Ashok Goel is a Professor of Computer Science and Human-Centered Computing at Georgia Institute of Technology and Chief Scientist at Georgia Tech’s Center for 21st Century Universities (C21U). He also serves as Executive Director of the NSF-funded National AI Institute for Adult Learning and Online Education (AI-ALOE). His research spans cognitive systems, artificial intelligence, and education, with a focus on computational design, creativity, and AI-driven educational technologies. Professor, School of Interactive Computing, Georgia Tech Chief Scientist, Center for 21st Century Universities Executive Director, NSF’s National AI Institute for Adult Learning and Online Education Goel’s research explores the intersection of AI and cognitive science, particularly in computational design, creativity, and biologically inspired design. His recent work emphasizes AI in education, including virtual teaching assistants like Jill Watson (powered by ChatGPT) and frameworks for scalable, human-centric AI-augmented learning. He investigates explainable AI, multimodal educational systems, and bidirectional feedback mechanisms to enhance personalized learning experiences. Award highlights include: AAAI’s Outstanding AI Educator Award Fellow of AAAI and Cognitive Science Society University System of Georgia Regent’s Award for Scholarship of Teaching and Learning Goel leads the Design Intelligence Laboratory at Georgia Tech, mentoring a team of graduate and undergraduate researchers. His contributions to AI education include pioneering Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program and developing blended learning frameworks. He also co-founded the AI-based educational startup Beyond Question (LLC) in 2020.
Mathias Unberath is the John C. Malone Associate Professor in the Department of Computer Science at Johns Hopkins University, with secondary appointments in Ophthalmology and Otolaryngology—Head and Neck Surgery at the School of Medicine. He is a core faculty member of the Laboratory for Computational Sensing and Robotics (LCSR) and the Malone Center for Engineering in Healthcare, and affiliate faculty at the Institute for Assured Autonomy and Data Science and AI Institute. Education: PhD in Computer Science from Friedrich-Alexander University of Erlangen-Nürnberg (2017), MSc in Optical Technologies (2014), BSc in Physics (2012) His research focuses on computer-assisted medicine, integrating computer vision, machine learning, and medical robotics to develop human-centered solutions through mixed reality and embodied technologies. His work addresses surgical phase recognition, explainable AI, and digital twin representations for clinical workflows. Unberath's 15 most recent publications demonstrate expertise in surgical AI (7/15), medical imaging (12/15), and mixed reality (8/15), with specific subfields including segmentation frameworks (3 papers), cognitive load estimation (4 papers), and surgical robotics (5 papers). NSF CAREER Award NIH NIBIB Trailblazer R21 Google Research Scholar Award Inaugural DSAI Junior Faculty Award IPCAI 2025 Best Paper Award He teaches graduate courses in machine learning, AI system design, and interpretable machine learning. His group, the ARCADE Lab, develops technologies for computer-assisted interventions, emphasizing robustness, explainability, and human-AI collaboration in clinical settings.
Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge's Department of Computer Science and Technology. He also holds positions as a Senior AI Fellow at The Alan Turing Institute and a Visiting Professor of Machine Learning at the University of Sheffield. After three years as Director of Machine Learning at Amazon, Lawrence recently returned to academia, bringing extensive industry experience to his academic work. Lawrence's research focuses on the intersection of machine learning with the physical world, particularly in uncertainty quantification and end-to-end solutions for real-world applications. His work was initially inspired by deploying machine learning systems in African contexts, where comprehensive solutions are often required. His technical expertise spans over two decades in machine learning methods, with a growing interest in public understanding of machine learning, policy decisions, and data governance implications. His recent publications reveal a diverse research portfolio spanning climate science, healthcare applications, systems engineering for AI deployment, data governance, and theoretical machine learning. Lawrence's work demonstrates a consistent theme of bridging theoretical machine learning with practical applications across multiple domains, with particular attention to uncertainty quantification and the societal implications of AI systems. Lawrence serves on the board of the AISTATS conference and the ELLIS Foundation, and acts as the founding and series editor for the Proceedings of Machine Learning Research. He is also the co-host of the Talking Machines podcast, demonstrating his commitment to public engagement with machine learning concepts. At Cambridge, Lawrence teaches Advanced Data Science (Part II) and Machine Learning and the Physical World (MPhil ACS, Part III), contributing to both undergraduate and postgraduate education in computer science. His work with the Accelerate Programme for Scientific Discovery and the Data Trusts Initiative positions him at the forefront of developing frameworks for responsible and effective AI deployment in scientific and societal contexts.
