Find Academic Supervisors

• Software Engineering
• Model-Driven Engineering
• Automotive Embedded Systems
• +3 more

Vera Pantelic is an Adjunct Assistant Professor in the Department of Computing and Software at McMaster University. Her research focuses on software engineering practices for model-based development in automotive systems, particularly centralized Electrical/Electronic (E/E) architectures, Simulink modeling, and supervisory control of probabilistic discrete event systems. Education: Not explicitly mentioned in the text. Her scholarly activity includes extensive contributions to conferences and journals in automotive software engineering, model transformation, and real-time systems. Her work addresses challenges in modularity, documentation, and compliance within automotive embedded systems. Her recent publications emphasize advancements in centralized E/E architectures, model-driven testing, and assurance cases for automotive safety. She collaborates on topics integrating software engineering principles with automotive domain requirements. Scientific Awards: No specific awards mentioned in the text. She serves as an advisor in software engineering, though specific student names are not listed. Her projects involve simulation-based testing, model refactoring, and compliance frameworks, supported by industry partnerships and academic grants. Her work contributes to labs and teams focused on automotive software reliability and model-driven engineering. No explicit lab or team affiliations are detailed in the provided text.

Anne Elisabeth HaxthausenView profile

Associate Professor

• Formal methods
• Specification languages
• Semantics
• +6 more

Anne Elisabeth Haxthausen is an Associate Professor at the Software Systems Engineering section within DTU Compute , Technical University of Denmark . Her work focuses on formal methods, railway control systems, and safety-critical software engineering. Founder and leader of the DTU Railway Verification Group Member of European Technical Working Group on Formal Methods in Railway Control Editorial board member for Springer Formal Aspects of Computing Journal Active in the Overture Language Board Her research emphasizes formal verification of railway interlocking systems, particularly through compositional approaches and automated tools. She has contributed to projects like RobustRailS, Overture, and RAISE, focusing on model-based development and verification. She serves as a tutor for bachelor students and contributes to the advisory committee for DTU's Computer Science and Engineering MSc program. Her recent publications explore challenges in verifying autonomous and AI-driven railway technologies.

Sudhir KumarView profile

Professor

• Molecular Evolution
• Phylomedicine
• Functional Genomics
• +5 more

Sudhir Kumar is a Professor and Principal Investigator at Temple University, leading a research laboratory focused on molecular evolution, phylomedicine, and functional genomics. His lab develops mathematical methods, computational algorithms, and software packages for analyzing genomic variation across populations, pathogens, tumors, and species. Key contributions include the widely used MEGA software (www.megasoftware.net) for molecular evolutionary analysis and the TimeTree knowledge-base (www.timetree.org) that synthesizes evolutionary knowledge on species divergence times. Dr. Kumar's research interests center on integrating mathematical and computational techniques into evolutionary biology and biomedicine. His lab pursues a holistic paradigm where evolutionary and genomic patterns are discovered through comparative analysis of big datasets, then used to reveal underlying biological processes and develop predictive models. His work spans phylomedicine of genetic diseases, molecular phylogenomics, and the timetree of life, with recent innovations including Bayesian methods, machine learning algorithms, and statistical approaches for inferring molecular phylogenies, divergence times, and pathogenic mutations. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning to evolutionary genetics, with multiple 2025 papers focused on sparse learning techniques, transformer-based models, and AI-assisted analytical protocols. His work increasingly bridges evolutionary biology with cancer genomics and precision medicine applications. His scientific achievements have been recognized with the prestigious 2025 George W. Beadle Award from the Genetics Society of America, which honors his "efforts to democratize evolutionary genetics." Dr. Kumar has mentored numerous doctoral candidates, postdoctoral researchers, and graduate students, many of whom have gone on to faculty positions at institutions including Oakland University and universities in Brazil. His lab includes current doctoral candidates working in bioinformatics and statistical molecular evolution, supported by technical staff including programmers, genome tech specialists, and informatics specialists. The Kumar Laboratory operates as an interdisciplinary research hub with multiple projects including MEGA (Molecular Evolutionary Genetics Analysis), TimeTree, myPEG (web-based evolutionary tools), and FlyExpress (a knowledge base for Drosophila melanogaster embryo images). The lab emphasizes green computing efforts aimed at democratizing scientific practice and making big data analytics more accessible.

