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Pharm.D. & M.S. in Artificial Intelligence

PHARM.D. & M.S. in ARTIFICAL INTELLIGENCE

We experience artificial intelligence (AI) every day in our interactions with the world: voice recognition embedded in our phones, advertisements customized to our e-mail accounts based on e-mail content and Google search history, and self-driving cars. The more a software application is used, the more it “learns” from the available data, and the more accurate the application’s responses become. Technology’s ability to self-improve is the essence of machine learning. AI already plays a role in health informatics, discovery and design of new drugs, and personalized medicine, and will one day allow clinical trials to be conducted virtually. The modern pharmacist must be conversant with the subjects included in this M.S. degree option, including computational sciences, bioinformatics, “big data” mining and analysis, robotics, statistical learning, and deep learning.

M.S. in Artificial Intelligence (30 credits)

4-Year Plan

5-Year Plan


All courses are three (3) credit hours, coded as accessible, advanced or challenging

Required courses:

 

AI 681: Machine Learning and Pattern Recognition (Somewhat advanced, but a prerequisite for many courses)

Fall; No prerequisites  

AI 700: Applicable Deep Learning                

Spring; Prereq: AI 681

 

 

Core module courses (choose four (4):

 

AI 602: Programming in Python

 

AI 632: Algorithms and Data Structures in Python

Spring; Prereq: AI 602

I 680: Artificial Intelligence: Present and Future

Fall; No prerequisites

AI 682: Data Mining and Exploration

Fall; Prereq: AI 681

AI 683: Statistical Learning    

Fall; No prerequisites

AI 686: Automatic Speech Recognition        

Fall; Prereq: AI 681

AI 688: Image and Vision Computing

Spring; Prereq: AI 681

 

 

Specialization courses (choose two (2):

 

AI 687: AI and Machine Learning in Bioinformatics

Spring; Prereq: AI 681

AI 689: Computational Neuroscience, Cognition
           and Artificial Intelligence     

Fall; Prereq: AI 681

AI 701: Intelligent Autonomous Robotics

Spring; Prereq: AI 688 and AI 700

AI 790: Special Topics in Artificial Intelligence I

Fall; Prereq: AI 680 and AI 681 

AI 791: Special Topics in Artificial Intelligence II

Fall; Prereq: AI 680 and AI 681

Electives (6 credits):

Non-thesis option: Two (2) elective courses from Depts. of AI, Computer Science or Data Analytics

Thesis option: Six (6) credits of AI thesis research


Course descriptions:

AI 602: Programming in Python
Problem solving, algorithmic design, and implementation using the Python programming language are presented. Topics include fundamental data types and associated collection data types, I/O processing, conditional and loop constructs, and use and implementation of functions. This first part of the course is complemented with a through presentation of object-oriented programming. Select advanced features for both procedural programming and object-oriented programming are introduced. Throughout the course, sound programming styles and development are emphasized. Three credits; one-hour laboratory. Prerequisites: none.

AI 632: Algorithms and Data Structures in Python
A comprehensive study of the design and analysis of efficient data structures and algorithms in Python. The course provides the fundamentals of data structures and algorithms, including their design, analysis and implementation. Fundamental data abstractions include linear lists, stacks, queues and deques, priority queues, multi-linked structures, trees and graphs, maps, hash tables, and internal and external sorting and searching.  Three credits; one-hour laboratory.  Prerequisite: AI 602.

AI 680: Artificial Intelligence: Present and Future
AI systems now outperform humans on tasks that were once thought to require a uniquely human intelligence (e.g., playing chess). How far can this go in the future? What are the assumptions behind different approaches to AI? What dangers can there be from AI systems, and how should AI practitioners take these into account? The course gives a quick overview of the background and contemporary work in symbolic AI, and looks at the relationship between statistical and two logical approaches to AI. It also addresses some of the philosophical and ethical issues that arise. The course surveys the state of the art in current AI, looking at systems and techniques in various subfields (agents and reasoning; planning, constraints and uncertainty; Google search and the semantic web; dialogue and machine translation; varieties of learning). Three credits; one-hour laboratory. Prerequisites: none.

