Find Academic Supervisors

Amal ZouaqView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Semantic Web
• +5 more

Dr. Amal Zouaq is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. She holds the FRQS (Dual) Chair in AI and Digital Health, serves as Director of the LAMA-WeST research laboratory, and is an Associate Member of MILA. Her work bridges artificial intelligence with applications in digital health, cultural heritage, and educational technologies, positioning her at the forefront of interdisciplinary AI research in Canada. Her research focuses on Artificial Intelligence , particularly Natural Language Processing and the Semantic Web . Specific interests include knowledge representation, ontology learning, SPARQL query generation, bias mitigation in language models, and clinical text processing. Her work spans multiple domains including healthcare, cultural heritage, and educational technology, with emphasis on developing practical AI solutions that address real-world challenges in knowledge management and information extraction. Analysis of her recent publications reveals a strong trajectory in advancing NLP techniques for knowledge-intensive applications. Her work increasingly focuses on domain-specific applications in healthcare and cultural heritage, with growing emphasis on ethical AI considerations like bias mitigation. The research demonstrates progression from foundational semantic web technologies toward more sophisticated neural approaches while maintaining strong theoretical grounding in knowledge representation. Scientific Recognition: Holder of the FRQS (Dual) Chair in AI and Digital Health Dr. Zouaq has supervised 23 graduate students to completion, including 1 PhD and 22 Master's theses, with research spanning ontology learning, knowledge representation, and NLP applications. Her supervision record demonstrates consistent mentorship in cutting-edge AI research with practical applications across multiple domains. She actively serves on program committees for major conferences in knowledge engineering, data mining, and semantic web technologies. She directs the LAMA-WeST (Web, Semantics and Text) laboratory , which specializes in natural language processing and artificial intelligence research. The lab focuses on knowledge representation, semantic technologies, and their applications in healthcare, cultural heritage, and educational contexts. As a member of IVADO and MILA, she collaborates with leading AI researchers across Montreal's vibrant AI ecosystem.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +3 more

Chun-Liang Li is a research scientist at Apple MLR and an affiliate assistant professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His work bridges machine learning theory with practical applications in computer vision and natural language processing, focusing on efficient model training and representation learning. His educational background includes: Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019), supervised by Prof. Barnabás Póczos B.S. and M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013), supervised by Prof. Hsuan-Tien Lin Li's research centers on generative models and representation learning , with significant contributions to document understanding (FormNet series), multimodal systems (Pic2word), and large language model efficiency . His work consistently addresses real-world challenges like reducing training costs while maintaining performance, as seen in distillation techniques and synthetic data optimization. Analysis of his 2022-2024 publications reveals three dominant trends: (1) LLM efficiency through curriculum training and model updating, (2) structural document understanding via graph-based methods, and (3) multimodal representation learning for vision-language tasks. These reflect his cross-cutting approach to improving model scalability and applicability. His scientific recognition includes: IBM Ph.D. Fellowship (2018) Best student paper runner-up at IJCAI (2017) Double first-place wins in KDD Cup Tracks (2011, 2013) While specific grant details aren't listed, his award-winning KDD Cup performances and extensive publication record suggest strong funding support. He collaborates widely with students and researchers, though formal advisees aren't specified. His current roles at Apple MLR and UW position him at the industry-academia interface for cutting-edge AI development. At Apple, Li contributes to the Machine Learning Research group's core vision-language projects, while his UW affiliation enables academic mentorship and cross-institutional collaboration on foundational ML research.

