Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Maryland, College Park . He earned his Ph.D. in Computer Science from Dartmouth College and holds prior degrees from Shanghai Jiao Tong University. His research focuses on advancing the foundations and applications of artificial intelligence, particularly in machine learning, data mining, natural language processing, computer vision, and biomedical informatics . His work integrates large-scale optimization, fairness, and robustness in deep learning systems. Heng Huang’s recent publications demonstrate a strong trend in large language models, federated learning, model watermarking, continual learning, and medical image analysis . His work appears consistently in top venues like NeurIPS, ICML, CVPR, ICLR, and MICCAI, reflecting a broad impact across theoretical and applied AI. He actively mentors students and postdocs, seeking highly motivated researchers in machine learning and related domains. His work has significant implications for healthcare, privacy, and trustworthy AI.
Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University, with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology. He will be joining NYU in September 2025, bringing expertise at the intersection of machine learning and computational biology. Prior to joining NYU, he was a Postdoctoral Fellow at Genentech and Stanford Medicine from 2021 to 2025, working with Jonathan Pritchard and Aviv Regev. Dr. Lopez received his educational training at prestigious institutions: PhD in Computer Science (2021) from the University of California, Berkeley, advised by Mike Jordan and Nir Yosef M.S. in Applied Mathematics (2016) from École polytechnique, Palaiseau, France Dr. Lopez's research focuses on developing machine learning methods to understand biological systems at the cellular level. His work bridges computational techniques with biological applications, particularly in single-cell and spatial omics analysis. He pioneered probabilistic approaches for single-cell analysis with scVI and co-developed scvi-tools, now widely adopted tools in the field. His research spans deep generative models, causal inference, perturbation modeling, and representation learning for biological data. His publication record demonstrates a consistent trajectory of innovation in computational biology, with recent work focusing on spatial biology, disentangled representations of cellular perturbations, and causal modeling of cellular responses. He has made significant contributions to the field of single-cell analysis, developing methods that help scientists interpret complex cellular data and predict how cells respond to various perturbations. Dr. Lopez has received numerous honors and awards for his research: Best Paper Award from the ICML Workshop on AI for Science (2024) Best Paper Award Honorable Mention from the AAAI Conference on Artificial Intelligence (2021) Best Student Poster Award from the ICML Workshop on Computational Biology (2019) UC Berkeley EECS Departmental Graduate Fellowship (2016) Carnot Foundation Fellowship (2016) Monahan Foundation Fellowship (2016) French National Defence Medal, Bronze Echelon (2014) At NYU, Dr. Lopez will lead the Biological Machine Learning group, which develops probabilistic machine learning methods to uncover biological mechanisms governing cellular behavior and disease. His lab focuses on creating tools that transform complex cellular data into biological insights, with applications in understanding cancer, immune responses, and fundamental cellular processes. His work has significant implications for precision medicine and drug discovery.
Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.
Yaojun Zhang is an Assistant Professor in the Department of Physics & Astronomy and the Department of Biophysics at Johns Hopkins University. She earned her PhD in Physics from the University of California, San Diego (2015), followed by postdoctoral fellowships at the Princeton Center for Theoretical Science (2015-2018) and the Princeton Center for the Physics of Biological Function (2018-2021). Her research focuses on biological physics, particularly the complex behaviors of biomolecules and their assemblies across scales—from single-molecule folding to intracellular transport and biomolecular phase separation. She employs theoretical, mathematical, and computational tools to bridge biological questions with physical principles. Education PhD in Physics, University of California, San Diego (2015) Postdoctoral Fellowships: Princeton University (2015-2021) Research Interests Her group studies biomolecular condensates and liquid-liquid phase separation, exploring how microscopic interactions determine macroscopic properties of cellular compartments. Key areas include: Biomolecular condensate formation and dynamics Phase separation in cellular environments Interactions between biomolecules and cellular components Biophysics of intracellular transport Collaborations & Tools Zhang collaborates with experimentalists to validate theoretical models and develops frameworks for understanding condensate functions, such as surface tension, stoichiometry, and phase diagrams. Her work addresses challenges like condensate stability, molecular exclusion, and biological function regulation. Labs & Resources She leads the Zhang Lab , which integrates experimental and computational approaches. Her team’s research is supported by resources at the Bloomberg Center for Physics and Astronomy.
