Shang-Tse Chen is an Associate Professor at the Department of Computer Science and Information Engineering and Graduate Institute of Networking and Multimedia , National Taiwan University . He leads the NTU AI Security Lab , focusing on applied and theoretical machine learning with emphasis on cybersecurity, adversarial ML, and ML privacy/fairness. Education: PhD in Computer Science (Georgia Tech, 2019), BSc in CSIE (NTU, 2010) Awards: K. T. Li Young Researcher Award (2025), IBM PhD Fellowship (2018), KDD Best Student Paper Runner-Up (2016), NSF SaTC Grant (2017-2021) His research spans adversarial ML, certified defenses, model inversion attacks, and intersection with differential privacy/fairness. Recent work includes physical adversarial attacks on object detectors and practical defenses using JPEG compression. He teaches courses like Security and Privacy of Machine Learning and Introduction to Medical Informatics . Key publication trends show focus on adversarial robustness (ICML/NeurIPS/ICLR), cybersecurity applications (ACSAC), and ML fairness (ACL/EMNLP). Collaborations include industry partnerships with Intel Labs and Symantec. Scientific Awards: K. T. Li Young Researcher Award (2025) ACM TiiS Best Paper Honorable Mention (2020) IBM PhD Fellowship (2018) KDD Audience Appreciation Award Runner-Up (2018) Symantec Fellowship Runner-Up (2016) KDD Best Student Paper Runner-Up (2016) NSF Grant (2017) He advises 13 current students (PhD/MS/Undergrad) and has mentored alumni now at CMU/UC Berkeley. The lab actively recruits postdocs and students across levels.
Chang S. (CS) Nam is a Professor of Industrial and Systems Engineering at North Carolina State University, with affiliations in the UNC/NCSU Joint Department of Biomedical Engineering and the Department of Psychology. He holds editorial roles as Editor-in-Chief of Brain-Computer Interfaces journal and is a Fellow of the Human Factors and Ergonomics Society. His research focuses on advancing human-centered technologies through neuroergonomics, brain-computer interfaces (BCIs), and rehabilitation engineering, supported by grants from NSF, AFRL, AFOSR, and the Laboratory for Analytic Sciences. Education: Ph.D. in Industrial Engineering, Virginia Tech (2003) MSIE in Industrial Engineering, State University of New York at Buffalo (2000) Research Interests: His work spans neurorehabilitation, haptic interaction design, and BCI applications, aiming to bridge human cognitive and neural processes with technological systems. Key areas include adaptive human-computer interaction, EEG-based emotion recognition, and spatial augmented reality for skill training. Grants & Awards: Recipient of NSF CAREER Award (2010), Air Force Summer Faculty Fellowship (2018), and the Leland S. Kollmorgen Spirit of Innovation Award (2019) Editor of Brain-Computer Interfaces Handbook: Technological and Theoretical Advances (CRC Press) Advising & Labs: Advises students like Lingchao Mao and Priscille Koutouan Leads the Brain-Computer Interface and Neuroergonomics Lab, developing BCI systems for communication in vegetative states and neurorehabilitation
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Tatsunori Hashimoto is an Assistant Professor of Computer Science at Stanford University, specializing in artificial intelligence, machine learning, and natural language processing. His research focuses on developing robust and ethical language models, addressing challenges in bias mitigation, fairness, and transparency. He leads projects like the Stanford Alpaca, exploring instruction-following models and their societal impacts. Key research interests include generative models, AI ethics, and privacy-preserving techniques. His recent work examines language model behaviors, security risks, and the societal implications of AI systems. Notable contributions include frameworks for auditing language models, improving factual accuracy, and reducing disparities in speech recognition. His publications highlight advancements in long-context processing, few-shot learning, and automated benchmarking. He emphasizes practical applications of AI while addressing dual-use concerns and ensuring alignment with human values.
David Salesin is an Affiliate Professor in the Department of Computer Science & Engineering at the University of Washington and a Principal Scientist/Director at Google Research since 2019. He has held academic roles at Cornell University (Visiting Assistant Professor, 1991-92) and guest professorships at Zhejiang University. His career spans academia and industry, including leadership at Adobe's Creative Technologies Lab (2005-17) and Microsoft Research (1999-2005). PhD, Stanford University (1991) Sc.B., Brown University (1983) His research focuses on computer graphics, particularly non-photorealistic rendering, digital typography, color science, and adaptive document layout. He pioneered techniques in image-based rendering, pen-and-ink illustration, and facial animation, with applications in multimedia and user interface design. Article Trends : His work bridges procedural content generation, 3D visualization, and artistic computing, emphasizing user-driven tools for creative industries. Key subfields include texture advection, multiresolution modeling, and real-time camera control for virtual cinematography. Scientific Awards : ACM Fellow (2002) ACM SIGGRAPH Achievement Award (2000) Carnegie Foundation Professor of the Year (1998) NSF Presidential Faculty Fellow (1995-98) Alfred P. Sloan Research Fellowship (1995-97) Numerous industry grants and lab donations He has advised over 30 PhD and Master's students, including leaders at Microsoft, Pixar, and Google. His labs at UW and Adobe focused on graphics, imaging, and creativity tools.
