Cornell University Registrar

Applications of quantum chemistry to (1) the interaction of electromagnetic radiation with matter for spectroscopy and strategic bond breaking and (2) the behavior of electrons in solids - insulators, conductors, and semiconductors. Quantum and statistical mechanics for classical thermodynamics - 1st and 2nd laws, phase equilibrium, chemical equilibrium, and heat pumps. Devising rate equations for chemical reactor design from experiment and theory - kinetic theory of gases and transition state theory.

An introduction to modern biology including aspects of biochemistry and molecular and cellular biology intended for students with no significant background in this area. An emphasis on practical applications of this knowledge in a variety of settings including the production of industrial enzymes, pharmaceuticals, and biologics.

Weekly presentations by visiting chemical and biomolecular engineers to describe career paths and current professions. Job overviews and day-to-day details. Lessons learned from experiences.

Studies the first and second laws and their consequences for chemical systems. Covers thermodynamic properties of pure fluids, solids, and mixtures; phase and chemical reaction equilibrium; heat effects in batch and flow processes; and power cycles and refrigeration.

Fundamentals of fluid mechanics. Macroscopic and microscopic balances. Applications to problems involving viscous flow.

Fundamentals of heat and mass transfer. Macroscopic and microscopic balances. Applications to problems involving conduction, convection, and diffusion.

Analysis and design of chemical separation processes involving phase equilibria and mass transfer. Topics include: continuous and batch processing; counter-current and co-current flow patterns; tray columns and packed columns for distillation, gas absorption/stripping, and liquid-liquid extraction; batch separation by selective adsorption on solids; continuous separation by selective permeation through membranes; and choosing among separation options.

Modeling and analysis of the dynamics of chemical processes, Laplace transforms, block diagrams, feedback control systems, and stability analysis.

Study of chemical reaction kinetics and principles of reactor design for chemical processes.

Genomic and proteomic thinking and tools have revolutionized the way scientists study biology and medicine. We are now beginning to understand the molecular level mechanisms that underlie normal and pathologic cellular functions. As a consequence, novel molecular level approaches provide the basis for better diagnostic and therapeutic strategies to effectively treat or prevent human diseases. This course aims to present a broad overview of molecular level techniques that are relevant in many aspects of biomedical engineering. We will discuss the underlying principles, how to interpret representative data, limitations of current approaches, and engineering challenges for the development of new and improved techniques.The lectures will cover existing and emerging technologies and instrumentation critical to molecular - level analysis in biomedical engineering. These will include DNA recombinant technology, design of primers, vectors and gene-modified organisms, gene therapy approaches, DNA sequencing, quantification of RNA expression, fundamentals of protein biochemistry and biophysics, protein structure determination, mass spectrometry, protein purification, thermodynamic principles of biomolecular interactions, enzyme kinetics and modes of inhibition, and design and application of nano- and microtechnologies for diagnosis and therapeutic applications.The laboratory work consists of three modules: DNA isolation and sequencing, surface plasmon resonance technique, and design of microfluidic systems for molecular biology applications.

Introduces the fundamental concepts of nuclear science and engineering, including nuclear structure, radioactivity, nuclear reactions and the interaction of neutrons, charged particles, x-rays and gamma-rays with matter. Discusses the neutron chain reaction and its control in the core of a fission reactor. Different reactor designs are introduced and discussed along with their safety features. Other topics include radiation shielding and aspects of the nuclear fuel cycle, including isotope separation, fuel reprocessing, waste disposal and sustainability

Laboratory experiments in fluid dynamics, heat transfer, mass transfer, separations, process control, and other unit operations fundamental to large-scale chemical processing. Data collection, analysis, and interpretation. Technical report writing. Process design and scale-up based on pilot-plant data.