Timothy Menzies is a full Professor in the Department of Computer Science at North Carolina State University's College of Engineering. He serves as the director of the Irrational Research lab (mad scientists r'us) and holds editorial positions as editor-in-chief of the Automated Software Engineering journal and associate editor for IEEE Transactions on Software Engineering. With over 300 publications and more than 24,000 citations, Menzies is a globally recognized leader in software engineering research. Menzies' research focuses on developing computer systems that make optimal decisions with minimal data, specializing in artificial intelligence, intelligent agents, data sciences, analytics, and software engineering. His pioneering work in data-driven, explainable, and minimal AI for software systems has redefined defect prediction, effort estimation, and multi-objective optimization. He is particularly known for his contributions to empirical software engineering, emphasizing transparency and reproducibility. As the co-creator of the PROMISE repository, he helped establish modern empirical software engineering by demonstrating that small, interpretable AI models can outperform larger, more complex ones. Menzies' recent publications reveal several key trends in his research: a growing emphasis on ethical considerations in AI deployment, particularly in sensitive domains like legal systems; continued innovation in software analytics with a focus on hyperparameter optimization tailored specifically for software engineering tasks; exploration of causal relationships in software analytics; and development of techniques that work effectively with limited data, including landscape analysis, surrogate learning, and active learning approaches. Mining Software Repositories Foundational Contribution Award (2017) Carol Miller Graduate Lecturer Award (2016) IBM Faculty Award (2016, 2017) ACM Fellow (2025) ASE Fellow (2024) IEEE Fellow Professor Menzies has advised 24 Ph.D. students throughout his career, with recent completions including Andre Motta (April 2025) and Xueqi Yang (October 2024). His research has secured over $19 million in funding from prestigious agencies including NSF, DARPA, and NASA, as well as industry partners like Meta, Microsoft, and IBM. Current grants focus on improving machine learning model efficiency, adapting empirical software engineering methods to computational science, vulnerability detection, and software analytics at scale using transfer learning across 10,000+ GitHub projects. Menzies has developed innovative approaches to help developers navigate the challenges of AI implementation while maintaining ethical standards and practical effectiveness. As director of the Irrational Research lab, Menzies leads a team focused on creating AI tools that are not only intelligent but also fair, transparent, and trustworthy. The lab's work emphasizes practical applications of AI in software engineering while addressing the human factors involved in developer-AI collaboration. Current projects include developing methods for better fuzzing with L3harris, improving vulnerability detection through smart pruning techniques, and creating AI platforms for workforce empowerment through credential gap diagnostics.
Hakan Basarir is a Professor in the Department of Mining Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. His research and teaching focus on mining rock mechanics, rock mass characterization, underground support systems, and the application of soft computing methods in mining engineering. PhD in Mining Engineering (2002) 20+ years of research and teaching experience 60+ publications in journals and conferences Research Interests include rock mass property prediction using measurement while drilling (MWD) techniques, numerical modeling of mining structures, optimization of mine support systems, and sustainable material development. His work integrates machine learning and computational methods to address challenges in mining geomechanics and backfill design. Recent Publications highlight advancements in AI-driven lithology prediction, eco-concrete formulation, and backfill mixture optimization. He has also contributed to tunnel stability analysis and seismic rock slope modeling. Teaching includes advanced courses in mining engineering, mineral production modeling, and specialization projects in geotechnology.
Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University, part of the Ira A. Fulton Schools of Engineering. He maintains his office in BYENG 506 at the Tempe campus and can be reached at yanjie.fu@asu.edu. Dr. Fu received his Ph.D. from Rutgers University in 2016, the B.E. degree from the University of Science and Technology of China, and the M.E. degree from the Chinese Academy of Sciences. His industry research experience includes positions at Microsoft Research Asia and IBM Thomas J. Watson Research Center. His research focuses on developing disruption-robust machine intelligence that can handle imperfect and complex data. Dr. Fu's work spans two major efforts: Data for AI (D4AI), exploring how structure knowledge of data can guide AI, and AI for Data (AI4D), investigating how AI can augment, reprogram, and knowledgeize data. His current research interests include space-time intelligence, data-centric AI, sim2decision, multimodal reasoning, and LLM with agentic AI. His lab has contributed projects including D4AI-spatial, D4AI-timeseries, D4AI-causal outliers, AI4D-RL, AI4D-Gen, and AI4D-LLM. Dr. Fu's recent publications reveal a strong trend toward integrating causal reasoning with deep learning for robust anomaly detection, advancing time series forecasting with novel normalization techniques, and applying generative AI to urban planning. His work increasingly bridges traditional machine learning with large language models, particularly focusing on data-centric approaches for tabular data transformation and feature engineering. US NAE FOE early career engineer (2023) US NSF CAREER (2021) NSF CRII (2018) ACM KDD18 Best Student Paper Finalist IEEE ICDM Best Paper Finalist (2014, 2021, 2022) ACM SIGSpatial Best Paper Runner-up (2020) 2022 Baidu Scholar global top Chinese young scholars in AI 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention Dr. Fu has successfully mentored multiple Ph.D. students who have secured tenure-track faculty positions at prestigious institutions including University of Kansas, Chinese Academy of Sciences, Great Bay University, Portland State University, and University of Macau. His research has been supported by significant grants including the NSF CAREER award, and he currently serves as Associate Editor of ACM Transactions on Knowledge Discovery from Data. He is also a senior member of both ACM and IEEE. Dr. Fu leads a research group focused on developing trusted and safe machine intelligence. The lab connects computing issues across representation learning, self-supervised learning, interactive learning, adaptive learning, and stream learning to build disruption-robust frameworks. The group executes two key steps: data representation construct (integrating structure knowledge, self-optimization, explainability) and learning strategy construct (integrating robust representations with adaptive and interactive learning).
Prof. Dr.-Ing. Marc Reichenbach serves as the Chair of Integrated Systems at the Institute for Applied Microelectronics and Data Technology at the University of Rostock. His office is located at Albert-Einstein-Straße 26, 18059 Rostock, Room 102 (1st floor), with contact information including telephone (0381) 498 7270 and email marc.reichenbach@uni-rostock.de. Professor Reichenbach's research focuses on the intersection of hardware design and artificial intelligence, with particular expertise in memory technologies and computing architectures. His work spans several key areas: Development of specialized computer architectures for deep learning applications Advanced VLSI design and CPU architecture Emerging memory technologies, particularly RRAM (Resistive Random-Access Memory) FPGA-based acceleration systems Hardware implementations for neural networks and AI applications Analysis of Professor Reichenbach's recent publications (2023-2025) reveals a strong focus on memory computing technologies, particularly RRAM-based systems. His work demonstrates expertise across multiple dimensions of computer architecture including ASIC design, FPGA acceleration, and novel memory systems. The publications show a clear trajectory toward implementing AI and machine learning capabilities directly in hardware, with applications ranging from edge computing to satellite systems. A significant portion of his recent work addresses the challenges of implementing neural networks using emerging memory technologies, focusing on efficiency, reliability, and performance optimization. Professor Reichenbach teaches several advanced courses including: Computer architectures for deep learning applications Project seminar Embedded Systems Advanced VLSI Design (Advanced CPU Design) His research group appears to be actively engaged in several cutting-edge projects related to hardware acceleration for AI applications, memory computing, and embedded systems design. The group collaborates on projects involving digital twins for hardware systems, real-time operating systems for heterogeneous architectures, and specialized computing systems for various applications from medical devices to drone technology.
