AI Augmented Biology                                   
                                                                        

Hoifung Poon is General Manager at Health Futures in Microsoft Research and an affiliated faculty at the University of Washington Medical School. He leads biomedical AI research and incubation, with the overarching goal of structuring medical data to optimize delivery and accelerate discovery for precision health. His team and collaborators are among the first to explore large language models (LLMs) and multimodal generative AI in health applications, producing popular open-source foundation models such as PubMedBERT, BioGPT, BiomedCLIP, LLaVA-Med, BiomedParse, with tens of millions of downloads. His latest publication in Nature features GigaPath, the first whole-slide digital pathology foundation model pretrained on over one billion pathology image tiles. He has led successful research partnerships with large health providers and life science companies, creating AI systems in daily use for applications such as molecular tumor board and clinical trial matching. He has given tutorials on these topics at top AI conferences such as ACL, AAAI, and KDD, and his prior work has been recognized with Best Paper Awards from premier AI venues such as NAACL, EMNLP, and UAI. He received his PhD in Computer Science and Engineering from the University of Washington, specializing in machine learning and NLP.

Dr. Xin Gao is Chair and Professor of Computer Science Program at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He is also the Co-Chair of Center of Excellence for Smart Health, Theme Lead of Center of Excellence on Generative AI, Chair of Bioinformatics Platform, and the Lead of the Structural and Functional Bioinformatics (SFB) Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center for Computational Biology at Carnegie Mellon University. He earned his bachelor degree in Computer Science in 2004 from Tsinghua University and his Ph.D. degree in Computer Science in 2009 from University of Waterloo.

He is a world-renowned expert on developing AI solutions for diagnostics of genetic diseases and cancers, drug development, and biomedical imaging. He is co-founder of Syneron Technology and NANOFAI. He serves as the scientific advisor to a number of companies and organizations, such as SFDA in Saudi Arabia, Bioaster in France, DermAssure in USA, Myant in Canada, BioMap in China, and Proteinea in Egypt. He is an elected Fellow of National Academy of Artificial Intelligence and Life Fellow of Royal Society of Arts.

He has published more than 410 papers in the fields of bioinformatics and machine learning, with a total citation of over 13000 and h-index of 61. He is the leading inventor of over 60 international patents, and have been the PI on research grants of more than USD$30 million. He is the associate editor of Bioinformatics, npj Artificial Intelligence, Journal of Translational Medicine, Genomics, Proteomics & Bioinformatics, Big Data Mining and Analytics, BMC Bioinformatics, Journal of Bioinformatics and Computational Biology, Quantitative Biology, Complex & Intelligent System, and International Journal of Artificial Intelligence and Robotics Research, and the guest editor-in-chief of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Methods, and Frontiers in Molecular Bioscience. He has been invited to review research grant proposals for various funding agencies, such as the Swiss National Science Foundation, Italian Ministry of Health, Research Grant Council of Hong Kong, UK Research and Innovation-BBSRC, The Netherlands e-Science Center and The Dutch Research Council, National Science Centre of Poland, and Ministry of Health of Saudi Arabia. Twenty four of his former students and postdocs are now independent PIs at leading institutes all over the world, such as Tsinghua University, Chinese University of Hong Kong, City University of Hong Kong, Toyota Technological Institute at Chicago, MBZUAI, Shandong University, King Abdulaziz University, and University of Benevento, Italy.

Iqbal leads the Bioinformatics and Machine Learning group in the Ladders to Cures (L2C) accelerator – a rare disease accelerator – at the Broad Institute of MIT and Harvard. The focus of the Iqbal lab is connecting genetic discovery to proteins and mechanisms via several aims: building bioinformatics resources to bridge the gap across complex multi-omics data types, developing methods to unveil the molecular effect of genetic/synthetic mutations on protein structure-function relationships, and building AI-driven innovative tools for small-molecule hit identification using data from DNA-encoded library screening, and other approaches. She developed the Genomics 2 Proteins portal, a discovery tool for linking genetic screening outputs to protein sequences and structures.

