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Morgan G. AmesView profile

Adjunct Assistant Professor

• Science and Technology Studies
• Anthropology
• Human-Computer Interaction
• +5 more

Morgan G. Ames is an Assistant Adjunct Professor at the UC Berkeley School of Information and serves as Associate Director of Research for the Center for Science, Technology, Medicine & Society. She chairs the Designated Emphasis in Science and Technology Studies and is affiliated with multiple research centers including the Algorithmic Fairness and Opacity Working Group, the Center for Science, Technology, Society and Policy, and the Berkeley Institute of Data Science. Her educational background includes a Ph.D. in Communication with a minor in Anthropology from Stanford University (2013), an M.S. in Information Management and Systems from UC Berkeley (2006), and a B.A. in Computer Science from UC Berkeley (2004). Prior to her academic career, she worked as a researcher at Google, Yahoo!, Nokia, and Intel. Ames researches the ideological origins of inequality in the technology world, with a focus on utopianism, childhood, and learning. Her work critically examines how technology design practices shape identities and social structures. Current projects include 'Seeing Like a Valley: the Moral Visions of Silicon Valley,' 'Algorithms in Culture,' and 'Countercultures of Technology Use.' She has published extensively on One Laptop per Child, Minecraft, and the social implications of algorithmic systems. Her publication record shows a consistent focus on the cultural dimensions of technology, particularly examining how utopian visions shape technology design and implementation. Recent work increasingly addresses algorithmic systems and their cultural impacts, while maintaining her longstanding interest in educational technology and youth technology practices. Ames has received significant recognition for her scholarship, including: 2020 Best Information Science Book Award 2020 Sally Hacker Prize 2021 Computer History Museum Prize She advises students on interpretive research methods, particularly ethnography, and serves on doctoral committees though cannot be a primary advisor for PhD students. Her research has been supported by multiple interdisciplinary collaborations, including the 'Algorithms in Culture' conference series she co-organized through the Center for Science, Technology, Medicine & Society. Ames leads the 'Seeing Like a Valley' research collective that brings together scholars from across UC Berkeley and Silicon Valley to examine how the region's industrial practices shape moral visions that influence global technological development and social values.

Kanwaljeet S. AnandView profile

Professor

• Pediatric Pain and Stress Neurobiology
• Neonatal and Fetal Pain Assessment
• Biomarker Development
• +3 more

Dr. Kanwaljeet S. Anand is a dual-appointed Professor of Pediatrics (Pediatric Critical Care) and Anesthesiology, Perioperative & Pain Medicine at Stanford University School of Medicine. As director of the Pain/Stress Neurobiology Lab and Jackson Vaughan Critical Care Research Fund, he serves as Editor-in-Chief of Pediatric Research and maintains active membership in Bio-X, MCHRI, and Wu Tsai Neurosciences Institute. Rhodes Scholar with D.Phil from University of Oxford Harvard postdoctoral fellowship and Boston Children's Hospital residency Founded Harmony Health Clinic - Arkansas' largest charitable medical-dental facility A translational researcher with 30+ years of impact, Dr. Anand established the first scientific framework for infant pain perception and developed novel pain assessment methodologies. Current research focuses on: Hair biomarker analysis for stress and social affiliation (cortisol/oxytocin) Machine learning systems for objective pain detection in non-verbal infants Biopsychosocial interventions for stress reduction in disadvantaged youth Neurotoxicity mechanisms of sedatives in developing brains Global NICU opioid usage patterns through the NeoOpioid Consortium His work has yielded over 260 publications and significant advances in: Pediatric pain management protocols Neonatal stress biomarker development Critical care neurobiology insights Community health initiatives AI-driven clinical decision support systems Scientific Recognition 9th Annual 'In Praise of Medicine' Public Address, Erasmus University (2014) Nightingale Excellence Award (2016) Honorary Doctorate from University of Örebro (2019) NIH SBIB-H82 Study Section Chair (2018) Multiple IASP and American Pain Society awards Swedish Academy of Medicine's Nils Rosén von Rosenstein Award (2009) St. Jude Endowed Chairholder (2010) As mentor to Med Scholar Anjali Gupta and advisor to numerous professional bodies, Dr. Anand maintains active clinical leadership in Pediatric Intensive Care while advancing computational approaches to pain detection through collaborations with Stanford's AI researchers.

