جست‌وجوی استاد و پژوهشگر

Atika Khuranaمشاهده پروفایل

دانشیار

• Developmental Psychology
• Prevention Science
• Adolescent Risk-Taking
• +۴ مورد دیگر

Atika Khurana is an Associate Professor in the Counseling Psychology and Human Services Department at the University of Oregon's College of Education, and a Research Scientist at the Prevention Science Institute. Her work focuses on developmental-ecological approaches to adolescent risk-taking behaviors. Postdoctoral training (2010-2012): Annenberg Public Policy Center, University of Pennsylvania PhD (2010): The Ohio State University (Major: Human Development and Family Science, Minor: Quantitative Psychology) Her research examines: Self-regulation development in adolescents Media/technology effects on health behaviors Personality-risk interactions (impulsivity, sensation seeking) Longitudinal modeling of behavioral trajectories Parenting behavior moderation Substance abuse prevention Recent publications analyze adolescent risk behaviors through the lens of media exposure, peer influences, and neurocognitive development, employing advanced statistical methods like latent variable modeling and multi-level analysis. Her work has been funded by NIDA, NICHD, and IES. NIDA Early Career Poster Award (2014) Distinguished Research Fellow, Annenberg Public Policy Center (2013) University of Oregon Faculty Research Award (2013) Dr. Khurana serves as Research Scientist at the Prevention Science Institute and will accept new PhD students in Counseling Psychology and Prevention Science in 2026-2027. Her methodological expertise includes missing data analysis, mediation models, and longitudinal research design.

Kent McIntoshمشاهده پروفایل

استاد

• Positive Behavior Support
• Equity in School Discipline
• Sustainability of Evidence-Based Interventions
• +۳ مورد دیگر

Kent McIntosh is a Professor and Philip H. Knight Chair in Special Education at the University of Oregon’s College of Education . He directs Educational and Community Supports and co-directs the Center on Positive Behavioral Interventions and Supports (PBIS) , focusing on equity in school discipline and sustainability of evidence-based practices. Education: PhD in School Psychology, University of Oregon (2005) MS in Special Education, University of Oregon (2003) Certificate in Curriculum and Instruction, Lesley University (2002) BA in English, Duke University (1997) His research interests include: Racial and ethnic equity in school discipline Implicit bias mitigation strategies Positive Behavioral Interventions and Supports (PBIS) Culturally responsive practices Implementation science for educational interventions The articles reflect trends in school discipline reform , PBIS scalability , and equity-focused interventions , with subfields spanning implicit bias analysis , tiered support systems , and school climate assessment . Scientific Awards: 2023 Advancing Diversity, Equity, and Inclusion Outstanding Research Award 2022 APA School Psychology Article of the Year 2015 Council for Exceptional Children Distinguished Early Career Award He leads federal grants testing interventions to reduce disciplinary disparities and improve high school completion rates . His work emphasizes collaborative teaming and systemic PBIS implementation .

Stephen C. Gravesمشاهده پروفایل

استاد

• Operations Research
• Supply Chain Optimization
• Production Planning
• +۵ مورد دیگر

Stephen C. Graves is the Abraham J. Siegel Professor of Management and a tenured Professor in the Operations Management group at MIT Sloan School of Management, with a joint appointment in the Department of Mechanical Engineering and affiliations at Singapore University of Technology and Design. Education : AB in Mathematics and Social Sciences, Dartmouth College MBA, Dartmouth College MS and PhD in Operations Research, University of Rochester Research Focus : Applications of operations research in manufacturing, distribution, and service operations, particularly in supply chain optimization, strategic inventory positioning, production planning, and online retailing challenges. Key Article Trends : Recent works explore digital twin technology, closed-loop supply chains, multi-echelon coordination, and automation in fulfillment systems, reflecting his expertise in integrating data analytics with operational frameworks. Honors : INFORMS Kimball Medal (2023) INFORMS Prize National Academy of Engineering Induction Fellowships: INFORMS, MSOM, POMS Advising & Industry Impact : Supervised 22+ PhD students, collaborated with companies like Amazon, IBM, and Boeing, and served as Chief Science Advisor to JDA Software.

