Artificial Intelligence

Faculty

E. A. Rundensteiner, The William Smith Dean's Professor and Program Head; Ph.D., University of California, Irvine, 1992. Big data systems, big data analytics, visual analytics, machine learning/deep learning, health analytics, AI and fairness.

B. Calli, Associate Professor; Ph.D., Delft University of Technology, 2015. Robotic manipulation, robot vision, machine learning, dexterous manipulation, environmental robotics.

C. Chamzas, Assistant Professor; Ph.D., William Marsh Rice University, 2023. Integrating learning and planning, planning under uncertainty, motion planning, task and motion planning, and visual task planning.

F. Emdad, Teaching Professor; Ph.D., Colorado State University, 2007. Business analytics, computational and applied mathematics.

L. Fichera, Assistant Professor; Ph.D., University of Genoa/Italian Institute of Technology.Continuum robotics, medical robotics, surgical robotics, image-guided surgery, laser-based surgery, medical devices.

T. Ghoshal, Assistant Teaching Professor; Ph.D., University of Mississippi, 2020. Feature Engineering, Deep Learning, and Natural Language Processing.

X. Kong, Associate Professor; Ph.D., University of Illinois, 2014. Data mining and big data analysis, with emphasis on addressing the data variety issues in biomedical research and social computing, and healthcare analytics.

K. Lee, Associate Professor; Ph.D., Texas A&M University, 2013. Information retrieval, natural language processing, social computing, machine learning, and artificial intelligence.

K. Leahy, Assistant Professor; Ph.D., Boston University, 2017. Robotics, formal methods, multi-agent systems, and artificial intelligence.

J. Li, Assistant Professor; Ph.D., University of California, Santa Cruz, 2014. Human-robot interaction and interface, human-robot mutual adaptation, remote robot manipulation, nursing assistant robots.

Y. Li, Associate Professor; Ph.D., University of Minnesota, 2013. Ph.D., BUPT, Beijing, China, 2009. Data mining and artificial intelligence with applications in urban computing, smart transportation, and human mobility analysis.  

X. Liu, Associate Professor; Ph.D., Syracuse University, 2011. Natural language processing, deep learning, information retrieval, data science, and computational social sciences.

O. Mangoubi, Assistant Professor; Ph.D., Massachusetts Institute of Technology, 2016. Optimization, Machine learning, Statistical algorithms.

R. Moraffah, Assistant Professor; Ph.D., Arizona State University, 2024. Machine learning, data mining, artificial intelligence, and causal inference.

F. Murai, Assistant Professor; Ph.D. University of Massachusetts, Amherst, 2016. Application of mathematical modeling, statistics and machine learning to computer, informational and social networks.

C. Ngan, Assistant Teaching Professor; Ph.D., George Mason University, 2013. Time Series Analysis, Decision Guidance and Support Systems.

R. C. Paffenroth, Associate Professor; Ph.D., University of Maryland, 1999. Large scale data analytics, statistical machine learning, compressed sensing, network analysis.

C. Pinciroli, Associate Professor; Ph.D., Université Libre de Bruxelles, Belgium, 2014. Swarm robotics, multi-agent systems, software engineering, programming languages, human-swarm interaction.

C. Ruiz, Professor; Ph.D., University of Maryland, 1996. Data mining, machine learning, artificial intelligence, health, clinical medicine.

N. Sanket, Assistant Professor; Ph.D., University of Maryland, College Park, 2021. Robot perception, artificial intelligence, aerial robotics, computer vision, sensor fusion, SWAP-Aware minimalist autonomy.

R. Shraga, Assistant Professor; Ph.D., Technion - Israel Institute of Technology, 2020. Database systems, data discovery and integration, applied machine/deep Learning, human-in-the-loop, human-AI collaboration, information retrieval.

E. T. Solovey, Associate Professor; Ph.D., Tufts University, 2012. Human-computer interaction, user interface design, novel interaction modalities, human-autonomy collaboration, machine learning.

J. Xiao, Professor; Ph.D., University of Michigan. Robotic manipulation and motion planning, artificial intelligence, haptics, multi-modal perception.

R. Zekavat, Professor; Ph.D., Colorado state University, 2002. Statistical Signal Processing, Sensor Data Analysis and Machine Learning.

H. Zhang, Associate Professor; Ph.D., Johns Hopkins University, 2017. Biomedical robotics, biomedical imaging, ultrasound and photoacoustic instrumentation, functional imaging of brain and cancer, image-guided therapy and intervention.

