جست‌وجوی استاد و پژوهشگر

Laika Satishمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• Artificial Intelligence Ethics
• Machine Learning Applications
• +۳ مورد دیگر

Laika Satish is an Assistant Teaching Professor in the Department of Computer Science at the University of Missouri–St. Louis , affiliated with the College of Arts and Sciences . Her office is located in 330 ESH, and she maintains regular student consultation hours through both Zoom and in-person sessions. She earned her MS in Computer Applications from the University of Mumbai in 2006. Her research spans interdisciplinary domains including: Computer Science Education : Developing AI-enhanced pedagogical tools, process visualization techniques, and integrity-preserving frameworks for programming education AI Ethics & Accountability : Examining trust models, algorithmic transparency, and societal impacts of generative AI Biomedical Informatics : Creating machine learning approaches for Alzheimer's biomarker discovery and COVID-19 proteomic analysis Algorithmic Foundations : Optimizing data cleaning processes and analyzing computational complexity of evolutionary algorithms Publication analysis reveals three dominant themes across her recent work (2021-2025): Innovations in computer science education leveraging explainable AI and process visualization Ethical frameworks for algorithmic accountability and trust in AI systems Computational methods for biomarker discovery in neurodegenerative and viral diseases Her work consistently bridges theoretical computer science with applied domains including healthcare, education, and social informatics.

Hans-Jörg Schulzمشاهده پروفایل

دانشیار

• Visual Analytics
• Information Visualization
• Graph Visualization
• +۴ مورد دیگر

Hans-Jörg Schulz serves as an Associate Professor in the Department of Computer Science at Aarhus University, Denmark, specializing in visual analytics and information visualization. His research bridges computer science with interdisciplinary applications in food science, neuroscience, and material engineering. He earned his Doctorate in Computer Science (2010) and Diplom (Master's equivalent, 2004) from the University of Rostock, focusing on explorative graph visualization and visual data mining for complex structures. His academic trajectory reflects deep expertise in transforming complex data into actionable visual insights through innovative methodological frameworks. Schulz's research centers on advancing visual analytics methodologies, particularly progressive visual analytics where data processing and user interaction occur simultaneously. His work establishes foundational techniques for visual guidance systems, explainable AI interfaces, and specialized visualization tools for domains like EEG analysis and food rheology. Recent publications demonstrate a strategic expansion into haptic feedback integration, agent-based visualization design, and cross-disciplinary applications requiring novel visual fingerprinting techniques. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Human-centered progressive analytics with focus on trust calibration and cognitive load management, (2) Domain-specific visualization frameworks for complex data like anisotropic food structures and EEG artifacts, and (3) Novel interaction paradigms incorporating force feedback and malleable interfaces. His work consistently emphasizes practical usability while pushing technical boundaries in visual representation. Schulz's contributions have been recognized with significant awards including IEEE InfoVis Best Poster (2010), EuroVis 3rd Best Paper (2012), VDA Best Paper (2015), Cybercartography Competition 1st Place (2022), and IEEE SciVis Contest 2nd Place & Most Innovative Work (2023). These accolades highlight both theoretical innovation and practical impact in the visualization community. As an educator, he supervises PhD candidates and teaches core visualization courses including Data Visualization, Information Visualization, and Visual Analytics. His current ArtiPlex project (2024-present) develops multiplex analytics for EEG artifact detection, securing active research funding while demonstrating his commitment to translating visualization research into domain-specific solutions. Collaborative patterns across his 79 publications indicate strong partnerships with food scientists, neuroscientists, and HCI researchers.

