جست‌وجوی استاد و پژوهشگر

Daniel Epsteinمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Ubiquitous Computing
• Social Computing
• +۶ مورد دیگر

Daniel Epstein is an Associate Professor in the Department of Informatics at the University of California, Irvine (UCI), where he leads the Personal Informatics Everyday (PIE) Lab. He holds a courtesy appointment in the Department of Computer Science and is affiliated with the Connected Learning Lab, Institute for Future Health, and Accessibility Research Collective. Epstein earned his Ph.D. in Computer Science & Engineering from the University of Washington and a B.S. in Computer Science from the University of Virginia. His research focuses on how personal tracking technology can better serve diverse users' needs, emphasizing ethical design, health equity, and long-term user engagement. Notable areas include pregnancy tracking, wearable technology for children and ADHD management, and AI-driven health chatbots. Epstein has received grants from NSF, Snap Inc., and UCI programs, totaling over $650k in funding. Epstein's work bridges HCI and health informatics, with over 60 peer-reviewed publications. He advises six Ph.D. students and has mentored numerous undergraduates. Awards include the ACM Senior Member distinction (2025), CHI Best Paper (2023), and NSF CAREER Award (2023). Professional service includes program committees for CHI, CSCW, and UbiComp, plus leadership roles in workshops like the Workgroup on Interactive Systems in Healthcare (WISH). Beyond academia, Epstein enjoys hiking, collecting turtle figures, and advocating for accessible technology.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۳ مورد دیگر

Geoffrey E. Hinton is a distinguished Professor in the Department of Computer Science at the University of Toronto. He is renowned for his foundational contributions to machine learning, particularly in the development of deep learning and neural networks. His research focuses on understanding learning processes in both artificial and biological systems, with key contributions including Boltzmann machines, backpropagation, and deep belief networks. He teaches advanced machine learning courses such as CSC2535, emphasizing topics like graphical models, variational inference, and deep learning architectures. His work has been published extensively in top journals and conferences, with recent papers exploring forward-forward algorithms, analog diffusion models, and scalable neural network training methods. Hinton has advised numerous PhD and master's students and collaborates with institutions like Vector Institute. He is a central figure in the global AI community, regularly presenting at conferences (e.g., 2023 talks on CBS 60 Minutes, BBC, and PBS). His lab focuses on advancing machine learning theory and applications, addressing challenges in vision, language, and generative models.

Sanjiv Singhمشاهده پروفایل

استاد پژوهشی

• Aerial Robotics
• Robotics in Agriculture and Forestry
• Mining Robotics
• +۱۱ مورد دیگر

Sanjiv Singh is a Research Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science. His academic journey at CMU spans from Systems Scientist (1995-2001), to Senior Research Scientist (2001-2003), Associate Research Professor (2003-2007), and finally Research Professor since 2007. He also holds an adjunct faculty position in Mechanical Engineering since 2009. Singh serves as Editor-in-Chief of the Journal of Field Robotics, demonstrating his leadership in the robotics community. His educational background includes a Ph.D. and M.S. in Robotics from Carnegie Mellon University (1995, 1992), an M.S. in Electrical Engineering from Lehigh University (1985), and a B.S. in Computer Science from the University of Denver (1983). Dr. Singh's research focuses on three primary themes: Autonomous Navigation (developing motion planning and control for ground and air vehicles with applications in agriculture, exploration, and low-flying aircraft), Coordinated Multi-Robots (examining team-based tasks like structure assembly and search/rescue operations), and Forceful Interaction with the world (using physical models to enable robots to handle complex, high-force interactions). His work spans aerial robotics, agricultural and forestry robotics, mining robotics, 3D vision, sensing and perception, visual servoing, motion planning, and field service robotics. Analysis of his recent publications (2016-2020) reveals a strong focus on collision avoidance algorithms, sensor fusion techniques, and real-time navigation systems. His research demonstrates consistent advancement in SLAM (Simultaneous Localization and Mapping) technologies, particularly in GPS-denied environments, with increasing sophistication in handling complex aerial maneuvers and multi-robot coordination. Editor-in-Chief of Journal of Field Robotics Dr. Singh has advised numerous graduate students throughout his career, with current advisees including Matt Aasted (Ph.D), Andrew Chambers (M.S), Hugh Cover (M.S), Michael Dille (Ph.D), and Justin Haines (M.S). His past students include prominent researchers like Sebastian Scherer, Joe Djugash, Fred Heger, Geoff Hollinger, and Ji Zhang who have gone on to make significant contributions in robotics. His research has been supported through various projects including CASC (agricultural applications), Riverine, Transformer, Trestle, and Ember. His laboratory work focuses on developing practical robotic systems capable of operating in challenging real-world environments, with particular emphasis on agricultural applications, search and rescue operations, and coordinated multi-robot teams that can work effectively alongside humans.

