Find Academic Supervisors

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +5 more

Dr. Stan Ahalt serves as the inaugural Dean of the UNC School of Data Science and Society (SDSS) and holds a professorship in the Department of Computer Science at UNC-Chapel Hill. He previously directed the Renaissance Computing Institute (RENCI) and currently acts as its executive advisor. Additionally, he is the associate director of informatics and data science at NC TraCS, the translational research institute at UNC. His academic journey includes a 22-year tenure as professor of electrical and computer engineering at Ohio State University and leadership roles at the Ohio Supercomputer Center. Ahalt’s research focuses on applying data science to societal challenges such as healthcare, climate, and judicial reform. Key initiatives include the National Consortium for Data Science and the iRODS project, which advanced enterprise data management systems. Ahalt has secured over $34.5M in grants to lead high-impact projects like the NHLBI BioData Catalyst (unifying biomedical data), the NCATS Translator (accelerating clinical discovery), and the NIH HEAL Initiative (ensuring FAIR data standards). He mentors students across doctoral and master’s programs, teaching data science fundamentals in courses like COMP 116: Introduction to Scientific Programming. His work emphasizes interdisciplinary collaboration, evidenced by roles in RENCI, NC TraCS, and the SDSS. Ahalt’s contributions span cyberinfrastructure development, educational innovation, and translational research, positioning UNC as a national leader in data science.

• Data Science
• High-Performance Computing
• Biomedical Informatics
• +4 more

Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.

• AI for drug discovery
• Generative AI models
• Geometric deep learning
• +5 more

Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech and a field faculty member in Computer Science at Cornell University. Previously, she was a postdoctoral researcher at Microsoft Research New England and completed her PhD at Stanford University’s Institute for Computational & Mathematical Engineering. Her research focuses on algorithmic fairness, computational social science, and causal inference in public health, addressing disparities in automated systems like speech recognition and policy decision-making. Education : PhD, Stanford Institute for Computational & Mathematical Engineering MA/MS, Stanford University BA/BS, Massachusetts Institute of Technology Research Interests : Dr. Koenecke’s work bridges economics and computer science, emphasizing equity in AI systems. Key areas include: Algorithmic bias in speech recognition (e.g., racial disparities in voice assistants) Fairness in policy tools like environmental justice data systems Causal analysis in public health interventions Ethical implications of large language models in education Media & Impact : Her research has been featured in outlets like New York Times , Science , and Scientific American . Notable studies include exposing racial gaps in speech-to-text systems and advocating for inclusive dataset development. She also explores societal impacts of AI in education and governance. Awards : Sloan Research Fellow in Computer Science Forbes 30 Under 30 in Science NSF Awards Cornell CIS Teaching Excellence Award (2024) Teaching & Outreach : She teaches courses like Designing Fair Algorithms and Data Science for Global Development , emphasizing interdisciplinary collaboration. Her PhD Professionalization course addresses hidden curricula in academia. She advises on AI ethics for nonprofits, tech companies, and government agencies.

• Algorithmic Fairness
• Computational Social Science
• Public Health
• +4 more

David R. Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the School of Medicine. He holds roles such as Associate Director of the Joint CMU-Pitt Computational Biology PhD Program (CPCB) and is involved in multiple graduate programs including Intelligent Systems and Computational Biomedicine. His research focuses on developing computational algorithms and systems for drug discovery, emphasizing open-source software and machine learning applications in biomedical data. Koes teaches courses like MSCBIO2025 (Bioinformatics Programming in Python) and MSCBIO2065 (Scalable Machine Learning for Big Data Biology). He has secured NIH funding (R35GM140753) and collaborated on projects with institutions like NVIDIA and Google Cloud. His lab develops tools such as GNINA, Pharmit, and 3Dmol.js, and actively contributes to open drug discovery initiatives. Education: PhD in Computer Science from Carnegie Mellon University (CMU). Research Interests: Leveraging computation and AI for drug design, molecular docking, pharmacophore modeling, and open science. Specific areas include developing scalable machine learning pipelines, virtual screening systems, and tools for 3D molecular analysis. Grants and Funding: Current NIH R35 grant and prior support from NSF, Relay Therapeutics, and others. His work emphasizes translating computational methods into practical drug discovery solutions. Lab and Teams: Directs a lab focused on computational drug discovery, collaborating with multiple academic and industry partners. Supervises graduate students and postdocs in projects spanning AI-driven drug design, molecular modeling, and software development.

