جست‌وجوی استاد و پژوهشگر

Siyu Tangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Digital Humans
• +۵ مورد دیگر

Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.

Mark W. Newmanمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Ubiquitous Computing
• Health Informatics
• +۴ مورد دیگر

Mark W. Newman is an Associate Professor in the School of Information at the University of Michigan, with a joint appointment in the EECS Department. His research focuses on human-computer interaction, ubiquitous computing, and health informatics, emphasizing the design of technologies that integrate seamlessly into everyday life. He teaches courses on user experience research, programming, and application development, contributing to curriculum design efforts such as the User Experience Design track and programming curricula for undergraduate and graduate programs. His work spans smart home technologies, mobile health interventions, and collaborative health management systems. Notably, he collaborates across disciplines to address challenges in healthcare technology, including patient-generated data integration and sustainable energy solutions. Newman actively mentors students and leads design projects that prioritize user-centered approaches, such as the UX Research & Design Specialization on Coursera. His recent scholarship explores adaptive mHealth interventions, context-aware systems, and participatory design methods to enhance health equity.

JJ Parkمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +۵ مورد دیگر

Jeong Joon (JJ) Park is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on computer vision, graphics, and artificial intelligence with applications in 3D/4D reconstruction, generative modeling, robotics, and medical imaging. He holds a position in the College of Engineering and actively seeks PhD students and postdoctoral researchers aligned with his research interests. Dr. Park’s work emphasizes interdisciplinary approaches, combining geometric deep learning with generative models to address challenges in scene understanding, novel view synthesis, and multi-modal perception. His lab explores both foundational techniques and applied systems, often collaborating with industry and academia on real-world problems. Key research directions include diffusion models for sparse data restoration, trajectory-conditioned 4D generation, and uncertainty-aware sensor fusion for autonomous systems. His publications span top-tier conferences like CVPR, ICCV, and NeurIPS, reflecting a strong publication record in computer vision and graphics. He teaches courses in computer vision and advises students on advanced projects requiring significant weekly commitments. Prospective applicants are encouraged to apply through the U-M CSE PhD program and contact him directly for collaboration opportunities.

WonSook Leeمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

WonSook Lee is a tenured Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa’s Faculty of Engineering. Her expertise spans medical imaging, machine/deep learning, computer graphics, and computer vision. She earned her Ph.D. in Computer Science from the University of Geneva (Switzerland) and holds degrees from POSTECH (Korea) and NUS (Singapore). Before academia, she worked at Korea Telecom, Samsung Advanced Institute of Technology, and Eyematic Interfaces Inc. (USA). Her research focuses on applications such as virtual/augmented reality, MRI/CT/Ultrasound analysis, and 3D mesh modeling. She has authored over 130 publications, including 30+ journal papers, and serves on conference committees and editorial boards. Lee has secured major grants (NSERC, CFI, ORF) as Principal Investigator and contributed to global initiatives like South Korea’s National Research Foundation. Her lab explores cutting-edge techniques in medical imaging, AI-driven object detection, and multimodal systems. Notable projects include adversarial perturbation analysis for model robustness, cross-domain GANs for semantic segmentation, and real-time ultrasound-enhanced pronunciation training. She actively promotes interdisciplinary research in healthcare technology and autonomous systems.

Shiva Nejatiمشاهده پروفایل

استاد

• Software Engineering
• Cyber-Physical Systems
• Autonomous Vehicles
• +۷ مورد دیگر

Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.

Julia Kempeمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Quantum Computing
• +۳ مورد دیگر

