Find Academic Supervisors

Md. Tahmidul Islam MollaView profile

Assistant Professor

• Wearable Technology
• Pervasive/Ubiquitous Computing
• Human-Computer Interaction
• +1 more

Dr. Md. Tahmidul Islam Molla is an Assistant Professor of Practice and Director of Undergraduate Studies in the Department of Computer Science at Marquette University. He specializes in wearable technology, pervasive computing, human-computer interaction, and data science. Previously, he held a Postdoctoral Researcher position at Cornell University and earned his Ph.D. from the University of Minnesota in 2020. His teaching roles include instructing core and advanced courses in computer science, data science, and embedded systems. He leads the NSF S-STEM project at Marquette, providing scholarships for low-income graduate students transitioning into computing careers. Courses taught include COSC 1000, COSC 2100, COSC 4290/5290, COSC 5500, COSC 6500, and COSC 6510. Research interests focus on wearable systems, soft/flexible electronics, and on-skin interfaces. His work explores manufacturing techniques for e-textiles and user experience in thermal microclimate control. Collaborative projects include the development of SkinKit and WovenProbe for prototyping on-skin interfaces. Dr. Islam’s research has led to innovations in e-textile fabrication, including surface-mount methods and laundering-resistant designs. He actively contributes to conferences like ACM International Symposium on Wearable Computers and ACM Designing Interactive Systems.

Feng ChenView profile

Associate Professor

• AI Safety
• Ethical AI
• Machine Learning
• +7 more

Feng Chen is an Associate Professor in the Department of Computer Science at The University of Texas at Dallas (UT Dallas), part of the Erik Jonsson School of Engineering & Computer Science. He directs the AI Safety Laboratory and holds tenure. His research focuses on AI safety, ethical machine learning, uncertainty quantification, and fair algorithms. He earned a Ph.D. from Virginia Tech (2012), M.S. from Beijing University of Aeronautics & Astronautics (2004), and B.S. from Hunan University (2001). His research spans AI safety frameworks, resilient AI systems, fairness in healthcare and finance, cybersecurity, and environmental AI applications. Key contributions include uncertainty-aware deep learning, causal representation learning, and adversarial vulnerability analysis. He has authored over 150 peer-reviewed publications, including top venues like NeurIPS, ICML, KDD, and ICDM. Grants: $4.2M+ in funding from NSF, Army Research Office, IARPA, and industry partners. Teaching: Courses in data mining, anomaly detection, and artificial intelligence since 2014. Notable achievements include an NSF CAREER Award (2018) and leadership in projects like EMBERS (social media event prediction). His work bridges theory and practice, addressing societal challenges through ethical AI principles. Labs/Teams: AI Safety Laboratory (UT Dallas), collaborative projects with Virginia Tech, Carnegie Mellon, and industry partners on software vulnerability analysis and causal AI. Awards: Multiple best paper nominations, including 2nd Place in NAE Security Category (2009) and UT Dallas Outstanding Research (2021-2022).

Jonathan ClarkView profile

Professor

• Robotics
• Legged Locomotion
• Dynamic System Analysis
• +4 more

Dr. Jonathan Clark is a Professor and Director of the CISCOR at the FAMU/FSU College of Engineering's Department of Mechanical Engineering. His research focuses on bioinspired robotics, legged locomotion, and mechanical design, emphasizing robots for unstructured environments. He holds a Ph.D. (2004), M.S. (2000), and B.S. (1998) in Mechanical Engineering from Stanford University and Brigham Young University, respectively. Key research areas include dynamic climbing robots, compliant leg design, and shape deposition manufacturing. Notable projects involve the Sprawl family of robots and the development of multimodal robots like STARQ and Aquaclimber. His work bridges biomechanics and robotics, with applications in rescue, exploration, and industrial automation. Publications emphasize gait optimization, material science for robotics, and energy-efficient locomotion. Recent trends include bioinspired swimming robots and terrain-adaptive systems. Clark collaborates with biologists and engineers to advance robotics in complex environments. He advises on grants relating to dynamic systems and robotics education, and leads CISCOR's initiatives in advanced manufacturing and robotics innovation. Labs and teams include the CISCOR facility, focusing on prototyping and testing next-generation robotic platforms.

