Find Academic Supervisors

Brian KulisView profile

Associate Professor

• Machine Learning
• Statistics
• Large-scale Data Analysis
• +5 more

Brian Kulis is an Associate Professor at Boston University with appointments in the Department of Electrical and Computer Engineering, Computer Science, Systems Engineering, and the Faculty of Computing and Data Sciences. He holds the Peter J. Levine Career Development Professorship and has previously been an Amazon Scholar at Alexa AI (2019–2023) and an assistant professor at Ohio State University (2012–2015). His research focuses on machine learning, including large-scale optimization, metric learning, deep learning, Bayesian methods, and applications in audio and visual data analysis. He earned his PhD in Computer Science from the University of Texas at Austin (2008) and a BS in Computer Science and Mathematics from Cornell University. Key awards include the NSF CAREER Award (2015), CVPR Best Student Paper (2008), and ICML Best Student Paper (2007, 2005). His work spans publications in top venues like CVPR, NeurIPS, ICML, and ECCV, emphasizing scalable algorithms and domain adaptation. Current research explores metric learning, adversarial audio augmentation, and HPC anomaly detection. He advises multiple PhD students and collaborates on grants such as the NSF Traineeship for Sustainable Energy Solutions (2024). He teaches advanced courses in machine learning, deep learning, and data structures. His lab focuses on foundational and applied ML challenges, with affiliations in the Intelligent, Autonomous & Secure Systems group. Recent service includes senior area chair roles at AAAI, NeurIPS, and ICML.

Gemma BoledaView profile

Research Professor

• Computational Linguistics
• Linguistic Theory
• Semantics
• +3 more

Gemma Boleda is an ICREA Research Professor at Universitat Pompeu Fabra in Barcelona, Spain, where she co-directs the Computational Linguistics and Linguistic Theory (COLT) research group. Her research focuses on understanding how humans convey meaning through language, investigating the formal properties that support communication, and exploring how languages are shaped by cognitive and communicative factors. Her primary interests include lexical semantics, cross-linguistic variation, and the integration of linguistic theory with computational methods. She employs interdisciplinary approaches combining linguistics, artificial intelligence, and cognitive science, utilizing large-scale data analysis to study universal patterns and variations across languages. Boleda's publications demonstrate a consistent focus on computational semantics, lexical variation, and language evolution. Her recent work explores the intersection of symbolic and neural approaches to language processing, lexical creativity across development and evolution, and computational models of semantic phenomena like colexification and polysemy. She teaches Computational Semantics in the Master's in Theoretical and Applied Linguistics program and has secured significant research funding including ERC Starting Grants. Her work has contributed valuable linguistic resources such as the ManyNames dataset and Database of Catalan Adjectives.

Cody BuntainView profile

Assistant Professor

• Crisis Informatics
• Online Political Engagement
• Information Quality
• +3 more

Cody Buntain is an Assistant Professor at the College of Information Studies (iSchool) at the University of Maryland (UMD), serving as Co-Director of the Center for AI, Data, and Conflict. He is also an Affiliate Fellow at UMD's Honors College – Artificial Intelligence Cluster and a research affiliate at NYU’s Center for Social Media and Politics (CSMaP). His research focuses on crisis informatics, online political engagement, and information quality, particularly examining how AI shapes information ecosystems during crises, conflicts, and social unrest. Dr. Buntain’s work addresses disinformation, health misinformation, and AI-driven methods for emergency response. Key affiliations include the Global Elections and Information Security (GEIS) initiative and the Tech Policy Research & Education Hub. His research has been featured in major media outlets like the New York Times, Washington Post, and WIRED. Education: PhD in Information Studies from University of Maryland (2016), postdoctoral research at NYU’s SMaPP Lab (2018-2019) and UMD’s Human-Computer Interaction Lab (HCIL, 2016-2018). Previously served as Assistant Professor at NJIT’s Informatics department and Adjunct Faculty at American University. Professional experience includes Director of Research at Pikewerks Corporation (2008–2013). Current projects include E-VERIFY (HMT-ISR analysis), M3I (maps/models for influence efforts), and the Incident Streams TRECIS initiative. His GitHub repositories reflect technical contributions to crisis informatics, including tools for social media analysis during disasters.