Julia Kempe is a Silver Professor of Computer Science, Mathematics, and Data Science at New York University (NYU), holding joint appointments at the Courant Institute and the Center for Data Science (CDS). She serves as Director of the CDS and is on research leave at the CSD, ENS, Paris (2023–24). Her expertise spans interdisciplinary research in quantum computing, machine learning, and data science. She holds PhDs in Mathematics (UC Berkeley, 2001) and Computer Science (École Nationale Supérieure des Télécommunications, Paris, 2001), alongside advanced degrees in theoretical physics and mathematics from prestigious institutions in France and Austria. Research Interests: Data Science, Machine Learning (theoretical foundations and applications to physics), and past contributions to quantum computing. She focuses on robustness in machine learning models, adversarial examples, and interdisciplinary applications of physics-informed AI. Awards and Honors: Knight of the National Order of Merit (France, 2010), Femme en Or de la Recherche (France, 2010), ERC Starting Grant (2007, top-ranked in Europe), and numerous academic fellowships. She is a member of Academia Europaea (2018) and a Fellow of the Asia-Pacific Artificial Intelligence Association (2022). Grants and Leadership: Principal investigator of NSF NRT grants for CDS PhD programs, co-PI on NASA TCAN grants, and leader in NYU’s Senior Leadership Team. She designed NYU’s Data Science undergraduate programs and expanded interdisciplinary collaborations in machine learning and quantum computing. Labs and Teams: Directs the CDS, collaborates with the Courant Institute, and leads research initiatives in Paris. Her work bridges theoretical computer science, physics, and applied data science, emphasizing interdisciplinary innovation.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Lerrel Pinto is an Assistant Professor of Computer Science at New York University's Courant Institute, where he leads the General-purpose Robotics and AI Lab (GRAIL). His research focuses on enabling robots to generalize and adapt in unstructured environments through advancements in robot learning, decision making, and multimodal sensing. Before joining NYU, he completed a postdoc at UC Berkeley, a PhD in Robotics at Carnegie Mellon University, and an undergraduate degree in Mechanical Engineering at IIT Guwahati. Key research areas include large-scale robot learning, representation learning for sensory data, reinforcement learning for adaptability, and open-source robotics hardware. Notable achievements include the Sloan Fellowship (2025), NSF CAREER Award (2024), and Best Paper Awards at multiple robotics conferences. Pinto's lab has developed influential systems such as the AnySkin tactile sensing framework and the OPEN TEACH teleoperation system. Education highlights include a PhD from CMU (2019) under Abhinav Gupta, a postdoctoral stint with Alexei Efros and Pieter Abbeel at Berkeley, and undergraduate studies at IIT Guwahati. He has authored over 65 publications in top conferences like ICRA, NeurIPS, and CVPR. Pinto teaches courses on robotics, reinforcement learning, and AI at NYU. His service contributions include roles on program committees for ICML, NeurIPS, and IROS, as well as organizing workshops on topics like Dexterous Manipulation and Vision-Language Models for Robotics. His team actively collaborates through the GRAIL lab, with current projects exploring tactile sensing, zero-shot policy deployment, and multimodal robot learning systems. Ongoing research emphasizes bridging the gap between human and robotic dexterity through novel reward structures and adaptive control frameworks.

Ryo Suzukiمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Robotics
• Tangible User Interfaces
• +۶ مورد دیگر

Ryo Suzuki is an Assistant Professor in the Department of Computer Science at the University of Calgary's Faculty of Science. His research focuses on Human-Computer Interaction (HCI) and robotics, particularly in tangible user interfaces, swarm robotics, and shape-changing interfaces. He holds a PhD in Computer Science from the University of Colorado Boulder (2020) and has conducted research internships at Stanford University, UC Berkeley, the University of Tokyo, and Adobe Research. His educational background includes advanced coursework in robotics and HCI, with a strong emphasis on interdisciplinary innovation. He teaches courses such as Human-Computer Interaction II, Human-Robot Interaction, and Special Topics in Mixed Reality Application Design. Research interests include designing novel interfaces for AR/VR/MR systems, haptic feedback mechanisms, and collaborative robotics. His work often bridges physical and digital spaces, such as through projects like LiftTiles (shape-changing building blocks) and RoomShift (room-scale haptic environments). Key awards include the UIST 2020 Honorable Mention Paper Award, DIS 2019 Best Paper Award, and the Ministry of Internal Affairs and Communications in Japan Innovation Award. His research has been featured in IEEE Computer Graphics and Applications, TechXplore, and Wired. He actively explores future work in symbiotic AI systems, AI-in-the-loop AR applications, and scalable shape-changing robotics. His lab emphasizes hands-on prototyping and user-centered design principles.