Iqbal started her lab at the Broad Institute in 2023, after two years of Postdoctorate research at the Analytic and Translational Genetics Unit of MGH, Harvard Medical School, and the Stanley Center for Psychiatric Research, and spending three years as a research scientist at the Center for Development of Therapeutics at the Broad. Iqbal has earned multiple awards including SPARC award, the Merkin Institute award for Transformative Technologies in Healthcare, and the Broad Ignite award.

Iqbal holds a Professional Certification in Data-driven Decision Making from MIT, a Ph.D. in Computer Science and Applied Mathematics from the University of New Orleans, and a M.Sc. and B.Sc. in Computer Science and Engineering from the Bangladesh University of Engineering and Technology.

Contact Sumaiya Iqbal via email at sumaiya@broadinstitute.org.

Dr. Yu Xue is a professor at the Department of Bioinformatics & Systems Biology, College of Life Science and Technology of Huazhong University of Science and Technology. His major interests are focused on the development of AI-augmented algorithms and tools, for understanding the regulatory roles of protein chemical modifications in dynamic life processes, such as metabolism, cellular homeostasis, and autophagy. Dr. Xue has published > 130 papers in a number of high-profile journals, such as Nature Metabolism, Nature Biomedical Engineering, Nature Communications, Nature Protocols and Immunity, with > 15,000 citations. He served as an associate Editor of Science Bulletin, Scientific Data, and Genomics, Proteomics & Bioinformatics. He is a co-founder of Artificial Intelligence Biology (AIBIO) subgroup in Biophysical Society of China, and serves as the secretary-general of this community. 

Jovan Tanevski is a group leader at the Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, where he also heads the computational platform of the Translational Spatial Profiling Center. His research lies at the intersection of systems sciences and machine learning, focusing on the development of explainable AI/ML and optimization-based methods for data analysis, hypothesis generation, and computational discovery in spatial omics to advance translational biomedicine. In addition to his position in Heidelberg, he is also affiliated with the Department of Knowledge Technologies at the Jožef Stefan Institute in Ljubljana, Slovenia, where he works on applying machine learning to model dynamic biological systems and developing machine learning approaches for surrogate-based modeling of complex dynamical systems.

Xiaomin Ying is a professor at Beijing Institute of Basic Medical Sciences. She received her B.S. degree in Automatic Control and Applications and her Ph.D. in Control Science and Engineering. Since then, she has been dedicated to interdisciplinary research on machine learning and biology. Her research interests include multimodal data integration and AI algorithm development for deciphering the mechanisms of biological processes and major diseases. She has received many national grants, including the National Key R&D Program of China (where she served as Chief Scientist) and the National Natural Science Foundation of China. Her research has been published in top-tier journals such as Nature Biotechnology and Gut.

Professor Kai Ye

Position: Professor and Director, Bioinformatics Institute of Xi’an Jiaotong University

Institution: Xi’an Jiaotong University, China

 

Research Focus:

Professor Kai Ye is an expert in bioinformatics, specializing in developing advanced algorithms and tools for analyzing complex genomic data. His research encompasses structural variant detection, single-cell multi-omics, and systems biology approaches to elucidate mechanisms underlying human diseases and evolution.

 

Education and work experience

• 1995-09~1999-06 Wuhan University, China, B.S.
• 2000-09~2003-06 Wuhan University, China, M.S.
• 2003-07~2003-12 Wuhan University, China, lecturer
• 2004-01~2008-12 Leiden University, the Netherlands, PhD
• 2008-07~2009-06 European Bioinformatics Institute, United Kingdom, Postdoctoral
• 2009-07~2012-11 Leiden University Medical Center, the Netherlands, Assistant Professor
• 2012-12~2016-02 Genome Institute at Washington University in St. Louis, United States, Assistant Professor
• 2016-02~now      Automation department, Xi’an Jiaotong University, China, Professor