Christian EngwerView profile

Professor

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +7 more

Christian Engwer is a full Professor at the University of Muenster in the Institute for Applied Mathematics, specializing in Analysis and Numerics. He leads the Engwer Group focused on Applications of Partial Differential Equations and is actively involved in the Cells in Motion initiative as a supervisor in the CiM-IMPRS Graduate Programme. His research centers on developing numerical methods for partial differential equations, particularly addressing challenges in complex geometries and multi-physics applications. He specializes in Unfitted Discontinuous Galerkin methods, which allow simulations on complex geometries without requiring domain-fitted meshes. His work spans porous media modeling, biological systems, and bioelectromagnetism applications, with significant contributions to EEG/MEG forward modeling in neuroscience. Analysis of his recent publications reveals a strong focus on model order reduction techniques, stabilized numerical schemes for cut-cell meshes, and applications in bioelectromagnetism. His work demonstrates a consistent trajectory toward developing robust, efficient numerical methods applicable to real-world problems in medical imaging and biological modeling, with increasing emphasis on high-performance computing implementations. Professor Engwer actively supervises doctoral students, with recent completions including Lukas Renelt (2025), Michael Wenske (2021), and Maria Carla Piastra (2019), among others working on topics related to numerical methods and biomedical applications. He leads several major research projects including BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging (NIH-funded since 2019), multiple EXC 2044 Cluster of Excellence projects through 2025, and the InterKI interdisciplinary teaching program on machine learning and artificial intelligence. His group develops several important software packages including DUNE (Distributed and Unified Numerics Environment), duneuro (for bioelectromagnetism applications), and TPMC (Topology Preserving Marching Cubes). These tools support research in numerical methods and their applications to complex scientific problems.

Reza SamaviView profile

Associate Professor

• Information Security
• Machine Learning
• Trustworthy AI
• +5 more

Dr. Reza Samavi is an Associate Professor at Toronto Metropolitan University's Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering & Architectural Science. He is also a Faculty Affiliate with the Vector Institute for Artificial Intelligence and directs the Trustworthy AI Research Lab (TAILab). Previously, he served as Assistant Professor and eHealth Graduate Program Coordinator at McMaster University's Department of Computing and Software (2014-2020). Holding a PhD in Computer Science (University of Toronto, 2013), his academic journey bridges industry experience with rigorous scholarly contributions. His research lies at the critical intersection of Trustworthy AI , Machine Learning Security , and Medical Informatics . He investigates Safety & Security of ML Algorithms Privacy-Preserving AI Systems Transparency Frameworks for Medical AI Blockchain-enabled Privacy Auditing Game Theory for Model Robustness Optimization-based Anonymization Techniques The TAILab research group under his leadership has produced groundbreaking work in Uncertainty Quantification for Neural Networks Robustness Against Adversarial Attacks Medical Image Analysis Clinical Decision Support Systems Emergency Medicine Predictive Modeling His recent projects focus on enhancing migrant youth mental health through LLM-based conversation agents and developing certified robustness guarantees for ensemble networks. Dr. Samavi's scholarly excellence is recognized through Privacy Technologies Research Award (IBM) Privacy By Design Research Award (Ontario IPC) Bridging Divides Emerging Research Grant (TMU) NSERC PGS-D Recipient (Co-supervised student) SOSCIP Accelerator Grant He has secured major funding from NSERC , SOSCIP , MITACS , HHS , and IDEaS programs. As a dedicated educator, Dr. Samavi teaches graduate courses in Secure Machine Learning and Software Testing while mentoring 15+ graduate students across PhD , MASc , and MEng programs. His lab has presented at premier venues including AAAI , IJCAI , and IEEE Transactions while maintaining active collaborations with institutions like Harvard, ETH Zurich, and the University of Waterloo.

• Computer Supported Cooperative Work (CSCW)
• Human-Centered Computing
• Human-Computer Interaction
• +7 more

Pernille Bjørn is a Professor in Computer Supported Cooperative Work (CSCW) at the Department of Computer Science , University of Copenhagen (DIKU), where she has been since May 2015. Her research investigates collaborative work practices to design cooperative technologies, focusing on domains like healthcare, global software development, startup companies, and digital fabrication. Faculty of Science, University of Copenhagen Human-Centred Computing Section Research Interests : Bjørn’s work spans CSCW , Human-Computer Interaction , and Digital Fabrication , with applications in healthcare systems, cross-cultural software development, and inclusive technology design. She explores collaborative virtual reality training, FemTech, and crisis computing. ACM Distinguished Member (2024) Publications : Published in top venues like ACM Transactions on Computer-Human Interaction , CSCW , and CHI , her recent work examines hybrid work asymmetry, neurodiverse accessibility, and art-driven collaborative research.

