جستجوی استاد راهنمای دانشگاهی

Shinji Watanabeمشاهده پروفایل

دانشیار

• Automatic Speech Recognition
• Speech Enhancement
• Spoken Language Understanding
• +۳ مورد دیگر

Shinji Watanabe is an Associate Professor at Carnegie Mellon University's Language Technologies Institute and a Courtesy Professor in the Electrical and Computer Engineering department. He holds a Ph.D. (Dr. Eng.) from Waseda University, Japan, and has held research roles at NTT Communication Science Laboratories, Mitsubishi Electric Research Laboratories (MERL), and Johns Hopkins University. His research focuses on automatic speech recognition, speech enhancement, and machine learning for speech processing. Watanabe has published over 300 peer-reviewed papers and received the Best Paper Award at IEEE ASRU 2019. His work emphasizes robust speech processing in challenging environments, multilingual models, and neural audio codecs. He leads the ESPnet toolkit development for end-to-end speech processing systems and contributes to technical committees like IEEE SLTC and APSIPA SLA. Recent research trends include streaming speech systems, universal speech enhancement (URGENT challenges), and fusion of discrete speech units with self-supervised representations. He explores scalable speech foundation models through benchmarks like ML-SUPERB 2.0 and investigates cross-modal audio-visual processing in challenges like MISP 2025. Education : B.S., M.S., Ph.D. (Waseda University) Affiliations : CMU Language Technologies Institute, CMU ECE, Former roles at MERL and Johns Hopkins Key Projects : ESPnet, OpenWhisper-Style Models, URGENT Challenge Frameworks

Ke Wangمشاهده پروفایل

استاد

• Data Mining
• Database Systems
• Data Privacy
• +۳ مورد دیگر

Ke Wang is a Professor in the School of Computing Science at Simon Fraser University . His research focuses on Data Mining , Database Systems , Data Privacy , and Graph and Network Data . He holds a Ph.D. and M.Sc. from the Georgia Institute of Technology (1986 and 1984, respectively). Teaching includes courses like Database Systems II , Introduction to Data Mining , and Special Topics in Databases . He has advised numerous students and alumni, many of whom now work in tech, academia, and industry. Notable awards include the 2013 Faculty of Applied Sciences Research Excellence Award and the ECIR 2019 Best System Paper . His work emphasizes privacy-preserving techniques and has led to contributions like the Introduction to Privacy-Preserving Data Publishing textbook. He has served as a conference chair for major data mining events like SDM 2015/2016 and holds editorial roles in journals like ACM TKDD. His lab, the Database and Data Mining Laboratory , focuses on actionable solutions for real-world data challenges.

Tuuli Toivonenمشاهده پروفایل

استاد

• Geoinformatics
• Spatial Analytics
• Machine Learning
• +۷ مورد دیگر

Tuuli Toivonen is a Professor of Geoinformatics at the Department of Geosciences and Geography, University of Helsinki. She leads the transdisciplinary Digital Geography Lab , which addresses human-scale spatial analytics for sustainable societies. She serves as Vice-Director of Geography Degree Programs (post-2022) and previously held the Director role (2020-2022). After receiving the ERC Consolidator Grant in 2022, she continues advancing open science through active memberships in HELSUS and URBARIA . PhD in Geography (University of Turku, 2006) Specialist Vocational Qualification in Leadership (2023) Life Member, Clare Hall, University of Cambridge (since 2021) Her research focuses on Human-Place Interactions through accessibility/mobility lenses, combining Open Data , Machine Learning , and Spatial Analytics . Key application areas include Urban Geography , Conservation Science , and Governance Policy . Recent publications address Dynamic Cities (2018), Social Media for Conservation (2019), and Environmental Exposure During Travel (2021). Her lab produces datasets like the Helsinki Region Travel Time Matrix series (2014-2023). Scientific Awards: European Open Data Champion (2017) Open Science Price (2017) University of Helsinki Geography Award (2018) She supervises Master's thesis work and serves as Opponent for doctoral defenses across Europe. Her teaching portfolio includes Advanced Geoinformatics and Digital Geographies courses.