• Multimodal Learning
• Multilingual NLP
• Vision-Language Models
• +4 more

Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.

• Machine Learning
• Artificial Intelligence
• Combinatorial Optimization
• +13 more

Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .

Alan RitterView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +3 more

Alan Ritter is an Associate Professor at the School of Interactive Computing, Georgia Institute of Technology (Georgia Tech), affiliated with the Machine Learning Center (ML@GT). His research focuses on natural language processing (NLP), machine learning, and robust computational models. He completed his Ph.D. at the University of Washington and a postdoctoral fellowship at Carnegie Mellon University's Machine Learning Department. Education : - Ph.D. in Computer Science, University of Washington - Postdoctoral Research, Machine Learning Department, Carnegie Mellon University Research Interests : Ritter's work emphasizes developing models that operate across domains and languages with minimal supervision. His projects include systems analyzing social media data for cybersecurity threats, cultural bias in LLMs, and privacy-preserving dialogue agents. His group also explores efficient fine-tuning of language models and cross-lingual information extraction. Recent Activities & Awards : - NSF CAREER Award - Amazon Research Award - Best Social Impact Paper Award (ACL 2024) - Program Chair for NAACL 2025 Advising & Students : Ritter advises Ph.D. and M.S. students in Georgia Tech's ML and CS programs. Notable advisees include Yang Chen (Ph.D. 2024, now at NVIDIA) and Fan Bai (Ph.D. 2023). Labs & Affiliations : - Machine Learning Center (ML@GT) - Collaborations with institutions like AI2, Stanford, and Microsoft Research

Mykola PechenizkiyView profile

Professor

• Data Science
• Machine Learning
• Predictive Analytics
• +5 more

Mykola Pechenizkiy is a Full Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), holding the Data Mining Chair. He also serves as an Adjunct Professor in Data Mining for Industrial Applications at the University of Jyväskylä. His research focuses on predictive analytics, data mining, and responsible AI, addressing real-world challenges in industry, healthcare, and education. He leads the Customer Journey research program at the Data Science Center Eindhoven, emphasizing ethical and transparent analytics. Academically, he holds a PhD from the University of Jyväskylä (2005) and has held visiting researcher positions at institutions like Columbia University and NYU. He has co-authored over 300 peer-reviewed publications and serves on editorial boards and committees for leading conferences (e.g., AAAI, IJCAI). He is the President of the International Educational Data Mining Society (IEDMS). His research interests include concept drift adaptation, sparsity techniques in neural networks, and fairness-aware AI. He has led projects such as the TKI PPS KPN Smart Two initiative and collaborates with industries like ASML, Philips, and Rabobank. His work contributes to UN SDGs, particularly in sustainable development through AI-driven solutions. Awards: Best Demo Paper Award (IEEE ICDE 2023), Best Paper Awards (ALA 2022, LoG 2022), and SensorKDD 2009 recognition. Grants/Projects: Active projects include TKI PPS KPN Smart Two (2019–2025) and Smart One W&I TKI KPN Flagship (2018–2022). Labs/Teams: Affiliated with EAISI Health, SIKS Scientific Board, and the University of Waikato’s AI Institute.