Lisa P. Ramsey is a Professor of Law at the University of San Diego School of Law, where she teaches Trademark Law, International Intellectual Property, Civil Procedure, Trademark Litigation, Intellectual Property Survey, and Intellectual Property Seminar. She joined the USD law faculty in 2004, progressing from Assistant Professor (2004-2006) to Associate Professor (2006-2009) and then to full Professor (2009-present). Professor Ramsey's research focuses on the intersection of trademark law and free speech rights, examining how trademark protection can conflict with First Amendment protections. Her scholarship addresses potential conflicts between trademark laws and free expression, explaining how trademark protection of certain inherently valuable words, symbols, and product features can harm fair competition and freedom of expression. She has written extensively on non-traditional trademarks, the impact of Supreme Court decisions like Matal v. Tam on trademark registration, and the application of First Amendment principles to trademark enforcement. Her publications reveal a consistent focus on balancing trademark rights with free expression, with particular attention to how trademark law affects artistic expression, political speech, and competition. Recent work examines the implications of the Supreme Court's decisions in cases like Jack Daniel's v. VIP Products and Vidal v. Elster for trademark enforcement and registration. Thorsnes Prize for Outstanding Legal Scholarship (2020-2021) Class of 1975 Endowed Professorship (2017-2018) Order of the Coif Women of Influence in Law 2025 Honoree (San Diego Business Journal) Professor Ramsey actively participates in professional organizations, serving on the Trademark Law Committee of the American Intellectual Property Law Association and contributing to the International Trademark Association's Model Trademark Law Guidelines. She has testified before the U.S. Senate Judiciary Committee's Intellectual Property Subcommittee regarding the First Amendment implications of the proposed No FAKES Act. Her upcoming book, 'Trademarks and Free Speech: Conflicts and Resolutions,' is scheduled for publication by Cambridge University Press in December 2025.
Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology (TU/e), Netherlands. Her research bridges data-driven methodologies (machine learning, statistical modeling) with human-centered design to enhance learning analytics, explainable AI, and writing process analysis. She holds a joint PhD (cum laude) from Antwerp University and Tilburg University, and an MSc (cum laude) in Human-Technology Interaction from TU/e. Academic Background: MSc (2015, TU/e, cum laude), PhD (2020, Antwerp University & Tilburg University, cum laude). Research Focus: Learning analytics, keystroke logging, explainable AI for education, data dashboards, and self-regulated learning dynamics. Teaching: Courses in Advanced Research Methods, Human-AI Interaction, Behavioral Research Methods, and AI ethics in education. Her recent publications explore parallel language planning in writing, longitudinal self-regulated learning strategies, and generalizability of academic performance prediction models. She leads an NWO Veni project on Human-Centered AI in education, emphasizing tailored explanations for student-AI collaboration. Scientific awards include cum laude distinctions for her MSc and PhD, and the NWO Veni grant. Collaborative work spans institutions in the Netherlands, Norway, and the U.S., with applications in intelligent tutoring systems and ethical AI deployment in exams. Key trends across her work: integration of machine learning with educational theory, leveraging keystroke data for cognitive process insights, and prioritizing actionable, explainable AI systems for student support. Publications span journals like the Journal of Experimental Psychology: General , Computers and Education , and IEEE Transactions on Learning Technologies . Scientific Awards: NWO Veni grant for Human-Centered AI in education Cum laude for MSc and PhD Grants & Collaborations: National Science Foundation grants (2016868, 2302644) for biometric feedback in writing UK Research and Innovation grant (ES/W011832/1) for real-time AI scaffolding TU/e Boost! Program grant for self-regulated learning analysis Labs & Teams: EAISI Foundational (Eindhoven AI Systems Institute) Human Technology Interaction group at TU/e Collaboration with Norwegian Reading National Center (University of Stavanger) Project teams for Waterproof ITS and ProWrite grants