Alexandru G. BardasView profile

Associate Professor

• Cybersecurity
• Systems Security
• Moving Target Defenses
• +9 more

Alexandru G. Bardas is an Associate Professor at the University of Kansas in the Department of Electrical Engineering & Computer Science (EECS) and the Institute for Information Sciences (I2S) . He received his PhD from Kansas State University under advisors Xinming (Simon) Ou and Scott A. DeLoach. His research focuses on cybersecurity from a systems perspective , including moving target defenses, security operations center (SOC) metrics, DevOps security, power grid cybersecurity, and defensive technologies for political activists. He explores UDP-based DDoS detection, DNS traffic analysis, and the intersection of AI with cybersecurity, emphasizing foundational knowledge over tool-specific training. Key research areas: Cybersecurity, Systems Security, Moving Target Defenses, SOC Metrics, DevOps Security Recent publications in ACSAC 2024 , USENIX Security 2024/2023 , and IEEE Security & Privacy 2022 Dr. Bardas has received significant recognition including: NSF CAREER Award (2022) for SOC automation Bellows Scholar (2021) at KU NSA SoS Honorable Mention (2023) He actively advises students across disciplines, with graduates now at Sandia National Laboratories , Blue Cross Blue Shield , and Pacific Northwest National Laboratory . Dr. Bardas participates in NSF grant reviews , serves on program committees for SOUPS and MILCOM , and leads outreach initiatives like the GenCyber Summer Camp .

Yang WangView profile

Associate Professor

• Computer Vision
• Machine Learning
• Domain Adaptation
• +3 more

Dr. Yang Wang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on computer vision and machine learning, with a particular emphasis on domain adaptation, meta-learning, and privacy-preserving techniques. He actively advises prospective graduate students through a dedicated webpage outlining application procedures. Research interests include few-shot learning, test-time adaptation, and cross-modal applications such as handwritten text recognition and gaze estimation. His work explores how models can adapt dynamically to new domains using limited labeled data, with applications in crowd counting, medical data analysis, and cybersecurity. He also investigates privacy-preserving methods for deep learning models to protect user attributes and sensitive information. Recent publications highlight advancements in meta-auxiliary learning frameworks and efficient user adaptation techniques. His contributions span journals and conferences, showcasing innovations in both foundational machine learning methodologies and real-world applications.

Aldeida AletiView profile

Professor

• Automated Software Engineering
• Artificial Intelligence
• Optimization
• +2 more

Aldeida Aleti is a Professor in the Department of Software Systems & Cybersecurity at Monash University. Her research focuses on Automated Software Engineering, leveraging AI and optimization techniques for tasks like software design, testing, and repair. She has held roles including Chief Examiner for units like FIT4002 and FIT5136, and has contributed to teaching FIT3077 and FIT1008. Education: PhD in Software Engineering (Swinburne University of Technology, 2012), Master of Computer Engineering (Polytechnic University of Tirana, 2008), and Bachelor Honours in Computer Engineering (Yildiz Technical University, 2005). Research Interests: Automated software engineering, fitness landscape analysis, optimization, and search-based techniques. She leads projects like RAISE (Responsible AI Software Engineering) and collaborates on quantum computing and healthcare AI initiatives. Awards include the FIT Dean's Award (2016), Best Paper Awards (2015, 2011), and the Heidelberg Laureate Forum invitation (2014). She has been a grant assessor for the Australian Research Council since 2015. Advising: Accepting PhD students in AI-driven software engineering, optimization, and automated testing. Active in committees like the Faculty Research Committee and Early Career Researcher committee.

Bashir M. Al-HashimiView profile

Professor

• Energy-efficient mobile computing systems
• Low-power test and test-data compression of digital integrated circuits
• Energy-harvesting computing
• +1 more

Professor Sir Bashir M. Al-Hashimi is currently Vice President (Research & Innovation) at King’s College London and holds the ARM Professorship in Computer Engineering there. He is also a Visiting Professor in Electronics and Computer Science at the University of Southampton. Prior to academia, he worked in the electronics design industry for eight years before joining the University of Southampton in 1999, where he became a personal Chair holder in 2004. His research focuses on energy-efficient computing systems, low-power testing, and energy-harvesting technologies, with a strong emphasis on smart city applications and wearable computing. He has led numerous interdisciplinary projects funded by the EPSRC and industry, including the PRiME Programme Grant and the EPSRC-funded Spatial Computational Learning consortium. He has supervised 45 PhD students and authored/co-authored nearly 400 technical papers, earning eight best paper awards and contributing to five books. His honors include a CBE (2018), knighthood (2025), Fellowship of the Royal Society (2023), and roles on the Research Excellence Framework panels. He founded the Arm-ECS industry-academia center in 2008, promoting energy-efficient computing research.