Tara BoroushakiView profile

Assistant Professor

• Wireless Networking
• Cyber-Physical Systems
• Robotics
• +3 more

Tara Boroushaki is an incoming Assistant Professor in Electrical & Computer Engineering at Yale University. She completed her Ph.D. at MIT (expected May 2025), advised by Prof. Fadel Adib, with a focus on sensing and mobile technologies. Her research spans wireless networking, robotics, and human-computer interaction, emphasizing multi-modal sensing for environmental perception. Key achievements include the Microsoft Research PhD Fellowship (2022–2024) and the IEEE RFID '23 Best Paper Award. Her work on RF-based 'X-ray vision' has been featured in TEDxMIT and media outlets like the BBC and World Economic Forum. She co-founded Cartesian Systems, deploying sensing technologies in retail and supply chain. Research interests include non-line-of-sight perception, RFID localization, and robotic grasping. She has developed systems like FuseBot and RFusion, highlighted as transformative in MIT's '103 Ways to Make the World Better' initiative.

Luca DemetrioView profile

Assistant Professor

• Adversarial Machine Learning
• Malware Detection
• Cybersecurity
• +3 more

Luca Demetrio is an Assistant Professor at the University of Genoa, Italy, specializing in adversarial machine learning and cybersecurity. Previously, he was a Post-doctoral Researcher at the PRA Lab within the Department of Electrical and Electronic Engineering at the University of Cagliari. He holds bachelor's (2015), master's (2017), and Ph.D. (2021) degrees from the University of Genova, with his doctoral thesis focusing on formalizing evasion attacks against security detectors. His research emphasizes enhancing the robustness of machine learning models against adversarial attacks, particularly targeting malware detectors, SQL injection defenses, and Windows security systems. He leads the development of SecML Malware, a Python library for generating adversarial Windows malware, and contributes to the SecML framework. His work has been published in top-tier journals like ACM TOPS and IEEE TIFS. Key research interests include adversarial example generation, malware analysis, and cybersecurity defense mechanisms. He has explored query-efficient attacks on phishing detectors, certified adversarial robustness via randomized smoothing, and robust synthetic data-driven threat detection. His recent studies (2023–2025) address challenges in hardening machine learning models against evasion attacks, adversarial SQL injection countermeasures, and securing autonomous driving systems from adversarial reinforcement learning attacks.

Fethiye Irmak DoganView profile

Researcher

• Human-Robot Interaction
• Machine Learning
• Deep Learning
• +4 more

Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.

Liu LiliView profile

Lecturer

• Machine Learning
• Computer Vision
• Multi-modal Learning
• +7 more

Liu Lili is a Lecturer (Educator Track) in the Department of Computer Science at the School of Computing, National University of Singapore. She holds a Ph.D. from Nanyang Technological University and a Master's in Computer Science from Shanghai University. Prior to NUS, she served as a Senior Research Scientist at Singapore Polytechnic and a Scientist at A*STAR's Institute of High-Performance Computing. Her research focuses on Machine Learning, Computer Vision, and Multi-modal Learning, with applications in FinTech, Social Media Analysis, and Algorithms & Theory. Notable projects include AI-driven coating inspection systems for marine assets and behavioral competency assessment tools for navigational safety. She has contributed to robotics for construction quality assessment and interactive virtual environments for rehabilitation. Liu's publications span AI applications in finance, robotics, and material science, reflecting her expertise in bridging theoretical computer science with practical industrial solutions. Her work emphasizes automation, anomaly detection, and multi-modal data integration.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +9 more

Professor Mounim A. El Yacoubi holds positions at Institut Polytechnique de Paris, Institut Mines-Télécom, and Telecom SudParis. His research focuses on AI, machine learning, and deep learning applied to e-Health (neurodegenerative disease detection, diabetes management), biometrics (gait, vein, and handwriting recognition), and smart systems (agriculture, surveillance, robotics). He leads the SAMOVAR CNRS Lab and has supervised 17 PhDs and 30+ master's students. Education: PhD (1996, Université de Rennes 1), HDR (2014, Paris-Saclay University). Experience: Senior Researcher at Parascript (2001–2008), Visiting Scientist at CENPARMI (1997–1998), Associate Professor at PUCPR (1998–2001). Research Interests: AI applications in healthcare, biometrics, pattern recognition, and smart technologies. Recent work includes Alzheimer’s detection via handwriting analysis, diabetes prediction using PPG signals, and palm/vein recognition systems. Grants & Leadership: Program Chair of ICPRAI 2022, ICCPRA 2024. Editor of IEEE Access and journals on cyber-physical intelligence. Authored books on Pattern Recognition and AI.