• Software Security
• Formal Verification
• String Analysis
• +۵ مورد دیگر

Dr. Fang Yu is an Associate Professor at the Department of Management Information Systems, National Chengchi University, specializing in software security, formal verification, and string analysis. They hold a Ph.D. in Computer Science from the University of California, Santa Barbara. Research Expertise: Dr. Yu focuses on cybersecurity, formal methods for software verification, and machine learning applications in data clustering and adversarial example detection. Their work bridges theoretical computer science with practical security solutions. Publication Trends: Recent articles address biomedical data clustering ( scGHSOM ), explainable AI ( XFlag ), and adversarial defense mechanisms. Topics span bioinformatics, security verification, and fairness testing in neural networks. Awards: 資深優良教師(10年) (2020, National Chengchi University) 國科會研究獎勵 (2019, National Science Council, Taiwan) Projects: Principal Investigator for 15+ grants from Taiwan's National Science and Technology Council and Ministry of Education, focusing on AI security, IoT verification, and financial technology.

• Computer vision and pattern recognition
• Digital image processing
• Medical image processing
• +۴ مورد دیگر

Santa Di Cataldo is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino. His research focuses on computer vision, pattern recognition, digital image processing, and medical image processing, with applications in industrial systems and AI for manufacturing. Scientific Branch: IINF-05/A - Information Processing Systems ERC Sectors: PE6_8 - Computer graphics, computer vision, multi media, computer games ERC Sectors: PE6_11 - Machine learning, statistical data processing His work includes developing AI-driven anomaly detection frameworks, physics-informed neural networks for additive manufacturing optimization, and neuro-symbolic approaches for Industry 4.0 applications. He supervises PhD students in Artificial Intelligence and Computer Engineering programs, collaborating on projects like BIG (Blue Is Green) and PNRR-Complementary Plan. Premio Donna Innovazione (2010) He leads courses such as Machine Learning in Applications and Applied AI and Machine Learning , while contributing to bioinformatics and robotics-related teaching. His research is supported by IAM@PoliTo and EDA groups, utilizing LADISPE laboratory facilities.

Thomas Villmannمشاهده پروفایل

استاد

• Computational Intelligence
• Machine Learning
• Vector Quantization
• +۷ مورد دیگر

Thomas Villmann is a Professor of Computational Intelligence and Techno-Mathematics at Mittweida University of Applied Sciences in Germany. He serves as Deputy Spokesperson for the Mathematics Department, AI Coordinator at the university, Director of the Saxon Institute for Computational Intelligence and Machine Learning (SICIM), and President of the German Chapter of the European Neural Network Society (GNNS). His academic credentials include the German 'Dr. rer. nat. habil.' designation, indicating both doctoral and habilitation qualifications in natural sciences. Professor Villmann's research focuses on computational intelligence with particular emphasis on vector quantization, learning vector quantization, neural networks, and interpretable machine learning. His work spans theoretical developments in mathematical foundations of machine learning algorithms as well as practical applications in bioinformatics, remote sensing, medical diagnostics, and autonomous systems. He has developed mathematically sound methods for data-based analysis (clustering), decision support systems, data-based prediction models, and visualization of complex data. His recent publication record demonstrates a strong trend toward interpretable and explainable AI, with emphasis on vector quantization techniques applied across diverse domains including medical diagnostics (particularly breast cancer detection), fairness in machine learning, satellite remote sensing, and autonomous vehicle systems. Villmann's work consistently bridges theoretical mathematics with practical applications, maintaining a focus on making machine learning models more transparent, reliable, and ethically sound. As Director of SICIM and leader of the Computational Intelligence Research Group, Professor Villmann oversees an integrated research ecosystem focused on problem-oriented intelligent data analysis. His institutes aim to stimulate interest in computational intelligence among students and young researchers while supporting public institutions, authorities, and companies in data analysis both regionally and internationally. His leadership extends to organizing academic events and workshops, including serving as president of the German Chapter of the European Neural Network Society.