Sean Caoمشاهده پروفایل

دانشیار

• Artificial Intelligence in Finance
• Machine Learning Applications
• Capital Market Research
• +۵ مورد دیگر

Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.

Maja J Matarićمشاهده پروفایل

استاد

• Socially Assistive Robotics
• Human-Robot Interaction
• Assistive Technology
• +۷ مورد دیگر

Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.

Olga Sorkine-Hornungمشاهده پروفایل

استاد

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +۵ مورد دیگر

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

Blase Urمشاهده پروفایل

دانشیار

• Computer Security
• Privacy
• Human-Computer Interaction (HCI)
• +۴ مورد دیگر

Blase Ur is an Associate Professor of Computer Science at the University of Chicago, leading the UChicago SUPERgroup (Security, Usability, & Privacy Education & Research group). His research focuses on data-driven methods to improve online security, privacy, and usability of complex systems. He holds a PhD from Carnegie Mellon University and an AB from Harvard University. Education: PhD in Societal Computing, Carnegie Mellon University (2016) AB in Computer Science, Harvard University Research Interests: Blase's work spans computer security, privacy, HCI, and ethical AI. He develops tools like password meters, privacy transparency systems, and IoT security solutions. His SUPERgroup emphasizes interdisciplinary collaboration, with projects addressing data-driven decision-making and usability challenges in AI and IoT. Articles Trends: Recent work includes studies on ad transparency systems, user perceptions of privacy in AI, and barriers to passwordless authentication. Research often combines empirical user studies with technical implementations to bridge gaps between security mechanisms and user needs. Awards: NSF CAREER Award (2021) Quantrell Award for Teaching (2021) SIGCHI Outstanding Dissertation Award (2018) Advising & Grants: Advisor to PhD students like Kevin Bryson. Secured grants from NSF, Mozilla, and Meta. Active in outreach to broaden CS participation, including K-12 programs. Labs & Teams: Co-director of the SUPERgroup and affiliated with the Systems Group and CERES Center for Unstoppable Computing at UChicago.

Alane Suhrمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Alane Suhr is an Assistant Professor at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department and a member of the Berkeley Artificial Intelligence Research Lab (BAIR). Her research focuses on natural language processing (NLP), machine learning, and computer vision, emphasizing systems that interact with humans through language. She designs models and datasets for language grounding (e.g., NLVR) and develops algorithms for learning through interaction. Education: PhD in Computer Science, Cornell University (2022), advised by Yoav Artzi Bachelor's in Computer Science and Engineering, Ohio State University (2016), with a Linguistics minor Research Interests: Interactive systems for collaborative language use (e.g., CerealBar) Language grounding in multimodal contexts Embodied agents and reinforcement learning Generalization in NLP and the role of language in learning Key Contributions: Developed the NLVR dataset for visual reasoning with natural language Pioneered work on SWE-Gym for training software engineering agents Contributed to research on language models' sensitivity and commonsense reasoning Awards: ACM Doctoral Dissertation Award (2022) Outstanding Paper Awards at ACL 2023 and EMNLP 2021 Professional Activities: Organized workshops at ICML, NeurIPS, and ACL on topics like agents, generalization, and theory of mind Active in academic outreach and conference speaking (e.g., ICML, NeurIPS, CVPR) Labs/Teams: Berkeley Artificial Intelligence Research (BAIR) Lab SWE-Gym research group

