جستجوی استاد راهنمای دانشگاهی

David Garlanمشاهده پروفایل

استاد

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +۴ مورد دیگر

David Garlan is a Professor at the Software and Societal Systems Department within the School of Computer Science at Carnegie Mellon University , where he also serves as Associate Dean for Master’s Programs . He received his Ph.D. from Carnegie Mellon in 1987 after working in industry as a software architect. His research focuses on controlling complexity in large software systems through formalized architectural design, self-adaptive systems, and cyber-physical systems. He developed AcmeStudio , a widely used architecture design environment, and pioneered formal representation and analysis of software architecture. Education : Ph.D. in Computer Science (Carnegie Mellon, 1987) Research Interests include: Software Architecture: Formal methods for architectural design, end-user composition, and architectural styles Self-Adaptive Systems: Stochastic planning, model checking, security adaptation, and uncertainty reduction Cyber-Physical Systems: Multi-view design methods, consistency checking, and automotive systems Recent Article Trends address microservice resiliency, hybrid planning (combining formal methods and ML), simulation-augmented robotics, and sustainable machine translation. Themes include stochastic modeling , probabilistic verification , and adaptive decision-making . Scientific Awards : Stevens Award Citation (2005) ACM SIGSOFT Outstanding Research Award (2011) Allen Newell Award for Research Excellence (2016) IEEE TCSE Distinguished Education Award (2017) Nancy Mead Award (2017) Fellow of IEEE and ACM Advising and Grants : He has advised 25+ graduate students and collaborated on projects with Toyota and the Software Engineering Institute. His work includes model-based adaptation, automated planning, and formal verification of adaptive systems. Labs & Teams : Affiliated with the Institute for Software Research and works on tools like AcmeStudio, Rainbow, and IPL for architectural modeling and self-adaptation.

• Audio Signal Processing
• Music Technology
• Digital Audio Recording
• +۲ مورد دیگر

Tom Sullivan is Teaching Professor in Electrical and Computer Engineering and Lecturer in Music at Carnegie Mellon University. His research interests center on signal processing applications for music and audio systems, including digital audio recording technologies, musical instrument synthesis, and controller interfaces for performance systems. Teaches foundational courses including Introduction to Electrical and Computer Engineering, Electro-acoustics, Signals and Systems, and core components of the Music Technology program. Actively involved in STEM outreach initiatives targeting K-12 students from underrepresented communities, demonstrating engineering applications in music and media contexts.

Feras Saadمشاهده پروفایل

استادیار

• Probabilistic Programming
• Artificial Intelligence
• Programming Languages
• +۴ مورد دیگر

Feras Saad is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the Principles of Programming and Artificial Intelligence groups. He received his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 2022, where his dissertations on probabilistic programming systems earned him the George M. Sprowls PhD Thesis Award and Charles & Jennifer Johnson MEng Thesis Award. His research focuses on developing scalable computing systems for probabilistic modeling and inference, integrating ideas from programming languages and probabilistic AI. Key research themes include probabilistic programming languages, automated probabilistic model discovery, statistical estimation and testing, random sampling algorithms, and applications in science and engineering. His lab explores new techniques to improve reasoning systems through automation, accuracy, and scale. Dr. Saad has published extensively in top venues including PLDI, POPL, ICML, and Nature Communications. His work spans foundational computational questions to practical software systems for probabilistic inference. Research trends show consistent focus on bridging theoretical computer science with practical applications in probabilistic modeling, with recent advances in random sampling algorithms and probabilistic programming systems. Awards and honors include: George M. Sprowls PhD Thesis Award in Artificial Intelligence and Decision Making (2023) Charles & Jennifer Johnson MEng Thesis Award in Computer Science (2017) Editor's Highlight for Nature Communications paper (2024) He currently advises graduate students Gaurav Arya and Thomas Draper in the Probabilistic Computing Systems Lab. His research is supported by software libraries including GenSQL, BayesNF, and SPPL that enable practical applications across scientific domains.

