Population biology phenomena such as the spread of alleles in populations, the coexistence of competing species in an ecological community, the branching patterns in phylogenetic trees, and the dynamics of infectious disease are situated in different biology subdisciplines, e.g. demography, ecology, epidemiology, or evolution. However, these disparate phenomena are modeled using shared mathematical approaches. These approaches draw from diverse areas of mathematics, including stochastic processes, mathematical statistics, combinatorics, and dynamical systems. Across biological disciplines, population biologists share interests in the identification and analysis of equilibria, investigation of mechanistic dynamics, network-based methods and spatial modeling, modeling of stage transitions, biological optimality arguments, and the precise mathematical formulation and exploration of the consequences of verbal models. Some mathematical approaches are unique to population biology phenomena, e.g. the exponential amplification of initially rare beneficial variants such that the law of large numbers never applies poses a profound challenge, one which continues to call for new math in the area of stochastic processes.

This workshop aims to crystallize central ideas common across mathematical modeling and theory in population biology; to disseminate mathematical innovations from one biological subdiscipline to another; to promote mathematical studies at intersections among biological subdisciplines, and to deepen interactions among scientists working on similar problems but separated by disciplinary structures within biology, or between biology and the mathematical sciences. The goals are to build a community of theoretical population biologists that spans biological subdisciplines, invite new entrants to the field, encourage early-career scientists, showcase outstanding research, demonstrate impact in societally relevant problems, and uncover promising new directions where modeling and theory can make a difference in studying new types of biological data obtained at the level of populations.

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The Department of Sociology's Urban & Community Workshop presents:

Evie Lopoo (NU): Understanding Creative Labor in the Indie MusicScene: Competition and Community in the Age of AI

Sophia Costa (U Chicago): Responding to Poverty Ungovernance During the COVID-19 Pandemic

(A. Fahlberg, C. Martins, A. C. Araujo, L. Santos & J. L. da Silva)

Parkes Hall, Room 222 | Zoom

4:00 PM to 5:30 PM

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Population biology phenomena such as the spread of alleles in populations, the coexistence of competing species in an ecological community, the branching patterns in phylogenetic trees, and the dynamics of infectious disease are situated in different biology subdisciplines, e.g. demography, ecology, epidemiology, or evolution. However, these disparate phenomena are modeled using shared mathematical approaches. These approaches draw from diverse areas of mathematics, including stochastic processes, mathematical statistics, combinatorics, and dynamical systems. Across biological disciplines, population biologists share interests in the identification and analysis of equilibria, investigation of mechanistic dynamics, network-based methods and spatial modeling, modeling of stage transitions, biological optimality arguments, and the precise mathematical formulation and exploration of the consequences of verbal models. Some mathematical approaches are unique to population biology phenomena, e.g. the exponential amplification of initially rare beneficial variants such that the law of large numbers never applies poses a profound challenge, one which continues to call for new math in the area of stochastic processes.

This workshop aims to crystallize central ideas common across mathematical modeling and theory in population biology; to disseminate mathematical innovations from one biological subdiscipline to another; to promote mathematical studies at intersections among biological subdisciplines, and to deepen interactions among scientists working on similar problems but separated by disciplinary structures within biology, or between biology and the mathematical sciences. The goals are to build a community of theoretical population biologists that spans biological subdisciplines, invite new entrants to the field, encourage early-career scientists, showcase outstanding research, demonstrate impact in societally relevant problems, and uncover promising new directions where modeling and theory can make a difference in studying new types of biological data obtained at the level of populations.

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Register Now

Population biology phenomena such as the spread of alleles in populations, the coexistence of competing species in an ecological community, the branching patterns in phylogenetic trees, and the dynamics of infectious disease are situated in different biology subdisciplines, e.g. demography, ecology, epidemiology, or evolution. However, these disparate phenomena are modeled using shared mathematical approaches. These approaches draw from diverse areas of mathematics, including stochastic processes, mathematical statistics, combinatorics, and dynamical systems. Across biological disciplines, population biologists share interests in the identification and analysis of equilibria, investigation of mechanistic dynamics, network-based methods and spatial modeling, modeling of stage transitions, biological optimality arguments, and the precise mathematical formulation and exploration of the consequences of verbal models. Some mathematical approaches are unique to population biology phenomena, e.g. the exponential amplification of initially rare beneficial variants such that the law of large numbers never applies poses a profound challenge, one which continues to call for new math in the area of stochastic processes.