Karsten Borgwardtمشاهده پروفایل

استاد

• Machine learning
• Systems biology
• Bioinformatics
• +۳ مورد دیگر

Prof. Dr. Karsten Borgwardt is Director of the Research Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany. A leading figure in the intersection of machine learning, bioinformatics, and systems biology, he heads a multidisciplinary team that develops novel computational methods to extract knowledge from large biomedical data sets. Research Mission: The Borgwardt lab converges big data analytics and biomedical research . Two overarching goals drive their work: (1) Automatically generating new biological and medical knowledge from massive data via state-of-the-art machine-learning algorithms. (2) Understanding the molecular underpinnings of biological system function, with emphasis on personalized medicine and biomarker discovery. Their methodological toolbox spans graph neural networks, kernel methods, conformal prediction, deep learning on sequences and structures, and topological data analysis . Application domains include antimicrobial resistance prediction, protease engineering, acute-kidney-injury forecasting, coronary-artery-disease diagnostics, single-cell spatial proteomics, and Long-COVID immune profiling. Recent Publication Landscape (2023-2025): The group’s latest articles demonstrate a clear trend toward translationally relevant machine learning . High-impact venues such as Nature Communications , Science , ICLR , and RECOMB feature their work on: Data-driven protein engineering using DNA-recorded deep mutational scanning. Guaranteed antimicrobial resistance detection from MALDI-TOF spectra via conformal prediction. Graph-based biomarker discovery with theoretical guarantees. Deep phenotyping of human iPSC-derived neuronal networks to study disease mutations. Multi-modal learning that fuses genomics, proteomics, and clinical data for patient stratification. These contributions collectively advance both the theoretical foundations and real-world deployment of machine learning in medicine. Scientific Awards & Honors: While no explicit award list is provided, the breadth and impact of publications, invited book chapters, and keynote-level conference presentations (ICLR, RECOMB, ISMB/ECCB) testify to sustained international recognition. Laboratory & Collaboration Ecosystem: The Borgwardt lab operates at the Max Planck Institute of Biochemistry —a world-leading biomedical research campus. Collaborations span multiple Max Planck centers, university hospitals across Europe, and international consortia such as the EyeConic study on optogenetics therapy. The lab’s open-source footprint includes the Multi-SConES R package for multi-task network-regularized feature selection, fostering reproducible science across the community.

Tomaž Hočevarمشاهده پروفایل

استادیار

• Algorithms and Data Structures
• Machine Learning
• Network Analysis
• +۳ مورد دیگر

Tomaž Hočevar serves as an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana, where he is an active member of the Bioinformatics Laboratory. He teaches core undergraduate courses including Algorithms and Data Structures 1, Programming 2, and Machine Learning for Data Science 1, while significantly contributing to competitive programming through national contest organization and mentorship for both university and high-school students. His research integrates Algorithms and Data Structures, Machine Learning, and Network Analysis with a focus on local structural properties in biological networks. Current applications target circular RNA in liver cancer (project J3-4513), explainable foundation models for human gene expression (project L2-60154), and water-linked epidemiology of plant viruses (project L4-3179). He extends this work to educational domains through the DALI4US project developing data literacy curricula for primary schools. While only one 2014 publication on graphlet counting is explicitly cited, his active research portfolio reveals a strong trajectory in computational biology. Current projects emphasize explainable AI for genomic data interpretation, nanopore sequencing applications, and interdisciplinary approaches bridging computer science with oncology and plant virology, indicating significant methodological innovation in biological data analysis. No scientific awards were mentioned in the provided text. Dr. Hočevar secures competitive research funding primarily through the Slovenian Research Agency (ARRS) and Structural Funds: Current projects: J3-4513 (circular RNA in liver cancer, 2022-2025), DALI4US (data literacy for primary schools, 2024-2026), L2-60154 (explainable foundation models for gene expression, 2025-2027) Past projects: ARRS AI research program (2015-2020), BioPharm.si biologics initiative (2016-2020), L7-2632 nanopore sequencing (2020-2023), L4-3179 tobamovirus epidemiology (2021-2024) He maintains active collaboration within the Bioinformatics Laboratory at FRI, focusing on genomic data analysis pipelines, machine learning tool development for biological applications, and interdisciplinary research teams addressing cancer genomics and agricultural virology challenges through computational approaches.