As biology turns increasingly into a data-rich science, the massive amount of data generated by high-throughput technologies present both new opportunities and serious challenges. As a bioinformatician, Dr. Ge Gao is interested in developing novel computational technologies to analyze, integrate and visualize high-throughput biological data effectively and efficiently, with applications to decipher and understand the function and evolution of gene regulatory systems. Since 2011, when he was first recruited as a Principal Investigator (tenure-track) by Peking University, Dr. Gao has developed fourteen online bioinformatic software tools and databases for efficient analyses of large-scale omics data. More than 1.5 billion hits for these resources as well as 30,000+ citations for 30+ published peer-reviewed papers from world-wide research community during past five years well demonstrates their global significance and impact. Taking advantage of these powerful bioinformatics technical infrastructures, Dr. Gao has been delineating the regulatory map and characterizing the functional genome in action globally. Dr. Gao is an active member of global bioinformatic society. He has been elected as a member of Executive Committee and the China Liaison for Asia-Pacific Bioinformatics Network (APBioNET) since 2011, and the Vice President on Education during 2016 and 2018. He is also a Founding Member of Expert Committee for Computational Biology and Bioinformatics, Chinese Society of Biotechnology (established in 2014), as well as of Expert Committee for Big Data and Biocuration, Genetics Society of China (established in 2015). His academic achievement has been well recognized through the Clarivate Highly Cited Researcher, the Elsevier Chinese Most Cited Researchers, the Bayer Investigator Award, the Cheung Kong Scholar and the National Top-notch Young Professionals programs. In the coming years, Dr. Gao will continue his scientific pursuit to decipher the “coded messages” in genomes with cutting-edge bioinformatic and genomic technology.

Henrik Nielsen works in the bioinformatics section at Department of Health Technology, Technical University of Denmark, where he has been an associate professor since 2006. He holds an M.Sc. in biology from University of Copenhagen (1993) and a PhD in biochemistry / theoretical chemistry from Stockholm University (1999). His main research interest has always been the prediction of protein subcellular location in all domains of life. Where is the information that tells the cell where to put which proteins, and what is the nature of this information? To answer these questions, he has used various kinds of machine learning algorithms, notably artificial neural networks and hidden Markov models. His most well-known contribution to the field is the program and web site SignalP which predicts secretory signal peptides. The SignalP web server was launched in 1995 and is now in its sixth major version, based on protein language models (https://services.healthtech.dtu.dk/services/SignalP-6.0/). It is used more than 1,000 times daily, thousands of users have downloaded the program for use on their own computers, and the papers about SignalP have been cited more than 30,000 times.

Web site: https://www.healthtech.dtu.dk/protein-sorting

Dr. Li received her PhD in Biostatistics from the University of Michigan in 2005. Initially trained as a statistical geneticist, she transitioned her research focus to statistical genomics after joining the faculty at the University of Pennsylvania in 2006. Her work aims to deepen our understanding of the molecular mechanisms underlying human diseases. The central theme of her current research involves leveraging statistical, machine learning, and artificial intelligence methods to explore cellular heterogeneity in disease-relevant tissues, characterize gene expression diversity across cell types, study patterns of cell state transitions, and investigate cell-cell crosstalk using single-cell and spatial omics data. More recently, Dr. Li has expanded her expertise to computational pathology, a critical area for processing and analyzing spatial omics data. In addition to developing algorithms and tools, she collaborates with researchers to identify susceptibility genes and key acting cell types for complex diseases. At the University of Pennsylvania, she serves as the Director of the Statistical Center for Single-Cell and Spatial Genomics and chairs the Graduate Program in Biostatistics. Dr. Li is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association, and a Fellow of the American Association for the Advancement of Science.

