My goal as an educator is to help students develop critical thinking, independent decision-making, and collaborative problem-solving skills. I want learners to approach complex problems not only through a technical lens, but also with an understanding of their historical, ethical, and societal context, particularly in the domain of artificial intelligence.
As AI increasingly influences healthcare delivery, access to services, and clinical decision-making, it is essential that students recognize how these systems are shaped by human assumptions and remain vulnerable to bias and error. I aim to foster professional skepticism alongside a strong sense of social responsibility, encouraging students to question assumptions and design solutions that are both technically rigorous and ethically grounded.
Throughout my education, I experienced both passive and active learning environments, and those experiences strongly shaped my approach to teaching. Early education in the former Soviet Union emphasized memorization and unquestioned acceptance of authority. Learning was largely passive, and success depended on accurate recall rather than inquiry.
Later, at San Jose State University, I encountered a more active learning model that emphasized discussion, critical analysis, and engagement with primary sources. Although this approach initially felt unstructured, I came to appreciate its ability to cultivate independent thinking, argumentation skills, and intellectual curiosity. These experiences taught me that while students benefit from clear structure and well-designed lectures, deep learning occurs when they actively engage with ideas.
I therefore try to balance structure with participation. I value lectures that provide historical or conceptual context, and I often organize content as narratives to help learners understand how ideas evolve over time. For example, discussing the development of the perceptron within the scientific understanding of neuroscience in the 1950s allows students to see how technological models reflect the limitations and assumptions of their era. This framing helps students understand knowledge as dynamic rather than fixed.
Before beginning my PhD, I taught clinical practice courses in the Adult Gerontology Acute Care Nurse Practitioner program at the OHSU School of Nursing. I used an inverted classroom model, designing case-based activities aligned with didactic material to strengthen clinical reasoning. To increase engagement, I created fictional case scenarios using familiar cultural references and detailed background histories, encouraging students to interpret clinical information within broader social contexts.
I also incorporated gamified elements by dividing students into teams, with one group designing clinical scenarios and another working through diagnostic and decision-making processes. Students reported that this format improved their confidence and helped them prepare for real-world clinical situations by practicing analytical thinking in a supportive environment.
During my PhD, I participated in mentored teaching experiences with Dr. Karen Eden in Medical and Healthcare Decision Making and with Dr. Vishnu Mohan in Design and Evaluation in Health Informatics. These experiences expanded my skills in hybrid and online teaching formats. While I appreciated the flexibility of prerecorded lectures combined with structured online discussions, I find hybrid teaching particularly effective because real-time interaction allows me to adapt to students’ questions, clarify misunderstandings quickly, and maintain a collaborative learning environment.
I am particularly interested in teaching courses at the intersection of clinical reasoning, informatics, artificial intelligence, and decision science. My goal is to help students think critically about how information is developed, presented, and applied in real-world healthcare settings, especially in time-sensitive and high-uncertainty environments such as trauma or acute care.
I would welcome opportunities to teach or co-teach courses focused on applied clinical decision support, health data science, human factors in informatics, and the ethical implications of AI in healthcare.
A major focus of my teaching interests is the use of artificial intelligence to design, evaluate, and implement decision support tools both at the bedside and at the systems level, including healthcare planning, forecasting, and resource allocation. I am especially drawn to interdisciplinary learning environments where learners from clinical and technical backgrounds collaborate and develop shared frameworks for problem-solving. Over time, I hope to contribute to curriculum development that bridges informatics theory with implementation science, patient safety, and equitable healthcare delivery.
Mentorship has been a meaningful part of my professional work. I have supported students and early-career professionals in both academic and clinical settings, helping them clarify goals, develop career plans, and build confidence in decision-making. This has included mentoring interns in trauma care to strengthen clinical reasoning and helping nurses at OHSU pursue educational and professional advancement. Outside formal academic environments, I have also mentored members of the Russian-Ukrainian-Jewish immigrant community as they navigated educational and career pathways.
One particularly rewarding example involved mentoring an intern who transitioned from an unsatisfying general surgery residency to an OB/GYN residency, where she ultimately flourished and now practices as an attending physician. More recently, I have mentored a pulmonary and critical care fellow developing a large language model to support clinical rounding. Supporting mentees as they translate ideas into practical solutions is one of the most rewarding aspects of academic work for me.
In all of my teaching, I strive to create learning environments where students are empowered to think critically, engage actively, and take intellectual risks. Whether in the classroom, online, or clinical setting, I believe meaningful learning occurs when students are both challenged and supported. My goal is to cultivate spaces where inquiry is encouraged, mistakes are part of growth, and learners develop into thoughtful professionals who can navigate complexity with confidence and integrity.
These lectures were developed for weekly graduate seminars in the OHSU School of Nursing AG-ACNP program. The sessions focused on clinical reasoning and decision-making under uncertainty using interactive case-based discussions.
This lecture focuses on EKG interpretation and helps students differentiate between left bundle branch block, right bundle branch block, and non-specific intraventricular conduction delay.
This lecture begins with a patient losing consciousness at home and guides students through causes of syncope, prioritization of life-threatening diagnoses, collection of history and vital signs, recognition of a heart murmur, and the selection of appropriate labs and imaging. It concludes with prognosis, management, and the importance of guideline-based assessment.
This lecture follows the same case-based template as most of my seminars and focuses on seizures. Students usually enjoy the reference to Cinderella.
This lecture focuses on neurotrauma and reinforces student understanding of altered level of consciousness. Students especially enjoyed the references to Tom Cruise and Jack Reacher.
This lecture is based in part on John J. Nance’s book Why Hospitals Should Fly. It teaches graduate AG-ACNP students the importance of communication and documentation in reducing legal risk and improving patient safety. Topics include patient-provider communication, closed-loop communication, shared decision-making, patient rights, and common legal pitfalls.
This lecture was designed for my graduating AG-ACNP class and introduces the concept of checklists as tools for clinical decision support. It is largely inspired by the work of Atul Gawande on surgical and patient safety. Students are introduced to human factors through the story of the B-17 prototype crash and then guided to discover the checklist as a strategy for reducing error in complex environments.
This is one of my favorite lectures. It is intended for the final term of a graduating class, when students are often exhausted by cognitive overload. Beyond its entertainment value, it explores how intuition may serve as an augmentation to clinical decision-making.
The lecture opens with a discussion of what intuition is, whether it is rational to act on it, and whether students have examples from their own experience. It then moves through historical examples of decision-making under time pressure and insufficient information. One of its central goals is to show that acting and not acting are both decisions, each with trade-offs and consequences.
The discussion of dopamine and spindle neurons helps establish a physiological rationale for rapid judgments based on prior experience and explains why clinicians should pay attention when “the hairs on the back of the neck stand up” — whether their own or those of the bedside nurse.