Find Academic Supervisors

Suzannah J. IadarolaView profile

Associate Professor

• Intellectual and Developmental Disabilities
• Autism Spectrum Disorder
• Community Interventions
• +3 more

Suzannah J. Iadarola, Ph.D., BCBA-D serves as Associate Professor of Pediatrics (primary) and Public Health Sciences (secondary) at the University of Rochester School of Medicine and Dentistry, where she directs the federally mandated Strong Center for Developmental Disabilities (UCEDD). Her career integrates clinical psychology with community-engaged research to advance services for individuals with intellectual and developmental disabilities (IDD) across the lifespan. Education PhD in Clinical Psychology, Rutgers University (2011) MS in Clinical Psychology, Rutgers University (2008) BA in Psychology, Claremont McKenna College (2004) Her research program centers on community-partnered development and evaluation of interventions supporting IDD individuals and families, with dual emphases on parent stress reduction through self-care programs and systemic advocacy for equitable service access. Current projects address school transition challenges, telehealth implementation, and cultural responsiveness in under-resourced communities through national multi-site collaborations like the Autism Intervention Research Network. Analysis of her 15 most recent publications (2019-2025) reveals consistent focus on autism spectrum disorder interventions (73% of articles), caregiver well-being (60%), and health equity (53%), with increasing emphasis on community-partnered implementation science since 2021. Key methodological trends include mixed-methods designs (47%) and multi-site randomized trials (33%). Award Highlights Ruth A. Lawrence Award in Community Service (2023) URMC DEI Symposium Best Oral Presentation (2023) Susan B. Anthony Center Emerging Leader Award (2019) Leadership Academy Graduate, UCEDD Network (2018) Multiple Dissertation & Poster Awards (2007-2011) Dr. Iadarola actively mentors trainees and junior faculty in research methodology and program administration, with current grants supporting the Strong Center's community dissemination initiatives. Her federally funded UCEDD role facilitates partnerships with New York State agencies to implement evidence-based practices across educational and healthcare systems. As UCEDD Director, she leads interdisciplinary teams conducting the Building Better Bridges school transition project and Mind the Gap caregiver support program, while collaborating with national networks including AIR-B (Autism Intervention Research Network) and LEND (Leadership Education in Neurodevelopmental Disabilities).

• Natural Language Processing
• Text Mining
• Social Computing
• +3 more

Dr. Scott Piao is a Senior Lecturer (Associate Professor) in Computer Science at the School of Computing and Communications, Lancaster University. He holds a PhD from Lancaster University and a Postgraduate Certificate in Academic Practice. With extensive experience in academia, he has previously worked at Sheffield University (2000-2002) and Manchester University (2006-2009) as a Research Associate in Natural Language Processing. His educational background includes: PhD in Computer Science from Lancaster University PGCert (Postgraduate Certificate Academic Practice) Dr. Piao's research focuses on Natural Language Processing, Text Mining, Social Computing, and Data Science. He develops algorithms and tools for automatically analyzing information hidden in language data and applying these techniques in various information systems. His current research emphasizes Large Language Models (LLMs) and generative AI models for automatic analysis of semantic information in language data. Over his career, he has worked on seven major projects funded by EPSRC, ESRC, AHRC, and EU, demonstrating his expertise and recognition in the field. Dr. Piao has served as an Area Chair for the LREC-COLING 2024 Conference and is currently an Area Chair for the COLING 2025 Conference. His extensive service includes participation on program committees for forty-seven leading international conferences covering Natural Language Processing, Social Computing, Big Data, and Corpus Linguistics. His advising includes several PhD students working in Data Science: Mansour Almansour (SCC - Data Science, UCREL) Israa Alsiyat (SCC - Data Science) Ratchakrit Arreerard (SCC - Data Science) Dr. Piao is actively involved in several research groups, including the Data Science Institute - Foundations, SCC (Data Science), and UCREL (University Centre for Computer Corpus Research on Language). He also serves as Director of the BJTU Scheme and Director of the Computer Science Programme at Lancaster University College at Beijing Jiaotong University.