Roberto Martin-Martinمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Roberto Martin-Martin is an Assistant Professor of Computer Science at the University of Texas at Austin, where he leads the Robot Interactive Intelligence (RobIN) Lab. His research bridges robotics, computer vision, and machine learning to enable robots to operate autonomously in human-centric environments like homes and offices. Previously, he was a Postdoctoral Scholar at the Stanford Vision and Learning Lab working with Fei-Fei Li and Silvio Savarese, and an AI Researcher at Salesforce AI. Education: Ph.D. and M.Sc. in Robotics from Technische Universität Berlin (TUB), advised by Professor Oliver Brock B.Sc. from Universidad Politécnica de Madrid Dr. Martin-Martin's research focuses on developing AI algorithms that combine reinforcement learning and imitation learning with advanced planning and control to address core challenges in robot perception. His work spans mobile and whole-body manipulation, dexterous and contact-rich interactions, and long-horizon tasks in unstructured environments. He takes inspiration from human cognition through psychology and cognitive science to develop solutions for skills ranging from simple pick-and-place operations to complex tasks like cooking and furniture assembly. His recent publications demonstrate a strong trend toward enabling robots to learn from human demonstrations, particularly through video, and to safely adapt these demonstrations to their own morphology. There's significant emphasis on mobile manipulation, bimanual tasks, and developing hardware that supports robust robot learning through trial and error. His work shows increasing integration of large language models and vision-language models to enhance robot understanding and task execution. Scientific Awards: RSS Pioneer (2020) Winner of Amazon Picking Challenge (2015) RSS Best Systems Paper Award (2016) ICRA Best Paper Award IROS Best Mechanism Award Amazon Faculty Award AAAI Young Faculty IJCAI Early Faculty Nominated for Best Paper at IROS (2014, 2017) Dr. Martin-Martin advises PhD students including Arpit Bahety, who is working on mobile manipulation and learning. He serves as Chair of the IEEE Technical Committee on Mobile Manipulation and is a co-founder of QueerInRobotics. His research is supported by industry partnerships and academic funding sources that enable his lab to develop both hardware and software innovations in robotics. He directs the Robot Interactive Intelligence (RobIN) Lab at UT Austin, which takes a holistic approach to robot intelligence, developing both the hardware (like the BaRiFlex gripper) and software frameworks necessary for robots to learn from interaction. The lab's research addresses the full pipeline from perception to action, with particular emphasis on learning from human demonstrations, safe exploration, and adapting to novel objects and environments.

Christopher G. Atkesonمشاهده پروفایل

استاد

• Humanoid robotics
• Human aware environments
• Machine learning
• +۱۳ مورد دیگر

Christopher G. Atkeson is a Professor at the Robotics Institute at Carnegie Mellon University (CMU), where he has been since 2000 after previously holding positions at MIT and Georgia Institute of Technology. His research focuses on fulfilling the science fiction vision of machines achieving human levels of competence in perception, cognition, and action, with particular emphasis on understanding how to get machines to generate and perceive human behavior. Atkeson's work spans two complementary approaches: humanoid robotics and human aware environments. His research interests include nonparametric learning, memory-based learning, reinforcement learning, learning from demonstration, and modeling human behavior. He is particularly known for his work on robot learning of challenging dynamic tasks such as juggling, trajectory-based optimization, and soft robotics (including his contributions to the Baymax character in Disney's Big Hero 6). His recent publications demonstrate a strong focus on tactile sensing (FingerVision), human-in-the-loop optimization for exoskeletons, deep learning for locomotion control, and trajectory-based optimization methods. His work consistently bridges theoretical foundations in machine learning with practical implementations on physical robots. Among his scientific recognitions are an NSF Presidential Young Investigator Award, a Sloan Research Fellowship, and a Teaching Award from the MIT Graduate Student Council. Atkeson has advised numerous students who have gone on to successful careers in academia and industry, including notable researchers like Andrew Moore and Stefan Schaal. His teaching includes courses on dynamic optimization, humanoids, kinematics, dynamics, and control.

Stefan Leuteneggerمشاهده پروفایل

استادیار

• Mobile Robotics
• SLAM (Simultaneous Localization and Mapping)
• Computer Vision
• +۷ مورد دیگر

Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.

Wei-Lun Chaoمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Autonomous Driving
• +۳ مورد دیگر

Wei-Lun (Harry) Chao is an Associate Professor in the Department of Computer Science and Engineering at the Ohio State University (OSU), College of Engineering. Promoted to this role in May 2025, he is also an Innovation Scholar and Distinguished Assistant Professor of Engineering Inclusive Excellence. His work spans machine learning, computer vision, and their applications in autonomous driving, healthcare, biology, and natural language processing. Research Focus: Machine learning with imperfect data, interpretable and personalized learning, robust perception for autonomous systems, and visual recognition in real-world scenarios. Awards: 2025 OSU Early Career Distinguished Scholar Award, CVPR Best Student Paper Award (2024), CSE Faculty Teaching Award (2024), Lumley Research Award (2023). Grants: Funded by NSF, NIH, ONR, Cisco, AWS, and Google. Notable Research Trends: The 15 most recent articles highlight his work on vision foundation models, federated learning, diffusion models for biological species generation, interpretable vision transformers, and robust perception systems for autonomous driving. Key subfields include sparse autoencoders, 3D object detection, semi-supervised learning, and anomaly detection in scientific domains. Scientific Awards: 2025 Early Career Distinguished Scholar Award (OSU) CVPR Best Student Paper Award (2024) CSE Faculty Teaching Award (2024) Lumley Research Award (2023) Mentoring & Grants: As an advisor for the OSU Buckeye AutoDrive Team and AI Club, he mentors graduate and undergraduate students. His research is supported by major grants from NSF, NIH, ONR, and industry partners like Cisco and Google.