Ruben Martinsمشاهده پروفایل

استاد پژوهش

• Constraint Programming
• Program Synthesis
• Software Analysis
• +۳ مورد دیگر

Ruben Martins is an Assistant Research Professor and Master’s Program Director at the School of Computer Science, Carnegie Mellon University. He holds a Ph.D. from the Technical University of Lisbon, followed by postdoctoral research at the University of Oxford and UT Austin. His work focuses on constraint programming, program synthesis, and formal verification, with applications in improving programmer productivity and automating data science tasks. Education: Ph.D. in Computer Science, Technical University of Lisbon (2013) Postdoctoral Researcher, University of Oxford (2014-2015) Postdoctoral Researcher, UT Austin (2015-2017) Ruben's research bridges constraint programming with program synthesis, aiming to automate tasks such as vulnerability detection, code repair, and SQL synthesis. He developed Open-WBO , a MaxSAT solver that won multiple gold medals and is used in real-world optimization scenarios like seating arrangements for events. His work integrates large language models (LLMs) with traditional methods to enhance software analysis and debugging. Awards: Distinguished Paper Award at PLDI 2018 Gold Medal in MaxSAT Competitions for Open-WBO Advising & Grants: Ruben advises Master’s students and directs courses such as 15639, 15604, and others. His research has been supported by grants focusing on program synthesis and cybersecurity. Labs/Teams: Leads development of Open-WBO and contributes to projects like Crabtree (Rust API testing) and Pryde (evasion attack analysis).

Azadeh O. Sawyerمشاهده پروفایل

استادیار

• Building Technology
• Lighting Design
• Sustainable Architecture
• +۵ مورد دیگر

Azadeh O. Sawyer is an Assistant Professor in Building Technology at Carnegie Mellon University's School of Architecture, serving as Track Chair for the Master of Science in Building Performance & Diagnostics (MSBPD) and PhD-BPD programs. Her research bridges architecture, engineering, and human perception, focusing on optimizing building envelopes and integrating virtual reality (VR) for evaluating lighting design and occupant comfort. Education: Ph.D. (2019) in Building Technology from the University of Michigan (Taubman College), M.Sc. in Architecture (University of Michigan), M.Des.S. from Harvard University Graduate School of Design, and B.Arch. (2008) from California College of the Arts. Research interests include: evaluating façade design through VR simulations, quantifying user perception of light distribution, and exploring kinetic systems/smart materials for health and circadian rhythm impacts. She emphasizes closing the gap between measured environmental data and human sensory experiences in 'green' buildings. Awards include the Dow Fellowship, Rackham Predoctoral Fellowship, and Harvard’s Daniel L. Schodek Award. Her work has been presented at the International Building Performance Simulation Association conference and published in peer-reviewed venues. Teaching includes courses on daylight simulation with VR (48-692), sustainable design synthesis (48-731/48-732), and thesis guidance for MSBPD students.

Olexandr Isayevمشاهده پروفایل

استاد

• Machine Learning
• Computational Chemistry
• Drug Discovery
• +۷ مورد دیگر

Olexandr Isayev is the Carl and Amy Jones Professor of Chemistry at the Department of Chemistry, Carnegie Mellon University (CMU), within the Mellon College of Science. He holds affiliate roles at the Scott Institute for Energy Innovation and the CMU-Pitt Computational Biology Program. His research focuses on applying artificial intelligence (AI), machine learning (ML), and computational chemistry to solve complex problems in drug discovery, materials science, and quantum mechanics. Notable collaborations include work with the CMU Cloud Lab and the development of the AIMNet2 molecular potential. Education: Postdoctoral Fellow at Case Western Reserve University (2009–2012), PhD in Theoretical Chemistry from Jackson State University (2008), M.S. in Chemistry from Dnepropetrovsk National University (2002). His research interests span generative AI for molecular design, ML-driven chemical intuition, and autonomous experimental platforms. Key achievements include groundbreaking work on universal ML potentials for quantum mechanics and GenAI methods for inverse molecular design. Awards include the Scialog Fellowship (2023) and NVIDIA GPU Computing Award (2014). Future goals aim to advance 'Chemical Intelligence' by integrating AI with expert decision-making frameworks. Awards: Scialog Fellow (2023) ACS Emerging Technology Award (2017, 2014) Eshelman Institute for Innovation Award (2016) Labs/Teams: Directs the Isayev Lab, which pioneers AI-driven molecular discovery through synergies between automated experiments and ML agents.