Mathias Payerمشاهده پروفایل

دانشیار

• Software Security
• System Security
• Binary Exploitation
• +۳ مورد دیگر

Mathias Payer is an Associate Professor at EPFL's School of Computer and Communication Sciences (IC), leading the HexHive Laboratory . His work focuses on software security, particularly addressing memory corruption and type violations through binary analysis and compiler-based techniques. He contributes to research in secure system design, fault isolation, and fuzzing methodologies. Current PhD students : Di Bartolomeo Luca, Feng Zhiyao, Hofhammer Florian, Lyu Tao, Mao Philipp Yuxiang, Zhang Chibin, Zheng Han Past EPFL PhD students: Badoux Nicolas Daniel, Bhattacharyya Atri, Hazimeh Ahmad His research explores software security in areas like: Protecting applications from vulnerabilities Binary exploitation and mitigation Compiler-driven security hardening Strong sanitization and privilege separation Memory corruption detection The HexHive group develops tools and frameworks for: Automated fuzz driver generation Gradual compartmentalization State inference for feedback optimization Secure cell architectures Recent publications highlight advancements in: Fuzzing hybrid approaches (e.g., DUMPLING, MendelFuzz) Memory safety validation (QMSan, Pacmem) Compiler-assisted defenses (Gradient, Type++)

Ju Sunمشاهده پروفایل

استادیار

• Machine Learning
• Numerical Optimization
• Computer Vision
• +۵ مورد دیگر

Ju Sun is an Assistant Professor at the University of Minnesota, Twin Cities, in the Computer Science & Engineering department. He leads the Group of Learning, Optimization, Vision, Healthcare, and X (GLOVEX) and plays key roles in the UMN Data Science Initiative (DSI), Program for Clinical AI, and AI-CLIMATE institute. Research Focus : Theoretical foundations of machine learning, computer vision, and numerical optimization with applications in healthcare, inverse problems, and medical imaging. Grants : $4.5M+ in funding including NSF ACED Program and NIH R01 grants for constrained deep learning and imbalanced classification. Teaching & Leadership : Featured in UMN seminars and AI institutes, with affiliations across Electrical and Computer Engineering, Health Informatics, and Medical School. Recent Publications address inverse problems, federated learning, imbalanced classification, and phase retrieval using deep generative priors and diffusion models. His group website details these innovations. Scientific Awards : McKnight Land-Grant Professorship (2025–2027) 2021 AAAI New Faculty Highlights Advising : Mentored three PhD graduates now at Meta, Amazon, and UCLA. Collaborations span medicine, materials science, and biomedical engineering, integrating physics-informed constraints into AI.

• Nanophotonics
• Metasurfaces
• Inverse Design
• +۳ مورد دیگر

Jonathan Fan is an Associate Professor at Stanford University in the Department of Electrical Engineering. His teaching portfolio includes graduate and undergraduate courses in electromagnetics, integrated circuit fabrication, and specialized studies across all quarters. EE 242: Electromagnetic Waves (Autumn) EE 312: Integrated Circuit Fabrication Laboratory (Winter) ENGR 42/EE 42: Electromagnetics and Applications (Spring) 11 independent studies and thesis courses (EE 190, EE 191, EE 300, etc.) His research focuses on nanophotonics and metasurface engineering , with particular emphasis on inverse design methodologies, machine learning -driven photonic optimization, and machine learning in electromagnetic simulation. His recent publications demonstrate a strong trend toward deep learning-enabled photonic design and high-speed optimization of complex optical systems. His work spans metamaterial fabrication , nonlocal effects in metasurfaces, and multi-functional optical devices such as spaceplates for aberration correction. Key technical contributions include physics-augmented neural networks , reparameterization techniques for design constraints, and topology-optimized metasurfaces .

Nathan Youngbloodمشاهده پروفایل

استادیار

• Photonics
• Neuromorphic Computing
• Artificial Intelligence Hardware
• +۳ مورد دیگر

Nathan Youngblood is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Pittsburgh , with a secondary appointment in the Department of Physics and Astronomy . His research focuses on reconfigurable photonic materials and devices for energy-efficient artificial intelligence applications. Educational Background: PhD in Electrical Engineering from the University of Minnesota Postdoctoral research at the University of Oxford (2017–2019) His work explores photonic in-memory computing, neuromorphic systems, and phase-change materials to minimize computing latency and energy consumption. Recent publications highlight advancements in magneto-optical non-reciprocity, coherent crossbar arrays, and plasmonic-enhanced phase-change devices. Scientific Awards: NSF CAREER Award (2024) AFOSR Young Investigator Award (2024) William Kepler Whiteford Faculty Fellowship (2024) Dr. Youngblood's lab develops photonic accelerators like LightML and LightBulb for machine learning, emphasizing scalable integration and novel material applications in silicon photonics.