Collaborative Faculty

M. Agheli, Associate Teaching Professor; Ph.D., Worcester Polytechnic Institute (WPI), 2013. Legged Robotics

E. O. Agu, Professor; Ph.D., University of Massachusetts, 2001. Mobile and ubiquitous health, machine and deep learning applications, and computer graphics.

V. Aloi, Assistant Teaching Professor; Ph.D., University of Tennessee, Knoxville. Robot dynamics, robotic control, and continuum robotics.

A. Arnold, Assistant Professor; Ph.D., Case Western University, 2014. Mathematical biology, bayesian inference, parameter estimation in biological systems .

D. Brown, III, Professor; Ph.D., Cornell University, 2000. Communication systems and networking, signal processing, information theory.

G. S. Fischer, Professor; Ph.D., Johns Hopkins University. Medical cyber-physical systems, surgical robotics, image-guided interventions, assistive technology, robot modeling and control, automation, sensors and actuators.

N. T. Heffernan, Professor; Ph.D., Carnegie Mellon University, 2001. Educational data mining, Machine Learning applied to educational context.  A/B testing.

B. Islam, Assistant Professor; Ph.D., University of North Carolina at Chapel Hill. Machine Learning, especially Efficient and On-Device Deep Learning, Mobile and Ubiquitous Computing, Cyber-Physical Systems, Internet of Things, and Mobile Health

N. Kordzadeh, Assistant Professor; Ph.D., University of Texas at San Antonio. Organizational and individual adoption and use of social media in healthcare; business intelligence and analytics with an emphasis on algorithmic fairness and ethical decision-making.

D. Korkin, Professor; Ph.D., University of New Brunswick, Canada, 2003. Big data analytics in life sciences, machine learning and its applications, visualization of complex biological data, network science, bioinformatics and personalized medicine.

G. C. Lewin, Associate Teaching Professor; Ph.D., University of Virginia, 2003. Systems integration, mobile robotics, mechatronics, sensors and control.

W. R. Michalson, Professor; Ph.D., Worcester Polytechnic Institute. Satellite navigation, real-time embedded computer systems, digital music and audio signal processing, simulation and system modeling.

R. Neamtu, Associate Teaching Professor; Ph.D., Worcester Polytechnic Institute.

M. Nemitz, Assistant Professor; Ph.D., University of Edinburgh, 2018. Robotic soft materials, magnetism, fluidics, machine learning.

C. Nycs, Assistant Research Professor; Ph.D., Worcester Polytechnic Institute, 2018. Healthcare cyber-physical systems, wearable robotics.
C. D. Onal, Associate Professor; Ph.D. Carnegie Mellon University, 2009. Soft robotics, printable robotics, origami-inspired robotics, bio-inspiration, control theory, human augmentation.

D. Reichman, Assistant Professor; Ph .D., Weizmann Institute, 2014 . Algorithms, Machine Learning, Artificial Intelligence .

A. Rosendo, Assistant Professor; Ph.D., Osaka University, 2014. Mobile robotics, mechatronics, machine learning.
A. C. Trapp, Associate Professor; Ph.D., University of Pittsburgh, 2011. Mathematical optimization and analytics with applications to benefit society, focusing on improving outcomes of vulnerable populations.

J. R. Whitehill, Associate Professor; Ph.D., University of California, San Diego, 2012. Machine learning, crowdsourcing, automated teaching, human behavior recognition.

Z. Wu, Associate Professor; Ph.D., Yale University, 2009. Big data statistical analytics, bioinformatics.

Z. Zhang, Assistant Professor; Ph.D., Oxford Brookes University, 2013. Computer vision and machine learning, object recognition/detection, data-efficient learning, with applications; deep learning, optimization.

Admissions Requirements

Applicants are expected to have earned the equivalent of a four-year U.S. bachelor’s degree with a quantitative and computational background including some coursework in programming, linear algebra and statistics. Students with bachelor's degrees in Computer Science, Data Science, Mathematics, Statistics, Electrical Engineering, Robotics Engineering, Information Technology, Business Analytics, Quantitative Sciences or other related fields are adequately prepared. Students from other backgrounds are welcome to apply if they can demonstrate their readiness through other means, such as GRE exams, professional certifications, or relevant technical work experience. The GRE is not required for admission. Non-matriculated students may enroll in up to two courses prior to applying for admission to the Master of Science in Artificial Intelligence. Students applying to pursue the graduate certificate in Artificial Intelligence should meet the same qualifications described above.