• Construction Engineering
• Computing in Civil Engineering
• Project Planning and Scheduling
• +6 more

Ming Lu is a Professor in the Department of Civil and Environmental Engineering at the University of Alberta, Faculty of Engineering. Specializing in Construction Engineering and Management (CEM), he leads the Construction Automation Lab (AutoLab) since 2010, focusing on integration, automation, and optimization in construction. Dr. Lu holds professional engineering licensure (PEng) in Alberta and has extensive academic experience across Canada, Hong Kong, and China. PhD in Civil Engineering (University of Alberta, 2000) B.Eng. in Road & Traffic Engineering (Tongji University, 1994) His research spans Construction Automation , Project Scheduling , and Resource Optimization , with over 150 publications in top journals. Recent work emphasizes model trees , time-window constraints , and labor cost regression . Publications appear in Automation in Construction , Journal of Computing in Civil Engineering , and ASCE Journal of Construction Engineering and Management . Notable awards include the 2022/23 CSCE Stephen G. Revay Award , Fiatech STAR Award (2013) , and multiple Best Paper Awards from ASCE. His software tools like SDESA and S3 revolutionized construction simulation and resource-constrained scheduling. Dr. Lu supervised numerous graduate students in projects involving BIM applications , earthwork optimization , and steel fabrication scheduling . He developed key courses like CIV E 406 (Construction Estimating) and CIV E 607 (Productivity Modeling), integrating simulation-based learning into construction education.

Daniel FremontView profile

Associate Professor

• Formal Methods
• Automated Reasoning
• Cyber-Physical Systems
• +6 more

Daniel Fremont is an Associate Professor of Computer Science and Engineering at the University of California, Santa Cruz, where he conducts research at the intersection of formal methods and autonomous systems. His work focuses on developing mathematical techniques to improve the reliability of software, hardware, and cyber-physical systems through precise specification, formal verification, automatic synthesis, and principled testing approaches. Dr. Fremont's research interests center on applications of logic in computer science, particularly using automated reasoning to enhance system reliability. His work spans formal methods for cyber-physical systems (CPS), especially autonomous systems that incorporate machine learning. Key research areas include algorithmic improvisation for creating systems with controlled randomness, probabilistic programming through the Scenic language for environment modeling, and formal verification techniques applicable to safety-critical autonomous systems. His group has successfully applied these methods to autonomous vehicles, aircraft systems, and robotics, with significant contributions to both theoretical foundations and practical implementations. The publication record reveals a strong trajectory from theoretical foundations of control improvisation toward practical applications in autonomous systems verification. Early work established the theoretical framework of control improvisation, while recent publications focus on applying these techniques to real-world challenges in autonomous driving, aircraft systems, and AI-based autonomy. A consistent theme across his research is the integration of formal methods with machine learning to address the verification challenges posed by complex, learning-based systems operating in uncertain environments. Best Paper Award at IoTDI 2016 for 'Control Improvisation with Probabilistic Temporal Specifications' Dr. Fremont leads a research group focused on formal methods for autonomous systems, with significant contributions to the development of tools like Scenic (a probabilistic programming language for scenario specification) and VerifAI (a toolkit for formal design and analysis of AI-based systems). His work bridges theoretical computer science with practical engineering challenges in safety-critical autonomous systems, receiving funding from various sources supporting research at the intersection of formal methods and artificial intelligence. The group's approach combines theoretical algorithm development with practical implementation and testing, often collaborating with industry partners working on autonomous vehicle technology. The research group maintains active development of several open-source tools, including the Scenic language for scenario specification and VerifAI for formal analysis of AI systems. They have demonstrated applications across multiple domains including autonomous vehicles, aircraft systems, and robotics, with particular emphasis on simulation-based testing and verification approaches that can provide formal guarantees about system behavior.

• Student Success in STEM
• Growth Mindset in Education
• Collaborative Learning
• +3 more

Sharon Mason is a Professor and Director of the School of Information at Rochester Institute of Technology (RIT), affiliated with the Golisano College of Computing and Information Sciences. She has been faculty since 1997, focusing on promoting success for underrepresented groups in STEM, particularly through growth mindset initiatives and collaborative learning strategies. Education: Bachelor of Science, Ithaca College Master of Science, Rochester Institute of Technology Doctor of Philosophy, University at Buffalo Research & Impact: Dr. Mason's work emphasizes faculty development and student retention, particularly for marginalized populations. Her research investigates classroom dynamics, career navigation for women in STEM, and institutional transformation. She has secured over $5M in grants, including NSF S-STEM and ADVANCE grants, and authored over 25 peer-reviewed articles. Grants & Awards: NSF S-STEM Grant (2020): ENGAgE program enhancing growth-mindset academic experiences for low-income computing students. NSF ADVANCE Grant (2012): CONNECT initiative increasing women STEM faculty representation. Department of Defense IASP Awards: Supporting cybersecurity graduate students. Leadership: Founded RIT's Women in Computing (WIC) program, co-chaired the NY Regional Women in Computing Conference, and served on NCWIT's Academic Alliance Committee. Managed scholarship distribution and student conflict resolution as GCCIS Faculty Associate. Advocacy: Presented nationally on technical career outreach for women, advised on recruitment/retention strategies, and contributed to GCCIS program assessments.