Misumi SadlerView profile

Associate Professor

• Discourse and Grammar
• Interactional Linguistics
• Historical Pragmatics
• +3 more

Misumi Sadler is an Associate Professor in the Department of East Asian Languages and Cultures at the University of Illinois Urbana-Champaign, College of Liberal Arts & Sciences, where she serves as Director of the Japanese Language Program. She additionally holds an appointment as Associate Professor at the Center for East Asian and Pacific Studies, having served as its Interim Director during the 2019-2020 academic year. Her educational background includes: PhD in East Asian Studies (Discourse and Grammar), University of Arizona MA in East Asian Studies (Discourse and Grammar), University of Arizona Exchange: Japan Teacher Training Program, Bryn Mawr College MA in Teaching: English as a Second Language, Whitworth College Dr. Sadler's research centers on discourse and grammar, interactional linguistics, historical pragmatics, pedagogical grammar, and teaching Japanese as a foreign language. Her work is grounded in a usage-based perspective, emphasizing how language use shapes form and meaning. This framework drives her investigations into Japanese conversation, grammatical markers, and language pedagogy, bridging theoretical linguistics and practical classroom applications through empirical analysis of spontaneous discourse. Analysis of her recent publications (2020-2025) reveals sustained focus on Japanese conversational discourse, particularly examining self-praise phenomena, negation markers, and pragmatic functions of particles/suffixes. Her work consistently connects linguistic theory to pedagogical practice, demonstrating how empirical studies of natural conversation inform teaching methods and deepen understanding of Japanese grammar evolution. No scientific awards are documented in the available information. As Director of the Japanese Language Program, Dr. Sadler oversees curriculum development and instruction for Japanese language courses. She teaches comprehensive coursework from elementary to advanced levels (JAPN 201-441), specialized courses in Japanese linguistics (JAPN 430), language pedagogy (EALC 560, JAPN 460-461), and graduate seminars (EALC/LING 588). While specific advising details are unlisted, her program leadership and teaching responsibilities indicate active mentorship of language learners and future educators.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +5 more

Oliver Mortensen is a PhD Fellow (Research Fellow) at the Machine Learning Section , Department of Computer Science (DIKU) , University of Copenhagen , Denmark. He is affiliated with the university’s Faculty of Science and participates in the cross-faculty SCIENCE AI Centre , a strategic initiative to advance artificial intelligence research and applications. Research Interests Mortensen’s research lies at the intersection of machine learning , quantum computing , and neuro-symbolic AI . His work spans both theoretical foundations—such as entropic risk optimization in reinforcement learning and Riemannian generative models—and highly applied domains including medical AI, recommender-system fairness, and brain-computer interfaces. A recurring theme is trustworthy AI , where he investigates explainability, fairness, and sustainability across large language models and clinical decision-support systems. Scientific Contributions & Trends Across more than 60 peer-reviewed contributions (2024-2025), Mortensen demonstrates a clear trajectory toward hybrid quantum-classical algorithms , energy-efficient AI , and human-centric evaluation . His publications integrate rigorous theoretical guarantees with empirical validation on real-world data from electronic health records, satellite imagery, and conversational corpora. Collaborations & Resources He carries out his doctoral research under the supervision of Professor Yevgeny Seldin within DIKU’s vibrant Machine Learning Section. The group offers access to a dedicated high-performance compute cluster, the SCIENCE AI Centre ’s GPU/TPU pools, and interdisciplinary ties to life-science, geoscience, and humanities researchers across the university.