Tim Van de Cruysمشاهده پروفایل

مدرس ارشد

• Computational Linguistics
• Natural Language Processing
• Computational Semantics
• +۵ مورد دیگر

Tim Van de Cruys is a Senior Lecturer at the Faculty of Arts, KU Leuven, serving as Head of the Centre for Computational Linguistics (CCL). He maintains significant affiliations with LECTIO (KU Leuven Institute for the Study of the Transmission of Texts, Ideas and Images), Leuven.AI (KU Leuven Institute for Artificial Intelligence), and LILI (KU Leuven Interdisciplinary Language Institute). His work bridges computational linguistics, artificial intelligence, and humanities research with practical applications across multiple disciplines. Dr. Van de Cruys specializes in computational semantics and creative language generation, with particular expertise in applying NLP techniques to historical and classical texts. His research spans multiple domains including: Natural Language Processing for ancient languages (Latin, Ancient Greek) Computational approaches to lexical and compositional semantics Large language models and their applications in humanities research Creative language generation and human-AI collaboration Named entity recognition and disambiguation in historical contexts Non-autoregressive modeling for sequential generation tasks His recent publications demonstrate a strong focus on applying cutting-edge NLP techniques to humanities challenges, particularly in processing ancient languages. He frequently employs transformer models to address named entity recognition, word sense discrimination, and semantic analysis in low-resource language contexts. His work consistently bridges formal linguistic theory with practical computational applications, creating valuable tools for digital humanities scholars. As promotor and co-promotor on numerous research projects extending through 2029, Dr. Van de Cruys supervises PhD students working at the AI-humanities intersection. His current major projects include "Living Corpora" (exploring human-AI collaboration in digital humanities), "Stochastic processes and non-autoregressive models for sequential generation," and "NIKAW" (exploring knowledge networks from classical antiquity). These projects demonstrate his commitment to advancing both theoretical understanding and practical applications of computational linguistics. He teaches various courses including Computational Linguistics, Scripting Languages, Programming for Humanities, Computational Creativity, and AI for Humanities, training students to work at this critical interdisciplinary crossroads. His leadership of the Centre for Computational Linguistics positions him at the forefront of computational linguistics research in Belgium, where he continues to expand the boundaries of what's possible at the intersection of language, computation, and humanistic inquiry.

B. Aditya Prakashمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Aditya Prakash is an Associate Professor and Associate Chair for Academic Affairs in the School of Computational Science and Engineering at Georgia Tech. He holds a PhD from Carnegie Mellon University (2012) and a B.Tech from IIT Bombay (2007). His research focuses on data science, machine learning, and AI applied to epidemiology, healthcare, security, and urban computing. His work has led to impactful tools used by organizations like CDC, ORNL, and Walmart. Notable awards include the NSF CAREER Award (2018) and IEEE's 'AI Ten to Watch' (2017). Education: PhD (Computer Science, CMU, 2012), B.Tech (IIT Bombay, 2007) Research Interests: Epidemic forecasting, network analysis, healthcare informatics, and large-scale data-driven solutions for societal challenges. He has authored over 80 papers and holds two patents. His lab develops methods for disease modeling, urban infrastructure analysis, and cybersecurity. Key projects include the NSF-funded BEHIVE initiative for pandemic prediction and collaborations with MIDAS network for infectious disease modeling. Awards: NSF CAREER, Facebook Faculty Award, IEEE AI Recognition, and multiple best-paper awards. His group advises students across PhD, MS, and undergraduate levels, with notable alumni in academia and industry. Labs & Affiliations: Core faculty at ML@GT (Machine Learning Center) and IDEaS (Institute for Data Engineering and Science). Active in organizing conferences like AAAI, KDD, and SIGMOD.

George H. Chenمشاهده پروفایل

دانشیار

• Trustworthy Machine Learning
• Time-to-Event Prediction
• Survival Analysis
• +۳ مورد دیگر

George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.

Maarten de Rijkeمشاهده پروفایل

استاد

• Information Retrieval
• Recommender Systems
• Retrieval Models and Ranking
• +۸ مورد دیگر

Maarten de Rijke is a Professor at the University of Amsterdam and affiliated with the Innovation Center for Artificial Intelligence (ICAI) . He is a leading expert in Information Retrieval , Recommender Systems , and Machine Learning , with over 500 publications and 10,000 citations. His work spans theoretical and applied domains, including conversational recommender systems , domain generalization , and neural ranking models . Research Pillars: Information retrieval, e-commerce search, learning to rank, and empathetic AI systems Awards: Best Paper (2x), Best Student Paper Community Roles: Organized workshops (MANILA25, SIGIR editions) His recent publications focus on robust recommendation systems , cross-domain contract extraction , and brain signal integration for query refinement. He leads the AIRLab (Amsterdam) and collaborates with institutions like Shandong University and the University of Chinese Academy of Sciences. Scientific Contributions : Over 500 publications in ACM Transactions, SIGIR proceedings, and journals Developed novel frameworks for learning-to-rank and user satisfaction modeling Pioneered research on conversational AI and adversarial attacks in retrieval He actively engages in community service, including organizing conferences and advocating for epilepsy research through initiatives like Emma’s collection box (over €24,448 raised). His work bridges theoretical rigor with real-world impact in search and recommendation technologies.