David R. KauchakView profile

Professor

• Natural Language Processing (NLP)
• Text Simplification
• Machine Learning
• +3 more

David R. Kauchak is a Professor of Computer Science at Pomona College, part of The Claremont Colleges. He has been affiliated with the institution since 2014. His academic background includes a Ph.D. (2006) and M.A. (2006) in Computer Science from the University of California, San Diego, and a B.S. (2005) in Computer Science from the University of Utah. His research focuses on Natural Language Processing (NLP) , particularly text simplification , aiming to reduce text complexity while preserving content. Applications include improving health literacy through simplified medical text and enhancing accessibility of technical documents. He has also contributed to machine learning methodologies and information retrieval systems. Key awards include the Distinguished Poster Award at AMIA 2016 and the Best Undergraduate Paper Award at SocalNLP 2018. His work frequently addresses interdisciplinary challenges in healthcare communication, education technology, and software tool development. Teaching highlights include courses in Artificial Intelligence , Algorithms , Natural Language Processing , and foundational computer science modules. He has advised numerous projects in computational linguistics and health informatics, though specific student names are not explicitly listed in the provided materials. His collaborations span academic and medical domains, including joint research with institutions like the University of Utah and projects funded through initiatives like the German Climate Modeling Initiative (though this appears unrelated to his primary work). He maintains an active presence in academic conferences, publishing in venues such as ACL, AMIA, and IEEE journals.

Miguel F. AnjosView profile

Professor

• Mathematical Optimization
• Smart Grids
• Operations Research
• +3 more

Miguel F. Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh , and holds the NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair on Optimization for Smart Grids at Polytechnique Montréal. He received his B.Sc. (1992), M.S. (1994), and Ph.D. (2001) from McGill, Stanford, and Waterloo respectively. Research Theme Head of Data and Decisions at Edinburgh Founding Director of Trottier Institute for Energy Editor-in-Chief of Optimization and Engineering Research Interests: His work bridges mathematical optimization with smart grid applications , focusing on conic optimization, optimal power flow, demand response, and facility layout. He applies these techniques to energy storage, electric transportation, and industrial systems. Scientific Awards: Méritas Teaching Award (2012) Humboldt Research Fellowship (2009) Queen Elizabeth II Diamond Jubilee Medal (2013) Elected Fellow of EUROPT and Canadian Academy of Engineering Academic Service: Served on Mathematical Optimization Society Council, SIAM Activity Group on Optimization, INFORMS Optimization Society Vice-Chair, and Mitacs Research Review Committee. Hosts benchmark datasets: QAPLIB, FLPLIB, Jones Benchmark.

