Rice

Courses of interest for CS

COMP 140: Computational Thinking - An introduction to computational problem-solving techniques and thinking.
COMP 182: Algorithmic Thinking - Focuses on data structures and algorithms, including their design, implementation, and analysis.
COMP 321: Introduction to Computer Systems - Covers topics such as computer architecture, assembly language, and performance optimization.
COMP 322: Principles of Parallel Programming - Introduction to parallel computing, covering principles and programming models.
COMP 431: Introduction to Artificial Intelligence - Examines the fundamental concepts and methods of AI, including machine learning, reasoning, and perception.
COMP 540: Machine Learning - Advanced course on machine learning algorithms and their applications.
COMP 542: Deep Learning - Covers the concepts and methods in deep learning, including neural networks and their applications.

Interesting professors and Research

Dr. Moshe Vardi - Renowned for his work in logic and computer science, particularly in database theory and automated reasoning.
Dr. Lydia Kavraki - Expert in robotics, AI, and computational biology, known for her work on motion planning and its applications in bioinformatics.
Dr. Richard Baraniuk - Known for his contributions to signal processing, machine learning, and compressive sensing.
Dr. Anshumali Shrivastava - Focuses on scalable machine learning, randomized algorithms, and big data analytics.
Dr. Luay Nakhleh - Specializes in computational biology, phylogenetics, and evolutionary genomics.

Cool Facilities

The Oshman Engineering Design Kitchen (OEDK) - A collaborative space for students to work on engineering design projects, equipped with advanced tools and resources.
The Rice Advanced Computing Center (RACC) - Provides high-performance computing resources for research in computational science and engineering.
Duncan Hall - Home to the Computer Science Department, featuring modern classrooms, research labs, and collaborative spaces.
The Moody Center for the Arts - A state-of-the-art facility for interdisciplinary collaboration, integrating technology, art, and design.
The Fondren Library - Offers extensive resources, including digital collections, study spaces, and research support services.

Difference Between BSCS and BA in Computer Science at Rice University

BSCS Degree: - Focuses on a more rigorous and technical curriculum. - Requires additional coursework in math, science, and engineering. - Prepares students for technical roles in industry or for graduate studies in computer science. - Includes a capstone project or senior design course.

BA Degree: - Offers more flexibility and a broader liberal arts education. - Requires fewer math and science courses. - Allows students to combine computer science with other fields of study. - Suitable for students interested in interdisciplinary applications or double majors.

BSCS and BA

Dartmouth

Courses of interest for CS

COSC 1: Introduction to Programming and Computation - A foundational course in programming and computational problem-solving.
COSC 10: Problem Solving via Object-Oriented Programming - Focuses on object-oriented programming concepts and techniques.
COSC 30: Discrete Mathematics in Computer Science - Covers discrete mathematics with applications to computer science.
COSC 50: Software Design and Implementation - Advanced course on software engineering principles and practices.
COSC 60: Computer Architecture - Introduction to computer systems and architecture.
COSC 74: Machine Learning and Statistical Data Analysis - Examines machine learning algorithms and statistical techniques.
COSC 89.26: Deep Learning - Covers advanced topics in deep learning, including neural networks and their applications.

Interesting professors and Research

Dr. Hany Farid - Known for his work in digital forensics and image analysis, focusing on media authenticity and misinformation detection.
Dr. V.S. Subrahmanian - Expert in AI, cybersecurity, and computational behavioral modeling.
Dr. Xia Zhou - Specializes in mobile computing, sensing systems, and human-computer interaction.
Dr. Andrew Campbell - Focuses on mobile health, sensor data analytics, and ubiquitous computing.
Dr. David Kotz - Researches security and privacy in pervasive computing systems, with an emphasis on health IT.

Cool Facilities

Sudikoff Laboratory - The main building for the Computer Science Department, equipped with modern classrooms, labs, and collaborative spaces.
The Neukom Digital Arts Leadership and Innovation (DALI) Lab - A creative space for students to work on innovative digital projects, integrating design, technology, and entrepreneurship.
The Magnuson Center for Entrepreneurship - Provides resources and support for student-led startups and entrepreneurial ventures.
The Dartmouth Computing Cluster - Offers high-performance computing resources for research in computational science and engineering.
The Berry Library - Part of the Baker-Berry Library complex, offering extensive resources, study spaces, and research support services.

General Information

Undergraduate Enrollment: ~4,400 students
Student-Faculty Ratio: 7:1

Academics

Flexible Curriculum: Liberal arts education with customizable schedules.
D-Plan: Unique academic calendar with four 10-week terms per year.
Research Opportunities: Significant funding and support for undergraduate research.

Campus Life

Housing: Guaranteed for all four years.
Extracurriculars: Over 160 student organizations.
Greek Life: Significant participation, around 60% of eligible students.

Notable Programs and Centers

Thayer School of Engineering: Integrates engineering with liberal arts.
Tuck School of Business: Renowned MBA program.
Geisel School of Medicine: Focus on research, clinical care, and education.
Irving Institute for Energy and Society: Research on energy challenges.

Study Abroad

Off-Campus Programs: Numerous global study opportunities.