Amalia Fokaمشاهده پروفایل

استادیار

• Artificial Intelligence Applications in Arts
• Computer Science for Creative Practices
• Digital Interaction
• +۴ مورد دیگر

Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.

Naoki Yoshinagaمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Multilingual and Multimodal NLP
• +۴ مورد دیگر

Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .

Arpit Agarwalمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Human-AI Interaction
• +۴ مورد دیگر

Arpit Agarwal is an Assistant Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology, Bombay. He previously held postdoctoral positions at FAIR Labs (Meta) working with Max Nickel and at the Data Science Institute at Columbia University hosted by Prof. Yash Kanoria and Prof. Tim Roughgarden. He completed his PhD from the Department of Computer & Information Science at the University of Pennsylvania under the guidance of Prof. Shivani Agarwal. His research focuses on the intersection of human behavior and machine learning systems, with particular interest in learning from implicit, strategic, and heterogeneous human feedback. His work spans multiple dimensions of human-AI interaction including understanding long-term dynamics between humans and AI systems, designing responsible AI, and studying misalignment between user preferences and system objectives. His research methodology often combines theoretical machine learning with practical applications in recommendation systems and social AI. His recent publications reveal a strong focus on bandit algorithms, preference learning, and recommendation systems, with increasing attention to responsible AI design and human-centered considerations. His work demonstrates expertise in theoretical machine learning with applications to real-world problems, particularly in understanding how humans interact with and are influenced by AI systems over time. Dr. Agarwal teaches advanced courses including CS767 Theoretical Machine Learning (Autumn 2025) and CS6103 Human-Centered AI: From Learning Models to Responsible Systems (Spring 2025), which covers topics such as AI alignment, learning from pairwise comparisons, crowdsourcing, human-in-the-loop decision making, recommendation systems, interpretability, privacy, fairness, causality, and AI governance.

Xiaohui Yuمشاهده پروفایل

استاد

• Big Data Management and Analytics
• Database Systems
• Machine Learning
• +۳ مورد دیگر

Xiaohui Yu is a Professor and Graduate Program Director in the School of Information Technology at York University. He holds a BSc from Nanjing University, an MPhil from the Chinese University of Hong Kong, and a PhD from the University of Toronto. His research focuses on the intersection of data management and machine learning, including ML-based database systems, large-scale machine learning, and spatio-temporal data analysis in contexts like intelligent transportation systems and social networks. Supported by grants from NSERC and industry partners, his work has been published in top venues such as SIGMOD, VLDB, and TKDE. He serves as an Associate Editor for journals like IEEE TKDE and ACM TKDD, and actively participates in conference program committees. Education: BSc (Nanjing University), MPhil (Chinese University of Hong Kong), PhD (University of Toronto). Research Interests: Big data management, database systems, machine learning, spatio-temporal data analytics, and video query processing. Recent articles emphasize ML-driven database components, efficient video query optimization, and scalable algorithms for large-scale data. His work addresses challenges in query processing, indexing, and real-time systems. Service: Serves on editorial boards (e.g., Information Systems), and chairs/workshops (e.g., Symposium on Data Markets). Active in program committees for SIGMOD, ICDE, and other leading conferences. Advising & Grants: Directs graduate programs and leads research groups. Collaborates with industry on data marketplaces and AI model integration. No specific student names listed, but actively recruits PhD/Master’s candidates.

Ng See Kiongمشاهده پروفایل

استاد

• Data Mining
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

See Kiong Ng serves as Professor of Practice in the Department of Computer Science at the School of Computing, National University of Singapore (NUS), while concurrently holding leadership roles as Director of AI Technology at AI Singapore and Deputy Director of NUS's Institute of Data Science (IDS). His work focuses on translational data science research and developing integrated capabilities for Singapore's Smart Nation initiative through industry and public agency collaborations. His academic credentials include a B.S. in Applied Mathematics (Computer Science Track) from Carnegie Mellon University (1989), an M.S.E. in Computer & Information Science (Artificial Intelligence) from the University of Pennsylvania (1990), and a Ph.D. in Computer Science from Carnegie Mellon University (1998), supported by Singapore's National Computer Board overseas scholarship. Professor Ng's research bridges artificial intelligence with real-world applications across diverse domains. His primary interests span Data Mining, Machine Learning, Natural Language Processing, Smart Cities, and Computational Biology, with emphasis on extracting value from big data through interdisciplinary approaches. He actively pioneers applications in urban systems and bioinformatics, demonstrating data science's transformative potential beyond traditional boundaries. His publication record reveals consistent innovation in algorithm development for complex data challenges, with recent work focusing on taxonomy construction, single-cell genomics analysis, urban transportation systems, and imbalanced time series classification. These contributions demonstrate his commitment to solving practical problems through cutting-edge data science techniques. His major recognitions include: MTI Borderless Award (2014) as Green Growth Working Group project member Minister for National Development's R&D Award 2017 (Distinguished Award) for city-level analytics platform innovation A*STAR Borderless Award (2014) as Urban Systems Initiative team leader MTI Innovation Award (2013) for Strategic Technology Translation in Business Analytics Professor Ng has established significant research infrastructure including founding A*STAR's Data Analytics Department and leading the Urban Systems Initiative. His translational research model emphasizes industry partnerships and practical implementation, particularly in smart city development where he connects data science with urban planning challenges across Singapore's government agencies.