Professor Jakob Kather holds dual appointments in medicine and computer science at the Technical University (TU) Dresden, Germany, serves as a senior physician in medical oncology at the University Hospital Dresden and holds an additional affiliation with the National Center for Tumor Diseases (NCT) in Heidelberg. His research is focused on applying artificial intelligence in precision oncology. Prof. Kather’s research team at TU Dresden is using deep learning techniques to analyze a spectrum of clinical data, including histopathology, radiology images, textual records, and multimodal datasets. Guided by the belief that medical and tech expertise needs to be combined, medical researchers in his team learn computer programming and data analysis, while computer scientists are immersed in cancer biology and oncology. Prof. Kather chairs the “Working group on Artificial Intelligence” at the German Society of Hematology and Oncology (DGHO) and is a member of the pathology task force of the American Association for Cancer Research (AACR). His work is supported by numerous European and national grants, which enable the team to develop new deep learning methods for medical data analysis techniques and to apply them in precision oncology.

Brian is an Assistant Professor of Chemical Engineering at Stanford University, the Dieter Schwarz Foundation Stanford Data Science Faculty Fellow, and an Innovation Investigator at Arc Institute, where his group conducts research at the intersection of biology and machine learning.

Arne Elofsson
Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University
Date of Birth: 10 Dec. 1966
Citizenship: Swedish.
Place of Birth: Stockholm, Sweden

Affiliations at Stockholm University
• Dep. of Biochemistry and Biophysics
• Stockholm Bioinformatics Center
• Center for Biomembrane Research
• Swedish E-science Research Center
• Science for Life Laboratory

Exams and Degrees:
• Med Kand degree at Karolinska Institutet: June 13 1988
• PhD at Karolinska Institutet: May 7 1993.
• Docentur at Department of Biochemistry and Biophysics, Stockholm University April 28 1999
• Professor at Department of Biochemistry and Biophysics, Stockholm University Febr 1, 2006

Publication record
• Total number of scientific publications: 174
• Number of peer reviewed original publications in journals: 162
• Number of peer reviewed original publications in conference proceedings: 2
• Number of review articles: 5
• Number of book chapters: 5
• Number of citations: 22869
• i10-index: 159
• H-index: 77

Historical overview of Grants
• Since 1995 received individual research grants for 150 MSEK (15000 KEuro)
• Research councils 35 000 KSEK
• European Union 2 603 KEuro
• The foundation for strategic research 9 835KSEK
• Knut and Alice Wallenberg 66 000 KSEK
• Other foundations 992 KSEK
• Equipment (computing clusters) 11 300 KSEK
• Co-applicant in center grants 110 000 KSEK (10MEuro)

Misc merits
• Sven och Ebba-Christina Högbergs pris 1999
• More than 40 invited talks to conferences and workshops
• Supervised 21 PhD students
• Opponent in 6 PhD defenses
• 1998-2000 Secretary in the Swedish Biophysical society.
• 2004-2015 President of the Society for Bioinformatics in the Nordic countries (SocBiN)
• 2008-14 Associate editor for Protein science
• 2009-2012 Associate editor for PLOS computational biology
• 2012- Deputy editor for PLOS computational biology
• 2019- Associate editor for Bioinformatics
• 2018- Swedish representative in Elexir nodes, 3dbioinfo and disordered proteins.
• 2017-2022 Director for National Graduate School in Medical Bionformatics (medbioinfo.se)
• 2020-Editor in Chief for Frontiers in Protein

Bioinformatics
• Organized SocBiN conference, 99 and 2012
• Co-organizer of ISMB-2010, Protein Society 2011
• One of 32 Swedish Scientists included in the list “Highly cited researchers by Thomson” in 2014.

RESEARCH GRANTS
Current grants
• Research councils:
• 2017-2025 VR (M) 2016-06301 “The Swedish
National Graduate School in Medical Bioinformatics” 30 000 KSEK
• 2022-2026 VR (NT) 2021-03979 “Predictions of protein-protein interactions by PconsDock, a fold-and-dock algorithm.” ¨4¨350¨KSEK • Other

