias has been a principal investigator on numerous competitive research projects, including funding from the European Research Council (ERC) and the Spanish Ministry of Science. He has advised doctoral students, such as Toni Hakoniemi, whose thesis on proof complexity he supervised. His extensive collaborative network includes leading researchers like Phokion Kolaitis, Anuj Dawar, and Victor Dalmau. He has also served on the scientific committees of major conferences, contributing to the academic community. He is a core member of the ALBCOM research group , a leading team at UPC focused on theoretical aspects of computer science, which provides a vibrant environment for research in algorithms, complexity, and formal methods. His work is also connected to the broader Institut de Matemàtiques de la UPC, fostering interdisciplinary collaboration between computer science and mathematics.

Raouf BoutabaView profile

Professor

• Network Management
• Systems and Services
• Resource Management in Wired/Wireless Networks
• +9 more

Raouf Boutaba is a Professor at the University of Waterloo , serving as Director of the David R. Cheriton School of Computer Science since July 2020. He holds prestigious fellowships including FRSC , FIEEE , FIEC , and FCAE . 2024: Inaugural Rogers Chair in Network Automation 2024: Ontario Research Fund–Research Excellence (ORF–RE) $2M grant for next-gen mobile networks 2021: University Professor title, University of Waterloo Research Interests span network automation, resource management in wired/wireless networks, network function virtualization (NFV), software-defined networking (SDN), cloud computing, blockchain, future Internet architecture, and cybersecurity. His work focuses on zero-touch networks, 5G/B5G slicing, and AI-driven orchestration. Scientific Contributions include 15+ recent publications on topics like reinforcement learning for RAN slicing, encrypted traffic classification, quantum network optimization, and self-driving infrastructure. His projects 5G LEAP and 5G ELITE explore network isolation and Open RAN principles. 2024: IFIP/IEEE CNOM Test of Time Paper Award 2024: Graduate Supervision Excellence Award 2021: Kenneth C. Sevcik Outstanding Student Paper Award (advisor) Teaching includes co-developing the NSERC CREATE Network Softwarization program, offering courses like Network Softwarization: Principles and Foundations (Winter 2024) and Technologies and Enablers since 2018. He emphasizes hands-on training in SDN, NFV, Open RAN, and 5G. Students and Collaborations : Supervised PhD students such as Shihabur R. Chowdhury (2021), Nashid Shahriar (2020), and undergrad Leni Aniva (2022 Gov. Gen. Silver Medal ). His team includes researchers working on 5G, blockchain, and AI-driven network management. Professional Leadership : Organized Rogers TEP Workshops (2024-2025), delivered keynotes at IEEE Globecom, ColCom, and BalkanCom, and served on expert panels for AI orchestration and 5G cybersecurity at major symposia.

Hakan AydinView profile

Professor

• Real-Time Embedded Systems
• Sustainable Computing
• Fault Tolerance
• +4 more

Hakan Aydin is a Professor and Director of the PhD Program in the Department of Computer Science at George Mason University's Volgenau School of Engineering. He has been teaching at George Mason University since 2001 and has established himself as a leading researcher in real-time embedded systems and energy-aware computing. Education: PhD in Computer Science from the University of Pittsburgh (2001) Hakan Aydin's research primarily focuses on sustainable computing, real-time embedded systems, fault tolerance, Internet-of-Things, and cyber-physical systems. His work bridges theoretical foundations with practical implementations, particularly in energy management for real-time systems. He has developed innovative techniques for reliability-aware power management, dynamic voltage scaling, and energy harvesting in wireless sensor networks. His research has significant implications for extending battery life in mobile devices, improving reliability in safety-critical applications, and enabling sustainable computing practices. Aydin's publications reveal a consistent research trajectory centered around energy efficiency and reliability in real-time systems. His work spans theoretical algorithm development, system-level implementation, and experimental validation. A notable trend is the evolution from single-processor systems to multicore and heterogeneous architectures, reflecting industry trends. His recent work increasingly addresses security aspects of real-time systems and the integration of IoT technologies. Scientific Awards: National Science Foundation CAREER Award (2006) George Mason University Computer Science Department Teaching Award (2006, 2009) Best Paper Award at IEEE Green and Sustainable Computing Conference (IGSC'20) Best Student Paper Award at IEEE International Conference on Embedded Software and Systems (ICESS'15) Best Paper Award at IEEE International Conference on Embedded Computing (EmbeddedCom'14) Best Paper Award at International Workshop on Highly-Reliable Power-Efficient Embedded Designs (HARSH'13) Best Paper Award at ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM'11) Hakan Aydin has advised seven PhD students to completion, including Vinay Devadas (2011), Baoxian Zhao (2012), Bo Zhang (2012), Mohammad Atiqul Haque (2016), Maryam Bandari (2016), Arda Gumusalan (2019), and Abhishek Roy (2021). His research has been generously supported by the National Science Foundation through multiple grants, including CSR: Small: Collaborative Research: Towards Reliability-Centric Real-time Computing on Heterogeneous Chip Multiprocessor Systems (2014-2017), CSR: Small: Energy Harvesting for Performance Sensitive Wireless Sensor Networks (2011-2015), and CSR: Small: Collaborative Research: Generalized Reliability-Aware Power Management for Real-Time Embedded Systems (2010-2014). Prof. Aydin has held significant leadership roles in the academic community, serving as the Technical Program Committee Chair of the IEEE RTAS 2011 and General Chair of IEEE RTAS 2012. He is also a member of the Editorial Board of Journal of Real-Time Systems (Springer). His work has established foundational principles in reliability-aware energy management for real-time systems, influencing both academic research and industrial practices in embedded computing.