Jinghai Raoمشاهده پروفایل

پژوهشگر

• Semantic Web Services
• Web Service Composition
• Security Policies
• +۳ مورد دیگر

Jinghai Rao is a Researcher at Carnegie Mellon University's School of Computer and Information Science within the Institute for Software Research International. His work focuses on semantic web services, security policies, and logic-based programming. He holds a PhD in Computer Science from the Norwegian University of Science and Technology (NTNU) and has Master's and Bachelor's degrees from Renmin University of China. His research emphasizes web service composition, policy enforcement, and automated reasoning. Notable contributions include the use of linear logic for service composition and frameworks for interleaving policy reasoning with service discovery. He has received a best paper runners-up award at ICWS 2004. Rao has been actively involved in professional activities, serving on committees for workshops like ECWS'06 and SOT'06, and as a reviewer for conferences such as IJCAI'05 and ISWC'05. His work bridges theoretical foundations of semantic web services with practical applications in security and collaborative systems.

• Machine Learning
• Artificial Intelligence
• Catalysis
• +۶ مورد دیگر

Gabriel Gomes is an Assistant Professor in the Departments of Chemistry and Chemical Engineering at Carnegie Mellon University, where he leads the Gomes Group research program focused on the intersection of machine learning and chemistry. His work centers on developing new chemical reactions, catalysts, and materials using state-of-the-art machine learning and automated synthesis techniques. Dr. Gomes received his Ph.D. in Chemistry from Florida State University in 2018 under Professor Igor V. Alabugin, where he researched stereoelectronic effects. Prior to joining CMU in 2022, he was a Banting Postdoctoral Fellow at the University of Toronto in the Matter Lab led by Professor Alán Aspuru-Guzik. He earned his B.S. in Chemistry from Federal University of Rio de Janeiro, Brazil in 2013. His research program focuses on developing autonomous platforms for reaction discovery, with emphasis on catalysis. Gomes aims to create inverse design frameworks where algorithms can propose new functional catalysts from scratch based on desired properties. His work integrates quantum mechanics with machine learning to teach computers chemical intuition, enabling novel approaches to catalyst design and chemical synthesis. He strongly advocates for the integration of AI with human creativity to transform chemical research. Gomes' recent publications demonstrate a clear trend toward integrating large language models with chemical discovery, developing transferable machine learning potentials for catalysis, and advancing molecular representations with quantum chemical insights. His work spans from fundamental quantum chemistry to practical applications in biocatalysis and materials design. Scialog Fellow (2023) C&EN Talented Twelve (2022) NSERC Banting Postdoctoral Fellowship (2020-2021) CAS SciFinder Future Leaders Program (2018) ACS COMP Chemical Computing Group Excellence Award (2018) FSU's Graduate Student Research and Creativity Award (2018) IBM Ph.D. Scholarship (2017) Dr. Gomes mentors several graduate students including Robert MacKnight and Daniil Boiko, who have contributed to projects like the Catnip web application for biocatalyst prediction. His research has received funding through prestigious programs like the Scialog Automating Chemical Laboratories awards. Gomes actively promotes open science initiatives and has contributed to the Open Reaction Database to improve data accessibility for machine learning in chemistry. The Gomes Group operates at the cutting edge of AI-driven chemistry research, developing tools like Coscientist that bridge the gap between natural language prompts and actual chemical reactions. Their work on autonomous reaction discovery systems represents a significant step toward fully automated chemical laboratories where machines can 'dream of molecules' with specific desired properties.

Stefanie Sydlikمشاهده پروفایل

استادیار

• Polymer Chemistry
• Materials Science
• Graphene Research
• +۴ مورد دیگر

Stefanie Sydlik is an Assistant Professor of Chemistry and Biomedical Engineering (courtesy) at Carnegie Mellon University's Mellon College of Science, Department of Chemistry. She joined CMU full-time in summer 2015 after completing a postdoctoral fellowship with Professor Robert Langer at MIT, where she developed biomimetic block copolymers for cartilage repair. Her research group focuses on the synthesis of novel polymers and materials through molecular design principles, with applications spanning electronic, mechanical, and biological materials. Dr. Sydlik's research interests center on polymer chemistry and biomaterials development, particularly in creating functional graphene-based materials for tissue engineering applications. Her work bridges the gap between fundamental molecular design and practical biomedical applications, with emphasis on bone regeneration, stem cell scaffolds, and degradable polymer systems. The Sydlik Group explores how molecular architecture influences material properties and biological responses, developing innovative solutions for regenerative medicine. Analysis of her recent publications reveals a strong focus on graphene functionalization techniques, particularly for bone regeneration applications. Her research demonstrates how tailored surface chemistry of graphene materials can direct stem cell behavior and enhance tissue integration. The trend shows increasing sophistication in material design, moving from basic graphene composites to precisely engineered peptide-functionalized systems that mimic natural extracellular matrices. American Chemical Society PMSE Young Investigator Award National Institutes of Health Ruth L. Kirschstein Postdoctoral Fellowship (2014-2015) National Science Foundation Graduate Research Fellowship (2008-2011) MIT Department of Chemistry Award for Outstanding Teaching (2008) Beckman Scholars Program Award Recipient (2006-2007) Professor Sydlik actively mentors a diverse group of students, including PhD candidates, master's students, and undergraduates working on projects ranging from macrocyclic ring-opening polymerizations to peptide-functionalized graphene materials. Her lab has received funding through prestigious fellowships and grants that support undergraduate research initiatives. The Sydlik Group maintains well-equipped laboratory facilities including chemical fume hoods, biosafety equipment, and advanced microscopy capabilities, supporting their interdisciplinary research at the chemistry-biology-materials science interface.