Students work in teams to address a chemical plant or chemical product design challenge. Teams will prepare, depending on the project type, a full-scale feasibility study of a chemical process including product supply and demand forecasts, assess product functionality through prototyping, scale-up risks, identify energy and waste minimization opportunities, develop mass and energy balances that results in a process flow sheet sufficient for estimating the capital and operating costs of the process facilities. Students also define off-plot support facilities and estimate the capital and operating costs of those facilities to develop an economic analysis of the facilities and to provide an ultimate recommendation as to the project's viability. Some teams will engage company sponsors for key process/product data and design constraints. Students develop presentation and teamwork skills through weekly presentations of their work culminating in a final presentation to a panel of internal and external appraisers.

Engineering practice increasingly relies on computational tools and data analysis approaches. The course introduces computational thinking into engineering analysis. Integrates data science and statistics, linear algebra, artificial intelligence, and mathematical modeling approaches into the context of contemporary problems in the design and analysis of processes, products, and systems. Focuses on paradigms, not syntax. Use of the Julia programming language and its associated toolchain to transition from idea to implementation. In addition, cloud computing resources for course materials, assignments, and projects. Weekly labs provide guided hands-on practice. Course assignments use data sets and examples from industrial practice to develop fluency and understanding of real-life problems.

Principles of chemical kinetics, thermodynamics, and transport phenomena applied to microchemical and microfluidic systems. Applications in distributed chemical production, portable power, micromixing, separations, and chemical and biological sensing and analysis. Fabrication approaches (contrasted with microelectronics), transport phenomena at small dimensions, modeling challenges, system integration, case studies.

This course is offered by six Departments at Cornell, in collaboration with five Universities in China and India. Video conferencing will be used to connect classrooms in the three countries in real time. Important issues related to the food, energy, and water nexus and its implications for nutrition security, one health, environmental sustainability,climate change, and economic development in the US, China, India, and other countries will be described. Challenges associated with these issues will be evaluated and strategies to address them will be proposed. Engagement of these countries with each other and the rest of the world will be explored. The course serves as a platform for students from Cornell, China, and India to learn from and interact with each other in the same class, and to share their thinking, creativity, and perspectives on these issues.

Research or studies on special problems in chemical engineering.

Research or studies on special problems in chemical engineering for visiting international students.

The development of professional skills and their application to a project-based working environment for students in the Master of Engineering (M.Eng.) program in the Department of Chemical and Biomolecular Engineering. Students will focus on developing their management, communication and presentation skills, project management skills, networking, innovative mindset, early phase market interpretation as it applies to new products/processes, self-awareness, working as a team and managing conflict. A focus on basic finance skills which enable scientists and engineers to make quantitative financial decisions in corporate and wealth management contexts. The material from this course can be applied to traditional economic and engineering fields while simultaneously providing a core set of tools for students interested in entrepreneurship or opportunities in the financial and consulting industries.

Soft materials have unique properties, exploited widely by nature and industry. They are made from a variety of building blocks, including small molecules, macromolecules, and particles. These basic constituents can be arranged into a vast range of microstructures that determine their macroscopic material properties. This course will explore this broad material space, identify unifying scientific ideas, and explore practical strategies for their design. A top-down approach, which emphasizes their macroscopic behavior, will be combined with a bottom-up approach, that describes their microscopic structure and dynamics.

Implementation of Process Safety Management (PSM) is a complex task that requires planning, coordination, and considerable resources. PSM is typically a risk-based management process with higher risk processing units receiving higher priority. PSM will be broken down into smaller, manageable pieces that can be developed and implemented in a logical sequence. Industry standard structure, covering the various aspects that define PSM, will be followed.

With the growing focus on renewable energy and electrification of transport for climate control, electrochemical engineering will play an increasingly critical role in enabling such a paradigm shift. This course introduces fundamentals of electrochemical conversion and storage including an introduction on electrochemical cells, characteristics of electrochemical reactions and Faraday's law as well as basic principles on cell potential and thermodynamics, electrochemical kinetics, and ionic mass transport. Students will also learn about standard electroanalytical techniques and applications of electrochemical engineering with particular focus on batteries. In addition to lecture-style classes, the course will engage the students in critical reading, presentation, and discussion of relevant research publications in energy storage systems providing them a holistic view of the current state of the field.