Christiane BarzView profile

Professor

• Operations Research
• Revenue Management
• Stochastic Dynamic Optimization
• +3 more

Christiane Barz is a Professor of Mathematics at the University of Zurich's Institute for Business Administration since 2016. Previously, she held academic roles at the UCLA Anderson School of Management, the Chicago Booth School of Business, and the Technical University (TU) Berlin. Her research focuses on stochastic dynamic systems, Markov decision processes, and their applications in revenue management. She emphasizes making mathematical tools accessible and practical for real-world problem-solving, particularly in optimizing decision-making under uncertainty. Education includes a degree in industrial engineering and a doctorate from the University of Karlsruhe (TH), Germany. Her career path includes postdoctoral research at the University of Chicago's Booth School of Business and roles as an Assistant Professor at UCLA. She combines academic excellence with balancing family life, advocating for gender equity in STEM fields. Her research explores risk-sensitive decision-making frameworks, dynamic pricing models for transportation and healthcare, and optimizing resource allocation in complex systems. Recent work includes applications in FlixBus, air cargo networks, and improving patient admission scheduling in hospitals. Barz's teaching philosophy prioritizes demystifying mathematics for students, encouraging critical engagement rather than fear of complexity. She collaborates with industry partners to apply operations research methods to real-world challenges, emphasizing both theoretical rigor and practical relevance.

• Online Learning
• Instructional Design
• Augmented Reality in Education
• +7 more

Dr. Wanju Huang is a Clinical Assistant Professor in the Learning Design and Technology program at Purdue University , where she focuses on enhancing online learning experiences through innovative instructional design and emerging technologies. Prior to joining Purdue in Fall 2016, she served as an instructional design manager at Teaching and Learning Technologies and spent six years as a lecturer and instructional designer at Eastern Kentucky University. Education: Ph.D. in Curriculum & Instruction (Technology concentration) from University of Illinois at Urbana-Champaign Her research interests center on technology-mediated online communities, instructor presence in digital learning, augmented reality applications, and faculty development programs. She has published extensively on these topics, including studies on multimedia interventions for teaching presence, immersive technologies in ESL education, and adaptive cyber training platforms. Dr. Huang's publications reflect a strong focus on EdTech innovation , with trends spanning AI integration in K-12 , model-based systems engineering , and microlearning strategies . Her work bridges theoretical frameworks like social constructivism with practical implementations in professional education. Professional memberships include the American Educational Research Association, Association for Educational Communications and Technology, and Quality Matters. She teaches courses on learning design foundations, e-learning systems, and professional competency demonstrations in LDT.

Lev SarkisovView profile

Professor

• Carbon Capture
• Material Informatics
• Molecular Simulation
• +6 more

Lev Sarkisov is a Professor of Chemical Engineering at the University of Manchester, leading the Sarkisov Research Group. His work focuses on advancing porous materials for carbon capture, energy storage, drug delivery, and sensing through multiscale computational workflows integrating molecular simulation, machine learning, and process modeling. He holds a Ph.D. from the University of Massachusetts Amherst (2001) and held roles at the University of Edinburgh, including Head of Chemical Engineering. Notable achievements include securing a £1M Wolfson Foundation grant for sustainable engineering (2022) and receiving the 2013 Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship. Education: Ph.D., Chemical Engineering, University of Massachusetts Amherst, 2001 M.Sc./B.Sc., Moscow Lomonosov Academy of Fine Chemical Technologies, 1995-1997 Research Interests: The group develops porous materials using AI-driven approaches for carbon capture, energy-efficient separations, and material informatics. Key areas include MOFs, adsorption phenomena, and open-source software for reproducible research. Grants & Awards: £1M Wolfson Foundation Grant (2022) Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship (2013) Edinburgh University Student Union Teaching Award (2019) Labs & Collaborations: The group collaborates globally, emphasizing open-source tools and reproducibility. Projects include CRAFTED (MOF adsorption database) and PoreBlazer v4.0.