Claudio J. Tessoneمشاهده پروفایل

استاد

• Blockchain Technology
• Cryptocurrency Systems
• Complex Socio-Economic Systems
• +۵ مورد دیگر

Prof. Claudio J. Tessone is a Professor of Blockchain and Distributed Ledger Technologies at the Department of Informatics, University of Zurich. He serves as Head of the Blockchain and Distributed Ledger Technologies group, Chairman of the UZH Blockchain Center, and is incharge of the NetSci Society. His academic background includes a PhD in Physics (Complex Systems) and an Habilitation in Complex Socio-Economic Systems from ETH Zurich. Education: PhD in Physics (2006): Thesis on synchronization in stochastic systems, Universitat de les Illes Balears, Spain Habilitation (2015): Thesis on agent-based modeling of socio-economic systems, ETH Zurich Master in Physics (1999): Thesis on stochastic resonance, Instituto Balseiro, Argentina Research Interests: Prof. Tessone specializes in modeling complex socio-economic and socio-technical systems, with a focus on blockchain-based systems. His work explores crypto-economics, blockchain scalability, decentralized finance (DeFi), and the interplay between micro-level agent behavior and macro-level emergent properties. Notable areas include transaction network analysis in Bitcoin/Ethereum, consensus mechanisms (Proof-of-Stake/Work), and blockchain governance models. Publications Trends: Recent articles emphasize empirical blockchain analysis (e.g., Ethereum microvelocity, Bitcoin mesoscopic structure), DeFi arbitrage strategies, and privacy-preserving blockchain applications in healthcare. His work bridges theoretical agent-based models with real-world blockchain datasets, addressing both technical and socio-economic dimensions of distributed ledger technologies. Grants & Labs: Director of the UZH Summer School on Blockchain and Certificate of Advanced Studies program. Active in interdisciplinary collaborations through the URPP Social Networks (2015–2021) and ETH Zurich’s Systems Design group (2007–2014). Labs/Initiatives: Leads the UZH Blockchain Center, a hub for academic-industry research on blockchain applications in finance, governance, and digital transformation.

• statistical machine learning
• optimization
• sparse methods
• +۵ مورد دیگر

Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.

• Machine Learning
• Data Mining
• Neural Networks
• +۳ مورد دیگر

Professor Irena Koprinska is a faculty member at the School of Computer Science, University of Sydney, specializing in Machine Learning, Data Mining, and Neural Networks. Her research focuses on practical applications in education, health, and energy sectors. She has received multiple awards, including the Dean’s Award for Outstanding Teaching (2017, 2008) and Best Paper Awards at CHI 2019 and other conferences. Koprinska has supervised 11 PhD and over 60 Honours students, many of whom have won prestigious scholarships like the Google Fellowship. Education: PhD and MSc in Computer Science, MEd in Higher Education. Research Interests: Develops algorithms for pattern extraction and predictive modeling in healthcare (e.g., sleep apnea prediction), education (student behavior analysis), and energy (solar power forecasting). Her work bridges algorithmic innovation with multidisciplinary collaboration. Publications: Over 100 articles in top journals/conferences, emphasizing applications of machine learning in health, energy, and education. Recent works include deep learning for sleep apnea and ensemble methods for solar forecasting. Awards: Highlighted awards include the Dean’s Teaching Awards, Best Paper recognitions, and the Thompson Research Fellowship (2018). Advising & Grants: Currently supervises Hanxue Yao. Previously led initiatives like the Data Science for Social Good workshop at ECML PKDD. Served as Associate Head for Research Education and Sub-Dean for Teaching & Learning. Labs/Teams: Leads the Computer Human Adapted Interaction Research Group, focusing on human-centric technology solutions.