Dr. Steinegger is an Associate Professor in the Biology Department at Seoul National University, with a joint appointment to the Interdisciplinary Program in Bioinformatics. He conducted his doctoral studies at the Max Planck Institute for Biophysical Chemistry and was awarded a Ph.D. in computer science with summa cum laude honors from the Technical University of Munich in 2018, followed by a postdoctoral fellowship at Johns Hopkins University. Dr. Steinegger has published more than 50 papers covering a wide range of topics in bioinformatics, from detecting genomic assembly contamination to organizing the protein structure space.   In 2024 he was awarded the Overton Prize for outstanding contributions to computational biology by the International Society for Computational Biology. He started his research group in 2020, focusing on the development of methods to analyze massive genomics and proteomic datasets. The group’s contributions to bioinformatics include widely used tools for predicting structures (ColabFold/AlphaFold2), clustering (Linclust), assembling (Plass), and searching sequences (MMseqs2) and protein structures (Foldseek). His group’s software and web services have been installed and used millions of times. Dr. Steinegger is an advocate for internationality at his home institution, open science and open source.

Jianyi Yang is a Professor of Mathematics and Interdisciplinary Sciences at Shandong University. He has made significant contributions to the field of protein and RNA structure prediction, co-developing several widely-used algorithms, including trRosetta, trRosettaRNA and I-TASSER. His research group achieved remarkable success, winning the protein structure prediction competitions in both CASP15 and CASP16.

Kotaro Tsuboyama

M.D., Ph.D.

Degrees

The University of Tokyo Ph.D. (Science)              2019

The University of Tokyo M.D.      2016

Research Experience

2023-present     Lecturer (PI), IIS (Institute of Industrial Science), UTokyo (The University of Tokyo)

2020-2023         Postdoctoral Researcher, Rocklin lab, Feinberg School of Medicine Northwestern University

                          Research Advider: Dr. Gabriel J ROCKLIN

2019-2020         Postdoctoral Researcher, RNA function Lab, IQB (Institute for Quantitative Biosciences, UTokyo

                          Research Adviser: Dr. Yukihide TOMARI

2016-2019        Graduate Student, RNA function Lab, IQB, UTokyo Research Adviser: Dr. Yukihide TOMARI

2014-2016        Undergraduate Student, Molecular biology Lab, Faculty of Medicine, UTokyo

                         Research Adviser: Dr. Noboru MIZUSHIMA

Honors and Awards (selected)

2019     JSPS Ikusi-Prize (The most prestigious prize for graduate students in Japan)

2019     The University of Tokyo President’s Award for Students (for graduate students)

2016     The University of Tokyo President’s Award for Students (for undergraduates)

Rama Ranganathan, M.D. Ph.D. is a Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker School of Molecular Engineering, and the College at the University of Chicago. Rama’s research has focused on understanding the basic principles of structure, function, and evolution in biological systems, particularly emphasizing the atomic and cellular scale. His work has led to new models for the architecture of natural proteins and new experimental tools for studying the physics and evolution of proteins and cellular systems. He leads the University of Chicago Center for Physics of Evolving Systems. He is the director of BioCARS beamline, a national user facility for structural biology at the Advanced Photon Source at Argonne National Laboratory.

He received his undergraduate degree in Bioengineering from UC Berkeley. He received his M.D. and Ph.D. degrees from UC San Diego, working jointly with Charles Zuker, Chuck Stevens, and Roger Tsien. He conducted brief postdoctoral studies at Harvard Medical School, working with Rod MacKinnon, and at the Salk Institute, working with Joe Noel.

Dr.  Lu received his bachelor’s degree in Biological Science in 2009, from University  of Science and Technology of China (USTC). Then he pursued his doctoral degree  in Prof. Yigong Shi’s lab at Tsinghua University until 2014. In 2015, he joined  Prof. David Baker’s lab at University of Washington for postdoctoral training. With his Ph.D. research focused on the structural  and biochemical studies of transmembrane proteins, Dr. Lu accurately designed  several multipass transmembrane proteins, as a postdoc, paving the way for the  design of multispan membrane proteins with new functions. He has published 14  research papers in journals including Science, Nature, PNAS and Cell Research.