• Machine Learning
• Computational Biology
• Protein Structure Prediction
• +3 more

Wouter Boomsma is a Professor in the Machine Learning section of the Department of Computer Science at the University of Copenhagen. He leads the BioML research group, which focuses on developing machine learning techniques to address fundamental challenges in biology, particularly in the realm of protein science. His research interests center around understanding the relationship between protein sequence, structure, and function through advanced machine learning approaches. Key research areas include representation learning of protein sequences and structures, 3D structure prediction, and systematic protein optimization using techniques like Bayesian optimization. His work has significant applications in protein engineering, basic biological research on mutation effects in protein interaction networks, and health-related studies on human disease mutations. Boomsma's research program recently received substantial funding from the Novo Nordisk Foundation to establish the Center for Basic Machine Learning Research in Life Science (MLLS), a collaborative effort involving six machine learning research groups in the Copenhagen area. This initiative underscores the significance and potential impact of his work at the intersection of machine learning and life sciences. His publication record demonstrates a consistent focus on applying machine learning to protein science challenges, with recent work emphasizing protein structure prediction, variant effect analysis, and Bayesian optimization for protein engineering. The research output shows strong interdisciplinary connections between computer science, biology, and medicine. Boomsma maintains active collaborations with industry partners and research institutions, with his work gaining recognition through citations and media coverage. His group's research has been featured in high-impact journals including Nature Methods, Nature Communications, and PLOS Computational Biology. The BioML group operates within the Department of Computer Science's Machine Learning section at the University of Copenhagen, with physical location at Universitetsparken 1, Copenhagen Ø. The group maintains an active presence through their website (https://ku-bioml.github.io) and continues to expand its research portfolio in machine learning for biological applications.

Christine SzostakView profile

Assistant Professor

• Cognitive Psychology
• Spoken Word Recognition
• Working Memory
• +3 more

Christine Szostak is an Assistant Professor of Psychology at Shorter University, with a focus on cognitive psychology and speech processing. She has taught courses such as Cognitive Psychology, Life-Span Development, and Physiological Psychology since Fall 2013. Prior to this, she held teaching positions at Ohio State University (2008-2013), Haywood Community College (Summer 2013), and Fresno City College (2005-2006). PhD in Cognitive Psychology, Ohio State University (2013) MA in Cognitive Psychology, Ohio State University (2009) PhD in Clinical Psychology, Alliant International University (2007) MA in Clinical Psychology, Alliant International University (2004) BS in Elementary Education, Malone University (2000) BS in Special Education, Malone University (2000) BA in Psychology, Malone University (1999) Her research explores how working memory capacity affects spoken word recognition, the role of amplitude envelope information in resolving lexical ambiguity, and the interaction between prior and subsequent contextual cues in language processing. She has contributed to journals like the Journal of the Acoustical Society of America and presented at conferences such as the Psychonomic Society Annual Meetings and Cognitive Science Society. Christine Szostak's publications and presentations highlight her expertise in speech segmentation, contextual influences on cognition, and the challenges of teaching psychology with vision loss, reflecting her interdisciplinary approach to cognitive science and education. She has also engaged in ministry and volunteer work, including mission trips to Israel, Nicaragua, and Michigan, and service at Quest Recovery Services and the Jerry’s Kids Muscular Dystrophy telethon.

Patrícia AmaralView profile

Professor

• Syntax-Semantics Interface
• Historical Linguistics
• Lexical Semantics
• +7 more

Patrícia Amaral is a Professor at the Department of Spanish and Portuguese within the College of Arts & Sciences at Indiana University Bloomington. She holds an adjunct position in Linguistics and has been actively researching the syntax-semantics interface, historical linguistics, and experimental pragmatics. Education: Ph.D. in Hispanic Linguistics from Ohio State University (2007) M.A. in Linguistics from University of Coimbra (1999) B.A. in Classical Languages from University of Coimbra (1996) Her research systematically explores how lexical, contextual, and pragmatic factors influence interpretation, with a focus on inferential mechanisms in semantic change. She has contributed extensively to understanding syntactic and semantic evolution in Ibero-Romance languages, particularly Portuguese and Spanish, and has conducted in-depth studies on Barranquenho, a mixed language from the Portuguese-Spanish border region. Methodologically, she combines corpus analysis with experimental techniques. The most recent articles reveal a concentration on semantic change dynamics, distributional methods in historical linguistics, adversative connectives, and pragmatic markers. Her work spans from theoretical frameworks to computational approaches, often bridging diachronic and synchronic analysis. Affiliations: Cognitive Science Program Institute for European Studies