Matthew CollinsonView profile

Senior Lecturer

• Logic
• Theoretical Computer Science
• Information Security
• +12 more

Matthew Collinson is a Senior Lecturer in Computing Science at the University of Aberdeen, where he also serves as Head of Computing Science and Academic Line Manager. He holds an affiliation with the Scottish Informatics and Computer Science Alliance (SICSA) and leads the EPSRC-funded project SSPEDI (Supporting Security Policy with Effective Digital Intervention). Education: BSc Mathematics, University of Edinburgh (1997) MSc Mathematical Logic, University of Manchester (1998) PhD Computer Science, University of Manchester (2003) Research Interests: His research spans theoretical computer science and cybersecurity , focusing on non-classical logics (intuitionistic, modal, substructural), semantics of computation , concurrency theory , and type theory . He applies these foundations to information security , particularly in modelling security policies, access control, and the economics of cybersecurity decisions. His work integrates formal verification , simulation tools (e.g., Gnosis), and game-theoretic models . Publications Trends: Recent publications (2016–2022) emphasize human-centred security , exploring how persuasion and behavioural interventions can reduce cybersecurity vulnerabilities. Earlier works (2008–2015) concentrate on mathematical systems modelling , layered graph logics , and trust domains , bridging high-level policy and low-level system configurations. Projects & Grants: SSPEDI (2017–2020, EPSRC): Human dimensions of cybersecurity policy compliance. ALPUIS (EPSRC consortium): Algebra and logic for security policy and utility. Trust Domains (RCUK/TSB, 2011–2014): Framework for modelling secure information sharing. Seconomics (EU FP7, 2012–2015): Socio-economic impacts of cybersecurity regulation. PhD Supervision: He has successfully supervised PhD students including Kevin McDonald (2014), Barry Taylor (2015), and Robert (Bob) Duncan (2016), whose theses addressed logic-based security architectures, vulnerability analysis, and cloud stewardship respectively. Labs & Teams: His research is conducted within the Computing Science section of the School of Natural and Computing Sciences, leveraging collaborations with National Grid, HP Labs, and other academic partners.

• Case-Based Reasoning (CBR)
• Artificial Intelligence
• Multi-Agent Systems
• +4 more

Dr. Pascal Reuss is a Researcher at the Intelligent Information Systems (IIS) Division within the Institute of Computer Science , University of Hildesheim . His work focuses on Case-Based Reasoning (CBR) systems, Multi-Agent Systems , and Knowledge Management applications. Active in CBR framework development and game-based AI research Teaching Computer Science III (Databases) for winter 2025/26 Participating in university sustainability initiatives like Stadtradeln 2024/25 Reuss contributes to AI education through practical implementations in gaming environments and has developed visualization tools for CBR agent behavior. His research spans multi-agent collaboration , dynamic case bases , and domain-specific language implementations for knowledge maintenance. Notable contributions include: Co-developing the FEATURE-TAK framework for knowledge extraction Designing case factories for distributed CBR systems Implementing finite state machines for tactical game agents Creating CBR-based fitness planning systems His work appears in various CBR and Game Development publications from 2011-2024. The research demonstrates practical applications of CBR in aircraft maintenance diagnostics , training plan generation , and educational technology contexts.

Dominique UnruhView profile

Professor

• Quantum Computing
• Quantum Cryptography
• Post-Quantum Cryptography
• +5 more

Dominique Unruh is a Professor at RWTH Aachen University , leading the Chair for Quantum Information Systems . Additionally, they hold a Professorship in Cryptography at the Institute of Computer Science of the University of Tartu , Estonia. Their research spans quantum computing , quantum cryptography , post-quantum cryptography , and formal verification of cryptographic protocols and programs. Research Focus : Quantum programs, zero-knowledge proofs, lattice-based cryptography, and quantum random oracle model. Key Contributions : Advancements in NTRU encryption efficiency, quantum Hoare logic, and rewinding techniques for security proofs. Tools : Active development in the EasyCrypt framework for cryptographic verification. Email : unruh@cs.rwth-aachen.de

• Software Engineering
• Model-Based Software Engineering
• Automated Reasoning
• +5 more

