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Kathryn PasternakView profile

Lecturer

• Video Production
• Post-Production
• Documentary Filmmaking
• +5 more

Kathryn Pasternak serves as a Lecturer of Video Production in the Department of Art within the Rome School of Music, Drama, and Art at Catholic University of America. With over 25 years of professional experience in television production, independent filmmaking, and interactive media, she brings extensive industry knowledge to her academic role. Her research interests focus on Video Production and Post-Production , Documentary Filmmaking , and Multimedia Exhibit Production . Specializing in wildlife, conservation, and cultural films, Pasternak approaches storytelling with an emphasis on making science accessible and engaging. Her expertise spans wildlife and natural history storytelling, content research, directing, interviewing, and editorial refinement. Pasternak has received significant recognition for her work, including two National Emmy Awards for Best Science and Nature Program and two additional Emmy nominations. Her award-winning independent feature documentary Doeville premiered at the DC Environmental Film Festival in 2015 and has since screened internationally. More recently, she produced three multimedia exhibits for an international traveling exhibition on Ramses the Great. Her professional journey includes 16 years at National Geographic Television working on high-end television programs for international distribution. Following her time at National Geographic, she lived on a wildlife reserve in Zimbabwe for a year while writing and producing a series for Animal Planet. Pasternak Media LLC, her production company, creates documentary, wildlife, and conservation media ranging from independent documentaries to high-end television programming and interactive museum exhibits. She has lived and worked professionally in New York, Los Angeles, Johannesburg, and Washington DC, bringing a global perspective to her teaching. An Edmonton, Alberta native, Pasternak graduated from Harvard University with a degree in Fine Arts before settling in the Washington, DC area.

• Machine Learning
• Bioinformatics
• Computational Biology
• +3 more

Viktoria Schuster is a Guest Researcher in the Department of Computer Science at the University of Copenhagen, specializing in machine learning applications for biological data analysis. Her work bridges computational methods with biomedical research challenges. Her primary research domains include: Machine Learning (deep generative models, representation learning) Bioinformatics (single-cell RNA sequencing, multi-omics integration) Immunoinformatics (TCR-peptide binding prediction) Computational Biology (genomic data modeling) Recent publications reveal a strong methodological focus on developing novel deep generative frameworks for biological data, particularly in single-cell genomics and immunology. Her 2024 Nature Communications paper on multi-omics integration and 2023 Genome Biology work on N-of-one differential expression demonstrate significant contributions to handling sparse biological datasets without traditional control samples. Collaborations span international institutions including the Wellcome Sanger Institute (Sarah Teichmann) and multiple Danish research groups, with notable impact evidenced by patent references to her NetTCR-2.0 work and substantial Mendeley readership across publications.

• Biogeochemical cycling
• Permafrost biogeochemistry
• Glacier biogeochemistry
• +5 more

Dr. Catherine Hirst is an Assistant Professor in the Department of Earth Sciences at Durham University, UK, where she has been employed since 2022. Her research focuses on biogeochemical processes in high-latitude systems, particularly carbon cycling in permafrost and glacial environments across Arctic and Antarctic regions. She earned her MSc in Earth Sciences from the University of Oxford (2008-2012) and completed her PhD at Stockholm University (2012-2018). Prior to Durham, she conducted postdoctoral research at UCLouvain's Earth and Life Institute (2018-2022) under Professor Sophie Opfergelt. Her research integrates field studies in Siberia, Canada, Greenland, and Antarctica with laboratory techniques including stable isotope analysis (Fe, Si, V), TEM microscopy, and XAS spectroscopy. Key interests include mineral-organic carbon interactions in thawing permafrost, winter biogeochemical processes, and the role of iron in carbon preservation. She emphasizes cross-disciplinary collaboration and maintains active partnerships with researchers across Europe and North America. Analysis of her 15 most recent publications (2019-2025) reveals dominant themes in Arctic biogeochemistry, with particular focus on permafrost degradation impacts, seasonal hydrological controls on carbon transport, and mineral-organic interactions in frozen soils. Her work frequently addresses climate change implications through investigations of winter processes and thermokarst dynamics. Dr. Hirst supervises doctoral students including Emily Fearns-Nicol and Katarina Dickson, and collaborates with Durham's environment and isotope teams featuring Dr. Jotis Baronas, Julia Knapp, and professors Fred Worrall, Kevin Burton, Darren Grocke, and Geoffrey Nowell. She serves on multiple departmental committees including Equality, Diversity and Inclusion; Research; Fieldwork Health and Safety; and Ethics. Teaching responsibilities include co-leading the first-year Sustainability course, directing the third-year Polar Quaternary Environmental Processes module, and participating in geological field trips. She actively seeks new students for projects investigating inorganic drivers of organic carbon cycling through experimental and field-based approaches.