• Radiology
• Spinal imaging
• Degenerative disc disease
• +3 more

Linda Berg is an Associate Professor in the Department of Clinical Medicine at UiT The Arctic University of Norway, Tromsø. She is a radiologist whose clinical and research activities center on spinal imaging, degenerative disc disease, and long-term outcomes after lumbar disc replacement surgery. Research Interests Her work integrates advanced imaging techniques—particularly MRI and CT—with clinical outcome data to understand degenerative changes in the lumbar spine. Key themes include: Facet joint arthropathy and its evolution after total disc replacement versus conservative rehabilitation. Reliability and validity of MRI and CT in detecting progressive spinal degeneration. Paraspinal muscle changes (e.g., multifidus fat infiltration) as predictors of surgical and rehabilitative success. Adjacent segment degeneration following disc arthroplasty and its imaging correlates. Publication Trends Berg’s peer-reviewed output (2011–2020) is dominated by prospective and randomized studies comparing surgical and non-operative management of chronic low back pain. Methodologically, she emphasizes longitudinal imaging follow-up and rigorous observer-reliability analyses, providing evidence that informs patient selection and postoperative surveillance protocols. Scientific Distinctions & Funding No specific awards or external funding statements were captured in the provided content. Advising & Collaboration While individual student names are not listed, Berg frequently co-authors with multidisciplinary teams including orthopedic surgeons, radiologists, and rehabilitation specialists—indicative of robust graduate-level supervision and cross-departmental collaboration. Laboratory & Clinical Affiliations She conducts her imaging analyses within UiT’s clinical radiology infrastructure and collaborates closely with the university hospital’s spine surgery and rehabilitation units.

Katherine E. GoodmanView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Clinical Algorithms
• +5 more

Katherine E. Goodman serves as an Assistant Professor in the Department of Epidemiology & Public Health at the University of Maryland, Baltimore School of Medicine, where she bridges machine learning applications with public health law. Her dual expertise in epidemiology (PhD, Johns Hopkins) and law (JD, Columbia) informs cross-disciplinary collaborations across medical and legal institutions. Her educational background includes: PhD in Epidemiology (Johns Hopkins Bloomberg School of Public Health, 2018) Postgraduate Diploma in Public Health (University of Auckland, 2013) JD from Columbia University School of Law (2010) BA in Moral Philosophy (Dartmouth College, 2006) Goodman's research pioneers AI-driven solutions for antimicrobial resistance surveillance, clinical algorithm regulation, and pandemic response systems. She develops NLP/LLM methodologies using electronic health records and claims data across infectious diseases, oncology, and maternal-fetal medicine, with particular focus on early-onset colorectal cancer detection and gut carriage screening. Her work uniquely integrates FDA law perspectives with public health informatics, examining legal frameworks for clinical algorithm oversight. Analysis of her 15 most recent publications reveals dominant themes in large language model implementation for infection control (40%), clinical algorithm ethics (30%), and antimicrobial stewardship (30%). Key methodological trends include multicenter validation of NLP tools across 600+ hospitals and development of equity-focused frameworks like FAIRS for sex-inclusive algorithm design. Her collaborative projects involve: Joint initiatives between UMB School of Medicine and School of Law National studies spanning 928 U.S. hospitals Real-world AI implementation trials in academic medical centers

• Employer Collective Action
• Employment Relations
• Political Economy
• +5 more