Course develops a foundational understanding of the glassy state and nature of the glass transition. Introduces phenomenology, chemistry and structure of key oxide glass families, with an emphasis on silicate glasses and the interaction of oxide components. Recent advances in glass relaxation and the implications of the statistical nature of glass structure are also discussed. Contemporary and emerging applications in optical communication, displays, and electronics packaging are explored within the context of key optical, mechanical and thermal properties. Students will gain an understanding of modern glass theory, familiarity with glass technology, and practical know-how of a glass power user.

Discusses principles involved in using biomolecules (e.g., antibodies, enzymes, DNA) and living organisms (e.g., bacteria, yeast, tissue cultures) for engineering biological processes. Examples will be taken from the following application areas: biopharmaceuticals, biofuels, biomedical technologies, foods, and environmental processes.

This course will cover the physical principles required for understanding the molecular basis of life and its use in biotechnologies. Emphasis will be placed on deconstructing biological phenomena from a quantitative perspective of core engineering principles. Specific topics will include: thermodynamics and kinetics of gene expression and genetic circuitry, biophysical principles of molecular interaction, molecular mechanisms in the definition of cell state and differentiation, molecular modes of cellular communication, and biophysical considerations for multicellular life.

This course will explore experimental and computational Systems and Synthetic Biology principles. It will address critical challenges in human health, biopharmaceutical production, and bioenergy systems. The primary emphasis will be on understanding the structure, properties, and role of signal transduction and metabolic pathways in these applications. Additionally, the course will cover essential tools and strategies used in the emerging fields of whole-cell and cell-free Synthetic Biology to program novel behavior in cells or cell-free systems. This course will prepare students for advanced research positions and professional practice.

Introduction to molecular simulation methods, Molecular Dynamics and Monte Carlo. Understanding options for interatomic and intermolecular modeling and appropriate representation of materials.

Chemical products range from specialty chemicals, biologic based products, to electromechanical devices that perform chemical transformations. This course integrates the steps of chemical product design from brainstorming and concept selection through design and manufacturing while recognizing the overlap with Lean operating principles. Students will be taught house of quality, robust design, failure modes and effects analysis. Other topics include multi-generational product planning, FMEA, sustainability and product life-cycle analysis, basic economic evaluations, process mapping, cycle time content and balance, lead-time assessment and management, quality performance analysis and remediation, entrepreneurship and new business development, as well as patents, and intellectual property. Case studies will illustrate concepts.

Design study and economic evaluation of a chemical processing facility, alternative methods of manufacture, raw-material preparation, food processing, waste disposal, or some other aspect of chemical processing.

Studio design team work sessions for the Master of Engineering chemical process project.

A quantitative finance course that enables scientists and engineers to make quantitative financial decisions in corporate and wealth management contexts. We'll use tools from engineering, statistics, artificial intelligence (AI), data science (DS), and machine learning (ML) to model, analyze, and ultimately optimize financial systems and financial decision-making. The material from this course can be applied to traditional economic and engineering fields while simultaneously providing a core set of tools for students interested in entrepreneurship or opportunities in the financial and consulting industries. Course assignments will be completed using LaTeX.

An introduction into Lean base process design and operations management based on the Toyota Production System model (Lean). A deeper dive into the specific focus points of effective and clear process design, effective problem solving along with managerial expectations will be investigated and discussed. Tools/principles such as process mapping, data collection and analysis, gap analysis, cycle time content and balance, lead-time assessment and management, quality performance analysis and remediation, sustainable control measures, demand/takt time analysis and relation to demonstrated equipment/process capacity, process stability assessment, six sigma will be covered in detail. These tools will drive towards effective observation and analysis of process performance so that relevant and impactful process improvements related to cycle time, quality, lead time, and cost can be achieved. Content of delivery is supported through business case analyses and class discussion/debate by the students along with hands-on team activities throughout the semester in order to link theory with some level of practical outcomes. Industrial examples will also be linked to the theory throughout the class as case studies or videos. Guest speakers may be included where possible to link theory with practice.