Tze Meng Lowمشاهده پروفایل

استاد پژوهش

• Parallel and High Performance Computing
• Formal Methods
• Graph Algorithms
• +۳ مورد دیگر

Tze Meng Low is an Associate Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. His research focuses on high-performance algorithms, formal methods, and hardware-software co-design, with an emphasis on achieving performance portability across architectures. He holds a Ph.D. and M.S. in Computer Science and dual B.A./B.S. degrees in Economics and Computer Science from the University of Texas at Austin. His research interests span parallel computing, graph algorithms, machine learning, and cyber-physical systems. He has contributed to projects like the DARPA BRASS initiative, collaborating on adaptive software systems for resource-challenged environments. His work also involves developing tools such as SMaLL and qLD, which address challenges in machine learning library instantiation and genomic analysis. Low has received the Dean’s Early Career Fellowship (2022) and led the $2.7M DARPA-funded BRASS project (2016–2020), supported by SpiralGen, Inc. and academic collaborators. His contributions include code generation frameworks like SPIRAL and advancements in linear algebra-based graph algorithms, emphasizing analytical models and automated code optimization. His research bridges theoretical formal methods with practical implementations, aiming to enhance software reliability and scalability in emerging domains. Collaborations include work on fault-tolerant coded computing and high-assurance systems for cyber-physical applications.

Marios Savvidesمشاهده پروفایل

استاد

• Biometric Identification
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Marios Savvides is the Bossa Nova Robotics Professor of Artificial Intelligence and a Full Tenured Professor in the Electrical and Computer Engineering Department at Carnegie Mellon University (CMU). He is also the Founder and Director of the CyLab Biometrics Center. His research focuses on AI algorithms for biometrics, facial recognition, iris scanning, and object detection under challenging conditions. He holds a BEng from the University of Manchester, an MS in Robotics from CMU, and a PhD in Electrical and Computer Engineering from CMU. Education: BEng, Microelectronics Systems Engineering, University of Manchester Institute of Science and Technology (1997) MS, Robotics, Carnegie Mellon University (2000) PhD, Electrical and Computer Engineering, Carnegie Mellon University (2004) Research Interests: Core AI and machine learning for robust biometric systems Long-range iris capture and matching Low-shot object detection and scene understanding Applications in retail automation, airport security, and medical imaging Key Achievements: Recipient of seven Best Paper Awards and the 2022 PIPLA Inventor of the Year Recipient of the 2015 Edison Gold Award, 2018 Immigrant Entrepreneur Award, and 2020 AI Excellence Award Developed AI algorithms deployed in over 3 million ADT security cameras and Bossa Nova robots Spun off startups including HawXeye and Oosto, serving as CTO/Chief AI Scientist Grants and Partnerships: Collaborations with Bossa Nova Robotics, Egen, UltronAI, and CMKL University Research on facial recognition for stock market prediction and medical imaging privacy risks Labs and Teams: Director of the CyLab Biometrics Center Member of IEEE Biometric Council and contributor to the IEEE Certified Biometrics Professional program