Ziyang Liمشاهده پروفایل

استادیار

• Neurosymbolic Programming
• Machine Learning
• Programming Languages
• +۵ مورد دیگر

Ziyang Li is an Assistant Professor of Computer Science at Johns Hopkins University and a member of the Data Science and AI Institute. He holds a Ph.D. in Computer Science from the University of Pennsylvania (2025) and dual bachelor's degrees in Computer Science and Mathematics from UCSD (2019). Research Areas: Neurosymbolic Programming, AI4Code His research bridges programming languages and machine learning, focusing on neurosymbolic methods that combine symbolic reasoning with learning-based techniques. Applications span software security, computer vision, natural language processing, bioinformatics, and clinical decision-making. He developed Scallop , a neurosymbolic programming language, and Lobster , a GPU-accelerated framework for neurosymbolic applications, with impacts in cybersecurity and biomedical domains. Recent publications highlight neurosymbolic approaches for RNA structure prediction, Long COVID modeling, and safety-critical systems. His work emphasizes data-efficient learning, weak supervision, and hybrid AI for scalable reasoning. Scientific Awards : AWS Fellowship (2023) KPCB Fellows, Engineering (2018) NIH L3C Honorable Mention Award Li has mentored students including Jason Liu, Felix Zhu, and Eric Zhao, and served as Teaching Assistant for courses at UPenn and UCSD. He co-organized the TACPS Workshop and reviewed for NeurIPS, ICLR, and ICML.

Shmuel Avidanمشاهده پروفایل

استاد

• Object Tracking
• 3D Object Modeling
• Computer Vision
• +۳ مورد دیگر

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

Radu Zabetمشاهده پروفایل

مدرس ارشد

• Computational Biology
• Genomics
• Gene Regulation
• +۴ مورد دیگر

Dr Radu Zabet is a Senior Lecturer in Computational Biology at the Blizard Institute , Queen Mary University of London. He leads a research group within the Centre for Genomics and Child Health and is affiliated with the QMUL Epigenetics Hub . PhD in Computer Science from University of Kent Postdoctoral experience at University of Kent and University of Cambridge Established independent lab at University of Essex (2015) before relocating to Queen Mary in 2020 Research Interests: Computational modeling of transcription factor-DNA interactions Mechanistic understanding of 3D chromatin organisation Epigenetic regulation through histone modifications and DNA methylation Explainable AI applications in genomics Environmental epigenetics (air pollution exposure) Recent Publications Analysis: Key contributions to 3D chromatin organisation in model organisms Development of epigenetic analysis tools like DMRcaller Integration of machine learning with experimental validation Investigations into enhancer biology and histone code interpretation Environmental epigenetics through DNA methylation profiling Translational focus on genomic medicine applications Scientific Achievements: Awarded MRC Training Fellowship in Bioinformatics Maintains active research group with multiple projects spanning chromatin dynamics and epigenetic regulation Collaborations across computational biology , molecular biology , and medical research Supervision: Currently supervising 5 PhD students in genomic and epigenetic research Lab members: Duarte Polvora Brandao and team

Kenney Ngمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Biomedical Informatics
• +۶ مورد دیگر

Kenney Ng is a Researcher at IBM Research and the science program manager for the MIT-IBM Watson AI Lab . He focuses on applying data mining, machine learning, and AI techniques to real-world healthcare data, particularly in cardiovascular disease, diabetes, and genomic risk modeling. His career spans roles at IBM Software Group, iPhrase Technologies, and MIT-affiliated labs. Bachelor's, Master's, and PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology Kenney Ng’s research interests include biomedical informatics , machine learning , and genomic data analysis . His work connects AI with clinical applications, such as predicting disease progression, analyzing cardiometabolic risks, and developing causal inference frameworks. His publications emphasize AI in healthcare (e.g., drug screening, diabetes prediction, cardiovascular risk modeling) and methodological advancements in machine learning (e.g., bias formulas, latent space exploration). Fellow of the American Medical Informatics Association Senior Member of IEEE Kenney Ng collaborates with institutions like the Broad Institute , Massachusetts General Hospital , and Cleveland Clinic . He co-authored over 100 publications and 25 patents, with leadership roles in IBM’s Center for Computational Health and the MIT-IBM Watson AI Lab .