• Computational Drug Discovery
• Machine Learning in Biology
• Open Source Software
• +3 more

Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.

• Natural Language Processing
• Knowledge Graphs
• Machine Learning
• +5 more

Karan S Dixit, MD is an Assistant Professor in the Department of Neurology at Northwestern University's Feinberg School of Medicine, specializing in neuro-oncology and serving as Fellowship Director for the education program. His educational background includes: MD from Louisiana State University (2011) Internal Medicine Internship at University of Illinois at Chicago (2012) Neurology Residency and Chief Medical Resident at University of Illinois at Chicago (2015) Neuro-Oncology Fellowship at Northwestern University McGaw Medical Center (2016) He maintains board certification in Neurology (American Board of Psychiatry and Neurology) and Neuro-Oncology (United Council for Neurologic Subspecialties). Dr. Dixit's research centers on CNS lymphoma, lower-grade gliomas, and glioblastoma, with emphasis on novel therapies for treatment resistance. He investigates biomarkers for disease monitoring and explores neurocognitive decline mechanisms related to neural connectivity changes during CNS cancer treatment. His work bridges clinical neurology with translational oncology through molecular diagnostics and therapeutic innovation. His 2025 publications reveal three critical research trajectories: molecular characterization of glioma mutations (TERT/IDH), liquid biopsy applications for CNS lymphoma diagnosis, and repurposing antiseizure drugs with antineoplastic potential. These studies collectively advance precision medicine approaches for treatment-resistant CNS malignancies. Professional recognition includes: John A Kessler Teaching Award (2022, 2019) NeuroNEXT Fellow (2020) Society of Neuro-Oncology Clinical Trials Course (2019) University of Illinois Teaching Excellence Awards (2014-2015) As Fellowship Director, Dr. Dixit mentors neuro-oncology trainees while leading three active clinical trials: Vorasidenib/Pembrolizumab for IDH-mutant glioma (NCT05484622), NX-5948 BTK degrader for CNS lymphoma (NCT05131022), and emavusertib for relapsed PCNSL (NCT03328078). His industry collaborations include Bluprint Oncology Concepts and Servier Pharmaceuticals for therapeutic development. He maintains active membership in the Feinberg Academy of Medical Educators, American Neurological Association, American Academy of Neurology (Neuro-Oncology Section), American Society of Clinical Oncology, and Society of Neuro-Oncology, collaborating through Northwestern Medicine's Robert H. Lurie Comprehensive Cancer Center and Clinical and Translational Sciences Institute.

• Neuro-Oncology
• Brain Tumors
• Glioblastoma
• +4 more

Professor Thomas Meier is a Visiting Professor at the Department of Life Sciences, Imperial College London (since 2015), and Director of the Centre for Structural Biology (2017–2021). He leads research on ATP synthase structure, drug targets for tuberculosis, and molecular mechanisms of disease. Previously, he was a Group Leader at the Max-Planck-Institute of Biophysics (2006–2015) and ETH Zurich's Institute of Microbiology. His work combines structural biology (X-ray crystallography, electron microscopy) with biochemical studies. Education: Dr. sc. nat. (2002) and Dipl. sc. nat. (1998) from ETH Zurich. Awards include the Wellcome Trust Investigator (2015–present). Research focuses on ATP synthase's role in energy conversion, drug development, and structural biology. His lab includes postdocs and students like Lisa Uhrig and Anthony Cheuk. Key affiliations: Centre for Structural Biology, Membrane Biology Group, and Bacterial Pathogenesis studies. Languages: German, English, French (fluent), Latin (read/write). Publications span structural biology, planetary science, and drug discovery. His work on ATP synthase inhibitors for TB has clinical implications, while astrophysical studies explore planetary formation via giant impacts.