• Personality disorders
• Borderline Personality Disorder
• Clinical psychology
• +7 more

William Ellison is an Assistant Professor of Psychology at Trinity University, where he teaches Psychopathology, Personality and Individual Differences, and Statistics and Research Methods. Originally from Greenville, South Carolina, Ellison earned his bachelor's degree in History from Princeton University before pursuing graduate studies in clinical psychology at Pennsylvania State University. He received additional clinical training at the Massachusetts Mental Health Center/Harvard Medical School and at Rhode Island Hospital/Alpert Medical School of Brown University. Ellison's research focuses on personality disorders, particularly Borderline Personality Disorder (BPD), examining the mechanisms, diagnosis, and treatment of personality pathology. He develops person-specific models of psychopathology using ecological momentary assessment techniques and employs meta-analytic methods to research psychotherapy processes. His scholarly work spans diverse areas including the dynamics of borderline personality and anxiety features, self-concept clarity, narcissism, and emotion regulation. His publication record shows consistent output from 2016 through 2023, with recent work examining professionals' attitudes toward personality disorders, psychotherapy efficacy across treatment modalities, and methodological advances in psychological assessment. Ellison's research often investigates the intricate relationships between psychological constructs and their implications for clinical practice, with particular attention to how personality pathology manifests in daily life through real-time assessment methods. Ellison maintains an active research program with numerous projects examining the clinical significance of individual features of borderline personality disorder, the epidemiology of personality disorders, and educational approaches to improving quantitative abilities in clinical psychology students. His work demonstrates a commitment to advancing both clinical practice and psychological science through rigorous methodology and innovative assessment approaches.

Jeppe Dyrberg AndersenView profile

Associate Professor

• Forensic Genetics
• Shotgun Sequencing
• Forensic Investigative Genetic Genealogy
• +7 more

Jeppe Dyrberg Andersen serves as Associate Professor in the Section of Forensic Genetics at the University of Copenhagen's Faculty of Health and Medical Sciences. His research focuses on advanced genetic analysis techniques for forensic applications, particularly with degraded biological samples from crime scenes. His primary research domains include: Forensic investigative genetic genealogy using shotgun sequencing Molecular autopsy for sudden unexpected death investigations Pigmentation genetics (hair, skin, eye color prediction) Ancestry determination using AIMs Epigenetic analysis for age/lifestyle prediction Skin cancer and melanoma biomarker research Recent publications demonstrate leadership in developing RNA and DNA analysis protocols for challenging forensic samples, with 77 research outputs including high-impact journals like Forensic Science International: Genetics. His work integrates methylome, transcriptome, and genome analyses to solve complex forensic cases. Professional activities include 14 recorded engagements such as lectures at ISFG 2024 and workshops on shotgun sequencing preparation. His technical expertise spans whole genome/exome/transcriptome sequencing, bioinformatics, and high-throughput data analysis for forensic contexts.

• Neurodegenerative Diseases
• Hypothalamus Function
• Amyotrophic Lateral Sclerosis
• +5 more