Mathieu Salzmannمشاهده پروفایل

عضو هیئت علمی

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Mathieu Salzmann is a Senior Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) in the School of Computer and Communication Sciences (IC). He also holds a courtesy appointment with the EPFL College of Humanities and serves as Deputy Chief Data Scientist at the Swiss Data Science Center (SDSC). He has held concurrent roles in teaching units including SIN, SODH, and SSC, reflecting his interdisciplinary engagement. His research focuses on the intersection of machine learning and computer vision, particularly in deep learning for 2D and 3D visual scene understanding, efficient and robust models, domain adaptation, and interpretable AI. These interests are evident across his extensive publication record in top-tier venues. His recent publications (2023–2024) show a consistent trend in advancing deep learning methods for visual recognition, with strong representation at CVPR, ICCV, ECCV, ICML, ICLR, and NeurIPS. Topics include domain generalization, 3D understanding, model robustness, and multimodal learning, often with applications in real-world systems. His editorial roles as Associate Editor for IEEE TPAMI and Action Editor for TMLR further highlight his leadership in the field. Area Chair: ICML 2023, CVPR 2023, ICCV 2023, NeurIPS 2023, AAAI 2024, ECCV 2024 Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Action Editor: Transactions on Machine Learning Research (TMLR) Mathieu Salzmann has supervised numerous PhD students at EPFL, both current and past, including Bouquet Yann Yanis, Javed Saqib, Li Shuangqi, and others. He has also been involved in research grants and collaborative projects, such as his work with S. Süsstrunk and R. Baroni on comics reconfiguration. His part-time role as Senior GNC Engineer at ClearSpace (2020–2024) illustrates his applied research engagement in aerospace systems. He is actively involved in EPFL’s data science and AI research ecosystem through SDSC and multiple labs.

Fadel Adibمشاهده پروفایل

دانشیار

• Wireless Technologies
• Underwater Communication
• Health Monitoring
• +۵ مورد دیگر

Fadel Adib is an Associate Professor with Tenure at MIT, holding a joint appointment in the MIT Media Lab and the Department of Electrical Engineering and Computer Science (EECS). He leads the Signal Kinetics research group at the Media Lab and is the founder and CEO of Cartesian Systems. His work focuses on novel wireless technologies for sensing and connectivity in challenging environments, including underwater communication, health monitoring, and robotics. Education: B.Sc. from American University of Beirut (2011), M.Sc. and Ph.D. in Computer Science from MIT (2013 and 2016), where he won best thesis awards. His research has led to startups such as Cartesian Systems and Emerald Innovations, addressing applications in climate monitoring, healthcare, and logistics. Research Interests: Underwater backscatter networking, batteryless systems, non-line-of-sight perception, RFID localization, and wireless health monitoring. His innovations include the first battery-free underwater camera and systems for contactless vital signs monitoring. Awards: Sloan Research Fellowship (2021), NSF CAREER Award (2019), ACM SIGMOBILE Rockstar Award (2022), and Technology Review 35 Under 35 (2014). Grants: ONR Young Investigator Award, Google Faculty Research Award. Teams: Signal Kinetics research group at MIT Media Lab. Labs and Projects: Focus on cross-disciplinary initiatives such as wireless mapping (Cartesian Systems), underwater-to-air communication, and implantable medical devices.

Anamaria Crisanمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Data Visualization
• Applied AI/ML
• +۳ مورد دیگر

Anamaria Crisan is an Assistant Professor at the University of Waterloo, affiliated with the Insight Lab. Her research focuses on interdisciplinary work at the intersection of Human-Computer Interaction (HCI), Data Visualization, and Applied AI/ML. She explores human-centered approaches to AI/ML systems, visualization design for decision-making, and healthcare data science applications. Dr. Crisan holds a PhD in Computer Science from the University of British Columbia (2019), an MSc in Bioinformatics (2010), and a BComp in Biomedical Computing from Queen’s University (2008). Her educational background bridges computer science, biology, and healthcare informatics. Her research interests include responsible AI/ML systems, interactive visualization for data-driven decisions, and leveraging visualization in healthcare to improve outcomes. She emphasizes transparency, trustworthiness, and human alignment in AI technologies. Her work spans diverse applications such as genomic epidemiology, dashboard design, and ethical AI evaluation. Notable contributions include studies on human-AI collaboration, visualization linters, and scalable dashboard census methodologies. She has published widely in top-tier venues like IEEE VIS and ACM CHI. Dr. Crisan’s lab (UW Insight Lab) focuses on human-centered approaches to automating data science and improving visualization practices in critical domains like healthcare and public health.