Machine Learning for Engineering Design Professors and Supervisors in Qatar

Rachel Dzombakمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Rachel Dzombak is the Distinguished Service Professor of Design and Innovation and Senior Innovation Advisor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. She also holds teaching roles in the Human-Computer Interaction Institute, Tepper School of Business, and College of Engineering. Her work focuses on AI ethics, innovation strategy, and digital transformation, with an emphasis on responsible AI adoption in public and private sectors. Dr. Dzombak earned her PhD in Civil and Environmental Engineering from UC Berkeley, followed by a postdoctoral fellowship at Berkeley’s Haas School of Business. She holds a BS in Bioengineering from Penn State University. Her research spans AI governance, circular systems, and sustainable engineering, with notable contributions to design thinking, technology policy, and social entrepreneurship. She founded Outcome x Design, a consultancy advising organizations on AI adoption and innovation processes. Previously, she led digital transformation initiatives at CMU’s Software Engineering Institute and contributed to Berkeley’s Master’s in Development Engineering program. Her articles and engagements emphasize ethical AI practices, systemic accountability, and bridging technology with societal needs. Collaborations include the DoD’s National Security Innovation Network and projects in Kenya’s healthcare sector. Dzombak’s teaching and advisory work aim to foster innovation that addresses global challenges while prioritizing human and environmental well-being.

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Peter Zhangمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Peter Zhang is an Assistant Professor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy, with a courtesy appointment in Civil and Environmental Engineering. He holds a PhD in Engineering Systems from MIT (2019), an MASc in Mechanical and Industrial Engineering from the University of Toronto (2013), and a BASc in Engineering Science from the same university (2011). His research focuses on optimization theory, applied to supply chains, transportation systems, health, and socio-technical systems. He emphasizes robust optimization, dynamic and bilevel problems, and their applications in policy design and high-stakes decision-making. Education PhD in Engineering Systems, MIT (2019) MASc in Mechanical and Industrial Engineering, University of Toronto (2013) BASc in Engineering Science, University of Toronto (2011) Research Interests Robust optimization and its applications Transportation safety and last-mile delivery systems Supply chain resilience and policy design Machine learning integration with optimization Awards & Recognition INFORMS Koopman Prize (2020) First Place, INFORMS Junior Faculty Forum (2022) Ford Engineering Excellence Award (2015) INFORMS Daniel H. Wagner Prize (2014) Advising & Mentorship Supervised PhD students including Hao Hao and Guanting Wu, with postdoctoral mentorship for Ningji Wei and Alberto Japón Sáez. Research teams have participated in datasets like the U.S. Household Commuting Dataset and NASA’s Blue Skies Competition. Labs & Collaborations Collaborates on transportation fairness, hydrogen aviation decarbonization, and school bus optimization through interdisciplinary projects involving data science and policy analysis.

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Maria Kurnikovaمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Maria Kurnikova is a Professor of Chemistry at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. She holds concurrent roles as Co-Director of the Molecular Biophysics & Structural Biology (MBSB) Graduate Program and affiliated faculty in the Joint CMU-Pittsburgh Computational Biology Program. Her research focuses on computational chemistry, molecular modeling, and theoretical biophysics, particularly in understanding protein structure-function relationships through high-performance computing and multi-scale modeling approaches. Education: M.Sc., Moscow Institute of Physics and Technology, 1984–1990 Ph.D., University of Pittsburgh, 1998 Postdoctoral Fellowships: University of Tel Aviv (1998–1999) and University of Pittsburgh/NIST (1999–2001) Research Interests: Dr. Kurnikova's work spans computational modeling of membrane proteins, ion channels, and drug design. Her group develops hierarchical models to bridge atomistic and macroscale processes, with applications in understanding ligand binding, channel gating, and ion selectivity. Current projects include: Membrane protein receptors and ion channel dynamics In silico drug design using machine learning and free energy simulations Development of accurate force fields for molecular interactions Awards: 2024 Department of Chemistry Distinguished Alumni Award (University of Pittsburgh) 2002 Research Corporation Innovation Award Collaborations & Resources: Active user of NSF/NIH supercomputing facilities (e.g., Pittsburgh Supercomputing Center) Collaborations with Olexandr Isayev’s group on drug design methodologies Labs & Teams: The Kurnikova Research Group specializes in theoretical and computational approaches to biological macromolecules, with ongoing projects in TRPM7 channel regulation, AMPA receptor allostery, and heme transfer mechanisms.