This course emphasizes entrepreneurial driven technology designs (forward engineering) by integrating mechanical, chemical, and materials engineering through the understanding of early stage product development complexities. These complexities include staging invention and innovation via the critical selection of materials, assessing product mechanics and processes for final product function, performance, reliability, cost and technical marketability. Students will attend lectures, participate in establishing a Tech Startup integrated into the Johnson School MBA mentoring program, attend startup design reviews, give a series of individual/group presentations, and write a startup issue paper.

Statistical analysis of experimental data and processes, and the design of experiments are increasingly critical in both research and industrial environments. The course will review the fundamentals of probability and statistics, common probability distribution functions, data analysis and model parameter estimation, including characterization of sources of error and uncertainty, least-squares fitting, parameter correlation, as well as formal design of experiments (DOE) methodology.

Decision-making is essential to almost everything facet of life. However, are you making the best possible decisions? This course introduces quantitative decision-making tools and strategies that will give students a framework to make the best possible decisions in life, love, and finance. Students will be introduced to concepts such as utility maximization using personalized happiness functions. Students will also be introduced to modern machine learning and artificial intelligence approaches to decision-making, such as Markov Decisions Processes (MDP), Reinforcement Learning (RL), Imitation Learning (IL), and Multiagent decision-making tools and methods.

Introduces essential process engineering and lean tools used by leading companies to improve the design and performance of new and existing processes. Clearly defined problem statements with stakeholders, targeted data analysis, and effective implementation of solutions will be discussed. The following tools and principles, with a Lean philosophy overlay, will be covered: process mapping, data collection and analysis, standard operating setpoints, audits, training, change control, experiment planning, materials and tooling control, measurement and calibration systems, maintenance management, gap analysis, cycle time content and balance, lead-time assessment and management, quality performance analysis and remediation, sustainable control measures, demand/takt time analysis and relation to demonstrated equipment/process capacity, and process stability assessment. Six sigma tools will also be introduced. Effective use of these tools enables engineers to drive towards consistent process capability and improved cycle time, quality, lead time, and cost.

Engineering practice increasingly relies on computational tools and data analysis approaches. The course introduces computational thinking into engineering analysis. Integrates data science and statistics, linear algebra, artificial intelligence, and mathematical modeling approaches into the context of contemporary problems in the design and analysis of processes, products, and systems. Focuses on paradigms, not syntax. Use of the Julia programming language and its associated toolchain to transition from idea to implementation. In addition, cloud computing resources for course materials, assignments, and projects. Weekly labs provide guided hands-on practice. Course assignments use data sets and examples from industrial practice to develop fluency and understanding of real-life problems.

Engineering practice increasingly relies on computational tools, data analysis, and Machine Learning approaches. This one-semester course introduces machine learning, focusing on supervised learning and its theoretical foundations in the context of engineering practice. Topics include regularized linear models, boosting, kernel methods, deep learning approaches, generative modeling tools, decision-making in stochastic systems, and reinforcement learning approaches.

Energy Seminars will explore energy-related topics of emerging, contemporary and historical interest. An abbreviated list of subjects explored in the seminars includes: global energy resources, energy generation technologies (present and future), energy storage options, environmental impacts and climate change mitigation, energy policy, and energy delivery economics and systems. Seminar speakers will be distinguished practicing engineers and executives from industry and government as well as faculty members from several departments at Cornell, and other academic institutions. Students from any department in Engineering or the Physical Sciences should find these talks informative.

Energy Seminars will continue to explore energy-related topics of emerging, contemporary and historical interest. An abbreviated list of subjects explored in the seminars includes: global energy resources, energy generation technologies (present and future), energy storage options, environmental impacts and climate change mitigation, energy policy, and energy delivery economics and systems. Seminar speakers will be distinguished practicing engineers and executives from industry and government as well as faculty members from several departments at Cornell, and other academic institutions.

Students attend seminars of their selection and write one-page summaries. Eligible seminars include all listings that are related to medical and industrial biotechnology.

Nonthesis research or studies on special problems in chemical engineering.