Andre Levchenko is the John C. Malone Professor of Biomedical Engineering at Yale University, with secondary appointments in the Department of Neurosurgery and affiliations with the Cancer Signaling Networks, Immunology, and the Yale Program in Neurodevelopment and Regeneration. His research focuses on systems biology, signal transduction, and cell-cell communication, utilizing microfluidics and computational modeling to study cancer progression, stem cell behavior, and neurological disorders. PhD, Columbia University MEng, Moscow Institute of Physics and Technology Levchenko's work explores how cells process dynamic signals to make critical decisions, particularly in glioblastoma migration, organoid development, and cardiovascular tissue engineering. His lab develops innovative microfluidic platforms and mathematical models to dissect multicellular communication and signaling networks. Recent publications highlight his contributions to understanding YAP-driven cancer invasion , NOTCH signaling in angiogenesis , and metabolic regulation of hypoxia responses . He has pioneered methods for organoid modeling and single-cell analysis , advancing precision in biological signaling studies. Scientific Awards : Computational Molecular Biology Post-Doctoral Fellowship (Burroughs Wellcome Fund) National Academies Keck Futures Conference Invitee Distinguished Guest Lecturer, University of Virginia American Asthma Foundation Early Excellence Award Fellow, American Institute for Medical and Biological Engineering Levchenko leads the Levchenko Lab at the Yale Systems Biology Institute, collaborating with institutions like Mayo Clinic and Yale Cancer Center. His research has received recognition in Faculty of 1000 and multiple journal highlights.
James Briscoe is a Senior Group Leader at The Francis Crick Institute in London, where he leads a research group focused on developmental biology and morphogen signaling. He previously held positions at the Medical Research Council's National Institute for Medical Research, which later became part of the Francis Crick Institute. Education: BSc in Microbiology and Virology from the University of Warwick, UK PhD from Imperial Cancer Research Fund/King's College London Postdoctoral training at Columbia University with Thomas Jessell Dr. Briscoe's research focuses on the molecular and cellular mechanisms of graded signaling by morphogens and the role of transcriptional networks in cell fate specification. His laboratory employs a range of experimental and computational techniques using model systems including mouse and chick embryos and embryonic stem cells. His work has significant implications for understanding developmental processes and their relationship to disease. His recent publications demonstrate a continued focus on morphogen gradients, neural tube development, and computational approaches to understanding cell fate decisions. His research increasingly integrates single-cell technologies and computational modeling to unravel the complexities of developmental patterning. Scientific Awards and Honors: EMBO Young Investigator (2001) EMBO Gold Medal (2008) Elected to EMBO (2009) Fellow of the Academy of Medical Sciences (2019) Fellow of the Royal Society (2019) As Editor-in-Chief of the journal Development since 2018, Dr. Briscoe plays a significant role in shaping the field of developmental biology. His leadership extends to mentoring researchers and contributing to scientific policy discussions, as evidenced by his recent publication 'Science under siege: protecting scientific progress in turbulent times.' Dr. Briscoe's laboratory at the Crick Institute is well-equipped with access to advanced facilities including light microscopy, flow cytometry, genomics, and computational resources, enabling a multidisciplinary approach to developmental biology questions.