Professor Tomasz Kapitaniak is a distinguished academic in the field of nonlinear dynamics and theoretical mechanics. He serves as a Professor of Theoretical and Applied Mechanics and Head of the Division of Dynamics at the Faculty of Mechanical Engineering, Technical University of Lodz, Poland. His career spans over three decades at the university, where he has made significant contributions to the understanding of nonlinear systems, chaos theory, and mechanical oscillations. Professor Kapitaniak holds advanced degrees in both mechanics and applied mathematics from the Technical University of Lodz and the University of Lodz. His educational background includes: M.Sc. in mechanics, Faculty of Mechanical Engineering, Technical University of Lodz (1982) M.Sc. in applied mathematics, Faculty of Mathematics, Physics and Chemistry, University of Lodz (1985) Ph.D. in mechanics, Faculty of Mechanical Engineering, Technical University of Lodz (1985) D.Sc. in mechanics, Faculty of Mechanical Engineering, Technical University of Lodz (1988) Professor of technical science, title given by the President of Poland (1995) His research focuses on nonlinear dynamics, with particular emphasis on mechanical oscillations, stability, bifurcations and chaos, stochastic dynamics, and applications of nonlinear dynamics in mechanical engineering. Professor Kapitaniak is renowned for his work on the development of methods for controlling chaos without feedback, identification of new types of bifurcations, synchronization mechanisms in coupled mechanical oscillators, and explaining the origin of randomness in mechanical systems. His research has evolved from fundamental theoretical work to increasingly applied studies involving complex networks, biological systems, and engineering applications. Professor Kapitaniak has published over 300 scientific papers in renowned journals, cited over 8,000 times. His work exhibits a consistent focus on understanding complex nonlinear phenomena across various physical systems. The trend in his recent publications shows continued exploration of synchronization phenomena, extreme events in dynamical systems, and applications of nonlinear dynamics to biological, mechanical, and physical systems. His most recent work demonstrates a growing interest in multistability, chimera states, and the prediction of tipping phenomena in complex systems. Among his notable scientific achievements and distinctions are: Election as a member of the Polish Academy of Sciences (corresponding member in 2013, ordinary member in 2019) Election to Academia Europaea in 2021 Honorary doctorates from Saratov State University (Russia, 2001) and Lublin University of Technology (Poland, 2014) Multiple prestigious fellowships including the British Council Fellowship (1989), King Abdul Aziz Award Fellowship (1990), and Fulbright Fellowship (1997) Editorial roles including Associate editor of Chaos, Solitons and Fractals since 1990 and member of editorial boards of several other prestigious journals Throughout his career, Professor Kapitaniak has been actively involved in mentoring the next generation of researchers, having supervised numerous PhD students including Jerzy Wojewoda, Anton van Wyk, Barbara Błażejczyk-Okolewska, Andrzej Stefański, Andrzej Kozłowski, and Przemysław Szumiński. He has secured significant research funding from various national and international sources including the Ministry of Science and Higher Education (Poland), Deutscher Akademischer Austauschdienst, The Royal Society of London, and others. His research team has maintained strong international collaborations with institutions worldwide, including universities in the United States, United Kingdom, Germany, Brazil, Russia, and Ukraine. He leads the Division of Dynamics at the Technical University of Lodz, which serves as a hub for research in nonlinear dynamics, mechanical oscillations, and related fields. The division maintains strong international collaborations with institutions worldwide and continues to produce cutting-edge research in the field of nonlinear dynamics and its applications.