Sebastián Uchitel is a Professor at the Department of Computing, Imperial College London, UK. His research focuses on foundational aspects of Software Engineering, particularly in modeling and analysis for automated reasoning, verification of probabilistic systems, controller synthesis, and adaptive systems. He has led major research projects, including the ERC-funded IDEAS StG project on Partial Behaviour Modelling and the European FP6 SENSORIA project. Research Interests: Model-Based Software Engineering, Controller Synthesis, Probabilistic Systems, Adaptive Systems, Requirements Engineering External Roles: General Chair, International Conference on Software Engineering (2017); Associate Editor, Elsevier Science of Computer Programming; Steering Committee, International Conference on Software Engineering His recent work explores intersections between Software Engineering and AI, including assured adaptive systems and logic-based learning. A Senior Member of IEEE and Distinguished Scientist of ACM , he has received awards like the Houssay Prize (2015) and Philip Leverhulme Prize (2005). Collaborators include institutions in Argentina, Canada, and the UK. Selected Publications: 150+ peer-reviewed works spanning controller synthesis, requirements engineering, and formal methods Students: Supervised 12 PhD students since 2003 Grants: Principal Investigator for 3 major grants (2005-2016) totaling over $3.7M USD, including: 2013-2016: Technology Platform in Software Engineering (ANPCYT, $1.6M) 2009-2014: ERC IDEAS StG on Partial Behaviour Modelling (€1.4M) 2005-2008: FP6 SENSORIA Project (€0.7M)

• Computational Design and Fabrication
• Design for Additive Manufacturing
• Adaptive Process Planning
• +6 more

Dr. Xiong Yi is an Assistant Professor at the School of System Design and Intelligent Manufacturing (SDIM) at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He leads the Computational Design and Fabrication (CoDeFab) research group, focusing on the integration of computational design methods with advanced manufacturing technologies, particularly in the field of additive manufacturing. Dr. Xiong has established himself as a leading researcher in computational design for additive manufacturing, with a strong international research background spanning Europe and Asia. Dr. Xiong's educational journey includes: Doctor of Science (DSc) in Engineering Design and Production from Aalto University, Finland (2012-2016) Master of Science (MSc) in Machine Automation from Tampere University of Technology, Finland (2010-2012) Bachelor of Engineering (BEng) in Mechanical Engineering from Hubei University of Technology, China (2006-2010) Dr. Xiong's research primarily focuses on computational design and fabrication methodologies, with particular emphasis on design for additive manufacturing (DfAM), intelligent manufacturing systems, and smart materials. His work bridges the gap between theoretical design principles and practical manufacturing constraints, developing novel approaches for the production of complex engineered products. He has pioneered research in continuous fiber-reinforced composite additive manufacturing, developing innovative process planning and optimization techniques that enable the production of high-performance structural components. His research in electrothermally controlled origami and 4D printing of smart materials represents cutting-edge work at the intersection of materials science, mechanical engineering, and computational design. Dr. Xiong's recent publications reveal a strong focus on continuous fiber-reinforced composites, with significant contributions to 4D printing, metamaterials, and intelligent process planning. His work integrates computational design with manufacturing constraints, creating novel approaches for topology optimization, toolpath planning, and structural design that consider both performance requirements and manufacturability limitations. The research demonstrates increasing sophistication in materials science applications, particularly in programmable materials and multi-functional structures. Dr. Xiong has received multiple prestigious awards for his research contributions, including: Best Presentation Award at the 24th Chinese Conference on Mechanisms and Machine Science (IFToMM CCMMS2024) Best Presentation Award at the International Conference on Frontiers of Additive Manufacturing Research (RAAM 2024) Best Paper Award at the International Conference on Design for 3D Printing (ICD3DP 2023) PhD Scholarship from Aalto University (2016) Research Travel Grant from the International Association for Vehicle System Dynamics (IAVSD) (2013) National Scholarship from the Ministry of Education (2008) As a dedicated educator and mentor, Dr. Xiong serves as a PhD supervisor at SUSTech and has successfully guided students who have gone on to pursue advanced studies and careers at prestigious institutions including Hong Kong Polytechnic University, Beihang University, DJI Innovations, and Singapore's A*STAR research institute. His research is supported by multiple competitive grants, including key projects from the National Key R&D Program of China, the National Natural Science Foundation of China, and provincial and municipal funding agencies. Dr. Xiong also serves on the editorial board of the Journal of Engineering Design and as a guest editor for Composites Communications, contributing to the advancement of his field through scholarly service. Dr. Xiong leads the CoDeFab research group, which maintains a strong collaborative culture focused on 'design leading manufacturing, manufacturing driving design, and digital-intelligent integration.' The group has developed several advanced manufacturing platforms, including multi-axis continuous fiber-reinforced composite additive manufacturing systems, smart composite additive manufacturing platforms, and multifunctional soft matter open manufacturing platforms. With a focus on practical applications and innovation, the CoDeFab group actively collaborates with industry partners and has established a joint laboratory to bridge academic research with industrial implementation.