Matthias Beckerمشاهده پروفایل

استاد

• Human-Computer Interaction
• Simulation and Modeling
• Distributed Systems
• +۶ مورد دیگر

Matthias Becker is a Professor at the Institute for Practical Computer Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been a core member of the Human-Computer Interaction group since 2019. He serves as Internship Coordinator for Computer Science and Computer Engineering and holds key roles in the Computer Science Examination Board and Selection Committee, actively shaping academic governance and student development. His academic journey began with PhD studies at the University of Bremen (1996-2000) supported by a DFG grant, followed by a postdoctoral permanent position at Leibniz University Hannover (2000-2019), an Associated Assistant Professor role at École des Mines de Nantes (2000), and a Habilitation in Computer Science in 2013. This foundation enabled his transition to a full professorship in 2019. Becker's research spans Human-Computer Interaction, Simulation and Modeling, and Bio-inspired Computing, with applications in agriculture, renewable energy, and manufacturing. His work integrates distributed systems, optimization algorithms, and wireless sensor networks to solve complex real-world problems, such as greenhouse monitoring, wind farm logistics, and tire noise reduction. Recent publications reveal a strategic focus on practical validation of simulation models and cross-domain applications of nature-inspired algorithms. His 15 most recent publications (2018-2024) demonstrate consistent innovation in applying simulation techniques to offshore wind farm installation, agricultural pest management, and sports science. These works emphasize real-world validation, collaborative problem-solving, and the development of domain-specific optimization frameworks that bridge theoretical algorithms and industrial implementation. As Internship Coordinator, Becker facilitates critical industry-academia connections for students, while his examination board responsibilities ensure rigorous academic standards. His leadership in the Human-Computer Interaction group drives research on interactive systems for agriculture, energy, and health, with particular emphasis on user-centered design in complex operational environments like wind farm logistics and greenhouse automation.

• Sports Science
• Biathlon Training
• Physiological Responses
• +۷ مورد دیگر

Marko Laaksonen is a Professor at the Department of Health Sciences (HOV) at Mid Sweden University, specializing in winter sports biomechanics and performance analysis. He is affiliated with the Swedish Winter Sports Research Centre, focusing on biathlon and cross-country skiing. His research examines physiological responses to training, rifle carriage effects on skiing mechanics, and performance indicators in biathlon. Key areas include exercise intensity distribution, shooting precision, and muscle metabolism during endurance activities. Recent publications analyze biathlon shooting performance under varying conditions, training load measurement, and equipment impacts on athlete efficiency. Over 15 articles demonstrate expertise in winter sports analytics and physiological monitoring.

Jianke Zhuمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۳ مورد دیگر

Jianke Zhu is a Professor at the College of Computer Science and Technology of Zhejiang University . He obtained his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and conducted postdoctoral research at the BIWI Computer Vision Lab, ETH Zurich . His research focuses on Computer Vision and Machine Learning , with a particular emphasis on 3D scene understanding, LiDAR-based mapping, and neural rendering. Dr. Zhu’s research spans several subfields, including 3D Reconstruction , Semantic Segmentation , Multimodal Learning , and Autonomous Driving . His work integrates Neural Networks , LiDAR Processing , and Uncertainty Quantification to address challenges in real-time and adverse conditions. Selected Recent Trends: 2025 publications highlight his work in Hexagonal Mesh-based Neural Rendering , Instance-aware 3D Scene Understanding , and Efficient Visual Projectors for Multimodal LLMs . Earlier works include Box2Mask for Instance Segmentation (2024) and Token Selection for Point Cloud Learning (2025). Scientific Awards : Senior member of the IEEE Advising and Grants : As a Doctoral Supervisor , he mentors students in advanced topics like LiDAR Odometry and Multi-view Stereo Recovery . His projects have attracted funding for autonomous driving , 3D scene modeling , and neural rendering .