• Structural Biology
• Biochemistry
• Microbiology
• +5 more

Senior Lecturer Outi Salo-Ahen is affiliated with Åbo Akademi University's Faculty of Natural Sciences and Engineering , Department of Pharmacy. Her research focuses on computational pharmacology, drug design, and pharmaceutical chemistry, particularly targeting chemokine receptors (CCR5/CXCR4) and transient receptor potential channels (TRPA1) for therapeutic applications. Doctor of Pharmacy (2006, University of Kuopio/UEF) MSc in Pharmaceutical Chemistry (2001, UEF) BSc in Pharmacy (1999, UEF) University Pedagogy Modules 1-5 (2012-2015) Her work contributes to UN Sustainable Development Goals through education and pharmaceutical innovation . Recent research trends include: Antimicrobial resistance solutions TRPA1 channel modulation Nanotechnology-enabled drug delivery Multi-target HIV-1 inhibitors 3D printing of biocompatible materials Computational analysis of nucleic acid frameworks She actively supervises doctoral projects, serves on assessment panels, and leads collaborations like Nordic Pharmaceutical Translation and Innovation. Her 60+ publications demonstrate expertise in molecular modeling and drug discovery.

• Pharmaceutical Chemistry
• Molecular Modeling
• Drug Design
• +5 more

Suradip Das is a Research Assistant Professor in the Department of Neurosurgery at the Perelman School of Medicine, University of Pennsylvania, where he serves as a Senior Research Investigator. His work bridges neural engineering and regenerative medicine to address critical challenges in nerve and muscle repair. His academic training includes: B.Tech in Biotechnology from Heritage Institute of Technology (2010) PhD in Biosciences and Bioengineering from Indian Institute of Technology Guwahati (2016) Dr. Das specializes in biomaterials development , peripheral nerve injury models , neuromuscular interface engineering , and stem cell-based regeneration . His research pioneers innervated tissue-engineered muscle constructs, demonstrating how motor neurons and endothelial cells synergistically enhance skeletal myocyte maturation. He innovates custom mechanobioreactors that apply tensile forces to guide nanofiber alignment for optimal myofiber formation, significantly advancing volumetric muscle loss treatments. Analysis of his 15 most recent publications reveals a dominant focus on neuromuscular regeneration (75% of articles), with emerging exploration of psychedelic compounds in neural repair. His work consistently integrates human iPSC-derived models , multi-cellular co-cultures , and large-animal validation to address translational gaps. Key trends include optogenetic control of motor units (2023), porcine nerve injury models (2020), and the critical role of pre-innervation in creating pro-regenerative microenvironments (2020-2022). As a core member of the Cullen Lab, Dr. Das collaborates on developing biofabricated neural microtissues for delayed nerve fusion and rapid functional recovery. His research directly informs clinical strategies for peripheral nerve repair and muscle regeneration through rigorous mechanistic studies and innovative engineering solutions.

• Biomaterials
• Peripheral Nerve Injury
• Neuromuscular Modeling and Repair
• +2 more

Brent Page is an Associate Professor (tenured) in the Faculty of Pharmaceutical Sciences at the University of British Columbia (UBC) and maintains a research group at the Karolinska Institute , Department of Oncology and Pathology. His dual affiliation underpins a trans-Atlantic program that integrates cutting-edge chemical biology with medicinal chemistry for anti-cancer drug discovery. Education & Training PhD in Chemistry – University of Toronto (2013) HBSc in Chemistry (Honours) – University of British Columbia (2008) CIHR Postdoctoral Fellow – Karolinska Institute, Sweden (2008–2016) Assistant Professor (non-independent) – Karolinska Institute, Department of Oncology-Pathology (2017–2021) Research Focus Dr. Page’s laboratory operates at the interface of medicinal chemistry and chemical biology , aiming to identify and optimize small-molecule inhibitors for proteins previously considered “undruggable.” Core targets include STAT3, CLIC3, NUDT5/15 and SRPK3 . The group employs cellular thermal shift assays , isothermal ligand-induced resolubilization (ILIRA) , and CeTEAM technologies to quantify target engagement and refine structure–activity relationships in physiologically relevant models of breast cancer, triple-negative breast cancer, leukemia and atopic diseases . Funding & Collaborations His program is supported by Canadian Institutes of Health Research (CIHR) and other national and international agencies. Dr. Page actively participates in UBC’s Accelerated Translational Opioid Research Cluster and welcomes interdisciplinary collaborations and undergraduate research involvement. Equity, Diversity & Inclusion Committed to fostering an inclusive environment, Dr. Page mandates EDI training for all lab members and actively encourages participation from equity-deserving groups.