Francesco Roselli serves as an apl. Professor and Group Leader at the German Center for Neurodegenerative Diseases (DZNE) in Ulm, Germany. His research focuses on the critical yet understudied role of the hypothalamus in neurodegenerative diseases, particularly Amyotrophic Lateral Sclerosis (ALS) and Huntington's Disease (HD), where metabolic disturbances represent early and clinically significant features. Dr. Roselli's laboratory investigates fundamental questions about why hypothalamic dysfunction occurs in neurodegenerative diseases despite these neurons not being traditional targets of neurodegeneration. His team examines whether disruption of hypothalamic connections contributes to energy imbalance in these conditions and whether hypothalamic interventions can influence the vulnerability of other neuronal populations. His research program employs multiple sophisticated approaches: Development and application of state-of-the-art viral vectors to delineate connections between hypothalamic nuclei and the rest of the brain in ALS mouse models with varying metabolic phenotypes Artificial intelligence techniques to identify and map neurons projecting to specific hypothalamic nuclei over time Chemogenetic and multiplexed chemogenetic methods to activate or deactivate specific neuronal subpopulations and assess their impact on metabolic phenotypes Antibody arrays to characterize neurochemical changes in the hypothalamus before and after disease onset Translational analysis of biological samples from ALS patients to validate potential biomarkers Clinical evaluation of high-energy dietary supplements as therapeutic interventions Dr. Roselli's work bridges fundamental neuroscience with clinical applications, aiming to develop novel diagnostic approaches and therapeutic strategies targeting hypothalamic function in neurodegenerative diseases.

Jason W. MarionView profile

Professor

• Waterborne disease prevention
• Water quality
• Antimicrobial resistance in the environment
• +3 more

Jason W. Marion serves as Professor of Environmental Health Science at Eastern Kentucky University, where he directs research at the intersection of environmental science and public health. His work focuses on critical water safety and antimicrobial resistance challenges with direct community impact across Kentucky and international settings. His educational foundation includes: Ph.D. in Public Health - Environmental Health Sciences, The Ohio State University M.S. in Public Health - Environmental Health Sciences, The Ohio State University M.S. in Biology, Morehead State University B.S. in Environmental Science, Morehead State University Dr. Marion's research program centers on waterborne disease prevention through innovative water quality assessment methods, antimicrobial resistance tracking in environmental reservoirs, and occupational health interventions. His field work develops practical sanitation solutions for resource-limited settings while examining complex interactions between land use, climate factors, and pathogen emergence. Current projects investigate cyanotoxin dynamics in freshwater systems and behavioral drivers of health protection practices. Analysis of his 2017-2023 publications reveals consistent focus on environmental health threats through multiple lenses: water quality microbiology (Legionella, antibiotic-resistant E. coli), food safety contamination pathways, and human behavioral responses to health risks. His methodology integrates field sampling with epidemiological analysis, often featuring student collaborators in community-based studies across diverse environments from Kentucky watersheds to Kenyan dairy systems. Recognition for his contributions includes: Outstanding Research Poster Award in Environmental/Occupational Epidemiology (2023) Global Health Innovation Prize (2021) Dr. Marion actively mentors graduate students through co-authorship on significant publications, with advisees contributing to antimicrobial resistance studies and behavioral health research. His professional network spans key organizations including the National Environmental Health Association and American Public Health Association, facilitating collaborative grant work on water safety initiatives. Current projects likely involve Kentucky watershed monitoring and international food safety partnerships. His laboratory operations support field-intensive environmental sampling and microbiological analysis, with recent work emphasizing rapid detection methods for water contaminants. Community engagement remains central to his approach, translating research into practical sanitation guidelines for public health practitioners.