Karen Livescuمشاهده پروفایل

استاد

• Speech Processing
• Language Processing
• Machine Learning
• +۵ مورد دیگر

Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.

Ethan Mollickمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Generative AI Applications
• Entrepreneurship
• +۵ مورد دیگر

Ethan Mollick is the Ralph J. Roberts Distinguished Faculty Scholar, Rowan Fellow, and Associate Professor of Management at the Wharton School of the University of Pennsylvania. He serves as Co-Director of the Generative AI Labs at Wharton, where he researches AI's impact on work, entrepreneurship, and education. His academic background includes a PhD and MBA from MIT's Sloan School of Management and a bachelor's degree from Harvard University. Mollick's research spans artificial intelligence applications in business and education, entrepreneurship dynamics, and innovation management. He explores how generative AI transforms classroom education through simulations like PitchQuest, and investigates startup funding disparities with a focus on gender representation. His work on the 'jagged technological frontier' demonstrates how humans integrate AI capabilities through Centaur and Cyborg approaches. His publications reveal consistent focus on practical AI implementation, with recent work examining prompt engineering for diverse idea generation, AI-assisted teaching strategies, and gender representation tipping points in venture capital. The research shows increasing emphasis on educational applications since 2022, coinciding with the ChatGPT breakthrough. MBA Professor of the Year Award, Poets & Quants (2024) Named to TIME Magazine's Most Influential People in Artificial Intelligence ASQ Award for Scholarly Contribution (2024) Greif Research Impact Award (2020) McGraw-Hill/Academy of Management Innovation in Entrepreneurship Pedagogy Award (2016) As Co-Director of Wharton's Generative AI Labs, Mollick leads prototype development and research on human-AI collaboration. His teaching includes Technology Strategy, Entrepreneurship, and Innovation courses where he implements AI simulations. He has advised numerous organizations and co-founded a startup prior to academia, bringing practical experience to his research on solo versus team ventures. Mollick directs Wharton Interactive's game development initiatives, creating educational simulations including PitchQuest (AI-powered VC simulator), The Saturn Parable (crisis management), and The Entrepreneurship Game. His lab work focuses on creating scalable AI educational tools that provide personalized learning experiences through simulated practice environments.

Cecilia Mascoloمشاهده پروفایل

استاد

• Mobile Systems
• Wearable Computing
• Machine Learning
• +۵ مورد دیگر

Cecilia Mascolo is a Professor of Mobile Systems at the University of Cambridge , specifically in the Department of Computer Science and Technology . She co-directs the Centre for Mobile, Wearable System and Augmented Intelligence and is a Fellow of Jesus College, Cambridge . Her research focuses on mobile systems , machine learning for mobile health , and earable technology . She has been awarded prestigious grants such as the ERC Advanced Research Grant (2019-2025) and the EPSRC Open Research Fellowship (2025-2030). Currently on sabbatical at Harvard University , her work bridges systems and machine learning for health applications. Education: PhD in Computer Science from the University of Bologna, Italy. Previous Affiliation: Faculty at University College London before 2008. Her research spans mobile and wearable systems for health and behavior monitoring, focusing on on-device machine learning , uncertainty-aware models , and audio-based diagnostics . Key areas include federated learning , edge computing , and respiratory disease progression analysis via wearables. She explores earable technology for physiological monitoring, gait analysis, and even toothbrushing tracking using in-ear sensors. Her recent publications highlight advancements in earable-based health monitoring , including heart rate estimation , respiratory rate detection , and ECG analysis using machine learning. She emphasizes longitudinal health data from consumer devices, advocating for scalable diagnostics beyond traditional clinical standards. Scientific Awards: ERC Advanced Research Grant EPSRC Open Research Fellowship Best Paper Award - IEEE Percom 10-Year Impact Award - ACM Ubicomp Computer Laboratory Ring Hall of Fame Best Paper Award Student: Andrea Ferlini - ACM SIGMOBILE Doctoral Dissertation Runner-up She leads the Mobile Systems Research Laboratory , mentoring a team of 15 researchers (postdocs and PhD students), and has graduated over 25 PhD students. Her teaching includes Mobile Health courses at the University of Cambridge, and she serves as Director of Studies for Computer Science at Jesus College.