Georgia Gkioxari is an Assistant Professor in the Division of Computing and Mathematical Sciences at Caltech , with a part-time affiliation at Meta AI . Her work focuses on extending visual perception models through advanced 2D and 3D representation learning, spatial reasoning, and generative models. Education: Not explicitly mentioned in the text Research interests span 3D perception , spatial reasoning , and vision-language integration , with projects like Visual Agentic AI for Spatial Reasoning and Token-by-Token Multimodal Alignment . Her publications emphasize 3D object detection , reconstruction , and generative modeling techniques including diffusion models and transformers . Scientific recognition includes the Meta LLM Evaluation Research Grant , Okawa Research Grant , Google Faculty Scholar Award 2024 , and Amazon Research Award . She teaches courses like Large Language & Vision Models (EE/CS 148) and Learning & 3D (CS 101) at Caltech. Labs & Teams: Leads Glab with members including Ilona Demler, Ziqi Ma, and Damiano Marsili
John Paisley is an Associate Professor of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, and a member of Columbia's Data Science Institute (DSI). He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Duke University (2004-2010), followed by postdoctoral research in Computer Science at Princeton University and UC Berkeley. His research focuses on Bayesian models, posterior inference techniques for Big Data, and applications in data analysis, recommendation systems, information retrieval, and compressed sensing. He has pioneered methods like Bayesian Gaussian Process ODEs and Double Normalizing Flows, with recent work emphasizing uncertainty quantification in environmental modeling and neuroimaging analysis. His collaborative workflows (e.g., bneR ) address air pollution exposure and PM2.5 concentration uncertainties, combining Bayesian nonparametric ensembles with geospatial data. He has also developed frameworks for neural network interpretability, image denoising, and compressed sensing MRI. Paisley's work bridges statistical theory and applied machine learning, with applications in healthcare, environmental science, and geophysics. His academic contributions include over 50 publications since 2016, spanning topics like deep metric learning, adversarial learning, and variational inference optimization. He maintains an active research group and serves on editorial boards for machine learning and signal processing journals.
Michele Klingbeil is a Professor in the Department of Microbiology at the University of Massachusetts Amherst, where she leads the Klingbeil DNA Replication Laboratory. She received her PhD in Cell and Molecular Biology from the University of Toledo in 1996 and previously worked at Johns Hopkins School of Medicine before moving to UMass in July 2007. Her educational background includes: PhD in Cell and Molecular Biology, University of Toledo, 1996 Dr. Klingbeil's research focuses on the unique biology of trypanosomatid parasites, particularly Trypanosoma brucei , the causative agent of African sleeping sickness. Her laboratory investigates two main areas: (1) replication of the unusual mitochondrial DNA network called kinetoplast DNA (kDNA), and (2) nuclear DNA replication initiation. Her work on kDNA is particularly significant as this structure is essential for parasite survival but has no counterpart in mammalian hosts, making it an attractive drug target. She employs a combination of reverse genetics (RNAi), cell biology, and biochemistry to understand the replication and repair mechanisms of kDNA, with a special focus on a family of four DNA polymerases related to bacterial Pol I. Dr. Klingbeil's recent publications reveal her laboratory's deep investigation into mitochondrial DNA polymerases in trypanosomatids, with discoveries showing multiple polymerases having specialized functions in kDNA replication and repair. Her research has established that several of these polymerases are essential for parasite viability, opening new avenues for drug development. She has also made significant contributions to understanding the simplified Origin Recognition Complex in trypanosomatids compared to other eukaryotes. Dr. Klingbeil has received the Thomas G. Lessie Distinguished Lectureship Award for her impact on teaching at the graduate level. Her research is funded by the National Institutes of Health, U.S. Department of Agriculture, the Joeph P. Healey Endowment, and the University of Massachusetts Amherst. She has mentored numerous graduate and undergraduate students, including current PhD candidates Dave Bruhn, Jeniffer Concepción, and Juemin Luo, as well as visiting scholar Eva Vidal Rico. Her former students have gone on to positions at institutions including Dana Farber/Broad Institute, Regis College, and Flagship Ventures. The laboratory regularly participates in scientific conferences including the Molecular Parasitology Meeting at Woods Hole and the Kinetoplastid Molecular Cell Biology conference. Dr. Klingbeil teaches several courses including Parasitology (MICRO 590S), Parasitology Lab (MICRO 590L), Molecular Mechanisms of Pathogenesis (MICRO 797P), Advanced Cell Biology (MCB 641), and Writing in Microbiology (MICRO 360). Her laboratory organizes regular social events including pumpkin carving parties and outings to Six Flags New England and Mt. Sugarloaf.