Alipaşa Ayas is a Professor and Dean of the Faculty of Education at Bilkent University, where he also serves as Chair of the Department of Educational Sciences. His research focuses on science education, teacher training, conceptual understanding, and inclusive education. He has led numerous national and international projects, including UNICEF- and EU-supported initiatives. Ph.D., University of Southampton M.A., University of New Brunswick MSc., Karadeniz Technical University BSc., Karadeniz Technical University His research interests span teacher education, science education (particularly chemistry), concept development, curriculum design, and assessment strategies. He employs both qualitative and quantitative methods to explore these areas. His publications emphasize inquiry-based learning, conceptual change strategies, and the effectiveness of interventions such as the 5Es model, constructivist activities, and analogies in enhancing science learning. These works often address misconceptions in chemistry and methods to improve scientific process skills. He has contributed to projects supported by the World Bank, EU, and UNICEF, including initiatives on lifelong learning, education development, and inclusive teacher training. His administrative roles include a 6-year deanship at Karadeniz Technical University and leadership of Bilkent University’s Graduate School of Education since 2017.
Maria Luce Lupetti is a Fixed-term Assistant Professor at the Department of Architecture and Design (DAD) at Politecnico di Torino . She actively contributes to the College of Architecture and Design and is a member of the College of Mechanical, Aerospace and Automotive Engineering . Her academic role involves teaching Cognitive Ergonomics and HMI in the Automotive Engineering master's program and collaborating on Design and Communication courses. Editorial Roles: Associate Editor for the journal INTERACTIONS since 2024 Conference Leadership: Participating in organizing committee for CHI 2026 Research Projects: Scientific Director of PARJAI (2025-2028) on Participatory Design Justice for Ethical AI Transitions Research Focus : Maria's work bridges Design with Human-Robot Interaction , focusing on Ethical AI , Speculative Design , and Urban Robotics . Her recent publications explore: Speculative design for sustainable digital futures Contextual adaptability challenges in urban robotics Design taxonomies for demystifying AI Trustworthy embodied agents in healthcare Creative applications of AI in HRI Ethical frameworks for energy futures Design Philosophy : She emphasizes participatory approaches, critical design thinking, and transdisciplinary collaboration to address societal challenges at the intersection of technology, ethics, and human factors. Her work appears in leading venues including CHI , HRI , and Frontiers in Neurorobotics , while also contributing to edited volumes and journal special issues.
Souvik Paul is an Assistant Professor in the School of Physics at Indian Institute of Science Education and Research Thiruvananthapuram, where he leads the Computational Materials Science (CMS) Laboratory established in 2023. His research employs advanced computational techniques to investigate fundamental properties of magnetic materials and topological phenomena. Education: Ph.D. (2015), Indian Institute of Technology Guwahati, India M.Sc. (2008), Presidency College, Kolkata, India B.Sc. (2006), University of Calcutta, India Dr. Paul's research focuses on computational materials science with particular emphasis on magnetism in two and three dimensions, topological magnetic quasiparticles like skyrmions, surface physics, strongly correlated systems, and multifunctional materials including Heusler alloys. His work primarily utilizes Density Functional Theory (DFT) to predict and explain material properties at the atomic scale, bridging computational predictions with experimental observations through international collaborations. His publication record reveals a consistent trajectory in magnetic skyrmions research, transition metal systems, and Heusler alloys, with significant contributions to understanding spin interactions, stability mechanisms, and electronic properties. His work frequently appears in high-impact journals including Physical Review Letters, Nature Communications, and npj Computational Materials, demonstrating both theoretical depth and practical relevance to materials design. Scientific Awards: Prime Minister Early Career Research Grant (2025) from Anusandhan National Research Foundation Departmental Postdoctoral Fellowship (2015), Uppsala University Doctoral fellowship (2009), IIT Guwahati Graduate Aptitude Test in Engineering (GATE) (2009), MHRD, India Dr. Paul actively mentors graduate students including Moinak Ghosh and Bipin Babu. Through the International PhD Program, he has established formal collaborations with Prof. Stefan Heinze at CAU Kiel, Germany, providing students with international research opportunities, access to high-performance computing facilities, and extended research stays at partner institutions. His recently awarded Prime Minister Early Career Research Grant supports innovative work on antiferromagnetic skyrmions. The Computational Materials Science Laboratory employs Density Functional Theory to investigate structural, electronic, magnetic, and optical properties of materials at the atomic level. The lab maintains strong international collaborations with research groups at CAU Kiel and Forschungszentrum Jülich in Germany, focusing on discovering novel materials, explaining fundamental material behaviors, and developing predictive materials theory with applications in electronics and energy technologies.