Elina RönnbergView profile

Professor

• Discrete Optimisation
• Mathematical Optimisation
• Scheduling
• +18 more

Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.

• Combinatorial Optimization
• Scheduling Theory
• Operations Research
• +3 more

Professor Daniel Oron is affiliated with the University of Sydney, where he joined in 2004 after completing his PhD in Operations Research at the Hebrew University of Jerusalem. His research focuses on Combinatorial Optimization, particularly Scheduling Theory, addressing challenges like batch scheduling with setups, customer delivery models, and scheduling under deteriorating conditions. He teaches courses such as Quantitative Business Analysis, Management Science, and Business Analytics Honours. His editorial role includes serving on the board of the Journal of Industrial & Management Optimization . Recent research contributions span multi-agent scheduling, energy recharging in scheduling, and coupled task optimization. He advises two current PhD students: Johnson (Two-agent scheduling problems) and Renjie Yu (Multi-agent scheduling with parallel batching). Publications highlight advancements in scheduling algorithms, resource allocation, and optimization under constraints. Notable works include minimizing late jobs with step-learning models and analyzing parameterized complexity in single-machine scheduling.

Faidra MonachouView profile

Assistant Professor

• Market Design
• Algorithmic Fairness
• Public Policy Analysis
• +3 more

Faidra Monachou is an Assistant Professor of Operations Management at the Yale School of Management and a Faculty Fellow at the Center for Algorithms, Data, and Market Design at Yale (CADMY). She specializes in designing market mechanisms and operational interventions that balance efficiency and fairness, particularly in public policy contexts like education. Her research integrates tools from operations research, economics, and data science to address societal challenges. Education: Ph.D. in Operations Research from Stanford University's Department of Management Science and Engineering Postdoctoral Fellowship at Harvard University Bachelor's degree in Electrical and Computer Engineering from the National Technical University of Athens Her research interests focus on market design for public policy , including education systems, labor markets, and algorithmic fairness. Recent work examines human-algorithm trade-offs in college admissions and limitations of diversity interventions in hiring. She frequently collaborates with institutions like the Nokia Bell Labs and the University of Michigan Ross School. Recent Research Trends: Monachou's publications emphasize fairness, equity, and strategic behavior in allocation systems. Key themes include: Algorithmic fairness in education and labor markets Design of simple yet optimal allocation mechanisms Evaluating trade-offs between information and access in policy Awards: INFORMS DEI Best Student Paper Award (2022) ACM EAAMO 2021 Best Paper with a Student Presenter Award Her work has been published in top venues like ACM EC, Management Science, NeurIPS, and AAAI. She actively contributes to interdisciplinary initiatives such as the Mechanism Design for Social Good workshop.

Habeeb OlufowobiView profile

Assistant Professor

• Cyber-Physical System Security
• Embedded Systems Security
• IoT Security
• +10 more

Habeeb Olufowobi is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), where he leads the Cyber-Physical System Security Lab. He holds a PhD in Computer Science from Howard University (2019) and previously served as a Lecturer at Howard before joining UTA in 2020. His research is centered on the security and trustworthiness of embedded and distributed systems, particularly in the domains of autonomous vehicles, IoT, and healthcare AI. He investigates cybersecurity challenges at the hardware-software interface in real-time systems and develops AI/ML models that are transparent, explainable, and equitable. His interdisciplinary work integrates principles from cybersecurity, real-time systems, and machine learning. The recent publications reflect a strong trend in securing cyber-physical systems using advanced AI techniques, with emphasis on intrusion detection, secure communication (e.g., named data networking), and robustness of autonomous systems. His work frequently appears in top-tier venues such as IEEE Transactions, VehicleSec, and ICMLA. Project Management Professional (PMP), PMI (2012–Present) Member, Institute of Electrical and Electronics Engineers (IEEE) (2020–Present) Habeeb has secured significant research funding, including an NIH grant on ethical AI for Chagas disease prediction and an AIM-AHEAD grant focused on health equity. He mentors several graduate students, including Paul Agbaje and Afia Anjum, who have received awards and internships at prestigious institutions like Los Alamos National Laboratory. He teaches courses in cloud computing, embedded systems, and information security, and serves as a faculty advisor for the National Society of Black Engineers (NSBE) at UTA. His lab, the Cyber-Physical System Security Lab, focuses on developing scalable and reliable security solutions for critical infrastructure, with growing emphasis on healthcare applications and fairness in algorithmic decision-making.