Johannes Larsch is a tenure-track Assistant Professor at the Center for Integrative Genomics within the Faculty of Biology and Medicine at Université de Lausanne (UNIL). His research focuses on understanding the neuronal mechanisms underlying social interactions, using larval zebrafish as a model organism. He employs advanced techniques like virtual reality and optical imaging to study how social signals are processed in the brain. Education: Bachelor of Biology, University of Konstanz, Germany PhD in Neurobiology, Rockefeller University, USA (lab of Cori Bargmann) Postdoc at Max Planck Institute of Neurobiology, Germany (with Herwig Baier) Research interests center on neuronal circuits driving group behavior, social recognition, and the interplay between genetics and environment in shaping behavior. His lab investigates how individual brains coordinate collective social behaviors through studies of neuronal activity and circuit dynamics. Current lab openings include fully funded PhD and PostDoc positions focused on social neuroscience and neurogenomics. Contact: johannes.larsch@unil.ch .
Professor Yasamin Mostofi is a faculty member in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. She is also affiliated with the Department of Computer Science and the Center for Control, Dynamical Systems and Computation. Her work bridges wireless systems, robotics, and machine learning. PhD, Stanford University MS, Stanford University MS, Sharif University of Technology Research Interests : Her lab focuses on wireless systems and autonomous agents , developing novel mathematical models for RF sensing and communication-aware robotics. Current directions include WiFi/millimeter wave/6G-based imaging and analytics, networked robotics for cellular systems, human-robot collaboration, and machine learning integration. Recent Publications : Her work spans through-wall crowd analytics , robot-assisted connectivity , and vision-aided wireless sensing , with applications in smart health, security, and retail optimization. Key trends include cross-modal integration (WiFi and vision), synthetic data generation, and real-world clinical validation. Scientific Recognition : Presidential Early Career Award for Scientists and Engineers (PECASE) Antonio Ruberti Prize, IEEE Control Systems Society NSF CAREER Award IEEE Region 6 Outstanding Engineer Award IEEE Fellow (2020) Advising and Leadership : She mentors PhD students in systems research, with graduates joining leading companies like Qualcomm and Google. She co-founded NPJ Wireless Technology (Nature Portfolio) and serves on editorial boards including IEEE Transactions on Control of Network Systems .
Furkan Alaca is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. His research focuses on user authentication systems, addressing security and usability challenges. He holds a Ph.D. (2018) in Computer Science from Carleton University, an M.A.Sc. (2012) in Electrical and Computer Engineering, and a B.Eng. (2010) in Communications Engineering, all from Carleton University. His academic career includes teaching roles at Queen's University and the University of Toronto Mississauga, where he taught courses such as Cryptography, Cybersecurity, and Discrete Mathematics. He is affiliated with Queen's Security Research Group and Computer Security Research Lab. Research interests include computer and internet security, usable security, authentication mechanisms, and systems security. He has contributed to advancements in web authentication frameworks, malware analysis, and privacy-preserving technologies. His work spans conferences like IEEE and ACM, with notable publications in cybersecurity, machine learning, and network efficiency. Current teaching includes CISC 447 (Introduction to Cybersecurity) and CISC 468 (Cryptography). He has advised on courses ranging from undergraduate programming to graduate-level security topics.