Techniques covered include mathematical modeling, scaling, dimensional analysis, regular and singular perturbations, multiple scales, asymptotic analysis, stability analysis, linear and nonlinear ordinary and partial differential equations, numerical methods for initial and boundary value problems, finite Fourier transform method, and introduction to finite element analysis.

Molecular thermodynamics of gases, lattices, and liquids, including special applications to problems in chemical engineering.

Builds foundational knowledge of transport phenomena to provide conceptional and mathematical tools to address research topics in chemical and biomolecular engineering. Significant use will be made of primary literature from biological, materials, energy, and sustainability contexts to motivate and illustrate concepts.

Topics include derivation of conservation equations; conductive heat transfer; low Reynolds number fluid dynamics; lubrication theory; inviscid fluid dynamics; boundary layer theory; forced convection; and introduction to non-Newtonian fluid mechanics (polymeric liquids and suspensions), microfluidics, stability analysis, and turbulent flow.

Application of engineering design principles to problems in drug formulation and delivery. Specific topics include traditional drug formulation, mechanisms and kinetics of pharmaceutical stability, stimuli-sensitive systems, controlled-release devices, prodrugs, targeted drug delivery, transdermal drug delivery, biomaterials, and gene therapy.

Covers chemistry and physics of the formation and characterization of polymers; principles of fabrication.

Topics include microscopic and macroscopic viewpoints; connections between phenomenological chemical kinetics and molecular reaction dynamics; reaction cross sections, potential energy surfaces, and dynamics of biomolecular collisions; molecular beam scattering; transition state theory. Unimolecular reaction dynamics; complex chemically reacting systems: reactor stability, multiple steady states, oscillations, and bifurcation; reactions in heterogeneous media; and free-radical mechanisms in combustion and pyrolysis.

This course will cover the physical principles required for understanding the molecular basis of life and its use in biotechnologies. Emphasis will be placed on deconstructing biological phenomena from a quantitative perspective of core engineering principles. Specific topics will include: thermodynamics and kinetics of gene expression and genetic circuitry, biophysical principles of molecular interaction, molecular mechanisms in the definition of cell state and differentiation, molecular modes of cellular communication, and biophysical considerations for multicellular life.

Dynamics of micro-and nano-particles, which contain many molecules but are small enough that molecular effects are important. Topics include: the formation and growth of particles; their transport, theological and phase behaviors; and their role in technologies including paints, foods, health-care products, drug delivery, composite materials and air pollution control.

Quantitative methods of engineering and life cycle analysis for energy choices in a contemporary sustainability context. Fundamental principles of thermodynamics, transport, and reaction kinetics applied to representative energy supply and end use technologies. Topics include: resource assessment, energy extraction/capture, conversion, distribution, storage, consumption, environmental and economic consequences, local to global scales.

Energy technology module of CHEME 6660 will introduce students to issues and challenges in utilizing biomass feedstocks to produce bioenergy, biofuels and/or other products. The focus will be on converting biomass feedstocks to bioenergy using a variety of thermochemical processes. Case study material will include biomass feedstock cultivation and harvesting, processing and conversion technologies, co-products, and environmental and economic impacts over their full life cycle. The course will culminate in a final project in which students will use Life Cycle Assessment to measure the energetic viability and environmental performance.

Energy technology module of CHEME 6660 provides a comprehensive overview of solar energy conversion technologies. Major themes range from fundamental (nuts and bolts) solid-state concepts and operating principles of photovoltaics to manufacturing of cells and modules, balance of system aspects, and perspectives on second- and third-generation photovoltaic technologies. The module also summarizes solar thermal power technologies including passive and active solar heating, concentrated solar power plants.

Energy technology module of CHEME 6660 focuses on the utilization of low-temperature geothermal energy: geothermal heat pumps, district heating systems for heating and cooling, hybrid geothermal systems and cogeneration applications. It also discusses shallow and deep geothermal reservoir thermal modeling. Technical economic and environmental aspects of large scale geothermal deployment will be covered.