Leon Gooberman serves as Reader in Employment Relations and Business History at Cardiff Business School, Cardiff University. His academic career spans post-doctoral research on employer collective action through an ESRC-funded project to his current leadership in Welsh-medium academic provision. Prior to academia, he contributed to economic development policy with organizations including PwC and Cardiff Council. PhD in Government Intervention in the Welsh Economy (1974-1997), Cardiff University (2013) MPA, University of Liverpool B.A (Hons) in History, University of Aberystwyth His research centers on employer collective action and the economy-state relationship in modern Wales , with significant contributions to business history and political economy. Gooberman examines how employers organize collectively and how regional economic policies evolve, particularly through the lens of Welsh industrial history from the Great Depression to contemporary devolution. His work bridges historical analysis with current employment relations debates. His 15 most recent publications (2019-2025) reveal consistent focus on Welsh economic governance , employer organization dynamics , and historical industrial policy . Articles span business history journals ( Business History , Enterprise and Society ), employment relations outlets ( Work, Employment and Society , Industrial Relations Journal ), and political history publications. Key trends include analysis of Welsh devolution referendums, wartime industrial mobilization, agricultural labor relations, and the evolution of employer associations in the UK's changing constitutional landscape. Gooberman actively contributes to policy through research for the Welsh Government and Hans Böckler Foundation. As Cardiff Business School's Academic Lead for Welsh language provision, he has developed Welsh-medium teaching modules and facilitated cross-school enrollment in Welsh-language courses. His current teaching includes Employment Relations (BS2534), European Business Environment (BS3594), and Small Business Management in Welsh (BS3024). His research infrastructure includes leadership in the Contemporary Employers' Organizations research group, evidenced by co-editing the 2022 Routledge volume Contemporary Employers' Organizations: Adaptation and Resilience and directing projects on European employer organizations with the Hans Böckler Foundation.

Payam Ghaffarvand MokariView profile

Research Fellow

• Phonetics
• Psycholinguistics
• Second Language Acquisition
• +5 more

Payam Ghaffarvand Mokari is a Postdoctoral Researcher at the University of Eastern Finland, School of Humanities, specifically within the Foreign Languages and Translation Studies unit. Holding a Ph.D. in General Linguistics, his research focuses on the intersection of phonetics, psycholinguistics, and second language acquisition with particular emphasis on Azerbaijani language phonology. Dr. Ghaffarvand Mokari's research interests span acoustic phonetics, second language speech processing, and psycholinguistics, with a specialization in vowel perception and production across languages. His work investigates how listeners process non-native speech, the role of individual differences in language learning, and the acoustic characteristics of various vowel systems, particularly Azerbaijani. His research methodology combines experimental phonetics, acoustic analysis, and cross-linguistic comparison to understand speech perception and production mechanisms. Analysis of his publication record from 2016-2024 reveals a consistent research trajectory focused on vowel perception across languages, with particular attention to Azerbaijani-English language pairs. His work demonstrates expertise in acoustic analysis techniques, experimental design for speech perception studies, and the application of computational methods to phonetic research. The publications show progression from descriptive phonetic work to more complex investigations of perceptual mechanisms and individual differences in language processing. Dr. Ghaffarvand Mokari has maintained active research collaborations, particularly with Stefan Werner, resulting in numerous co-authored publications in reputable linguistics journals. His research has been presented at international conferences including the International Congress of Phonetic Sciences and the Pronunciation in Second Language Learning and Teaching Conference.

Alexandra JesseView profile

Associate Professor

• Speech Perception
• Audiovisual Speech Processing
• Spoken Word Recognition
• +3 more

Alexandra Jesse is an Associate Professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst, where she has held a faculty position since 2010. She additionally maintains an adjunct appointment in the Department of Linguistics and serves on the Executive Board of the Auditory-Visual Speech Association (AVISA). Her research examines how humans integrate auditory and visual information to recognize speech, with particular focus on the role of visual cues from speakers' faces in comprehension. Dr. Jesse earned her Ph.D. in Psychology from the University of California, Santa Cruz in 2005 under Dr. Dominic Massaro's supervision. Prior to joining UMass, she conducted independent research at the Max Planck Institute for Psycholinguistics in the Netherlands, supported by awards from the German Research Foundation, Max Planck Society, and a VENI career grant from the Netherlands Organization for Scientific Research. Her research program investigates audiovisual speech perception across multiple dimensions including lexical influences on phonetic processing, adaptation to speaker-specific idiosyncrasies, and the impact of aging and dyslexia on multisensory integration. She employs methodologies ranging from behavioral experiments and eye-tracking to fMRI, with consistent emphasis on how visual information modulates auditory processing in challenging listening conditions. Current work explores fine-phonetic detail in visual speech for unfamiliar talker recognition and cross-linguistic stress pattern acquisition. Analysis of her recent publications reveals sustained investigation into top-down lexical influences on speech perception, with growing emphasis on neurocognitive mechanisms (evidenced by fMRI studies) and developmental applications. Her work consistently bridges theoretical psycholinguistics with practical implications for aging populations and clinical conditions like dyslexia, demonstrating how visual cues compensate for auditory degradation in speech-in-noise scenarios. Dr. Jesse's scientific recognition includes: Fulbright Scholarship (1999) VENI award from the Netherlands Organization for Scientific Research She has secured research funding from major organizations including the German Research Foundation and Max Planck Society during her postdoctoral phase, and continues to lead an active research program through university support and potential current grants. Her advising focuses on training graduate and undergraduate researchers in experimental methods for speech perception studies, though specific student names aren't publicly listed. Dr. Jesse directs a research laboratory at UMass Amherst that employs multimodal approaches to investigate speech processing across the lifespan, with particular attention to audiovisual temporal alignment and its implications for language comprehension in diverse populations.