Ferreira Joao CarlosView profile

Professor

• Artificial Intelligence
• AI in Health and Medicine
• AI in Energy and Transportation
• +6 more

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

• Machine Learning
• Artificial Intelligence
• Genomics
• +4 more

Haohan Wang serves as Assistant Professor at the School of Information Sciences, University of Illinois Urbana-Champaign, with additional appointments as Affiliate at the Carl R. Woese Institute for Genomic Biology and Assistant Professor at the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges machine learning, genomics, and AI security, focusing on trustworthy systems for biomedical applications and foundational AI research. Wang's research centers on robust and secure artificial intelligence, with emphasis on large language model vulnerabilities (jailbreaking, safety evaluation), federated learning personalization, and genomic data analysis. He develops techniques for privacy-preserving dataset distillation, confounding factor correction in genome-wide studies, and multi-agent frameworks for scientific discovery. His fingerprint highlights expertise in Machine Learning (94%), Linear Mixed Models (87%), and Confounding Factor Correction (41%), reflecting his focus on methodological rigor in complex data environments. Analysis of his 2025 publications reveals dominant trends in AI security (jailbreak evaluation frameworks like GuardVal, adversarial attacks such as InfoFlood), biomedical AI (transcriptomic analysis, wearable data privacy), and foundational methods (federated learning optimization, synthetic data generation). These works consistently address real-world challenges in model trustworthiness while advancing computational techniques for genomics and healthcare. Through NCSA's high-performance computing resources and the Institute for Genomic Biology's collaborative ecosystem, Wang integrates supercomputing capabilities with biological research to tackle data-intensive problems in disease modeling and AI safety testing, as evidenced by media coverage of his team's AI security testing methods.

David CockView profile

Senior Lecturer

• Formal Verification
• Operating Systems
• Hardware-Software Co-Design
• +3 more

Dr. David Cock is a Senior Lecturer and Senior Researcher at ETH Zürich's Department of Computer Science, affiliated with the Systems Group. He holds a PhD from UNSW (2014) and a B.Sc. (hons) from UNSW (2004). His research focuses on formal verification, trustworthy systems, and hardware-software co-design, with notable contributions to projects like Enzian (a CPU/FPGA platform) and seL4 (formally verified kernel). He teaches Advanced Operating Systems and Informal Methods courses. Key achievements include the ACM Software System Award (2022) for seL4 and leadership in projects addressing hardware complexity and security. Research interests include formal methods for hardware modeling (Sockeye project), runtime verification, and mitigating timing channels. His work bridges theoretical foundations with practical systems, emphasizing secure and reliable computing platforms. Projects like Trustworthy BMC aim to enhance baseboard management systems' assurance. Collaborations span academia and industry, with open-source contributions to hardware designs and formal tools. Publications span formal verification, hardware modeling, and secure systems, with recent focus on heterogeneous computing and declarative hardware specifications. Teaching emphasizes practical formal techniques and OS design, leveraging real-world hardware (e.g., Barrelfish). His lab, the Systems Group, explores cutting-edge challenges in systems software and architecture.

• Computer Vision
• Machine Learning
• Medical Imaging
• +4 more

Edward Delp is the Charles William Harrison Distinguished Professor of Electrical and Computer Engineering at Purdue University's College of Engineering. He holds affiliations with both the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering. His research spans computer vision, medical imaging, and data forensics with a focus on synthetic media detection, deep learning applications, and healthcare technologies. Education: Not explicitly listed in the provided text. His work includes developing algorithms for speech forensics, microscopy image analysis, and food/nutrition assessment systems. He leads projects on synthetic speech detection, medical image segmentation, and automated crop disease measurement using RGB imaging. Delp collaborates across disciplines, integrating machine learning with healthcare and agricultural challenges. Recent work emphasizes ethical AI through fairness in synthetic media detection and explainable artifacts in biomedical imaging. He contributes to large-scale datasets like MetaFood3D and 3D nuclear segmentation frameworks for microscopy analysis. His grants and advising focus on interdisciplinary applications, though specific grant details are not provided. Delp is affiliated with the Purdue School of Biomedical Engineering and maintains active collaborations in medical imaging, computer vision, and aerospace anomaly detection.