AI 681: Machine Learning and Pattern Recognition
Fundamental theoretical concepts in machine learning and common patterns for implementing methods in practice are discussed. The course is intended for those wanting the background required to begin research and development on machine learning methods. The course provides foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems are covered. Three credits; one-hour laboratory. Prerequisites: none.

AI 682: Data Mining and Exploration
The aim of this course is to discuss modern techniques for analyzing, interpreting, visualizing and exploiting the data that are captured in scientific and commercial environments. The course will develop the ideas taught in various machine learning courses and discuss the issues in their application to real-world data sets, and will cover other techniques and data visualization methods. The first part of the course is lecture-based; occasional guest lectures from data mining practitioners are anticipated. This will be followed by student presentations of papers relating to relevant topics. Students will also carry out a practical mini-project on a real-world dataset. For both paper presentations and mini-projects, lists of suggestions will be available, but students may also propose their own, subject to approval from the instructor. Three credits; one-hour laboratory. Prerequisites: none.

AI 683: Statistical Learning
This course provides an introduction to the statistical methods commonly used in learning from data. Methodology with theoretical foundations and their computational aspects are covered. The course aims to assist in designing “good” learning algorithms and analyzing their statistical properties and performance guarantees. Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis are introduced. Topics covered include basic probability and statistics, including events, conditional probabilities, Bayes theorem, random variables, probability distributions, and hypothesis testing. Building on these concepts, the course provides in-depth coverage of supervised learning from data with focus on regression and classification methods. A few key unsupervised learning methods such as clustering (K-means and hierarchical clustering) are covered.  The “R” computer language is used throughout the course. Three credits; one-hour laboratory. Prerequisites: none.

AI 686: Automatic Speech Recognition
The course covers the theory and practice of automatic speech recognition (ASR), with a focus on the statistical approaches that comprise the state of the art. The course introduces the overall framework for speech recognition, including speech signal analysis, acoustic modelling using hidden Markov models, language modelling and recognition search. Advanced topics covered will include speaker adaptation, robust speech recognition and speaker identification. The practical side of the course will involve the development of a speech recognition system using a speech recognition software toolkit. Three credits; one-hour laboratory. Prerequisites: none.

AI 687: AI and Machine Learning in Bioinformatics
The digital revolution has seen a dramatic increase in data collection in various disciplines of the health sciences. The challenge of “big data” and “wide data” is especially pronounced in the biomedical space where, for example, whole-genome sequencing technology enables researchers to interrogate all 3 billion base pairs of the human genome. With an expected 50% of the world’s population likely to have been sequenced by 2025, the resulting datasets may surpass those generated in astronomy, Twitter and YouTube combined. Machine learning approaches are hence necessary to gain insights from these enormous and highly complex modern datasets, enabling the training of very sophisticated machine learning models in the context of artificial intelligence.

The course addresses various machine learning approaches that have been applied during the genomic revolution. Emphasis is placed on machine learning algorithms to recognize patterns in DNA sequences such as pinpointing the locations of transcription start sites (TSSs), identifying the importance of junk DNA in the genome, and identifying untranslated regions (UTRs), introns and exons in eukaryotic chromosomes.  The input data can include the genomic sequence, gene expression proļ¬les across various experimental conditions or phenotypes, protein-protein interaction data, synthetic lethality data, open chromatin data, and ChIP-seq data. Three credits; one-hour laboratory. Prerequisites: AI 681.

AI 688: Image and Vision Computing
The course addresses the analysis of images and video in order to recognize, reconstruct and model objects in the three-dimensional world. Emphasis is placed on studying the geometry of image formation; basic concepts in image processing such as smoothing, edge and feature detection, color, and texture; motion estimation; segmentation; stereo vision; 3-D modeling; statistical recognition. Three credits; one-hour laboratory. Prerequisites: AI 681.