Energy technology module of CHEME 6660. An overview of water and wind energy resources and technology both on and off shore. Emphasis will be placed on water power from conventional impoundment dams and run of river resources to pumped hydro, wave energy, and tidal basin systems. Covering water resource assessment, basic fundamentals of hydrokinetic energy capture, hydro turbine technology, designs and performance, wave power energy recovery systems, siting issues and environmental impacts, and cost estimates and projections.

Energy technology module of CHEME 6660 covering transportation energy systems. Focuses on understanding the link between transportation demand and energy consumption and on how to build a path for a conversion to sustainable energy sources. Covers engineering systems tools for analyzing the interactions among the transportation, economic, energy, and environmental systems. Analytical tools from transportation economics and engineering will be covered to assess the energy consumption and environmental effects of long-term projects over complex, large-scale transportation systems.

Energy technology module of CHEME 6660 covering fossil fuels.

Energy technology module of CHEME 6660 will provide a description of the operation of nuclear fission power plants in their several manifestations and the fuel cycle associated with them. Their benefits and reasons why they are cause for concern will be described. Topics will include the principles of reactor operation and control, reactor safety features (natural and engineered), normal and abnormal operation, spent fuel safety and nuclear waste disposal, etc. Advanced reactors now runder construction and to be constructed in the near and more distant future will be discussed.

Energy regulation, public interest, and the challenge of the social license are the main topics of this offering. This course will review the nature of energy regulation using the lens of government policy oversight, legal responsibilities, investment incentives, and the role of the public regulatory process in ensuring reliable, affordable, and environmentally responsible infrastructure development matched to technological innovation within North America.

The performance, cost, and safety of energy storage technology are recognized at the Achilles' heel in our transition towards a sustainable energy portfolio. The broad integration of inherently intermitted renewable energy sources (e.g., solar and wind) is critically dependent on technological advances in energy storage. The infrastructure used to store chemical, electrical, and thermal energy is extensive, multiscale, and capital intensive. Coverage in this module includes thermal energy storage, and electrical energy storage and conversion. Technologies evaluated include fuel cells, batteries, compressed air energy storage (CAES), pumped hydro, supercapacitors and flywheels.

Capstone energy analysis project covering a topic of interest. Relevant background information will be provided in CHEME 6660: Analysis of Sustainable Energy Systems - course lectures as well as from the other energy modules offered. Specific projects should include, to the extent possible, a quantitative discussion of resource assessment, energy extraction/capture, conversion, distribution, storage, and consumption; environmental and economic consequences spanning local to global scales.

Energy Economics for Engineers will prepare students to employ engineering and economic tools and techniques to analyze large and small energy projects and make effective recommendations on energy choices. The course will also cover energy systems, markets, innovation, and policy with a focus on aspects that are required to support thorough and complete project analysis. Energy technology module of CHEME 6660 - Analysis of Sustainable Energy Systems.

This course is offered by six Departments at Cornell, in collaboration with five Universities in China and one India. Video conferencing will be used to connect classrooms in the three countries in real time. Important issues related to the food, energy, and water nexus and its implications for nutrition security, one health, environmental sustainability, climate change, and economic development in the US and these two countries will be described. Challenges associated with these issues will be evaluated and strategies to address them will be proposed. Engagement of these countries with each other and the rest of the world will be explored. The course serves as a platform for students from Cornell, China, and India to learn from and interact with each other in the same class, and to share their thinking, creativity, and perspectives on these issues.

Systems optimization modeling, computation, and applications. Includes theory and algorithms of linear, nonlinear, mixed-integer linear, mixed-integer nonlinear, and deterministic global optimization, as well as stochastic programming, robust optimization and optimization methods for big-data analytics. Real-world applications of large-scale computational optimization in process manufacturing, bioengineering, energy systems, and sustainability.