DeeAnn ReederView profile

Professor

• Disease Ecology
• Biodiversity
• Conservation Biology
• +7 more

DeeAnn Reeder is a Professor of Biology at Bucknell University, where she teaches courses in Wildlife and Emerging Infectious Diseases and Mammalogy. She holds a Research Associate appointment at the Smithsonian Institution's National Museum of Natural History. Her research spans disease ecology, biodiversity, and conservation, with a particular focus on bat immunology and ecology. PhD, University of California, Davis M.S., University of California, Davis B.A., University of California, Berkeley Professor Reeder specializes in bat disease ecology, particularly white-nose syndrome (WNS) and viral pathogens including Ebola. Her lab employs diverse methods from traditional fieldwork to transcriptomics, examining the relationships between bat health, ecosystem health, and human disease risk. She has conducted extensive research in Africa (particularly Uganda and South Sudan) and North America, investigating bat immune responses to pathogens and biodiversity conservation. Her research program has received funding from the National Science Foundation, National Institutes of Health, National Geographic, US Fish & Wildlife Service, and the Woodtiger Fund. She is the editor of Mammal Species of the World (3rd edition) and author of 'The Lives of Bats: A Natural History' (2025). Her work includes the South Sudan Diversity Cam citizen science project cataloging animals from over 475,000 camera trap photos. Presidential Professor of Biology (2015-2018) Extensive publication record in leading journals including Nature Communications, PLoS Pathogens, and Science Editor of Mammal Species of the World (3rd edition) Author of 'The Lives of Bats: A Natural History' (2025) Professor Reeder mentors numerous students in her research, with many appearing as co-authors on publications. Her lab conducts field work in partnership with the Smithsonian Institution, Wildlife Services, Muni University, and others, focusing on biodiversity surveys in understudied habitats. She is actively involved in policy discussions about preventing future pandemics through ecological approaches, as featured in eCornell webinars and National Geographic.

Phillip D. VoegelView profile

Associate Professor

• Environmental Chemistry
• Electroanalytical Chemistry
• Sensor Development
• +5 more

Dr. Phillip D. Voegel serves as Associate Professor of Chemistry at Kentucky Wesleyan College, teaching core courses including Concepts in Chemistry, General Chemistry, Analytical Chemistry, Instrumental Techniques, and Advanced Environmental Chemistry. Previously, he held faculty positions at Southeastern Louisiana University (where he coordinated undergraduate chemistry and chaired the safety committee), Midwestern State University (as environmental science program coordinator), and Southern Illinois University at Carbondale as an instructor. B.A. in Chemistry, Wabash College (1991) Ph.D. in Chemistry, University of Louisville (1997) under Dr. Richard P. Baldwin His research spans environmental chemistry and electroanalytical techniques, evolving from fundamental electrochemical detector development to applied environmental monitoring. Early work focused on carbohydrate detection using active metal electrodes and reverse osmosis effluent impacts on river microbiology. At Southeastern Louisiana University, he investigated nutrient changes in Lake Maurepas post-Hurricane Katrina and hydrocarbon contamination from the Deepwater Horizon spill. Current research examines spatial distribution of coal-derived metal pollutants (cadmium, mercury, lead) along railway corridors using atomic spectroscopy of soil samples. Publications reveal a trajectory from capillary electrophoresis/electrochemical detection methods to environmental impact assessments, consistently featuring undergraduate co-authors. Key themes include sensor development for pollutants, disaster response monitoring, and community-focused educational outreach. His work bridges instrumental analysis with real-world environmental challenges. Finalist, American Chemical Society ChemLuminary Award for Student-to-Student Chemistry Initiative Dr. Voegel secured over $370,000 in research funding from Louisiana Board of Regents, NOAA, EPA, ACS Project SEED, and internal university grants. These projects engaged 50+ undergraduate and high school students, producing seven publications and 88 conference presentations, with over 60% of students presenting their work. His grant portfolio emphasizes student involvement in field sampling and instrumental analysis. He directs environmental research projects on coal transport pollution, training students in microwave plasma atomic spectroscopy and field sampling techniques. His laboratory synthesizes metal pyridinoporphyrazine compounds for electrochemical sensors and catalysis, maintaining strong connections to environmental monitoring applications in Kentucky and Louisiana watersheds.