• Longitudinal Data Analysis
• Latent Variable Methods
• Dyadic Analysis
• +۷ مورد دیگر

Dr. Susan M. Sereika is a Professor in the Department of Health and Community Systems at the University of Pittsburgh School of Nursing . She holds secondary appointments in the Department of Biostatistics and Health Data Science and the Department of Epidemiology in the School of Public Health, as well as affiliations with the Clinical and Translational Science Institute and UPMC Hillman Cancer Center's Biostatistical Facility. Associate Dean for Computing and Information Technology (School of Nursing) Faculty Statistician (Office of Research Scholarship) Active in Senate Computing & IT Committee (Pitt, 1992-present) Her statistical expertise focuses on: Longitudinal data analysis for intensive monitoring (Aardex MEMS, Fitbit, actigraphy) Latent variable methods (group-based trajectory, dual/multi-trajectory analyses) Dyadic analysis Model assessment She collaborates on weight loss, lifestyle self-management, symptom management, and regimen adherence research. Academic Contributions include: PhD-level courses: Advanced Quantitative Methods Seminar (NUR 3290) Independent study supervision (NUR 3060) Statistical mentorship for honors/masters/doctoral committees Consulting for NIH reviews and journal editorial boards

Colleen Marie Careyمشاهده پروفایل

دانشیار

• Health Economics
• Public Policy
• Disability Insurance
• +۴ مورد دیگر

Colleen Marie Carey serves as Associate Professor of Economics and Public Policy at Cornell University, Faculty Research Fellow at the National Bureau of Economic Research (NBER), and Co-Editor of the American Journal of Health Economics. Her research focuses on health care and health insurance market economics, with prior roles including Robert Wood Johnson Scholar at the University of Michigan, Staff Economist at the Council of Economic Advisers, and 2024-25 CMS Fellow. Dr. Carey's research centers on disability insurance dynamics, Medicare/Medicaid policy analysis, and health disparities. She investigates how economic conditions affect Social Security Disability Insurance enrollment across racial groups, examines provider payment reforms' impact on low-income populations, and analyzes pharmaceutical marketing influences on physician behavior. Her work consistently leverages administrative data to assess policy impacts on vulnerable communities, with particular attention to opioid epidemic interventions and racial/ethnic health inequities. Her publication trends reveal sustained focus on health insurance market design and vulnerable populations, with recent work expanding into pandemic response equity and pharmaceutical regulation. Articles increasingly integrate racial disparity analysis with economic modeling, demonstrating methodological evolution from single-program evaluations to cross-cutting policy assessments. Finalist for National Institute for Health Care Management Foundation’s 31st Annual Health Care Research Award Finalist for National Institute for Health Care Management Foundation’s 27th Annual Health Care Research Award Finalist for National Institute for Health Care Management Foundation’s 24th Annual Health Care Research Award Dr. Carey directs significant research grants through the Retirement and Disability Research Center, including project NB20-01 on health variations among disability insurance recipients. Her work receives consistent federal funding with direct policy relevance, evidenced by CMS fellowship appointment and frequent citations in government reports. While student advising isn't documented in source materials, her NBER affiliation and journal editorship indicate active mentorship within the health economics community. She contributes to the Retirement and Disability Research Center's collaborative framework, working with multi-institutional teams to analyze longitudinal administrative data from Medicare and Social Security systems.

Wayne Wobckeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.

Robert D. Kleinbergمشاهده پروفایل

استاد

• Algorithms
• Theoretical Computer Science
• Economic Aspects of Algorithms
• +۵ مورد دیگر

Robert D. Kleinberg is a Professor of Computer Science at Cornell University's Department of Computer Science and a member of the field of Information Science. He received his Ph.D. from MIT in 2005 and previously worked at Akamai Technologies designing internet content delivery networks. His research bridges theoretical computer science and practical applications in electronic commerce, networking, and information systems. Research Interests: Kleinberg's work focuses on the design and analysis of algorithms, with special emphasis on economic aspects of algorithms, online learning, random processes in networks, and applications to electronic commerce and information retrieval. His research combines theoretical rigor with real-world impact, exploring how algorithmic principles can optimize complex systems. Publication Trends: Kleinberg's recent publications demonstrate a consistent focus on online optimization, algorithmic fairness, network design, and learning algorithms. His work frequently appears in top-tier venues spanning theoretical computer science (STOC, FOCS), machine learning (NeurIPS, COLT), and networking (NSDI). The publications reflect interdisciplinary approaches combining computer science, economics, and applied mathematics. Awards and Honors: Microsoft Research New Faculty Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Best Paper Award at ACM EC 2014 Advising and Academic Leadership: Kleinberg actively mentors Ph.D. students and postdoctoral researchers, with current advisees including Raunak Kumar, Princewill Okoroafor, and Tegan Wilson. He has supervised numerous graduates who now hold academic positions worldwide. His teaching includes core algorithm courses (CS 4820, CS 6820) and specialized topics at both undergraduate and graduate levels.