Karoline Faust is an Associate Professor at KU Leuven, affiliated with the Laboratory of Molecular Bacteriology (Rega Institute) and the Faculty of Medicine . She contributes to the iSi Health and Leuven One Health institutes, and serves on senior academic councils. Her research spans microbial systems biology, focusing on community dynamics and network analysis. Education: PhD in bioinformatics (2010, KU Leuven) Affiliations: KU Leuven, ISME Journal editorial board, Belgian Society for Microbiology Her research investigates microbial community dynamics , systems biology approaches to microbiomes, and bioinformatics tool development . She specializes in modeling human gut microbiota , synthetic microbial communities , and environmental microbiomes (e.g., microplastic impacts on Daphnia microbiomes). Her work integrates metabolic modeling , network analysis , and experimental systems to understand microbial interactions. Recent publications highlight her contributions to microbial network inference , 16S rRNA sequencing protocols , microfluidics , and ecological modeling of microbiomes. She develops tools like manta , miaSim , and CoNet to analyze community structures. Teaching: Karoline co-teaches courses in microbiology, bioinformatics, and network analysis at KU Leuven, and has contributed to international workshops on microbial network inference. Scientific Engagement: She serves as Senior Editor at ISME Journal and Secretary of the Belgian Society for Microbiology .
Prof. Martin Haenggi is the Frank M. Freimann Professor of Electrical Engineering and Concurrent Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a Dr.sc.techn. (Ph.D.) from ETH Zurich and has been at Notre Dame since 2000. His research focuses on stochastic geometry and wireless networks, including cellular, heterogeneous, vehicular, and millimeter-wave systems. He has held sabbaticals at UCSD (2007–2008), EPFL (2014–2015), and ETH Zurich (2021–2022). Education: Dipl.-Ing. (M.Sc.), ETH Zurich, 1995 Dr.sc.techn. (Ph.D.), ETH Zurich, 1999 Research interests emphasize stochastic geometry for analyzing network performance, including coverage, interference, and reliability in wireless systems. Key areas include meta distributions, spatial-temporal analysis, and network optimization. His work has been recognized with IEEE Fellow status, Clarivate Highly Cited Researcher distinction, and NSF CAREER Award (2005). Grants and Awards: NSF Award (Deep Stochastic Geometry: 2020–2023) NSF Award (Toward a Stochastic Geometry for Cellular Systems: 2015–2019) Rice Prize (2017), Best Survey Paper Award (2017), and Best Tutorial Paper Award (2010) from IEEE Communications Society Teaching includes advanced courses on stochastic geometry, wireless networks, and signal processing. His lab focuses on theoretical and applied aspects of network modeling, with collaborations in industry and academia.
Ron Dror is the Cheriton Family Professor of Computer Science at the Stanford Artificial Intelligence Lab , with courtesy appointments in Structural Biology and Molecular & Cellular Physiology . He also holds affiliations with Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), the Institute for Computational and Mathematical Engineering (ICME), Sarafan ChEM-H, and the Wu Tsai Neurosciences Institute. Education: PhD in Electrical Engineering and Computer Science, MIT MPhil in Biological Sciences, University of Cambridge (Churchill Scholar) BS in Mathematics and Electrical & Computer Engineering, Rice University (summa cum laude) Ron leads a multidisciplinary research group that combines molecular simulation and machine learning to study biomolecular structure, dynamics, and function. His work focuses on developing computational methods to accelerate drug discovery by predicting molecular interactions and designing more effective therapeutics. Current projects include the PENSA software library for analyzing biomolecular ensembles and FRAME framework for structure-based ligand design. His research has produced groundbreaking work on G-protein-coupled receptors (GPCRs) , RNA structure prediction , and mitochondrial transport mechanisms . Key publications highlight applications of geometric deep learning and molecular dynamics simulations in structural biology. Scientific Awards: Cheriton Family Professorship (2023) Two Gordon Bell Prizes (2014, 2009) Best Paper Awards at NeurIPS (2021), IPDPS (2013), SC11 (2011), SC09 (2009), SC06 (2006) Science Magazine Top 10 Breakthrough (2010) Fulbright Scholarship , NSF Fellowship , DoD Fellowship , Whitaker Foundation Fellowship Ron has advised numerous doctoral and master’s students including EJ Fine , Masha Karelina , and Briana Sobecks . His lab collaborates with experimentalists across academia and industry, applying computational methods to diverse biomedical problems such as RNA structure prediction , GPCR signaling , and mitochondrial metabolism .