AI 689: Computational Neuroscience, Cognition and Artificial Intelligence
The course addresses foundational tools that connect cognitive science and computational neuroscience with artificial intelligence. Emphasis is placed on computational models that mimic brain information processing during perceptual, cognitive and control tasks tested with brain and behavioral data. Computational approaches to understanding cognitive processes using massively parallel networks are studied. Biologically-inspired learning rules for connectionist networks and their application in connectionist models of perception, memory and language are discussed. Three credits; one-hour laboratory. Prerequisites: AI 681.

AI 698: Research Thesis I
Preparation of a thesis under the supervision of a faculty adviser. The completed thesis is evaluated by the Department's Graduate Curriculum Committee. Three credits.

AI 699: Research Thesis II
Preparation of a thesis under the supervision of a faculty adviser. The completed thesis is evaluated by the Department's Graduate Curriculum Committee. Three credits.

AI 700: Applicable Deep Learning
Deep learning is one of the most highly sought-after skills in AI. In this course, the student will learn the foundations of deep learning, how to build neural networks, and how to lead successful machine learning projects. The course covers convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.

Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. For example, asked to recognize faces, a deep neural network may learn to represent image pixels first with edges, followed by larger shapes, then parts of the face like eyes and ears, and, finally, individual face identities. Deep learning is behind many recent advances in AI, including Siri’s speech recognition, Facebook’s tag suggestions and self-driving cars. The student will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing, and will master not only the theory but also see how it is applied in industry. These ideas will be explored using Python and TensorFlow. The student will likely find creative ways to apply what is learned to her/his career. This course culminates in a capstone project. Three credits; one-hour laboratory. Prerequisites: AI 681.

AI 701: Intelligent Autonomous Robotics
This course covers basic topics in autonomous robotics/systems. Intelligent autonomous robots and systems can sense their environment, make decisions on how to act based on the sensations, and execute these actions without human aid or intervention. The main focus of the course is on designing and building robotic systems that navigate independently in complex environments. It is a programming-intensive course that requires teamwork and collaboration, the use of the robotic hardware interface, and the implementation of several algorithms to address key areas for effective sensor processing, vision processing, and autonomous decision-making in a physical setting or a 3D-simulated environment. Three credits; one-hour laboratory. Prerequisites: AI 688 and AI 700.

AI 790: Special Topics in Artificial Intelligence I
A course for presenting timely advanced topics in artificial intelligence, including research topics. The topics may vary from year to year according to the interest of faculty and students. The course contents and objectives are aligned with the overall program learning goals. The course requires formal submission of the course topic and a detailed syllabus for department and faculty reviews and approvals. Three credits; one-hour laboratory. Prerequisites: AI 680 and AI 681.

AI 791: Special Topics in Artificial Intelligence II
A course for presenting timely advanced topics in artificial intelligence, including research topics. The topics may vary from year to year according to the interest of faculty and students. The course contents and objectives are aligned with the overall program learning goals. The course requires formal submission of the course topic and a detailed syllabus for department and faculty reviews and approvals. Three credits; one-hour laboratory. Prerequisites: AI 680 and AI 681. 

 



4-YEAR PLAN

Fall P-3 semester (1st professional year)

Spring P-3 semester (1st professional year)

Course Number & Title

Credits

Prerequisite(s)

Course Number & Title

Credits

Prerequisite(s)

PHM 310: Pathophysiology/Immunology

3

Completion of first two years

PHM 320: Molecular Biology

2

PHM 313

PHM 311: Pharmaceutics I (Pharmaceutical Calculations)

2

Completion of first two years

PHM 321: Principles of Pharmacology/Medicinal Chemistry/Toxicology

2.5

PHM 310

PHM  312: Pharmaceutics II (Basic Theories in Pharmaceutics)

2

Completion of first two years

PHM 322: Introduction to Pharmacy Law and the Integrated Pharmaceutical Care Laboratory

1

PHM 311

PHM 313: Biochemistry

3

Completion of first two years

PHM 323: Pharmaceutics III (Biopharmaceutics/Pharmacokinetics)