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Tai-Sing Leeمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Dr. Tai-Sing Lee is a Professor in the Computer Science Department at Carnegie Mellon University, with affiliations in the Center for the Neural Basis of Cognition (CNBC) and the Machine Learning Department. He holds adjunct roles at the University of Pittsburgh's Neuroscience Department. His research focuses on computational neuroscience, visual perception, and the intersection of biological and machine intelligence. Lee earned his S.B. from Harvard (1986), and dual Ph.D.s from Harvard (1993) and MIT (1993), followed by postdoctoral training at Harvard and MIT. He leads the Lee Lab for Biological and Machine Intelligence Research and directs programs such as the Peking University-CMU and Tsinghua-CMU summer programs in Computer Science. His research investigates computational principles of visual perception, leveraging neurophysiological, mathematical, and machine learning approaches. Key areas include statistical modeling of neural codes, learning/adaptation mechanisms in neural systems, and Bayesian inference frameworks. Lee has developed novel machine learning techniques for analyzing neural data, including studies on V1/V2/V4 neural coding and hierarchical perceptual inference. Lee has advised numerous Ph.D., M.S., and undergraduate students, many of whom pursued academic or tech careers. His honors include the NSF CAREER Award (2000) and ICCV Helmholtz Prize (2013). His lab’s projects include the MICrONS initiative to reverse-engineer brain algorithms and collaborations on predictive coding models in vision and music perception.

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Kotaro Haraمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Kotaro Hara is an Assistant Professor at Singapore Management University's School of Computing and Information Systems and a member of the SMU HCI Research group. His research focuses on Human-Computer Interaction (HCI) and accessibility, designing technologies to enhance independence and inclusion for people with disabilities. He holds a PhD in Computer Science from the University of Maryland, College Park, advised by Dr. Jon Froehlich, and a BEng from Osaka University, advised by Dr. Fumio Kishino. Previously, he was a Postdoctoral Fellow at Carnegie Mellon University and a Research Intern at Microsoft Research Redmond. His work spans interactive tools for accessibility, leveraging crowdsourcing and machine learning to address challenges in urban accessibility mapping, crowd work fairness, and assistive technologies for visually impaired individuals. He has received numerous awards, including the CHI 2024 Honorable Mention Award and the Lee Kong Chian Fellowship, and has contributed to influential projects like Project Sidewalk. Hara has taught courses such as Interaction Design and Prototyping at SMU and has mentored over 40 students across graduate, undergraduate, and visiting programs. His grants total over $1.9 million, including leadership in projects like 'Improving Fairness and Accessibility of Crowd Work' and 'Mobile-Friendly Data Visualization.' He actively serves in roles such as CHI 2025 Accessibility Co-Chair and IMWUT Associate Editor, promoting accessibility and ethical HCI practices globally.

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Yue Jiangمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Yue Jiang is an incoming Assistant Professor at the University of Utah (Fall 2025) and is completing their PhD at Aalto University and the Finnish Center for Artificial Intelligence (FCAI). Their research focuses on computational user interface understanding, eye tracking, and adaptive GUI layouts. They have held roles such as Accessibility Chair for CHI2023/2024 and have organized workshops on computational UI methodologies. Education : PhD in Intelligent Systems (Aalto University & FCAI, Finland) Visiting PhD Student (CMU's BIG Lab, 2024) Master's in Computer Graphics (UMD, USA) Bachelor's in Computer Science & Mathematics (U of Toronto, Canada) Research Interests : Developing human-centered technologies for adaptive UIs, eye tracking analysis, and AI-driven HCI. Key projects include Graph4GUI, EyeFormer, and computational methods for GUI layout optimization. Awards : Meta PhD Fellowship (2023-2025) Google Europe Students with Disabilities Scholarship (2022) CHI2022 Best Paper Honorable Mention Heidelberg Laureate Forum Young Researcher (2024) Service : PC Member for VL/HCC2025, CHI2026 Associate Chair for CHI2025/2026 Organized three Computational UI Workshops at CHI Labs & Collaborations : Collaborates with Prof. Jeffrey Bigham (CMU), Prof. Wolfgang Stuerzlinger (SFU), and Prof. Christof Lutteroth (U of Bath). Former internships at Apple AIML Lab and Adobe Research.