Dr. Ian Stavness is a Professor and Department Head in the Department of Computer Science at the University of Saskatchewan. His research focuses on interdisciplinary applications of computer science, including deep learning in agriculture, biomedical computation, and 3D display technologies. He leads the Biological Imaging & Graphics (BIGLAB) laboratory, which develops tools for plant phenotyping and musculoskeletal modeling. Education: Ph.D. in Computer Engineering, University of British Columbia, 2010 M.A.Sc. in Computer Engineering, University of British Columbia, 2006 B.Sc. in Computer Science & B.Eng. in Electrical Engineering, University of Saskatchewan, 2004 Research Interests: Deep Learning for Plant Phenomics & Agriculture 3D Displays and VR/AR Technologies Musculoskeletal Biomechanical Simulation (OpenSim, ArtiSynth) Computer Vision and Image Analysis Awards: ACM CHI 2019 Honourable Mention ACM VRST 2018 Polyphony Digital Award Key Projects: Deep Plant Phenomics Platform Parametric Human Project (Digital Human Modeling) P2IRC (Plant Phenotyping & Imaging Research Center)
Yuuko Uchikoshi Tonkovich is a Professor at the School of Education, University of California, Davis, where she has served since 2004, advancing from Assistant to Associate and then to full Professor. Her work centers on language and literacy development in young dual language learners, with a focus on bilingualism, early literacy, and the role of family and media in language acquisition. Her research interests include: Early Childhood Development Multilingual Learners and Bilingual Education Language Acquisition and Literacy Parent-Child Interaction and Home Language Environment Quantitative Analysis of Educational Outcomes Educational Television and Multimedia Her recent publications span topics such as cognitive distancing in parent-child reading, morphological awareness in bilingual readers, and the impact of teacher language use in multilingual classrooms. The research consistently emphasizes longitudinal, cross-cultural, and interdisciplinary approaches, particularly focusing on Chinese American and Mexican American immigrant families. Her scientific awards include: AERA Vocabulary SIG Notable Vocabulary Researcher Award (2025) Foundation for Child Development Young Scholars Program Grant International Reading Association Dissertation Fellowship and Finalist Award Harvard Graduate School of Education Fellowships She has advised numerous research projects and secured significant funding from NIH/NICHD, the Foundation for Child Development, and other organizations. She teaches courses such as Language Development, Early Literacy, and Research on Text Comprehension. She is actively involved in national initiatives, serving on the Technical Advisory Group for the First 5 California Dual Language Learner Study and as Associate Editor of Applied Psycholinguistics . She leads the Language & Literacy Development Lab at UC Davis, which investigates the cognitive, linguistic, and socio-emotional development of dual language learners.
Devi Parikh is an Associate Professor at the School of Interactive Computing, Georgia Institute of Technology, and a Research Director at Meta’s FAIR lab. Her research focuses on generative models, AI for creativity, computer vision, and natural language processing. Education: B.S. in Electrical and Computer Engineering from Rowan University (2005), M.S. and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University (2007, 2009). Research interests include embodied AI, human-AI collaboration, and creative applications of AI. She has held visiting positions at Cornell, MIT, CMU, and others. Awards include NSF CAREER Award, IJCAI Computers and Thought Award, and multiple fellowships. Led development of Habitat , a platform for embodied AI research, and contributed to the Open Catalyst Project for renewable energy storage.
Aykut Erdem is an Associate Professor of Computer Engineering at Koç University, affiliated with the KUIS AI Center. He earned his PhD, MSc, and BSc in Computer Engineering from Middle East Technical University (METU), with visiting researcher experiences at Virginia Tech (2004) and MIT (2007). His research focuses on learning-based approaches for visual data understanding, including image editing, visual saliency estimation, and vision-language integration. Research Interests: Vision and Graphics, Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, Generative Artificial Intelligence. Recent work includes text-guided image/video editing, diffusion models for object removal, and GAN-based frameworks for domain adaptation. Scientific Awards: Young Scientist Award (BAGEP 2021) by Science Academy in Computer Engineering Best Paper Award at 5th Multimodal Learning and Applications Workshop (2022) Collaborations and Funding: Principal investigator for TUBITAK 1001 project on generative AI for visual data (2021-2024), Adobe Research Gift (2023), and co-investigator for multiple TUBITAK grants. Serves as Associate Editor for IEEE Transactions on Image Processing (2022-present).
Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools
Erkut Erdem is a Professor in the Department of Computer Engineering at Hacettepe University, where he leads the Computer Vision Laboratory (HUCVL). His research focuses on computer vision and machine learning, particularly on incorporating different kinds of context (spatial, temporal and cross-modal) into visual processing across all levels from low to high-level vision. He received his Ph.D. (2008), M.Sc. (2003), and B.Sc. (2001) from Middle East Technical University. Prior to joining Hacettepe University in 2010, he completed a post-doctoral fellowship at Ecole Nationale Supérieure des Télécommunications (2009-2010) and held visiting researcher positions at UCLA (2007) and Virginia Tech (2004). His current research interests include Visual Saliency Prediction, Automatic Image Description, Video/Photoset Summarization, Image Filtering, and Image Editing. Recent work has focused on multimodal learning with video-language models, diffusion-based image editing, and event-based vision for low-light conditions. His research has been published in top venues including NeurIPS, ICLR, ICCV, SIGGRAPH, and ACL. He has received significant recognition including The Young Researcher Award from Turkish Academy of Sciences and being named a 2022 Outstanding Associate Editor of IEEE Transactions on Multimedia. He has secured multiple research projects funded by TUBITAK and received gift funds from Adobe Research for text-guided image synthesis work. Current Teaching: BBM202: Algorithms, AIN434/BBM444: Fundamentals of Computational Photography Graduate Supervision: 6 current Ph.D. students, numerous recent graduates including Burak Ercan (2024) and Aysun Kocak (2023) Professional Affiliations: Co-affiliated with Koç University and İş Bank AI Center (KUIS AI)
Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.
Prof. Jochen Hartmann holds the Digital Marketing professorship at the TUM School of Management (Munich). Previously, he was an assistant professor at the University of Groningen's School of Business and Economics and worked as a management consultant at McKinsey & Company. He earned his doctorate from the University of Hamburg and coordinated the DFG research group FOR 1452 (2019-2022). His research focuses on digital marketing and machine learning, particularly analyzing unstructured data (computer vision, NLP) and generative AI. Key themes include social media, algorithmic fairness, diversity in advertising, and human-machine interactions. Education: Ph.D. in Business Administration (University of Hamburg), Management Consulting experience at McKinsey & Company. Research interests combine cutting-edge AI techniques with marketing challenges. Recent work explores generative AI's impact on advertising, algorithmic bias in finance, and visual search innovations. His text/image mining studies rank among top-cited articles in marketing journals like the International Journal of Research in Marketing and Journal of Marketing Research. Awards include the EMAC-Sheth Sustainability Award, Lindau Nobel Laureate Meetings' Young Economist distinction, and multiple best dissertation awards. Grants: Led DFG-funded research group (2019-2022). Affiliated with Columbia Business School (visiting scholar) and Mannheim Business School (lecturer in machine learning). Labs/Teams: Active in interdisciplinary research groups focusing on AI applications in marketing and business analytics.
Franz Franchetti is the Kavčić-Moura Professor of Electrical & Computer Engineering at Carnegie Mellon University. He serves as Associate Dean for Research and Director of the Engineering Research Accelerator at CMU. Education: Ph.D. in Computational Mathematics (Vienna University of Technology, 2003) M.Sc. in Technical Mathematics (Vienna University of Technology, 2000) His research interests focus on automatic performance tuning and program generation for emerging parallel computing platforms , including multicore CPUs , GPUs , and 3DIC chip design . He leads the SPIRAL effort to automate highly optimized software libraries and explores domain-specific compiler transformations in HPC applications for smart grids and material sciences . Recent work extends SPIRAL to quantum computing . The scientific awards Franchetti has received include the Gordon Bell Prize (2006) , HPC Challenge Class II Award (2010) , and the CIT Dean's Early Career Fellowship (2013) . He and his students have won multiple Best Paper Awards at HPEC, DAC, and ISPA ACM TODAES Best Paper (2014) Student Research Competition wins (PACT 2024, CGO 2023) Franchetti has advised students like Richard Veras and Thom Popovici . He has secured significant grants from agencies such as DARPA, DOE, NSF, and industry partners (Intel, NVIDIA, Mercury). He co-founded SpiralGen, Inc. and holds leadership roles in organizations like ASciNA Western Pennsylvania and as Honorary Consul of Austria in Pittsburgh.
Dr. Andrew Hines is a Researcher at the School of Computer Science, University College Dublin, specializing in machine learning applications for signal processing in speech, audio, and video domains. His work focuses on Quality of Experience (QoE) modeling, speech quality assessment, and immersive media analysis. He has held leadership roles in European COST Actions like Qualinet and CryptoAction, and previously worked in industry as a Director of Engineering. University: University College Dublin Role: Director of Research, Innovation and Impact Key Collaborations: IEEE (Senior Member), Audio Engineering Society (Ireland) Research interests center on machine learning for QoE optimization, audio-visual integration, and healthcare applications like heart sound classification and stroke rehabilitation. His recent publications explore self-supervised learning, neural speech codecs, and contextual factors in speech/audio quality assessment. Scientific contributions include awards like IEEE Senior Membership, and his work spans both academic research and industrial engineering in finance and aviation sectors. He leads the QxLab research team at UCD and develops open-source platforms such as WARP-Q and AQP for quality metrics.