Cong PuView profile

Assistant Professor

• Network Security
• Data Privacy
• Applied Cryptography
• +4 more

Dr. Cong Pu is an Assistant Professor in the Department of Computer Science at Oklahoma State University (OSU), Stillwater, Oklahoma. He holds a Ph.D. and M.S. in Computer Science from Texas Tech University and a B.S. in Computer Science and Technology from Zhengzhou University, China. His primary research focuses on network security, data privacy, applied cryptography, wireless networking, and mobile computing. He leads the Security & Networking Lab at OSU and has secured grants from NSF, NSA, and other agencies. His work emphasizes secure IoT and drone networks, privacy-preserving protocols, and AI-driven cybersecurity solutions. Education Ph.D. and M.S. in Computer Science, Texas Tech University, USA B.S. in Computer Science and Technology, Zhengzhou University, China Research Interests Dr. Pu's research spans network security , data privacy , applied cryptography , wireless networking , and mobile computing . He specializes in designing lightweight authentication protocols for IoT and drone networks, enhancing privacy in distributed systems, and developing AI-driven cybersecurity frameworks. Recent efforts include blockchain-assisted authentication, fault-tolerant data aggregation, and resource-efficient cryptographic protocols. Grants & Awards NSF SaTC Award ($288,398) for securing Internet of Drones systems (2024) NSA Cybersecurity Training Program Grant ($127,128) (2023) Best Paper Award at IEEE CCNC 2024 Listed in Stanford/Elsevier Top 2% Scientists (2025) Advising & Training He mentors Ph.D., M.S., and undergraduate students in areas like network security and applied cryptography. Offers research assistantships, independent studies, and visiting scholar collaborations. Has developed training programs for K-12 educators in cybersecurity via NSF/OSRHE grants. Labs & Infrastructure Leads the Security & Networking Lab , focusing on secure IoT/drones, privacy-preserving systems, and AI-driven security tools. Collaborates on projects involving blockchain, reinforcement learning, and hardware-software co-design for defense mechanisms.

Kenneth M. AndersonView profile

Professor

• Software Engineering
• Crisis Informatics
• Human-Computer Interaction
• +3 more

Professor Kenneth M. Anderson is Chair of the Department of Computer Science and holds the Palmer Endowed Chair at the University of Colorado Boulder's College of Engineering & Applied Science. He co-directs Project EPIC, a $4M NSF-funded initiative on social media use during mass emergencies, and serves as Co-Director of the Center for Software and Society. Ph.D. in Computer Science, University of California, Irvine (1997) Joined CU Boulder in 1998; tenured in 2005 Former Associate Dean for Education (2016-2019) Former Associate Chair (2010-2013) His research bridges software engineering, crisis informatics, and human-computer interaction. Current projects focus on large-scale social media analytics, crisis response systems, and software architecture for data-intensive environments. Recent publications explore multilingual social media analysis (ML-EPIC), bug fix patterns in software development (FIXR), and collaborative big data platforms. Themes include disaster risk communication, data modeling challenges, and asynchronous analysis tools. ATLAS Fellow (2010) As department chair, he spearheaded initiatives for inclusivity, including creating a Bachelor of Arts in Computer Science and establishing the NSF Broadening Participation in Computing plan. He oversees major academic reforms and departmental operations.

Debswapna BhattacharyaView profile

Associate Professor

• Computational Biology
• Bioinformatics
• Machine Learning
• +5 more

Debswapna Bhattacharya is an Associate Professor in the Department of Computer Science at Virginia Tech. Her research focuses on computational biology, bioinformatics, and machine learning with applications in structural biology. She holds a Ph.D. from the University of Missouri-Columbia (2016) and previously served as an Assistant Professor at Auburn University (2017–2021). Her work develops AI-driven methods for biomolecular modeling, including RNA and protein structure prediction, quality assessment, and refinement. Notable contributions include software tools like lociPARSE, RNAbpFlow, and EquiPNAS. She has received prestigious awards such as the NSF CAREER Award (2020) and NIH MIRA Award (2020). Teaching includes courses on machine learning and AI in molecular modeling. Her lab collaborates on NIH-funded projects (R35GM138146) and NSF initiatives (DBI2208679). Recent work emphasizes equivariant neural networks and transformer-based models for biomolecular analysis.