Debabrota Basuمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۶ مورد دیگر

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.

• Artificial Intelligence
• Machine Learning
• Distributed Learning
• +۷ مورد دیگر

Berta María Guijarro Berdiñas is a Researcher in the Department of Computer Science and Artificial Intelligence at the University of A Coruña , Spain. She is affiliated with the Laboratory for Research and Development in Artificial Intelligence and teaches courses like Machine Learning , Development of Intelligent Systems , and Programming at both undergraduate and postgraduate levels. Research Focus: Her work lies at the intersection of Artificial Intelligence , Machine Learning , and Knowledge-Based Systems . Key contributions include frugal learning (limited data), anomaly explanation , and distributed learning for edge devices. She applies these to areas like health informatics , forest fire management , and human-robot interaction . Recent Publications span explainable AI , anomaly detection , multi-agent systems , and low-power machine learning . Her articles appear in top venues like Expert Systems with Applications and IEEE Transactions on Neural Networks and Learning Systems . Grants & Projects include EU-funded initiatives, Spanish Ministry of Science grants, and regional collaborations. She focuses on AI for healthcare , smart systems , and distributed learning .

• Statistical genomics
• Big omics data analysis
• Multivariate analysis
• +۵ مورد دیگر

Dr. Lin Chen is a tenured Professor of Biostatistics in the Department of Public Health Sciences at The University of Chicago, where they have been faculty since 2010, progressing from Assistant Professor (2010-2017) to Associate Professor (2017-2024) and now Professor (2024-present). Previously, they completed postdoctoral research at Fred Hutchinson Cancer Research Center (2008-2010) under Drs. Ross L. Prentice and Li Hsu, and at Princeton University (2008) under Dr. John D. Storey. Dr. Chen earned their Ph.D. in Biostatistics from the University of Washington in 2008 under Dr. John D. Storey, following a B.S. in Economics from Peking University (2002). Their research focuses on developing advanced statistical methods for statistical genomics , big omics data analysis , multivariate analysis , missing data analysis , and mixed-effects models . Their work has significantly advanced methodologies in Mendelian randomization, multi-omics integration, and genetic association studies. Dr. Chen's recent publications reveal a strong emphasis on integrative approaches that connect multiple layers of genomic data across different biological contexts. Their research demonstrates particular expertise in developing statistical frameworks that bridge genetic variation with molecular phenotypes and complex traits, often addressing critical challenges in causal inference and mediation analysis within genomic studies. The work consistently appears in top-tier journals including Nature Communications, Nature Genetics, and The American Journal of Human Genetics. The Best Paper in Genetic Epidemiology Award for the Year 2021 The Departmental Best Dissertation Award 2017-2018 (awarded to Jiebiao Wang) Dr. Chen actively mentors graduate students and postdoctoral fellows, with several former lab members now in faculty positions or industry roles at major pharmaceutical companies. Their lab currently has ongoing postdoctoral openings. They have developed multiple widely-used software packages including GMAC, Primo, mvMISE, ofGEM, MixRF, mixEMM, PEMM, Trigger, RHT, SNPath, and EigenR2, available through CRAN, GitHub, and Bioconductor. Dr. Chen teaches courses including Applied Regression Analysis, Introduction to Biostatistics, Statistical Analysis with Missing Data, and Introductory Statistical Genetics at the University of Chicago.