Daragh ByrneView profile

Academic

• Human-Computer Interaction
• Internet of Things (IoT)
• Tangible Interaction Design
• +4 more

Daragh Byrne is an Associate Teaching Professor at the Carnegie Mellon University School of Architecture , with courtesy appointments in the School of Design and Human-Computer Interaction Institute (HCII) . Previously an Assistant Research Professor at Arizona State University’s School of Arts, Media and Engineering, he manages the NSF-funded XSEAD project and leads MakeSchools , a catalog of making practices in higher education. PhD in Digital Media from Dublin City University (2011) M.Res. in Design and Evaluation of Advanced Interactive Systems from Lancaster University B.Sc. in Computer Applications from Dublin City University His research explores experiential media systems through Internet of Things and tangible interaction design , focusing on how computational tools can capture human experience and enable multidisciplinary collaboration. Key projects include Sentient Concrete (thermochromic architectural surfaces) and Spooky Technology (speculative design around invisible technologies). He has developed CMU’s Designing for the Internet of Things course since 2016, creating hands-on curricula for connected product design. Recent publications examine creative physical computing education , AI-driven documentation systems , and XR-enabled skill training . Awards include multiple CMU research grants and the CHI 2018 Best Paper Award . He actively advises PhD and Masters students in Computational Design, emphasizing human-centered design and speculative technology research .

Matt GianiView profile

Academic

• Educational Policy
• College and Career Readiness
• Career and Technical Education
• +5 more

Matt Giani is a Research Associate Professor in the Department of Sociology at The University of Texas at Austin , with additional affiliation at the Texas Behavioral Science Policy Institute (TxBSPI) . His work bridges sociology and educational policy, focusing on equity in postsecondary access, with particular emphasis on low-income students. Education: Ph.D. in Educational Policy and Planning, The University of Texas at Austin Expertise: Quantitative methods, causal inference, longitudinal data analysis Research Themes: Matt’s research explores how educational policies and interventions affect social mobility, particularly in dual enrollment, career and technical education (CTE), and community college transfer pathways. His recent articles highlight credit transfer barriers, CTE policy implementation, and financial certainty interventions for admissions. Methodological Strengths: He leverages national and statewide longitudinal datasets (e.g., Education Research Center at UT Austin) to evaluate the long-term impacts of educational reforms. His funding sources include competitive grants from the National Science Foundation , Spencer Foundation , and Bill & Melinda Gates Foundation .

Kobus BarnardView profile

Professor

• Computer Vision
• Machine Learning
• Scientific Applications
• +3 more

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.

• Educational Technology
• Instructional Psychology
• Scientific Reasoning
• +6 more