Zhongying DengView profile

Research Fellow

• Medical Imaging
• Computer Vision
• Deep Learning
• +3 more

Zhongying Deng is a Research Fellow in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work focuses on advancing medical imaging technologies and computer vision through deep learning and domain adaptation techniques. Key contributions include developing benchmark datasets like TrafficCAM and TrafficMOT for traffic analysis, A-Eval for abdominal organ segmentation, and foundational models for medical AI such as GMAI-VL. His research bridges theoretical advancements in neural networks and practical applications in healthcare and transportation. His research interests span image segmentation, domain adaptation, neural network architectures, and multimodal data integration. Notable projects include FCN+ for enhanced convolutional networks and Brain Foundation Models for neurodegenerative disease analysis. Deng collaborates extensively on interdisciplinary projects, combining mathematical modeling with computational tools to address real-world challenges in medical diagnosis and autonomous systems. Publications emphasize scalable medical image analysis frameworks (e.g., STU-Net, Sa-med2d-20m) and robust domain adaptation methods for cross-dataset performance. His datasets and models are widely recognized for enabling reproducible research and advancing state-of-the-art performance in critical areas like MRI reconstruction and multi-organ segmentation.

• Computer Networks
• Cybersecurity
• Distributed Systems
• +4 more

Saurabh Bagchi is a Professor at Purdue University, West Lafayette, USA. He holds a PhD in Computer Science from the University of Illinois Urbana-Champaign (2001). His research focuses on distributed systems security, networking, and embedded systems. Key areas include IoT security, cyber-physical systems resilience, and machine learning applications in edge computing. Bagchi's work spans theoretical and applied domains, addressing challenges in distributed algorithms, fault tolerance, and secure communication protocols. His contributions to firmware analysis, serverless computing optimization, and anomaly detection in industrial IoT systems have been widely recognized. He has published over 300 papers in top-tier conferences and journals such as IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Sensor Networks, and CVPR. He collaborates with researchers in academia and industry to advance resilient networked systems, including projects funded by NSF and industrial partnerships. His lab explores cutting-edge topics like federated learning security, edge computing architectures, and game-theoretic approaches to cyber defense.

David BlaauwView profile

Professor

• Ultra-Low Power Circuits
• IoT and Sensor Networks
• RF Communication
• +7 more

David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)

Gunnar BlohmView profile

Assistant Professor

• Sensorimotor Neuroscience
• Clinical Medicine
• Neurology
• +3 more

Gunnar Blohm is an Assistant Professor in the Department of Biomedical and Molecular Sciences at Queen's University, affiliated with the School of Medicine and Faculty of Health Sciences. His research focuses on sensorimotor neuroscience, particularly 3D sensorimotor control, eye-hand coordination, and computational modeling of neural processes. He holds a Ph.D. from Université Catholique de Louvain and has held postdoctoral positions at York University and his alma mater. Cross-appointed to the School of Computing, Department of Psychology, and Department of Mathematics and Statistics, he is also Vice-Director of the Connected Minds initiative. His research integrates behavioral experiments, brain imaging (MEG/EEG), and patient studies to understand how sensory information is transformed into goal-directed actions. Key areas include visuomotor transformations, multisensory integration, and Bayesian processes in neural computations. Blohm leads the Computational Sensorimotor Neuroscience Lab, emphasizing collaborative projects like Neuromatch Academy and contributions to open science initiatives. Affiliated with Queen's Centre for Neuroscience Studies and Ingenuity Labs, his work bridges computational approaches with clinical applications, aiming to develop frameworks for understanding brain dysfunction and clinical tools. His recent articles explore topics like saccade dynamics, pupil responses, and generative adversarial collaborations in scientific discourse.