Chulhee YunView profile

Assistant Professor

• Deep Learning Theory
• Optimization
• Machine Learning Theory

Chulhee Yun is an Ewon Assistant Professor at KAIST Kim Jaechul Graduate School of AI (KAIST AI), with a joint affiliation starting September 2025 at KAIST Graduate School of AI for Math and a part-time Visiting Faculty Researcher position at Google Research. He directs the Optimization & Machine Learning (OptiML) Laboratory at KAIST AI. Education: PhD in Elec. Eng. & Comp. Sci., 2016–2021, Massachusetts Institute of Technology MSc in Electrical Engineering, 2014–2016, Stanford University BSc in Electrical Engineering, 2007–2014, KAIST Chulhee Yun's research focuses on deep learning theory, optimization, and machine learning theory. His work bridges theoretical foundations with practical applications in neural network training, addressing fundamental questions about optimization dynamics, generalization capabilities, and the mathematical properties of deep learning models. His research spans from theoretical analyses of gradient-based optimization methods to practical techniques for improving neural network training and performance. His recent publications demonstrate a strong focus on understanding the fundamental properties of neural network training dynamics, optimization algorithms, and generalization. The research spans theoretical analyses of stochastic optimization methods like SGD, investigations into the properties of transformers and language models, and practical techniques for improving neural network training efficiency. A notable trend is the combination of rigorous theoretical analysis with practical implications for deep learning systems. Scientific Awards: KAIA Outstanding Paper Award at KAIA Summer Conference 2025 (CKAIA 2025) KT Best Paper Award at KAIA Summer Conference 2025 (CKAIA 2025) Best Paper Award at KAIA Fall Conference 2024 (JKAIA 2024) KT Best Paper Award at KAIA Summer Conference 2024 (CKAIA 2024) KAIA Outstanding Paper Award at KAIA Summer Conference 2023 (CKAIA 2023) NAVER Outstanding Theory Paper Award at KAIA-NAVER Joint Fall Conference 2022 (JKAIA 2022) Honorable Mention at NYAS Machine Learning Symposium 2020 Poster Awards Notable AC for NeurIPS 2023 Top Reviewer at ICML 2025 Professor Yun actively mentors PhD, Master's, and undergraduate students through the OptiML Laboratory. His research group includes several PhD students (Jaeyoung Cha, Hanseul Cho, Yujun Kim, Junghyun Lee, Junsoo Oh, Baekrok Shin), Master's students, and undergraduate interns. He has successfully guided former students such as Geonhui Yoo, Jaewook Lee, and Dongkuk Si to completion. His service to the academic community includes serving as Social Chair for ICML 2026, Conference Area Chair for ICLR 2025-2026 and NeurIPS 2023-2025, and as a reviewer for numerous top conferences and journals. Chulhee Yun directs the Optimization & Machine Learning (OptiML) Laboratory at KAIST, which focuses on advancing the theoretical foundations of machine learning and optimization. The lab investigates fundamental questions about neural network training dynamics, optimization algorithms, and generalization properties, with the goal of bridging theoretical insights with practical applications in deep learning systems.

Daniela HesseView profile

Assistant Professor

• Paediatric Dentistry
• Dental Caries
• Restorative Dentistry
• +3 more

Daniela Hesse is an Assistant Professor in the Department of Paediatric Dentistry at the Academic Centre for Dentistry Amsterdam (ACTA). Her ORCID identifier is 0000-0002-0912-4152. She teaches courses including Cariology and Preventive Dentistry and contributes to clinical education programs. Her research focuses on evidence-based paediatric dentistry with emphasis on: Clinical management of dental caries in children Restorative techniques including atraumatic treatments and glass ionomer cement applications Saliva composition analysis across developmental stages Clinical trial methodologies and systematic reviews Management of enamel defects like molar-incisor hypomineralisation Recent publications (2024-2025) demonstrate strong focus on paediatric oral health assessment methods, restorative treatment outcomes, and practice patterns analysis. Research methodologies prominently feature systematic reviews, scoping reviews, and survey-based studies across dental disciplines. She maintains active editorial roles as peer reviewer for multiple dental journals including European Journal of Oral Sciences , Community Dentistry and Oral Epidemiology , Evidence-Based Dentistry , Caries Research , and International Journal of Paediatric Dentistry .