Jan Elen is a full Professor at KU Leuven, specifically within the Instructional Psychology and Technology department of the Faculty of Psychology and Educational Sciences. They are also a member of DigiSoc – KU Leuven Institute for Digital Society and LIVO – KU Leuven Institute for Educational Research. Jan Elen's research interests focus on: Educational Technology and Digital Learning Instructional Psychology and Knowledge Scientific Reasoning and Argumentation in Education Educational Curation and Resource Management Teacher Education and Professional Development Assessment Methods and Educational Measurement Current research projects include Sabbatperiode Jan Elen: Fundamenten voor onderwijskundig redeneren (2023-2024), ICT in de lerarenopleiding: affordance versus daadwerkelijk gebruik in geselecteerde lerarenopleidingen in Ethiopië (2023-2027), and Naar een complementariteit tussen leraar en GenAI in de rol van de leraar als ontwerper van leeromgevingen (2022-2026). Their recent publications demonstrate a strong focus on the intersection of educational psychology, technology integration, and instructional design, particularly examining how teachers and students interact with digital learning environments and develop scientific reasoning skills, with emerging work on generative AI applications in education. Jan Elen serves in various academic capacities: Observer of the POC Criminological Sciences Observer of the POC Rights Member (as ZAP) of the PPW Faculty Council Member of the Assessment Committee of the Faculty of Psychology and Educational Sciences Observer of the OC Master of Psychology: Theory and Research Jan Elen teaches courses related to Leren in maatschappelijk betrokken onderwijs (Learning in socially engaged education) and supervises student placements in psychology and educational sciences across multiple formats including distance learning.

Danijel SkočajView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.

Jamal AtifView profile

Professor

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +4 more

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

Costas MarkidesView profile

Professor

• Business Models
• Innovation
• Business Model Innovation
• +7 more

Costas Markides is Professor of Strategic and International Management and Chairman of the Strategy Department at London Business School. With 88 publications and 9,493 citations, he is a globally recognized authority in strategic management and business model innovation. His work bridges academic research and practical business application, influencing both scholarly discourse and executive decision-making worldwide. Markides' research focuses on: Business model innovation and its strategic implications Disruptive innovation and competitive response strategies Strategic thinking frameworks for executives Innovation management in established organizations Global strategy and emerging market innovations Strategic ambidexterity in competing business models His publication record reveals a consistent exploration of how organizations can strategically innovate while maintaining core business operations. Markides' work on 'competing with dual business models' and 'strategic innovation at the base of the pyramid' has been particularly influential, offering practical frameworks for managing the tension between exploitation of existing business models and exploration of new ones. Markides' educational background includes a B.A. and M.A. in economics from Boston University, and an M.B.A. and D.B.A. from Harvard Business School. As Chairman of the Strategy Department, he has shaped one of the world's premier strategy research units while maintaining an active research agenda that continues to produce significant scholarly contributions. His work consistently addresses real-world strategic challenges, providing theoretically grounded yet practically applicable insights that have made him one of the leading voices in contemporary strategic management.

• Bioinformatics
• Computational Biology
• Pharmaceutical Drug Discovery & Development
• +13 more

Leonard Wesley is an Associate Professor in the Department of Computer Science at the College Of Science, San Jose State University. His research spans interdisciplinary domains at the intersection of Bioinformatics , Computational Biology , and Machine Learning , with specific applications in Pharmaceutical Drug Discovery , Genomic Data Analysis , and Autonomous Robotics . Education: Ph.D. in Computer Science, University of Massachusetts M.S. in Computer Science, University of Massachusetts B.A. in Physics and Math, Northeastern University Research Interests include Approximate Reasoning (probabilistic, evidential, and fuzzy logic), Agent-Oriented Systems , and Sensor Fusion . His work applies these methodologies to Drug Portfolio Management , Protein Structure Scoring , and Medical Diagnostics . Publication Trends show a consistent focus on Computational Biology , Robotics , and Uncertainty Quantification over four decades. Early work in Computer Vision evolved into modern applications in Pharmaceutical Analytics and AI in Aerospace . Key Projects include SVM-based drug affinity prediction, evidence-driven decision support systems for biopharma, and real-time agent development frameworks like ROADS. He has contributed to CFD code control and Mobile Network Congestion solutions. Collaborations with institutions like NASA, Los Alamos National Laboratory, and international conferences (WMSCI, ICINCO, AIAA) highlight his cross-disciplinary impact. His teaching includes Artificial Intelligence and Bioinformatics courses.