Juan Wachs is the James H. and Barbara H. Greene Professor at the Edwardson School of Industrial Engineering, Purdue University. He holds a courtesy appointment in Biomedical Engineering and is an Adjunct Professor of Surgery at the IU School of Medicine. His research focuses on the intersection of robotics, human-AI interaction, and healthcare systems, with a particular emphasis on surgical robotics, assistive technologies, and telemedicine. Education: PhD in Industrial Engineering (Intelligent Systems), Ben-Gurion University of the Negev MSc in Industrial Engineering (Information Systems), Ben-Gurion University of the Negev BEdTech in Electrical Education, ORT Academic College in Jerusalem Research interests include surgical telementoring via augmented reality, gesture-based interfaces for sterile environments, and semi-autonomous robotic systems for healthcare. His ISAT Lab develops solutions like the STAR telementoring system and robotic assistants like Gesturenurse and FIST-D for explosive ordnance disposal. Recent work emphasizes AI-driven medical decision support (Trauma THOMPSON), burn wound characterization, and robotic ultrasound automation. Key contributions include over 100 publications in robotics, medical AI, and human factors. Scientific Awards: James H. and Barbara H. Greene Professorship Purdue University Faculty Scholar Advising & Labs: Guides over 10 PhD/Master’s students in robotics and healthcare tech ISAT Lab fosters interdisciplinary projects in surgical robotics, human-robot interaction, and accessibility
David Garlan is a Professor at the Software and Societal Systems Department within the School of Computer Science at Carnegie Mellon University , where he also serves as Associate Dean for Master’s Programs . He received his Ph.D. from Carnegie Mellon in 1987 after working in industry as a software architect. His research focuses on controlling complexity in large software systems through formalized architectural design, self-adaptive systems, and cyber-physical systems. He developed AcmeStudio , a widely used architecture design environment, and pioneered formal representation and analysis of software architecture. Education : Ph.D. in Computer Science (Carnegie Mellon, 1987) Research Interests include: Software Architecture: Formal methods for architectural design, end-user composition, and architectural styles Self-Adaptive Systems: Stochastic planning, model checking, security adaptation, and uncertainty reduction Cyber-Physical Systems: Multi-view design methods, consistency checking, and automotive systems Recent Article Trends address microservice resiliency, hybrid planning (combining formal methods and ML), simulation-augmented robotics, and sustainable machine translation. Themes include stochastic modeling , probabilistic verification , and adaptive decision-making . Scientific Awards : Stevens Award Citation (2005) ACM SIGSOFT Outstanding Research Award (2011) Allen Newell Award for Research Excellence (2016) IEEE TCSE Distinguished Education Award (2017) Nancy Mead Award (2017) Fellow of IEEE and ACM Advising and Grants : He has advised 25+ graduate students and collaborated on projects with Toyota and the Software Engineering Institute. His work includes model-based adaptation, automated planning, and formal verification of adaptive systems. Labs & Teams : Affiliated with the Institute for Software Research and works on tools like AcmeStudio, Rainbow, and IPL for architectural modeling and self-adaptation.