Gabriel EilertsenView profile

Associate Professor

• Machine Learning
• Computer Vision
• Medical Imaging
• +5 more

Associate Professor Gabriel Eilertsen is affiliated with Linköping University's Department of Science and Technology (ITN) and the Media and Information Technology (MIT) research group. He holds a PhD in computer graphics and image processing, with expertise in high dynamic range (HDR) imaging and machine learning. As a core member of the Analytic Imaging Diagnostics Arena (AIDA) and affiliated researcher at CMIV (Center for Medical Image Science and Visualization) , he bridges academia and healthcare innovation. Research focuses on machine learning applications in computer vision , medical imaging diagnostics , and data-centric deep learning . Current projects include synthetic image generation for neural network training, evaluation of HDR reconstruction methods, and enhancing fairness in GANs. He leads a CENIIT-funded project on generative deep learning for medical imaging. Key contributions span synthetic data synthesis techniques, HDR imaging protocols, and AI-driven healthcare solutions. Notable work includes leveraging deepfake-like technology for generating AI-based medical images from the SCAPIS study. His interdisciplinary approach addresses challenges in domain adaptation, uncertainty quantification, and perceptual evaluation metrics for imaging systems. Affiliations include the Wallenberg AI, Autonomous Systems and Software Program (WASP) as an AI/MLX Assistant Professor, reflecting his leadership in national AI initiatives. His research emphasizes practical applications of AI in healthcare diagnostics and fundamental advancements in image generation and processing algorithms.

• Causal Machine Learning
• Healthcare Informatics
• Medical Data Analysis
• +3 more

Dr. Vinod Kumar Chauhan is a Research Fellow at the Institute of Biomedical Engineering, University of Oxford. His work focuses on causal machine learning and healthcare applications, including individualized treatment effects estimation and addressing sample selection bias in medical data. Previously, he held a postdoctoral position at the University of Cambridge and earned his PhD from Panjab University, Chandigarh, India. Education: BSc, MCA, PhD (Panjab University, India). Research interests include causal inference, healthcare informatics, and graph neural networks for EHR analysis. Recent projects involve postoperative atrial fibrillation prediction and optimizing treatment effects for composite outcomes. Key Awards: 2021 Institute for Manufacturing Postdoctoral Award (Cambridge), Travel Grant for Asian Conference on Machine Learning 2017. Selected Talks: Keynote on HyperNetworks (2023 Panjab University), invited talks on treatment effects estimation (IISER Pune, 2023). Labs/Teams: Computational Health Informatics group under Prof. David Clifton. Active in departmental EDI committee roles.

• Computer Vision
• Medical Imaging
• Artificial Intelligence
• +6 more

Seong Tae Kim is a Professor at Kyung Hee University's Department of Biomedical Engineering. He previously held affiliations at the Technical University of Munich (Germany) and KAIST's Image and Video Systems Laboratory in South Korea. His research focuses on computer vision, medical imaging, and AI applications in healthcare. Kim's work emphasizes explainable AI, neural network interpretability, and deep learning for medical diagnosis. He has contributed to advancements in knowledge graph-based reasoning, video object segmentation, and generative models for medical data synthesis. His research often bridges computer vision techniques with biomedical applications, such as cancer diagnosis and surgical phase recognition. Over 100 publications since 2003 highlight his expertise in neural networks, federated learning, and robust training methods. Notable projects include developing frameworks for COVID-19 CT analysis and generating realistic biomedical datasets. He collaborates widely with institutions like the Max Planck Institute for Informatics (via Nassir Navab's lab) and Samsung Research. Current research interests include interpretable machine learning models, longitudinal medical image analysis, and AI-driven healthcare solutions. His work frequently appears in top conferences like CVPR, MICCAI, and ECCV.

• Theoretical Computer Science
• Complexity Theory
• Machine Learning Theory
• +2 more

Li-Yang Tan is an Assistant Professor of Computer Science at Stanford University. His research focuses on theoretical computer science, particularly complexity theory and its intersections with machine learning and algorithm design. He holds a Ph.D. from Columbia University, advised by Rocco Servedio, and has been recognized with prestigious awards including the Sloan Fellowship and best paper awards at FOCS, CCC, and SAT conferences. His work bridges foundational theoretical contributions with practical algorithmic innovations, emphasizing topics like decision tree learning, computational hardness, and pseudorandomness. Notable achievements include polynomial-time decision tree learning algorithms, hardness results for learning problems, and contributions to coding theory and property testing. He collaborates extensively with researchers such as Guy Blanc, Caleb Koch, and Carmen Strassle on projects addressing core challenges in computational complexity. Tan's publications span top venues like FOCS, STOC, and JACM, reflecting his impact on areas such as query complexity, certification algorithms, and parallel computation lower bounds. His research also extends to cryptographic primitives and information-theoretic methods, showcasing a broad yet deeply technical approach to theoretical computer science.