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Ian LaneView profile

Associate Professor

• Natural Language Processing
• Speech Recognition
• Machine Translation
• +5 more

Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.

• Data Mining
• Machine Learning
• Natural Language Processing
• +5 more

Luca Cagliero is an Associate Professor in the Department of Control and Computer Engineering at Politecnico di Torino (Polytechnic University of Turin), Italy. His research spans multiple domains within computer science, with particular expertise in data mining, machine learning, natural language processing, and multimodal analysis. He has established a prolific research career with over 150 publications spanning from 2009 to the present, demonstrating consistent scholarly productivity. Dr. Cagliero's research interests focus on the intersection of artificial intelligence and practical applications. His work addresses fundamental challenges in data mining, information retrieval, and educational technology, with recent publications showing increasing emphasis on large language models, multimodal analysis, and explainable AI. He has made significant contributions to text summarization techniques, database systems, and applying machine learning to educational contexts. His recent publications (2023-2025) demonstrate a clear research trajectory toward multimodal AI systems, with particular attention to the integration of vision and language processing. His work spans theoretical contributions in machine learning methods as well as practical applications in educational technology, social media analysis, and document understanding. The breadth of his collaborations across different application domains indicates a versatile research profile that bridges theoretical and applied computer science. Dr. Cagliero has mentored numerous researchers who have become his frequent collaborators, including Lorenzo Vaiani, Moreno La Quatra, and Davide Napolitano. His work has appeared in top-tier venues including ACL, IEEE Transactions on Knowledge and Data Engineering, and Expert Systems with Applications, reflecting the high quality and impact of his research contributions.

• Machine Learning
• Explainable AI
• Fairness in AI
• +3 more

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.

Murat AkcacayaView profile

Associate Professor

• Machine Learning
• Statistical Signal Processing
• Optimization
• +5 more

Murat Akcacaya is an Associate Professor in the Electrical and Computer Engineering Department at the University of Pittsburgh and serves as the director of the Signal Processing and Statistical Learning (SPSL) Laboratory. He is also a Senior Member of IEEE. His research spans multiple disciplines with significant contributions to both theoretical and applied signal processing. PhD in Electrical and Systems Engineering, Washington University, 2010 MSc in Electrical and Systems Engineering, Washington University, 2010 BSc in Electrical and Electronics Engineering, Middle East Technical University, 2005 Dr. Akcacaya's research focuses on machine learning, statistical signal processing, and optimization with diverse applications. His work develops machine learning methods for human-computer interaction, human-in-the-loop systems, and assistive technologies, as well as techniques to analyze ambulatory and multimodal physiological data. He also creates cognitive systems for nonstationary environments and probabilistic models for complex physical systems. His space research aligns with these interests, particularly in physiological data analysis and cognitive systems for nonstationary environments. Analysis of his recent publications reveals a strong trend toward medical applications of signal processing and machine learning, particularly in cardiology (ECG analysis), neurorehabilitation (stroke recovery assessment), and autism research (EEG analysis). His work consistently integrates multimodal data fusion approaches, combining techniques from radar signal processing with biomedical applications. There's also a notable emphasis on practical implementation of theoretical concepts in real-world healthcare settings. Dr. Akcacaya has secured significant research funding from multiple federal agencies including the National Science Foundation (NSF), National Institutes of Health (NIH), Air Force Office of Scientific Research (AFOSR), and Department of Energy (DOE), supporting his diverse research portfolio. As director of the SPSL Laboratory, he mentors students working on cutting-edge projects at the intersection of signal processing, machine learning, and various application domains. The Signal Processing and Statistical Learning Laboratory serves as the hub for Dr. Akcacaya's research activities, providing a collaborative environment for developing innovative signal processing techniques and their applications across multiple domains including healthcare, aerospace, and human-computer interaction systems.

• Computer Architecture
• Parallel Programming
• Compilers
• +6 more

Martha A. Kim is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science. She serves as a Member of the Data Science Institute, Center Co-Chair of the Center for Computing Systems for Data-Driven Science, and Chair of the Computer Engineering Program. Professor Kim's research focuses on computer architecture with particular emphasis on the hardware-software interface. Her work develops tools and designs that help computer systems operate efficiently and intuitively, including database accelerators for energy-efficient queries, techniques for optimizing parallel software to better utilize hardware resources, and improving the versatility of specialized hardware accelerators. She leads the ARCADE Lab where she conducts research in computer architecture, parallel programming, compilers, and low-power computing. Her publication record shows consistent output at top-tier conferences including ASPLOS, MICRO, MEMOCODE, ISLPED, DAC, DaMoN, CC, and CGO, with recent work spanning video transcoding in the cloud, pipelined dataflow circuits, thermal monitoring, and database acceleration. Professor Kim has received several prestigious awards: 2013 Rodriguez Family Award 2015 Edward and Carole Kim Faculty Involvement Award 2013 NSF CAREER award 2016 Anita Borg Early Career Award She currently advises three doctoral students (Martha Barker, Thomas Repetti, and Andrea Lottarini) and has previously graduated PhD students Melanie Kambadur (2016) and Lisa Wu (2014). Her research is supported by C-FAR, DARPA, Google, Intel, and NSF. Professor Kim teaches Fundamentals of Computer Systems, Computer Architecture, and Principles and Practice of Parallel Programming courses at Columbia University. The ARCADE Lab provides opportunities for students to work on cutting-edge research in hardware-software interface optimization, with applications in energy efficiency, database acceleration, and parallel computing systems.

Umakishore RamachandranView profile

Professor

• Distributed Systems
• Edge Computing
• Sensor Networks
• +5 more

Umakishore Ramachandran is a Professor in the School of Computer Science at Georgia Institute of Technology's College of Computing, where he directs the Embedded Pervasive Lab. He received his Ph.D. from the University of Wisconsin-Madison in 1986 and has led transformative initiatives including the Online MS in Computer Science (OMSCS) program. His research spans distributed systems, edge computing, and real-time sensor networks, with applications in smart surveillance and connected vehicles. His research interests include architectural design of parallel/distributed systems, large-scale situation awareness using camera networks, cloud-edge continuum optimization, and latency-sensitive applications for geo-distributed infrastructures. Recent work focuses on elevating edge computing to parity with cloud resources. His publications show strong emphasis on edge computing innovations (MicroEdge, FogStore), real-time video analytics (EVA, ClairvoyantEdge), and adaptive mobile systems (Foresight). Trends include multi-tier architectures, quality-of-experience optimization, and scalable processing for IoT workloads. Major Awards: IEEE Fellow (2014) NSF Presidential Young Investigator (1990) ACM/IFIP Middleware Best Paper (2022) 3x College of Computing Dean's Awards He has advised 40+ PhD students, with recent graduates at Google, Microsoft, and academia. Current NSF/CPS grants support his work on geo-distributed latency-sensitive applications. He co-leads the STAR Center and Samsung-funded embedded software programs. His Embedded Pervasive Lab develops systems like Stampede (stream processing) and DFuse (sensor fusion), with testbeds in the Aware Home and transportation networks. Teams collaborate with Intel, Microsoft, and Bosch on edge-AI deployments.