• Medicinal Chemistry
• Chemical Biology
• Drug Discovery and Development
• +13 more

Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +3 more

Dr. Thomas A. Hughes is an Associate Professor of Cancer Biology at the University of Leeds and Professor of Biosciences at York St John University. As a Group Leader at the Leeds Institute of Medical Research, he focuses on gene regulation, tumour microenvironment, and nanomedicine approaches to improve cancer outcomes. Specializes in breast cancer, colorectal cancer, and rare diseases Develops therapeutic strategies using microRNAs and biomarkers Collaborates with clinicians, engineers, and chemists for translational research His research integrates molecular pathology with clinical data through partnerships with Leeds NHS Trusts, aiming to identify novel biomarkers and targets for therapy. Recent work emphasizes cholesterol metabolism, oxysterol signaling, and nanomedicine-based drug delivery systems. Key contributions include: Over 80 peer-reviewed publications in cancer biology and molecular therapeutics Leadership in MSc programs in Molecular Medicine and Cancer Biology and Therapy Extensive experience in grant review, editorial work, and doctoral supervision Scientific awards include Fellowship of the Higher Education Academy. His lab has mentored 26 doctoral students and numerous alumni in academia, clinical practice, and industry.

• Cancer Biology
• Gene Regulation
• Breast Cancer
• +6 more

Pengyu Chen is Francis Family Associate Professor and Ginn Faculty Achievement Fellow in Materials Engineering at Auburn University. His NIH-funded research develops nanoplasmonic biosensors for rapid disease diagnosis and immune monitoring, with applications in precision medicine and cancer immunotherapy. Research focuses on: Rapid diagnostic technologies for COVID-19 and other diseases Single-cell analysis platforms Nanoparticle-based cancer therapies Food/environmental safety monitoring Recent publications demonstrate innovations in nanoplasmonic sensing, targeted drug delivery, 3D-printed implants, and tumor microenvironment analysis. His work integrates nanotechnology, immunology, and microfluidics for biomedical applications.

• Nanoplasmonic Biosensors
• Single-cell Analysis
• Precision Medicine
• +3 more

Dr. Audrey Lamb is a Professor and Chair of the Department of Chemistry at The University of Texas at San Antonio (UTSA), within the College of Sciences. She joined UTSA in 2020 after rising to full professor at the University of Kansas, where she served as interim dean of graduate studies in 2019. Her leadership extends to professional organizations, including serving as an elected council member for the American Society for Biochemistry and Molecular Biology. Dr. Lamb received her B.S. in Chemistry from Furman University in 1993 and her Ph.D. in Biochemistry from Vanderbilt University School of Medicine in 1998. She completed postdoctoral studies in biochemistry at Northwestern University before beginning her academic career at the University of Kansas in 2003. Dr. Lamb's research focuses on understanding bacterial pathogenesis through mechanistic enzymology and structural biology. Her lab investigates how human pathogens biosynthesize metallophores for metal ion scavenging and riboflavin (Vitamin B2) biosynthesis pathways. These studies aim to identify targets for novel antibiotic development against multidrug-resistant pathogens. Her work spans bacterial enzymology, structural biology, and metabolic pathway analysis, with applications in antimicrobial drug design. Analysis of Dr. Lamb's recent publications reveals a consistent focus on enzyme mechanisms in bacterial metabolism, particularly in metallophore and riboflavin biosynthesis pathways. Her work combines structural biology with kinetic analysis to elucidate catalytic mechanisms. Many publications investigate enzymes from pathogens like Pseudomonas aeruginosa, Staphylococcus aureus, and Trypanosoma cruzi, highlighting the translational potential of her basic science research for antimicrobial development. Dr. Lamb has received notable recognition including: Election as a 2022 Fellow of the American Association for the Advancement of Science (AAAS) Award-winning teaching and mentoring at undergraduate and graduate levels Dr. Lamb has mentored numerous students and postdoctoral fellows, with many alumni now in academic, industrial, and research positions. Her lab has received funding from prestigious sources including the National Institutes of Health, National Science Foundation, American Lung Association, and W.M. Keck Foundation. She actively collaborates with researchers at Loyola University Chicago, Texas A&M University, University of Kansas Medical Center, and UTSA's Department of Molecular Microbiology and Immunology. The Lamb Lab maintains a comprehensive suite of equipment for protein biochemistry and structural studies, including multiple AKTA FPLCs, a stopped-flow spectrophotometer, crystallization robot, various spectrophotometers, and HPLCs. This infrastructure supports their research on enzyme mechanisms and structural biology of bacterial metabolic pathways.

• Mechanistic enzymology
• Metabolic pathways
• Structural biology
• +4 more