• Data mining and knowledge discovery
• Data engineering and data science
• Data quality
• +17 more

Prof. Ping Yu is a Professor at the University of Wollongong's School of Computing and Information Technology, where she has held leadership roles such as Director of the Centre for Digital Transformation (2013–2020). Her work focuses on digital health, health informatics, and data-driven solutions for aged care, with collaborations involving the World Health Organization (WHO), NSW Health, and over 11 aged care providers. She has pioneered projects like the WHO’s eSTEPS platform and AI-driven analytics to optimize healthcare resources. Her research integrates socio-technical perspectives to address challenges in technology adoption and healthcare system improvements. Her research interests span data engineering, data quality, ontology development, and generative AI applications in healthcare. Key areas include extracting insights from unstructured health records, improving clinical decision-making, and enhancing patient outcomes through mHealth and telemedicine innovations. She also explores the impact of green spaces on healthy aging and disparities in healthcare access across regions. Prof. Yu has secured over $4.5 million in research funding, including significant grants from the Australian Research Council. She has mentored 29 postgraduate students to completion, emphasizing interdisciplinary research and practical implementation. Her awards include the prestigious 2022 Telstra Brilliant Women in Digital Health Award and the 2008 Don Walker Award for contributions to health informatics. Her advising and grants narrative highlights her role as a mentor and her success in securing competitive funding. Her work bridges academia and industry, with projects like improving pressure injury risk management in aged care using EHRs and analyzing agitation in dementia through AI. She contributes to education policy, including co-designing academic leadership programs and enhancing clinical informatics curricula in the AI era. Prof. Yu leads multi-disciplinary teams at UOW and collaborates with global institutions, focusing on labs and initiatives like the Appendicectomy Surgical Pathway Ontology (ASPO) and the 6A framework for hypertension management via mHealth. Her research emphasizes real-world impact, from reducing hospital readmissions to optimizing surgical workflows and improving public health communication strategies during crises.

Lisa KervinView profile

Professor

• Early Childhood Education
• Digital Literacy
• Child Development
• +3 more

Professor Lisa Kervin is a leading academic in early childhood education at the University of Wollongong (UOW), serving as Director of Early Start Research and holding a Professorial appointment in the School of Education. Her research focuses on young children’s literate practices, play-based learning, and digital technology integration in education. She leads the ARC Centre of Excellence for the Digital Child and has secured significant funding, including a 2025 ARC Future Fellowship exploring intergenerational play. Kervin was honored as a Member of the Order of Australia (AM) in 2024 for contributions to early childhood digital literacy research. She has supervised over 35 research students, many now in academic and leadership roles, and currently oversees seven doctoral candidates. Her work bridges theory and practice through collaborations with educators and industry partners, emphasizing interdisciplinary approaches to child development and policy influence globally. Education: PhD in Language and Literacy from UOW (2001–2004). Professional roles include Principal Fellow of the Australian Literacy Educators’ Association and Director qualifications from the Australian Institute of Company Directors. Her research spans digital childhood ethics, play pedagogy, and literacy curriculum design, with outputs impacting government policies and international educational frameworks. Ongoing projects include the ‘DigIQ’ program promoting digital literacy and ‘Playgroups for All Ages’ fostering intergenerational engagement. Grants and collaborations involve partnerships across six universities and 33 global organizations. Her teaching innovations include the Early Childhood Elevate Mentoring Program to support educators’ academic progression. Kervin’s work addresses critical issues like screen time impacts, STEAM education, and culturally responsive pedagogies, positioning her as a key voice in 21st-century early childhood education.

