Exploring the architectures and symbolic frameworks that underlie intelligent behavior in machines. This theme bridges classical and contemporary AI approaches—including logic, language models, and neural architectures—with a special focus on how AI systems represent knowledge, make decisions, and relate to human users. Psychoanalytic theory is used to interrogate assumptions about mind, subjectivity, and trust.
Designing systems that enhance or extend human cognitive capacities through real-time feedback, machine learning, and symbolic modeling. This theme investigates how computational and robotic systems can support learning, decision-making, and self-reflection. It draws on psychoanalytic theory to understand the structural dynamics of attention, desire, and thought, and on engineering to design systems that operate in synchrony with embodied cognition.
Exploring how language models can support psychoanalytic interpretation without replacing the analyst. This theme investigates how AI systems, particularly large language models, can assist in the clinical or theoretical practice of interpretation—offering signifier chains, formalizations, or associative expansions—while preserving the analyst's role in navigating desire, contingency, and rupture.
Cultivating technically rigorous, ethically aware engineers through innovative pedagogy and interdisciplinary integration. This theme addresses curriculum development, active learning, and the inclusion of humanistic and psychoanalytic perspectives in engineering education. It supports the formation of students who can think critically about systems, ethics, and the social impact of their designs.
Designing systems that regulate themselves in the presence of uncertainty, noise, and delay. This theme focuses on classical and modern control theory, with applications in mechatronics, instrumentation, and robotics. Work includes system identification, nonlinear control, and real-time computing, often extending into philosophical questions about autonomy and agency.
Exploring the symbolic, embodied, and ethical dimensions of interaction between humans and intelligent systems. This theme examines the mutual shaping of humans and machines—how robotic and AI systems are interpreted, trusted, and engaged with by humans, and how those systems can be designed to accommodate subjectivity, ambiguity, and ethical asymmetry.
Developing symbolic frameworks for the organization, representation, and transmission of knowledge. This theme includes work on dialectical systems, knowledge graphs, and semantic embeddings that formalize how meaning is structured and transformed. Psychoanalytic and linguistic concepts are brought into dialogue with computation to explore how information systems shape (and are shaped by) subjectivity.
Developing learning algorithms for adaptive, nonlinear systems in uncertain and dynamic environments. This theme includes supervised, unsupervised, and reinforcement learning approaches applied to real-world systems. Particular emphasis is placed on interpretability, embodiment, and the use of machine learning to model or simulate symbolic structures, including those derived from psychoanalytic theory.
Pushing the limits of instrumentation and control at the nanoscale to image and manipulate spin systems. This theme encompasses earlier research on separative magnetization transport, spin dynamics, and feedback control in MRFM. It represents the foundations of your technical expertise in real-time measurement, physical modeling, and experimental design at the quantum-classical interface.
Arguing that current paradigms of trustworthy AI misunderstand the structural nature of trust. This theme critiques prevailing frameworks in AI safety by introducing the ethical subject as the only locus of ethical action.
Designing, modeling, and analyzing robotic systems that interact with and adapt to the physical world. This theme encompasses autonomous mobile robots, manipulation, and human-robot collaboration. Emphasis is placed on real-time operation, sensorimotor integration, and the philosophical implications of embodied agency.
Exploring what it means to build—or encounter—subjectivity in machines. This includes formalizing the structure of Lacanian subjectivity in computational terms and evaluating how current AI models instantiate (or fail to instantiate) subject-like behaviors.