3

PHM 312

PHM 314: Pharmacy Profession and the Health Care System

3

Completion of first two years

PHM 324: Biostatistics

2

3rd Year Standing

PHM 315: Pharmacy and Society

2

Completion of first two years

PHM 325: Introduction to Pharmacy Practice

3

3rd Year Standing

PHM 300: P-3 Introductory Pharmacy Practice Experience

0.5

Completion of first two years

PHM 326: Principles of Physical Assessment and Medication Administration

2

3rd Year Standing

Term credit total

15.5

 

Term credit total

15.5

 

Summer P-3 semester (1st professional year)

 

Course Number & Title

Credits

Prerequisite(s)

 

PHM 400: Community Practice, Introductory Pharmacy Practice Experience

4

Completion of 300 level courses

 

Term credit total

4

 

 


Fall P-4 semester (2nd professional year)

Spring P-4 semester (2nd professional year)

Course Number & Title

Credits

Prerequisite(s)

Course Number & Title

Credits

Prerequisite(s)

PHM 410: Human Genetics

2

PHM 320

PHM 420: Principles of Health Behavior and Patient-Provider Communication (replaces PHA 607: Behavioral Pharmacy)

3

4th Year Standing

PHM 411: Modular Organ Systems Therapeutics (MOST) Sequence I

3

PHM 321

PHM 421: Pharmaceutics IV (Dosage Forms and Principles of Compounding)

3

 

PHM 412: MOST Sequence II

3

PHM 321

PHM 422: Compounding Laboratory I

1

 

PHM 413: MOST Sequence III

2.5

PHM 321

PHM 423: Pharmacy Practice Laboratory II

1

 

PHM 414: Drug Information and Literature Evaluation  (Compensates for PHM 324 replacing PHA 010)

3

PHM 324

PHM 424: Modular Organ Systems Therapeutics Sequence IV

2.5

 

AI 680: AI: Present and Future

3

 

PHM 425: Modular Organ Systems Therapeutics Sequence V

3.5

 

AI 681: Machine Learning & Pattern Recognition

3

 

AI 682: Data Mining and Exploration

3

 

 

 

AI 602: Programing in Python

3

 

Term credit total

19.5

 

Term credit total

20

 

Summer P-4 semester (2nd professional year)

 

Course Number & Title

Credits

Prerequisite(s)

 

PHM 500: Institutional Practice Introductory Pharmacy Practice Experience

4

4th Year Standing

 

Term credit total

4

 

Red: AI graduate courses that replace Pharm.D. core courses

Blue: AI graduate courses that replace Pharm.D. electives

Green: Additional AI courses required for the M.S. degree

NOTE: 18 credits are included within the Pharm.D. curriculum; shared credit degree students will need to take and pay for an additional 12 credits.

 

Fall P-5 semester (3rd  professional year)

Spring P-5 semester (3rd professional year)

Course Number & Title

Credits

Prerequisite(s)

Course Number & Title

Credits

Prerequisite(s)

PHM 510: Health Care Informatics

2

5th Year Standing

PHM 521: Practice Management

2

5th Year Standing

PHM 511: Pharmaceutics V (Dosage Forms and Principles of Compounding)

3

PHM 311, 312

PHM 522: Public Health & Patient Safety

3

5th Year Standing

PHM 512: Compounding Laboratory II

1

PHM 511 is co-requisite

PHM 523: Pharmacogenomics

2

PHM 410

PHM 513: Pharmacy Practice Laboratory II

1

PHM 423

PHM 524: Clinical Pharmacokinetics

2

PHM 323

PHM 514: Practical Applications of the Biological Sciences

1

PHM 410

PHM 528: MOST Sequence VIII

3.5

PHM 321

PHM 515: Pharmacoeconomics and Pharmacoepidemiology

2

PHM 324

PHM 529: MOST Sequence IX

3

PHM 321

PHM 516: MOST Sequence VI

2.5

PHM 321

AI 687: AI and Machine Learning in Bioinformatics

3

PHM 517: MOST Sequence VII

2.5

PHM 321

 

AI 683: Statistical Learning

3

 

 

 

 

PHA 657: Principles and Practices of Regulatory Compliance and Enforcement

3

 

 

 

 

Term credit total

21

 

Term credit total

18.5

 