Anne LauscherView profile

Associate Professor

• Conversational Artificial Intelligence
• Fairness in AI
• Natural Language Processing
• +3 more

Prof. Dr. Anne Lauscher is an Associate Professor of Data Science at the University of Hamburg Business School, specializing in fair, inclusive, and sustainable conversational AI systems. Her research focuses on improving algorithmic fairness through demographic factors in NLP systems and exploring ethical implications of large language models. She holds a PhD from the University of Mannheim, where her work on computational argumentation was awarded summa cum laude, and has conducted research at Grammarly and the Allen Institute for AI. Key contributions include gender-fair machine translation datasets (e.g., Building Bridges), bias detection frameworks (e.g., SHADES), and multilingual benchmarking tools like MultiQ. Her work has been recognized with the Maria Gräfin von Linden-Award and inclusion in the '100 Brilliant Women in AI Ethics' list. Research spans ethical NLP, multilingual AI, and societal impacts of AI technologies. Education: PhD in Data and Web Science (University of Mannheim, 2021), Postdoc at Bocconi University's NLP group (2021-2022). Academic roles include adjunct positions and international collaborations across Europe and the US. Research Interests: Conversational AI fairness, multilingual NLP systems, ethical AI evaluation, bias mitigation in LLMs, and interdisciplinary applications of machine learning in scientific discovery. Publications (select highlights): Over 55 peer-reviewed works in top-tier venues like ACL, EMNLP, and AAAI. Recent focus on LLM hallucination analysis, cross-cultural NLP benchmarks, and gender-neutral language resources. Awards: 2021 Maria Gräfin von Linden-Award (Baden-Württemberg), 2023 '100 Brilliant Women in AI Ethics', 2022 Dissertation Award Nominee (GI). Labs/Teams: Leads the UHH Data Science Research Group, collaborating with industry partners like Grammarly and academic institutions worldwide. Active in initiatives promoting gender equity in STEM and sustainable AI development.

Sudip MittalView profile

Assistant Professor

• Cybersecurity
• Artificial Intelligence
• Cyber-Physical Systems

Dr Sudip Mittal is an Assistant Professor in Computer Science & Engineering at Mississippi State University and Associate Research Director of the PATENT Lab. His research spans cybersecurity, artificial intelligence, and cyber-physical systems, with a focus on building self-protecting systems and predictive security for unmanned vehicles. He leads the SECRETS Lab and has published over 70 papers in top venues, with work featured in The LA Times and WIRED. Research interests include: Autonomous intrusion response systems AI-driven threat detection in IoT/CPS Adversarial machine learning His publications (2019-2025) show a strong emphasis on AI security applications, particularly in malware detection, healthcare compliance, and anomaly detection using large language models. Recent articles explore MLOps security, adaptive cyber defense, and synthetic data generation for critical systems.

Nazli GoharianView profile

Clinical Professor

• Information Retrieval
• Text Mining
• Natural Language Processing
• +1 more

Nazli Goharian is a Clinical Professor of Computer Science at Georgetown University and Associate Director of the Information Retrieval Lab. She holds a PhD from Florida Institute of Technology and joined Georgetown in 2010 after industry experience and previous academic positions at Illinois Institute of Technology. Education: PhD Computer Science, Florida Institute of Technology (2001) MSc Computer Science, George Mason University (1995) BSc Computer Science, Dortmund University (1992) Her research spans information retrieval, text mining, and natural language processing with applications in health/medical domains. She focuses on developing computational methods for medical search, mental health analysis from social media, clinical text summarization, and adverse drug reaction detection. Her recent publications (2020-2016) predominantly focus on neural ranking models, transformer architectures for document retrieval, and clinical NLP applications. Notable trends include work on BERT-based re-ranking, zero-shot multilingual retrieval, and ontology-aware medical summarization. Awards & Honors: EMNLP 2017 Best Long Paper Award COLING 2018 Honorable Mention & Area Chair Favorite Julia Beveridge Award for Faculty (IIT, 2009) Multiple Teaching Excellence Awards (2002-2007) Research Leadership: She has supervised 6 PhD students to completion with placements at leading institutions. Secured over $500,000 in research funding from NSF, Adobe, and international partners. Founded the Semi-Annual Graduate Research Presentation Days at Georgetown and served as Program Chair for ECIR 2024. She leads the Information Retrieval Lab which focuses on developing novel algorithms for efficient document retrieval, cross-lingual search, and specialized applications in healthcare text analysis.