• Machine Learning
• Optimization
• Reinforcement Learning
• +۴ مورد دیگر

Karan Singh is an Assistant Professor of Operations Research at the Tepper School of Business , Carnegie Mellon University. His work bridges machine learning and optimization, focusing on algorithmic solutions for interactive learning paradigms such as reinforcement learning and online control. Education : PhD in Computer Science (Princeton University, 2022), MA in Computer Science (Princeton, 2017), BTech in Computer Science (IIT Kanpur, 2015) His research addresses computational and statistical challenges in feedback-driven learning systems, with key contributions in algorithmic reductions, nonstochastic control, and privacy-preserving online optimization. Recent work includes developing sample-efficient boosting methods and establishing regret bounds for differentially private learning. Notable scientific achievements include the Porter Ogden Jacobus Fellowship (Princeton's highest graduate honor), a Best Paper Award at the OptRL workshop (NeurIPS 2019), and a Spotlight Prize at the New York Academy of Sciences' ML Symposium (2018). Karan actively contributes to academic service as a reviewer for International Conference on Machine Learning (ICML) , NeurIPS , and Mathematics of Operations Research . He serves on CMU committees including the Business Analytics Teaching Track Search Committee and Special Taskforce for Testing Requirements .

Fas Lebbieمشاهده پروفایل

پژوهشگر

• Transition Design
• Human-Centered Design
• Social Enterprise
• +۷ مورد دیگر

Fas Lebbie is a PhD researcher and teaching fellow at Carnegie Mellon University's School of Design , specializing in Transition Design with focus on African mineral resource systems. His transdisciplinary work bridges design theory with technology, entrepreneurship, and social justice frameworks across four continents. Current industry roles: Product Design Manager at Meta , MIT GOV/LAB mentor Academic contributions: 15+ peer-reviewed case studies in fintech, AR design, and criminal justice reform Core research areas examine: Regenerative mining practices in West Africa Design interventions for financial inclusion Digital tools in industrial knowledge transfer Systems mapping of carceral economies Post-extractive economic transitions

Aaron Steinfeldمشاهده پروفایل

استاد پژوهش

• Human-Robot Interaction
• Advanced Transportation Systems
• Assistive Robotics
• +۴ مورد دیگر

Aaron Steinfeld is a Research Professor at Carnegie Mellon University 's Robotics Institute with a courtesy appointment in the Human-Computer Interaction Institute . His work bridges human-robot interaction and advanced transportation , focusing on inclusive design for users with disabilities. PhD in Industrial and Operations Engineering (1999) and MSE/BSE in the same field (1994/1993) from University of Michigan Postdoctoral work in Transportation Human Factors at UC Berkeley's California PATH program (2000) Research spans four key areas: Assistive Robotics : Developing inclusive technologies for visually impaired and disabled users Transportation Innovation : Redesigning transit systems through crowdsourcing and citizen science Human-Robot Group Dynamics : Studying social navigation and multi-robot coordination Trust & Communication : Creating frameworks for robot self-assessment and user trust alignment Recent publications emphasize social navigation benchmarks , pedestrian behavior modeling , and adaptive transit interfaces . Current projects include accessible delivery robots (NIDILRR 90IFDV0042) and FHWA-funded work on autonomous vehicle accessibility. Scientific Recognition NSF AI Institute (AI-CARING) co-PI ONR MURI grant recipient DARPA and multiple NSF grant collaborations Best Paper awards at CHI 2019 and IROS 2020 Advising both PhD and Master's students in human-robot interaction, his lab maintains partnerships with Georgia Tech, UMass Lowell, and Disney Research. He co-edited the book Accessible Public Transportation .

Matt Fredriksonمشاهده پروفایل

دانشیار

• Machine Learning
• Security
• Privacy
• +۲ مورد دیگر

Matt Fredrikson is an Associate Professor in the Computer Science Department at Carnegie Mellon University , affiliated with CyLab and the Principles of Programming Group . His research bridges security, privacy, and formal methods in machine learning and software systems. PhD in Computer Science, University of Wisconsin–Madison (2015) M.S. in Computer Science, University of Wisconsin–Madison (2012) Bachelor's in Mathematics and Computer Science, Duquesne University (2007) His work focuses on privacy in machine learning , particularly adversarial inference and differential privacy limitations. He develops formal methods for privacy-aware programming , using logics with counting to model adversarial uncertainty. Additionally, he explores probabilistic program analysis to enhance machine learning security and reliability. Recent publications highlight his contributions to LLM security , including attacks on alignment and robustness certification. His 2025 paper LLM Whisperer reveals biases in LLM responses, while 2024 works address certifiable robustness and automated adversarial attacks on coding models. Best Paper Award, USENIX Security Symposium 2014 He advises students on topics spanning AI ethics , program verification , and IoT security . Courses taught include Software Foundations of Security and Privacy and Bug Catching: Automated Program Verification and Testing .