Summer P-5 semester + P-6 (4th professional year)

 

Course Number & Title

Credits

Prerequisite(s)

 

PHM 610: Acute Care Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

PHM 611: Ambulatory Care Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

PHM 612: Community Practice Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

PHM 613: Institutional Practice Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

PHM 614: Internal Medicine Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

Selective I Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

Selective II Advanced Pharmacy Practice Experience (5 weeks) 

5

6th Year Standing

 

PHM 615: Senior Seminar

5

 

 

AI 700 Applicable Deep Learning (Spring P-6 semester)

3

 

 

Specialization: AI Special Topics or any AI course (Fall P-6 Semester)

 

 

 

 

Electives I and II (AI, Computer Sci. or Data Analytics) in Summer or Fall

OR

Thesis Research (AI 698 and AI 699).  Can be completed during summers

6

 

 

Term credit total

52

 

 

 

Red: AI graduate courses that replace Pharm.D. core courses

Blue: AI graduate courses that replace Pharm.D. electives

Green: Additional AI courses required for the M.S. degree

NOTE: 18 credits are included within the Pharm.D. curriculum; shared credit degree students will need to take and pay for an additional 12 credits.


 

5 YEAR PLAN


Fall P-3 semester (1st professional year)

Spring P-3 semester (1st professional year)

Course Number & Title

Credits

Prerequisite(s)

Course Number & Title

Credits

Prerequisite(s)

PHM 310: Pathophysiology/Immunology

3

Completion of first two years

PHM 320: Molecular Biology

2

PHM 313

PHM 311: Pharmaceutics I (Pharmaceutical Calculations)

2

Completion of first two years

PHM 321: Principles of Pharmacology/Medicinal Chemistry/Toxicology

2.5

PHM 310

PHM  312: Pharmaceutics II (Basic Theories in Pharmaceutics)

2

Completion of first two years

PHM 322: Introduction to Pharmacy Law and the Integrated Pharmaceutical Care Laboratory

1

PHM 311

PHM 313: Biochemistry

3

Completion of first two years

PHM 323: Pharmaceutics III (Biopharmaceutics/Pharmacokinetics)

3

PHM 312

PHM 314: Pharmacy Profession and the Health Care System

3

Completion of first two years

PHM 324: Biostatistics

2

3rd Year Standing

PHM 315: Pharmacy and Society

2

Completion of first two years

PHM 325: Introduction to Pharmacy Practice

3

3rd Year Standing

PHM 300: P-3 Introductory Pharmacy Practice Experience

0.5

Completion of first two years

PHM 326: Principles of Physical Assessment and Medication Administration

2

3rd Year Standing

Term credit total

15.5

 

Term credit total

15.5

 

Summer P-3 semester (1st professional year)

 

Course Number & Title

Credits

Prerequisite(s)

 

PHM 400: Community Practice, Introductory Pharmacy Practice Experience

4

Completion of 300 level courses

 

Term credit total

4

 

 



Fall P-4 semester (2nd professional year)

Spring P-4 semester (2nd professional year)

Course Number & Title

Credits

Prerequisite(s)

Course Number & Title

Credits

Prerequisite(s)

PHM 410: Human Genetics

2

PHM 320

PHM 420: Principles of Health Behavior and Patient-Provider Communication (replaces PHA 607: Behavioral Pharmacy)

3

4th Year Standing

PHM 411: Modular Organ Systems Therapeutics (MOST) Sequence I

3

PHM 321

PHM 421: Pharmaceutics IV (Dosage Forms and Principles of Compounding)

3

 

PHM 412: MOST Sequence II

3

PHM 321

PHM 422: Compounding Laboratory I

1

 

PHM 413: MOST Sequence III

2.5

PHM 321

PHM 423: Pharmacy Practice Laboratory II

1

 

PHM 414: Drug Information and Literature Evaluation (Compensates for PHM 324 replacing PHA 010)

3

PHM 324

PHM 424: Modular Organ Systems Therapeutics Sequence IV

2.5

 

AI 681: Machine Learning & Pattern Recognition

3

 

PHM 425: Modular Organ Systems Therapeutics Sequence V

3.5

 