This studio-style course emphasizes collaborative learning and innovation in sustainability. Students will explore foundational and cutting-edge literature, research, and potential future directions in AI for Sustainability. The course will cover a range of topics related to the use of AI and machine learning in sustainability science and engineering, including energy systems decarbonization, sustainable agriculture, climate modeling, resource optimization, and biodiversity conservation. Students will gain hands-on experience with AI/ML methodologies, tools, and software and engage in discussions on the latest advancements and applications of AI in addressing global sustainability challenges.

This course module of CHEME 6888 focuses on the application of artificial intelligence (AI) in materials science. Students will explore how AI and machine learning techniques can accelerate the design, discovery, and optimization of materials for energy storage, conversion, and sustainability. Key topics include using AI to predict material properties, enhance materials synthesis, and model complex material behaviors. The course emphasizes practical applications of deep learning models in materials science, featuring hands-on projects and case studies from recent research. Students will also discuss the challenges and opportunities of applying AI to advance innovations in materials science.

This course focuses on the application of artificial intelligence (AI) to energy systems over four weeks. Students will explore how AI techniques can optimize the performance of energy and power systems, with a particular focus on sustainable energy systems and renewable energy transition. Key topics include the optimization of energy generation, distribution, storage, and consumption. Specific case studies will cover topics such as optimizing solar and wind energy integration into the grid, improving battery storage management for renewable energy, and enhancing energy efficiency in smart grids. The course will also highlight AI applications in balancing supply and demand for renewable energy systems.

This course focuses on the application of artificial intelligence (AI) in the digital transformation of agriculture. Students will explore how AI techniques are applied to optimize and automate agricultural systems, improve productivity, and enhance sustainability. The course covers a broad range of topics, including AI-driven crop management, precision farming, livestock monitoring, and data analytics for sustainable agriculture. Case studies on AI applications in plant and animal production systems, as well as food supply chains, will provide practical insights into the future of farming. Students will engage in discussions on the ethical, social, and economic implications of AI in agriculture, while hands-on projects will offer experience in applying AI tools to real-world agricultural challenges.

This course covers the basic concepts, models and algorithms of Bayesian learning, classification, regression, dimension reduction, clustering, density estimation, artificial neural networks, deep learning, and reinforcement learning. Application and methodology topics include process monitoring, fault diagnosis, preventive maintenance, root cause analysis, soft sensing, quality control, machine learning for process optimization, data-driven decision making under uncertainty, missing data imputation, data de-noising, and anomaly/outlier detection.

This course provides a comprehensive overview of deep learning, covering basic concepts, models, algorithms, and applications. Topics include artificial neural networks, training techniques, convolutional neural networks, recurrent neural networks, generative deep learning, deep reinforcement learning, and deep learning hardware and software. Recent advances in deep learning, such as graph neural networks, attention, Transformer, ViT, BERT, and GPT, will also be discussed. The course explores deep learning-based applications in optimization, sensing, control, and automation, and in AI for Science, including molecular design, material discovery, and pharmaceutical development.

A colloquium/discussion group series for first-year graduate students. Topics include the culture and responsibilities of graduate research and the professional community; the mechanics of conducting research (experimental design, data analysis, serendipity in research, avoiding self-deception), documenting research (lab notebooks, computer files) and reporting research (writing a technical paper and oral presentations).

This course develops fundamental communication, coaching, mentorship and leadership skills for PhD students. It is designed specifically for PhD mentors in the Ezra's Bridge program; however, the course is appropriate for all PhD students who wish to be more effective lab members and leaders.

Intended for graduate students who are interested in teaching engineering or related fields as part of their future careers. Includes both discussion and practice of effective teaching techniques, assessments and technologies, an overview of current engineering education research, equity and inclusion in the undergraduate engineering classroom, and action research methods using qualitative/quantitative/mixed methodologies to develop teacher scholars.

Chemical Engineering graduate students in their third year and above must present an annual seminar on their research. Provides training in public presentation and dissemination of scientific data. Constructive feedback on the quality of the presentation and research will be submitted by attendees.

Introduction to molecular simulation methods, Molecular Dynamics and Monte Carlo. Understanding options for interatomic and intermolecular modeling and appropriate representation of materials.

General chemical engineering seminar.