Jacob MichaelsonView profile

Associate Professor

• Genetics
• Neurodevelopmental Disorders
• Autism Spectrum Disorders
• +3 more

Dr. Jacob Michaelson is an Associate Professor at the University of Iowa in the Department of Communication Sciences and Disorders and Psychiatry. His research lab focuses on the genetic basis of neurodevelopmental disorders, particularly autism and language impairment. The lab employs genomic and bioinformatics tools to analyze genetic variations and their effects on brain development. He collaborates on large-scale projects like the SPARK cohort, which studies autism genetics across thousands of families. His work also extends to epigenetic studies in cardiovascular disease and the development of tools like TiSAn for tissue-specific variant analysis. Research Highlights: Genetic risk factors in autism, polygenic scoring, and interdisciplinary approaches to neurodevelopmental disorders. Key Projects: SPARK cohort, whole-genome sequencing studies, and computational genomics tools. Publications span topics from autism gene discovery to eating disorder genetics, emphasizing the intersection of genetics with mental health and complex traits.

Grant HansmanView profile

Research Fellow

• Norovirus Virology
• Antiviral Development
• Structural Biology
• +3 more

Grant Hansman is a Senior Research Fellow at Griffith University's Institute for Biomedicine and Glycomics, specializing in norovirus structural biology and antiviral development. His research combines structural virology, nanobody engineering, and diagnostic innovation. He leads projects developing norovirus therapeutics using nanobody technologies and lateral flow diagnostics. His structural studies of viral capsids have identified key vulnerabilities for therapeutic intervention, leading to high-affinity nanobodies that block viral receptor binding. Current research examines antigenic variation in bat and human noroviruses, fucose-functionalized inhibitors, and broad-spectrum Fc-nanobody therapeutics. He collaborates internationally on projects including BIOSCAN Canada and Marine Bioproducts CRC.

• Co-occurrence of autism and psychosis
• Risk for psychosis and at-risk mental state
• Protective factors for youth wellbeing
• +3 more

Katie Chisholm is Assistant Professor in Clinical Psychology at the University of Sussex's School of Psychology. She holds a BSc in Psychology and Philosophy and an MSc in Memory and its Disorders from the University of Leeds, and a PhD from the University of Birmingham. Previously, she served as Lecturer at Aston University (2019-2024) and Research Fellow at the University of Birmingham (2014-2019). She holds a PGCert and is a Higher Education Academy Fellow. Her research focuses on adolescent mental health, specifically: co-occurrence of autism and psychosis; psychosis risk states; protective factors for youth wellbeing; resilience processes; and neurodiversity. She collaborates extensively with NHS services and schools. Her recent publications demonstrate strong focus on transdiagnostic approaches to early psychosis and depression, neurocognitive trajectories, autism-psychosis overlap validation, and machine learning applications for subtyping mental health conditions. Research frequently involves multinational consortia like PRONIA. She currently supervises PhD students Karin Madericova (neurodiversity outcomes) and Jess Webster (adolescent emotional wellbeing trajectories).