Akshay NarayanView profile

Senior Lecturer

• Artificial Intelligence
• AI Planning
• Reinforcement Learning
• +6 more

Akshay Narayan is a Senior Lecturer (Educator Track) at the School of Computing, National University of Singapore (NUS), where he teaches senior undergraduate and graduate-level courses in AI Planning and Decision Making, as well as introductory and intermediate-level Software Engineering courses. Education: Ph.D. in Computer Science from National University of Singapore (completed in 2020) M.Tech. in Information Technology from International Institute of Information Technology Bangalore, India B.E. in Computer Science & Engineering from Visveswaraya Technological University, India Research Interests: Dr. Narayan's research spans multiple domains within computer science with a primary focus on artificial intelligence and its applications. His current research centers on transfer learning in reinforcement learning, multi-agent decision making, and AI planning. He has also made significant contributions to cloud computing research, particularly in areas such as smart metering, chargeback systems, power-aware cloud metering, and workload analysis for virtual machine sizing. His work bridges theoretical foundations with practical applications, addressing real-world challenges in computing systems. He has recently expanded his research to include technology in education, exploring how AI can be integrated into teaching and learning processes. Publication Trends: Dr. Narayan's publication record demonstrates a clear evolution from foundational work in cloud computing to more recent explorations in reinforcement learning and AI education. His early work focused on practical applications in cloud systems, including smart metering and QoS monitoring. More recently, his research has shifted toward AI planning, decision making, and the educational applications of AI. This progression shows his ability to adapt to emerging fields while maintaining a strong foundation in systems research. Awards and Recognition: Teaching and Mentoring: Dr. Narayan teaches a variety of courses at NUS including CS2113 Software Engineering & Object-Oriented Programming, CS3219 Software Engineering Principles and Patterns, CS3268 Responsible AI: From Algorithms to Impact, and IT5100F Industry Readiness: Data Analytics and AI in Practice. He has also taught CS4246/CS5446 AI Planning and Decision Making. His teaching approach integrates his research expertise with practical applications, providing students with both theoretical foundations and hands-on experience. He has taught these courses across multiple academic years from AY-2013/14 through AY-2020/21. Research Groups and Collaborations: Dr. Narayan has collaborated with researchers across multiple institutions, including work with Prof. Tze Yun Leong at NUS (his PhD advisor), Shrisha Rao, Zhuoru Li, and others. His research has often involved interdisciplinary collaborations that bridge theoretical computer science with practical system implementations.

• Artificial Intelligence in Education
• Educational Technology
• Online Learning Systems
• +3 more

David Lefevre is a Professor of Practice at the Department of Management and Entrepreneurship, Imperial College Business School. His research focuses on AI applications in education, digital innovation in higher education, and tech-transfer. He co-founded the Edtech Lab in 2004, which pioneered Imperial’s online courses and Global Online MBA. The Lab has won awards including a Gold at IMS Learning Impact (2010) and Silver at QS Reimagine (2018). He also co-founded educational tech companies Epigeum (now part of Oxford University Press) and Insendi (now Study Group). He is Special Advisor on Digital Innovation at Study Group and a British Council Trustee, focusing on digital transformation. Lefevre’s work emphasizes strategic adoption of AI, online education quality, and bridging cultural barriers in learning. Education & Background: While formal education details are not provided, his career reflects deep expertise in education technology and entrepreneurship. His roles include: Professor of Practice, Imperial College Business School Co-founder & former leader of the Edtech Lab Co-founder of Epigeum and Insendi Expert in Residence, Imperial Enterprise Lab Research Interests: Lefevre’s work spans AI in education, online learning infrastructure, and cross-cultural e-learning. He advocates for data-driven strategies to improve online course design and learner engagement. His recent focus includes AI-driven feedback systems and holographic teaching methods. Key Achievements: Launched Imperial’s first online MBA program in 2015 Developed an LMS alternative using middleware Promoted adaptive learning environments for diverse learners Awards & Recognition: Gold award at IMS Learning Impact awards 2010 Silver award for Business Education at QS Reimagine Education 2018 Advisory & Grants: Advises Study Group and the British Council on digital innovation. His ventures have secured significant investments, though specific grant details are not disclosed. Labs & Collaborations: Leads the Edtech Lab and collaborates with Imperial Enterprise Lab. Active in global education networks such as QS Reimagine and the British Council.

Taylor JohnsonView profile

Associate Professor

• Formal Verification
• Cyber-Physical Systems (CPS)
• Artificial Intelligence
• +7 more

Taylor Johnson is an Associate Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and serves as a Senior Research Scientist in the Institute for Software Integrated Systems. Previously, he was an Assistant Professor at the University of Texas at Arlington from 2013 to 2016. His research focuses on formal verification techniques for cyber-physical systems (CPS), emphasizing safety, reliability, and security through hybrid systems, formal methods, and control theory. He has published extensively on neural network verification, earning best paper awards and recognition from IEEE, IFIP, and ACM. Education: Ph.D., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2013) M.Sc., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2010) B.S.E.E., Electrical and Computer Engineering (Rice University, 2008) Research Interests: Formal verification of neural networks and CPS, safety-critical systems, autonomous systems, and AI/ML security. His work bridges theoretical foundations (e.g., hybrid systems) with practical applications in aerospace, energy systems, and robotics. Key Contributions: Developed the NNV tool for neural network verification, led the Verification of Neural Networks Competition (VNN-COMP), and pioneered techniques for robust federated learning and malware detection. Awards: AFOSR YIP Award (2016), NSF CRII Award (2015), and multiple best paper honors. His research is funded by AFRL, NSF, Intel, NVIDIA, and industry partners. Labs & Collaborations: VeriVITAL Lab (Vanderbilt), collaborations with United Technologies Research Center, Boeing, and Toyota.