 

AI 602: Programming in Python

3

 

Term credit total

16.5

 

Term credit total

17

 

Summer P-4 semester (2nd professional year)

 

Course Number & Title

Credits

Prerequisite(s)

 

PHM 500: Institutional Practice Introductory Pharmacy Practice Experience

4

Completion of 300 level courses

 

Term credit total

4

 

 

Red: AI graduate courses that replace Pharm.D. core courses

Blue: AI graduate courses that replace Pharm.D. electives

Green: Additional AI courses required for the M.S. degree

NOTE: 9 credits are included within the Pharm.D. curriculum; shared credit degree students will need to take and pay for an additional 21 credits.

Fall P-5 semester (3rd professional year)

Spring P-5 semester (3rd professional year)

Course Number & Title

Credits

Prerequisite(s)

Course Number & Title

Credits

Prerequisite(s)

PHM 510: Health Care Informatics

2

5th Year Standing

PHM 521: Practice Management

2

5th Year Standing

PHM 511: Pharmaceutics V (Dosage Forms and Principles of Compounding)

3

PHM 311, 312

PHM 522: Public Health & Patient Safety

3

5th Year Standing

PHM 512: Compounding Laboratory II

1

PHM 511 is co-requisite

PHM 523: Pharmacogenomics

2

PHM 410

PHM 513: Pharmacy Practice Laboratory II

1

PHM 423

PHM 524: Clinical Pharmacokinetics

2

PHM 323

PHM 514: Practical Applications of the Biological Sciences

1

PHM 410

PHM 525: Pharmacy Law and Ethics

3

5th Year Standing

PHM 515: Pharmacoeconomics and Pharmacoepidemiology

2

PHM 324

PHM 528: MOST Sequence VIII

3.5

PHM 321

PHM 516: MOST Sequence VI

2.5

PHM 321

PHM 529: MOST Sequence IX

3

PHM 321

PHM 517: MOST Sequence VII

2.5

PHM 321

AI 682: Data Mining and Exploration

3

 

PHA 651: Pharmaceutical Labeling Advertisement and Promotion (replaces elective)

3

 

 

 

 

Term credit total

18

 

Term credit total

18.5

 

Summer following P-5 semester + P-6 (4th professional year)

 

Course Number & Title

Credits

Prerequisite(s)

 

PHM 610: Acute Care Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

PHM 611: Ambulatory Care Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

PHM 612: Community Practice Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

PHM 613: Institutional Practice Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

PHM 614: Internal Medicine Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

Selective I Advanced Pharmacy Practice Experience (5 weeks)

5

6th Year Standing

 

Selective II Advanced Pharmacy Practice Experience (5 weeks) 

5

6th Year Standing

 

PHM 615: Senior Seminar

5

 

 

AI 680: Artificial Intelligence: Present and Future (Fall P-6)

3

 

 

AI 687: AI and Machine Learning in Bioinformatics (Spring P-6)

3

 

 

Term credit total

46

 

 



Fall P-7 semester (5th professional year)

Spring P-7 semester (5th professional year)

Course Number & Title

Credits

Prerequisite(s)

Course Number & Title

Credits

Prerequisite(s)

AI 683: Statistical Learning

3

AI 700: Applicable Deep Learning

3

Specialization: AI Special Topics or any AI course

3

Non-thesis: Elective II (AI, Computer Sci, or Data Analytics)

 OR

Thesis:  AI 699: Thesis Research II

3

Non-thesis: Elective I (AI, Computer Sci, or Data Analytics),

OR

Thesis: AI 698: Thesis Research I

3

Term credit total

9

 

Term credit total

6

 

 

Red: AI graduate courses that replace Pharm.D. core courses

Blue: AI graduate courses that replace Pharm.D. electives

Green: Additional AI courses required for the M.S. degree

NOTE: 9 credits are included within the Pharm.D. curriculum; shared credit degree students will need to take and pay for an additional 21 credits.

 

CONTACT

LIU Pharmacy
Arash T. Dabestani
PharmD, MHA, FASHP, FABC
Dean

718-488-1004