Marco ValtortaView profile

Professor

• Artificial Intelligence
• Bayesian Networks
• Causal Inference
• +5 more

Marco Valtorta is a Professor and Graduate Director in the Department of Computer Science and Engineering at the University of South Carolina’s Molinaroli College of Engineering and Computing. He specializes in Artificial Intelligence, with a focus on normative reasoning under uncertainty, Bayesian networks, causal models, and computational complexity. His work includes developing algorithms for structure learning in graphical models, causal inference, and applications in multiagent systems. Education: Ph.D., Computer Science, Duke University (1987) M.A., Computer Science, Duke University (1984) Laurea, Electrical Engineering, Politecnico di Milano (1980) Research Interests: Dr. Valtorta’s work integrates logical and probabilistic reasoning, with contributions to causal models, chain graphs, and adversarial machine learning. His funded projects include collaborations with the Office of Naval Research (ONR), IARPA, and the U.S. Department of Agriculture (USDA). Notable collaborations include applying Bayesian networks to healthcare and developing frameworks for trustworthiness assessment in AI systems. Grants & Collaborations: Multi-institution IARPA project on Wigmorean/Bayesian networks for argumentation ONR-funded research on Markov properties of directed hypergraphs with Dr. Linyuan Lu Causal analysis for performance modeling of configurable systems His recent publications emphasize causal inference in AI, automated evaluation of text and sentiment analysis systems, and robustness of foundation models. He has pioneered algorithms for learning chain graphs and addressing adversarial attacks in probabilistic models.

Yang WangView profile

Associate Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Yang Wang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, holding an adjunct position since 2022. Previously, he served as an Associate Professor at the University of Manitoba (2012–2022) and worked as Chief Scientist in Computer Vision at Huawei Canada (2020–2022). He holds a PhD from Simon Fraser University, MSc from the University of Alberta, and BEng from Harbin Institute of Technology. His research focuses on computer vision, machine learning, and deep learning, particularly in meta-learning, test-time training, and continual learning. Key areas include crowd counting, anomaly detection, video highlight detection, and gaze estimation. His work has been recognized with awards such as the Falconer Emerging Researcher Rh Award (2017) and a Faculty of Science Research Chair (2019–2022). Recent research emphasizes AI models that are personalized and adaptable, leveraging techniques like meta-learning and few-shot learning. He has published extensively in top venues (CVPR, ICCV, ECCV) and holds patents in related fields. His group collaborates with industry partners like Huawei and Sightline Innovation.

Yang ChengView profile

Associate Professor

• Supply Chain Management
• Manufacturing Strategy
• Manufacturing Network
• +5 more

Yang Cheng is an Associate Professor at the Department of Materials and Production, Aalborg University, Denmark. He holds a PhD in Mechanical Engineering from the same institution (2011), focusing on manufacturing strategy and network dynamics. His research spans supply chain management, sustainability, and global operations, with a focus on integrating technology and environmental policies into manufacturing systems. He leads or participates in high-impact projects like MAASive (2024–2026) and the Sino-Danish Center Research Project (2011–present), addressing resilience in value networks and global operations innovation. Research Interests: Supply Chain Management & Integration Sustainability & Green Technologies Manufacturing Strategy & Networks Technology Policy & Digitalization Global Operations & Cross-Border Collaboration Recent Work Trends: Prof. Cheng's 2025 articles emphasize blockchain in sustainable supply chains, green technology investments under carbon policies, and digitalization's ethical implications. His 2024 research explores smart factories, EU battery regulations, and robotization in manufacturing. These studies blend quantitative models with case-based analysis to address real-world challenges. Awards: 2024 Emerald Literati Awards – Outstanding Reviewer Advising & Grants: As PI for multiple Global Operations Management PhD programs (2019–2025), he guides research on digital transformation and university-industry collaboration. His projects receive funding from Danish and international grants, focusing on innovation and resilience in manufacturing networks. Labs/Teams: Collaborates with the Center for Industrial Production at Aalborg University and engages in international partnerships through the Sino-Danish Center. Active editorial roles include Production Planning & Control and Journal of Manufacturing Technology Management .