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Michael Skirpanمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Michael Skirpan is an Assistant Professor at Carnegie Mellon University's School of Computer Science, Software and Societal Systems Department, with additional roles as executive director of Community Forge and co-founder of Probable Models . His work bridges technology, ethics, and education through innovative pedagogy and public engagement. PhD in Computer Science (socio-technical narratives) – University of Colorado Boulder His research focuses on ethics in computing , particularly in AI and machine learning, with interests spanning: Ethics of AI and data systems Design fiction for technology speculation Risk analysis frameworks Immersive education techniques Public engagement with algorithms Equity in classroom technology The 15 most recent publications reveal trends in machine learning ethics , design fiction , and public technology education , with a strong emphasis on contextualizing fairness in algorithms, participatory approaches to AI, and using creative methods like immersive theater for ethical discussions. Notable scientific achievement: Produced award-winning immersive play Project Amelia (2019-present), which explores AI ethics through interactive theater He advises initiatives around public use of algorithms and provides training to private/public sector entities. His community work includes rehabilitating an abandoned elementary school into a community center focused on education, arts, and local economy development.

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Dunja Mladenicمشاهده پروفایل

پژوهشگر

Carnegie Mellon UniversityQatar

Dunja Mladenic is a researcher at the Jožef Stefan Institute 's Department of Knowledge Technologies in Ljubljana, Slovenia. She has held visiting positions at Carnegie Mellon University 's School of Computer Science in 1996-1997 and 2000-2001, where she worked on text and data mining projects. Coordinator of the European project Sol-Eu-Net (2005) Tutorial chair for ECML/PKDD-2005 and ICML-2003 Key contributor to CMU's Text Learning Group Her research focuses on Text Mining , Data Mining , and Machine Learning , particularly in Web navigation assistance and intelligent agent design. She developed the Text Mining Group 's Personal WebWatcher system that uses machine learning to highlight interesting hyperlinks based on user behavior. Her publication trends show a strong emphasis on text learning and intelligent agent design, with technical reports and conference papers exploring feature selection, classifier comparison (kNN vs Naive Bayes), and document representation techniques for web browsing assistance. She maintains dual email contact through CMU and Jožef Stefan Institute . Her work spans multiple disciplines including biomedical data analysis, discrete event simulation, and encyclopedia typesetting using TeX.

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Jonathan Caganمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Jonathan Cagan is the George Tallman and Florence Barrett Ladd Professor in Engineering at Carnegie Mellon University's College of Engineering. His work bridges AI, machine learning, and cognitive science to enhance engineering design and decision-making. He co-founded CMU's Integrated Innovation Institute and held leadership roles including Associate Dean and Interim Dean. Research focuses on computational modeling of designer processes, biomechanical systems, and human-AI collaboration. Collaborations span psychology, neuroscience, computer science, and architecture. Recent publications highlight AI integration in design automation, additive manufacturing, and mixed reality systems. His work explores trust dynamics, confidence modeling, and optimization algorithms in human-AI teams. Scientific awards include the Robert A. Doherty Award for Excellence in Education and the ASME Design Theory and Methodology Award. He is a Fellow of ASME.

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Weina Wangمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, joining in Fall 2018. Her research lies at the intersection of applied probability , stochastic systems , and reinforcement learning , focusing on decision-making in large-scale systems with applications to computing resource orchestration, data privacy, and graph statistics. She has received prestigious awards including the NSF CAREER Award (2022) , ACM MobiHoc Best Paper (2022) , and ACM SIGMETRICS Rising Star Research Award (2023) . PhD in Electrical Engineering, Arizona State University (2016) Bachelor’s in Electronic Engineering, Tsinghua University (2009) Her recent publications span restless bandits , queueing theory , and attributed graph alignment , reflecting her dual focus on fundamental limits and algorithmic solutions. Notable collaborations include work on privacy-preserving data routing , phase-aware scheduling , and erasure-coded servers for heterogeneous traffic. She has advised PhD students Jalani Williams , Tuhinangshu Choudhury , and Yige Hong . Her research has been recognized with best paper awards and grants like the NSF CAREER . She also contributes to professional societies, recently joining the INFORMS Applied Probability Society council . Her teaching includes courses like Probability and Computing and Fundamentals of MDPs and Reinforcement Learning .

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