• Model-based Systems Engineering
• Safety-Critical Systems
• Systems Engineering
• +3 more

Dr. Siyuan Ji is a Reader in Model-based Systems Engineering (MBSE) at Loughborough University, serving as Deputy Head of the Manufacturing, Systems & Management Academic Community and Deputy Director of the Doctoral Training Centre in MBSE. He previously held a Senior Lecturer position in Systems Engineering at the University of York, where he led the MSc Programme in Safety-Critical Systems Engineering. His academic journey includes a PhD and MSc in Physics from the University of Nottingham, followed by research roles in model-based systems engineering at Loughborough University. His research focuses on advancing model-based techniques for systems engineering, particularly in safety-critical systems, formal methods, and complex system design. He has contributed to areas such as hazard management (e.g., BSafeML framework), response time analysis in real-time systems, and model synchronization for requirements engineering. His work bridges theoretical foundations with practical applications in automotive systems, embedded software, and educational technology. Dr. Ji holds the title of Fellow of the Higher Education Academy and has published extensively on topics ranging from quantum technology reporting to conversational tutoring systems. His research emphasizes interdisciplinary collaboration, evident in projects like the EPSRC-funded analysis of vehicles as complex systems. He actively contributes to both academic and industrial advancements in systems engineering methodologies and education innovation. His professional roles include managing doctoral training programs, overseeing academic communities, and advancing systems engineering education. Collaborations span industry partnerships and international academic networks, reflecting his commitment to impactful research and training the next generation of systems engineers.

• Intellectual Property Law
• Artificial Intelligence and Copyright
• Blockchain and Smart Contracts
• +7 more

Dr. Andres Guadamuz is a Reader in Intellectual Property Law at the University of Sussex, affiliated with the School of Law, Politics and Sociology. He serves as Editor in Chief of the Journal of World Intellectual Property . His research focuses on intersections of law and technology, including artificial intelligence and copyright, open licensing, cryptocurrencies, smart contracts, and blockchain. He has authored over 40 articles, book chapters, and two books, alongside regular blog posts on technology regulation. Research Interests: Artificial Intelligence and Copyright Blockchain Technology and IP Cryptocurrency Regulation Open Source Licensing Internet Governance Data Mining Legal Frameworks Publication Trends: His recent work addresses AI's impact on copyright law, NFTs, and legal challenges in smart contracts. He critically explores EU AI legislation, liability frameworks for AI inputs/outputs, and NFT authenticity issues. Teaching: Teaches courses on Digital Intellectual Property Law, Internet Law, and Information Technology IP Law, including developing the specialized LLM in IT and Intellectual Property Law. Grants/Advising: Supervises PhD/postgraduate students in his research areas. His work is widely cited in legal, academic, and policy circles, with over 100 Mendeley readers for key papers.

Sharad MehrotraView profile

Professor

• Data Management
• Distributed Systems
• Database as a Service
• +7 more

Sharad Mehrotra is a Distinguished Professor at the University of California, Irvine (UCI), leading the Center for Emergency Response Technologies (CERT) and directing the NSF-funded RESCUE project. He previously served at the University of Illinois, Urbana-Champaign, and holds a Ph.D. from the University of Texas at Austin (1993). His research focuses on data management, IoT systems, privacy-preserving technologies, and smart spaces, with contributions to frameworks like TIPPERS and MARS. Education: Ph.D., Computer Science, University of Texas at Austin, 1993 Research Interests: His work bridges database systems, security, and IoT, emphasizing privacy in smart environments. Notable projects include sentient space technologies for disaster response, cryptographic methods for encrypted data queries, and semantic IoT integration. Recent efforts address privacy in multi-owner data systems and resilient community water infrastructure. Awards & Recognition: ACM Fellow (2024) SIGMOD Best Paper (2001), DASFAA Best Paper (2004) NAVWAR Innovation Award (2021) Outstanding Graduate Mentor (2005) Grants & Leadership: As RESCUE PI, he managed $12.5M NSF funding, developing crisis-response software deployed by emergency agencies. Collaborations include the Cal-IT2 institute (UCSD/UCI) and the US Navy’s TIPPERS platform. He co-leads initiatives like the NSF Civic Innovation Challenge for disaster resilience in aging communities. Labs & Teams: Directs UCI’s Information Systems Group and CERT, fostering interdisciplinary research with 60+ members. His teams produce open-source tools (e.g., SEMIoTIC, PrivacySphere) and engage in global partnerships via Fulbright Visiting Scholar programs.