H. Jonathan ChaoView profile

Professor

• Networking
• Data Center Networks
• High-Speed Packet Processing
• +3 more

H. Jonathan Chao is a Professor in the Department of Electrical and Computer Engineering at New York University (NYU Tandon School of Engineering). He is the Director of the High-Speed Networking Lab, leading a team of 6 PhD students and 10 Master’s students. His research focuses on software-defined networking, network function virtualization, datacenter networks, and high-speed packet processing. Chao has held significant roles, including Head of the ECE Department (2004–2014) and former CTO of Coree Networks. He has authored over 200 publications and holds 58 patents. His awards include IEEE Fellow and National Academy of Inventors (NAI) Fellow. Education: B.S. and M.S. from National Chiao Tung University (Taiwan), Ph.D. from Ohio State University. Research Highlights Developing solutions for data center networks, network security, and quality of service control. Pioneering work in programmable packet schedulers, reinforcement learning for traffic engineering, and SDN security frameworks like SDNShield. Contributions to hybrid SDN networks, bufferless switch architectures, and energy-efficient data center designs. Awards Fellow of National Academy of Inventors (NAI) Fellow of IEEE Telcordia Excellence Award (1987) IEEE Best Paper Award (2001) IEEE New Jersey Coast Section Speaker of the Year (2003) Advisees & Labs Supervises 6 PhD and 10 Master’s students in the High-Speed Networking Lab. Collaborates with the Center for Advanced Technology in Telecommunications (CATT) to advance telecom innovations. Labs & Teams Directs the High-Speed Networking Lab, focusing on cutting-edge networking solutions, and contributes to CATT’s mission of technology transfer and entrepreneurship.

Linda NewnesView profile

Professor

• Transdisciplinary Engineering
• Manufacturing
• Whole Life Value Analysis
• +3 more

Professor Linda Newnes is a faculty member in the Department of Mechanical Engineering at the University of Bath, leading the Made Smarter Innovation: Centre for People-Led Digitalisation (£5M) and the TRansdisciplinary ENgineering Design (TREND) research group (£1.8M). Her work focuses on transdisciplinary engineering, whole life value analysis, and sustainable manufacturing. She directs The Foundry: Centre for Digital, Manufacturing & Design, emphasizing people-centric digitalization and cross-sector collaboration. Her research integrates natural/social sciences and industry stakeholders to address challenges in aerospace, defense, and energy sectors. Notable projects include models for whole life value (cradle-to-cradle) and tools for transdisciplinary working. She actively promotes Equality, Diversity & Inclusion (ED&I), leading the University’s Aurora programme and Athena SWAN submissions. Recent publications explore digital skill premiums, transdisciplinary frameworks, and AR deployment challenges. She advocates Industry 5.0 principles, emphasizing human-centric innovation and resilience in socio-technical systems. Current grants focus on net-zero transitions and cellular agriculture manufacturing. Her advising spans doctoral students in transdisciplinary engineering, Industry 5.0, and future manufacturing. She collaborates with industry partners like Airbus and Innovate UK to advance cost estimation, decision support tools, and lifecycle analysis.

Stefan HaefligerView profile

Professor

• Strategic Management
• Business Models
• Open Innovation
• +2 more

Stefan Haefliger is Professor of Strategic Management & Innovation at Bayes Business School, City, University of London. His research focuses on business model innovation, open strategy, and organization theory in technology-driven environments. Key research streams: Business model portfolios and corporate strategy diversification Organization theory and technology-mediated regulation Open strategy and innovation ecosystems His work examines how technology transforms organizational practices, with empirical studies on compliance systems in investment banks and digital infrastructure governance. Recent projects explore modularity in R&D teams, digital resilience, and human-AI interaction in organizations.