Scott Emrichمشاهده پروفایل

دانشیار

• Computational Genomics
• Bioinformatics
• Sequence Analysis
• +۶ مورد دیگر

Scott Emrich is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He also serves as the Interim Program Director for the UT-Oak Ridge Innovation Institute (UT-ORII) Ph.D. program in Data Science and Engineering. Dr. Emrich earned his B.S. in Biology and Computer Science from Loyola College in Maryland, followed by a Ph.D. in Bioinformatics and Computational Biology from Iowa State University. Upon completing his Ph.D., he received both the ISU Research Excellence Award and the university-wide Zaffarano Prize for Graduate Research. Before joining the University of Tennessee, he was the Director of Bioinformatics at the University of Notre Dame with faculty appointments in Computer Science and Engineering and Biological Sciences. His research focuses on genome-centric bioinformatics, parallel and distributed computing, and the integration of biological applications with second- and third-generation sequencing technologies. Dr. Emrich has published over 75 peer-reviewed publications in high-impact venues including Science (with 2 covers), PNAS, Nature, and Genome Research. His work has been cited over 14,300 times according to Google Scholar. His research group has introduced novel biological problems to the computer science community and developed bioinformatics frameworks that have contributed to high-impact publications in genomics. Dr. Emrich's publications demonstrate a strong trend toward integrating machine learning and AI approaches with traditional bioinformatics methods, particularly in codon usage analysis, genome assembly, and vector biology. His recent work shows increasing sophistication in language models for biological sequences and reference-free analysis techniques. His scientific awards include: 2008 Zaffrano Prize for Graduate Research ISU Research Excellence Award Dr. Emrich has advised numerous students throughout his career, including current PhD students in Genome Science and Technology and Data Science programs. His research has been supported by multiple NIH awards, including leadership roles on a NIAID contract (VectorBase) and a 2017-funded P01 grant on malaria genetics. Additional funding has come from the U.S. Department of Agriculture (USDA), the Department of Energy (DOE), and the state of Tennessee. He leads a research group focused on developing scalable computational tools that advance our understanding of genome biology. His group has introduced novel biological problems such as the influence of codon usage on protein folding to the broader computer science research community.

• Data Mining
• Machine Learning
• Network Science
• +۵ مورد دیگر

Tina Eliassi-Rad is the Inaugural Joseph E. Aoun Professor at Northeastern University . She is also an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Center . Her research lies at the intersection of Artificial Intelligence , Network Science , and their societal implications . Research Interests Data Mining & Machine Learning Network Science & Complex Systems Artificial Intelligence & Society Trustworthy Network Science Just Machine Learning Recent Article Trends Her recent work focuses on Graph Neural Networks , Hypergraph Mining , Adversarial Attacks , Algorithmic Fairness , and Human-AI Coevolution . Publications explore topics like Information Inequality , Network Resilience , and Explainable AI . Scientific Awards Inaugural Joseph E. Aoun Professor at Northeastern University Advising & Grants Current Students : Wan He (Network Science PhD), David Liu (CS PhD), Zohair Shafi (CS PhD), Samantha Dies (CS PhD) Major Funders : Defense Advanced Research Projects Agency (DARPA), National Science Foundation (NSF), Army Research Lab (ARL), Defense Threat Reduction Agency (DTRA), Lawrence Livermore National Laboratory (LLNL), MIT Lincoln Laboratory (MITLL), Volkswagen Foundation, PricewaterhouseCoopers (PwC), Washington Post Labs Labs & Teams She leads the RADLAB at Northeastern University and collaborates with the Network Science Institute . Her team includes postdoctoral researchers and PhD candidates working on AI, network science, and cybersecurity.