Emmanuel Morinمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Bilingual Terminology Extraction
• +۵ مورد دیگر

Emmanuel Morin is a Full Professor in Computer Science at the University of Nantes, France. He is affiliated with the Computer Science Department of Nantes Institute of Technology (IUT) and leads the Natural Language Processing (TALN) team at the Digital Sciences Laboratory of Nantes (LS2N UMR CNRS 6004). His research focuses on computational linguistics with particular emphasis on multilingualism and multimodality. His academic leadership includes serving as Head of the NLP team (2017-present), Co-responsible for the ATAL (Machine Learning and Natural Language Processing) option of the Computer Science Master (2017-present), and Director of the Nantes computer science training department (2014-present). He also co-edits the Traitement Automatique des Langues (TAL) journal and serves on the steering committee of ATALA since 2004. Current projects: Atlantic 2020, ALALA Project (2019-present), ANR ADDICTE (2017-present), and Atlanstic 2020 RAPACE Project (2016-present) Past projects: Labex CominLabs LIMAH (2014-2019), ANR CRISTAL (2012-2016), and European FP7-ICT TTC project (2010-2012) Professor Morin's research spans natural language processing with a focus on bilingual terminology extraction and comparable corpora analysis. His recent work (2020-2022) shows strategic expansion into medical informatics applications like FrenchMedMCQA, while maintaining his foundational work on domain adaptation of language models using graph-based networks. His publications demonstrate a consistent trajectory from early terminology extraction methods to contemporary applications in specialized language processing. He has supervised numerous PhD students, with current advisees including Kévin Espasa, Martin Laville, Merieme Bouhandi, and Antoine Caubrière. His past students have made significant contributions to the field of natural language processing, continuing his research legacy in academia and industry.

Zeynep Erson Omayمشاهده پروفایل

استادیار

• Computational Biology
• Genomics
• Epigenomics
• +۶ مورد دیگر

Dr. Zeynep Erson Omay serves as an Assistant Professor in the Department of Neurosurgery and Biomedical Informatics & Data Science at Yale School of Medicine. Her work bridges computational biology with neurosurgical oncology, focusing on precision medicine applications for brain tumors, with particular emphasis on understanding tumor heterogeneity and molecular mechanisms of CNS tumors. Dr. Erson Omay's educational background includes: PhD in Computer Science from Case Western Reserve University (2011) MS in Computer Science from Bilkent University (2005) BS in Computer Science from Bilkent University (2003) Her research focuses on computational analysis of multi-omic datasets to understand tumor heterogeneity, particularly in central nervous system tumors. Dr. Erson Omay specializes in genomic, transcriptomic, and epigenetic profiling of brain tumors, with emphasis on meningiomas, glioblastomas, and rare CNS tumor subtypes. She leads the Erson Lab, which develops bioinformatics approaches to study large datasets and reveal molecular mechanisms in tumor formation, progression, and clinical outlier subgroups. Her work in precision medicine aims to decipher the molecular architecture of individual tumors to guide personalized treatment approaches, with significant contributions to understanding tumor ecosystems and evolutionary patterns in brain cancers. Dr. Erson Omay's scientific contributions span neuro-oncology, computational biology, and precision medicine, with a strong emphasis on translating genomic findings into clinical applications. Her publications demonstrate consistent innovation in applying computational methods to complex neurosurgical problems, with particular focus on tumor heterogeneity, molecular classification, and racial disparities in tumor genomics. Her notable scientific awards include: 10x Genomics 2021 Pilot Award (2022) Mission Bio Tapestri Grant (2022) Case Western Reserve University, Research ShowCASE-Best Poster Award (2007) Dr. Erson Omay actively mentors students and researchers at various levels, including undergraduate students, graduate students, postdocs, and postgraduate associates. She collaborates extensively within Yale's neurosurgery department and across disciplines to advance computational approaches to brain tumor research. Her work is supported by various grants that enable the development of novel bioinformatics platforms for tumor genomic characterization. She leads the Erson Lab, which focuses on three major research areas: Tumor Ecosystem (studying interactions among tumor and immune cells), Tumor Evolution and Heterogeneity (understanding temporal and spatial tumor evolution), and Precision Medicine (applying genomic techniques to personalize brain tumor treatment). The lab employs diverse omics technologies to explore brain tumor biology and develop computational methods for precision medicine applications.