This workshop aims to crystallize central ideas common across mathematical modeling and theory in population biology; to disseminate mathematical innovations from one biological subdiscipline to another; to promote mathematical studies at intersections among biological subdisciplines, and to deepen interactions among scientists working on similar problems but separated by disciplinary structures within biology, or between biology and the mathematical sciences. The goals are to build a community of theoretical population biologists that spans biological subdisciplines, invite new entrants to the field, encourage early-career scientists, showcase outstanding research, demonstrate impact in societally relevant problems, and uncover promising new directions where modeling and theory can make a difference in studying new types of biological data obtained at the level of populations.

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The AI Graduate Group welcomes Asim Munawar, senior research scientist at IBM Research, for an AI industry talk.

Population biology phenomena such as the spread of alleles in populations, the coexistence of competing species in an ecological community, the branching patterns in phylogenetic trees, and the dynamics of infectious disease are situated in different biology subdisciplines, e.g. demography, ecology, epidemiology, or evolution. However, these disparate phenomena are modeled using shared mathematical approaches. These approaches draw from diverse areas of mathematics, including stochastic processes, mathematical statistics, combinatorics, and dynamical systems. Across biological disciplines, population biologists share interests in the identification and analysis of equilibria, investigation of mechanistic dynamics, network-based methods and spatial modeling, modeling of stage transitions, biological optimality arguments, and the precise mathematical formulation and exploration of the consequences of verbal models. Some mathematical approaches are unique to population biology phenomena, e.g. the exponential amplification of initially rare beneficial variants such that the law of large numbers never applies poses a profound challenge, one which continues to call for new math in the area of stochastic processes.

This workshop aims to crystallize central ideas common across mathematical modeling and theory in population biology; to disseminate mathematical innovations from one biological subdiscipline to another; to promote mathematical studies at intersections among biological subdisciplines, and to deepen interactions among scientists working on similar problems but separated by disciplinary structures within biology, or between biology and the mathematical sciences. The goals are to build a community of theoretical population biologists that spans biological subdisciplines, invite new entrants to the field, encourage early-career scientists, showcase outstanding research, demonstrate impact in societally relevant problems, and uncover promising new directions where modeling and theory can make a difference in studying new types of biological data obtained at the level of populations.

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The Language Resource Center and Media and Design Studio invite you to the final event of this year’s deep dive into Artificial Intelligence and Language Instruction. Together, we will review the latest student perspectives on the use of generative AI for learning and assignments, as well as opinions about instructors’ use of AI tools for the creation of teaching materials and assessments. We will use this data to brainstorm ideas for future curricular and program designs.

Population biology phenomena such as the spread of alleles in populations, the coexistence of competing species in an ecological community, the branching patterns in phylogenetic trees, and the dynamics of infectious disease are situated in different biology subdisciplines, e.g. demography, ecology, epidemiology, or evolution. However, these disparate phenomena are modeled using shared mathematical approaches. These approaches draw from diverse areas of mathematics, including stochastic processes, mathematical statistics, combinatorics, and dynamical systems. Across biological disciplines, population biologists share interests in the identification and analysis of equilibria, investigation of mechanistic dynamics, network-based methods and spatial modeling, modeling of stage transitions, biological optimality arguments, and the precise mathematical formulation and exploration of the consequences of verbal models. Some mathematical approaches are unique to population biology phenomena, e.g. the exponential amplification of initially rare beneficial variants such that the law of large numbers never applies poses a profound challenge, one which continues to call for new math in the area of stochastic processes.

This workshop aims to crystallize central ideas common across mathematical modeling and theory in population biology; to disseminate mathematical innovations from one biological subdiscipline to another; to promote mathematical studies at intersections among biological subdisciplines, and to deepen interactions among scientists working on similar problems but separated by disciplinary structures within biology, or between biology and the mathematical sciences. The goals are to build a community of theoretical population biologists that spans biological subdisciplines, invite new entrants to the field, encourage early-career scientists, showcase outstanding research, demonstrate impact in societally relevant problems, and uncover promising new directions where modeling and theory can make a difference in studying new types of biological data obtained at the level of populations.

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Just-in-time adaptive interventions (JITAI) are an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support healthy behaviors such as adherence to medications, increasing physical activity, managing stress and so on. JITAI decision rules input an individual's current internal and contextual state, such as their location, recent adherence and current stress and output behavioral support, such as a tip for how to be active in their current location or recommendations to practice a stress management strategy. Personalizing JITAIs (pJITAIs) deploy an AI algorithm to update and improve the JITAI decision rules as individuals experience the intervention. In this talk we discuss how we are using AI algorithms to personalize JITAIs.

2024-2025 Commencement Ceremony

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McCormick School of Engineering PhD Hooding and Master's Degree Recognition Ceremony. The most up to date information can be found on our graduation webpage.

McCormick School of Engineering Undergraduate Convocation. The most up to date information can be found on our graduation webpage.

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