Kapil Ahujaمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Computational Optimization
• +۴ مورد دیگر

Prof. Kapil Ahuja is a Full Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Indore (IIT Indore), where he heads the Mathematics of Data Science and Simulation (MODSS) research lab. After completing dual Master's degrees and a Ph.D. from Virginia Tech (USA) followed by postdoctoral work at the Max Planck Institute in Germany, he has held visiting positions at UT Austin, IMT Atlantique, Sandia National Labs, TU Dresden, and TU Braunschweig. His administrative roles include founding Dean of International Affairs and former Head of Computer Science & Engineering at IIT Indore. Education: Ph.D. in Mathematics, Virginia Tech (2011) M.S. in Mathematics, Virginia Tech (2009) M.S. in Computer Science, Virginia Tech (2007) B.Tech. in Mechanical Engineering, IIT (BHU) Varanasi (2001) Research Focus: Prof. Ahuja's work bridges theoretical advances with real-world applications, emphasizing machine learning algorithms for plant/cancer studies, game-theoretic poverty reduction models, exascale climate modeling solvers, and drone trajectory optimization. His interdisciplinary approach integrates numerical linear algebra with network science to solve complex systems problems across healthcare, agriculture, and climate science, supported by 4.85 Crores INR in external funding. Publication Trends: Recent work demonstrates growing emphasis on AI-driven optimization for physical systems (drones, climate models) and biomedical applications (cancer classification). His publications increasingly feature cross-disciplinary collaborations between computer science, biology, and economics, with notable contributions in explainable AI for healthcare and resource allocation algorithms for social networks. Scientific Recognition: National Teacher's Award (2024) from the President of India Five-time recipient of IIT Indore's Best Teacher Award (2013-2023) Best Poster Award at International Workshop on Game Theory & Networks (2019) Steeneck Graduate Research Fellowship (Virginia Tech, 2011) Multiple SIAM travel awards for international conferences Mentorship & Service: Prof. Ahuja has graduated 5 Ph.D. and 4 M.S. (Research) students while mentoring 75 B.Tech. projects. He serves as Associate Editor for Applied Intelligence Journal (Springer Nature) and Knowledge and Information Systems, organizes international conferences, and reviews for 35+ academic sources. His administrative leadership significantly expanded IIT Indore's global partnerships through the Research Park initiative. Research Infrastructure: The MODSS lab maintains active collaborations with Oak Ridge National Lab, Sandia National Labs, and European institutions. Current projects include AI-optimized drone swarms for agricultural monitoring and game-theoretic models for poverty intervention, utilizing high-performance computing resources for large-scale simulations.

Liang Chenمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Alternative Splicing
• +۷ مورد دیگر

Liang Chen is a Professor of Quantitative and Computational Biology at the University of Southern California . He leads research efforts in developing advanced statistical and computational frameworks to unravel gene expression regulation mechanisms in eukaryotes, with a focus on post-transcriptional processes and their implications in human diseases. Research Emphasis: Alternative pre-mRNA splicing as a driver of proteomic diversity and disease Advanced RNA-seq data analysis and transcriptome comparison Quantitative trait locus mapping (eQTL, NMD-QTL) Network modeling of splicing regulation Notable Software Contributions: Development of BCseq (single-cell RNA quantification), CellTICS (explainable AI for cell-type identification), and WemIQ (isoform quantification). Current Research Directions: Integrating multi-omics data to study splicing-phenotype relationships, leveraging interpretable machine learning for personalized medicine, and exploring evolutionary conservation of gene regulatory mechanisms. Education: Ph.D. in Computational Biology, Yale University (2006) B.S. in Bioinformatics, Tsinghua University (2001) Ph.D. Advisees: Wei Jiang, Zheyu Li, Qingyang Yin, and Xinyu (Brian) Guo.