Mithuna S. Thottethodi is a Professor and Interim Associate Head of Teaching and Learning at the Elmore Family School of Electrical and Computer Engineering at Purdue University. He holds a B.Tech. from the Indian Institute of Technology, Kharagpur (1996), and a Ph.D. in Computer Science from Duke University (2002). His research focuses on computer architecture, interconnection networks, distributed systems, and security, with notable work on sparse tensor accelerators and processing-near-memory architectures. His work has been funded by the NSF, AT&T, and SK Hynix. Research interests include security, interconnection networks in multicores, storage performance optimization, and memory hierarchies. Notable contributions span hardware accelerators for machine learning, secure speculative execution, and datacenter network congestion control. He has advised over 20 graduate students, many of whom have joined top tech firms like Google, Microsoft, and Intel. Awards: NSF CAREER Award (2007) Wilfred Hesselberth Teaching Excellence Award (2021) Multiple Eta Kappa Nu Outstanding Professor Awards Teaching includes undergraduate courses like EE 437 (Computer Design) and graduate courses such as ECE 666 (Advanced Computer Architecture). He also advises VIP and EPICS student teams.
Masood Masoodian is an Associate Professor in the Department of Art and Media at Aalto University, Finland. He leads the Visual Communication Design research group, focusing on interactive visualization for health, energy, and sustainability contexts. Previously, he held roles at the University of Waikato (2000-2016), University of Southern Denmark, and Massey University. Education: Doctoral degree in Other disciplines from the University of Waikato (1999) Research interests include design thinking, visualization of complex data, and creative aging interventions. Notable projects include the EU-funded INT-ACT initiative (2024-2026) addressing intangible cultural heritage. He has received an award for collaborative work on video game ludonarrative analysis (2019). Recent activities include organizing workshops on map-based interfaces, co-creating cultural heritage methods, and delivering public talks on digital design. Supervised two theses and contributed to 131 peer-reviewed outputs, emphasizing human-centered design and sustainability. Grants: Principal investigator for multiple EU projects totaling over 3 years of active funding. Awards: Prize for 'Comedy in the Ludonarrative of Video Games' (2019). Labs/Teams: Visual Communication Design group, collaborating internationally on projects like INT-ACT's cultural heritage mapping.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
Maciej A Mazurowski is an Associate Professor at Duke University School of Medicine, with dual appointments in the Department of Biostatistics & Bioinformatics and Radiology. He is also affiliated with the Department of Electrical and Computer Engineering and is a member of the Duke Cancer Institute. His research focuses on applying machine learning to medical imaging for improved diagnosis and treatment. Ph.D. in Computer Science from the University of Louisville (2008) Dr. Mazurowski's research emphasizes medical imaging , machine learning , and computer vision applications in radiology. His work includes automated segmentation , domain adaptation , prognostic modeling , and foundation models for MRI/CT analysis. His recent publications highlight trends in universal segmentation models (SegmentAnyBone, SegmentAnyMuscle), foundation models for MRI (MRI-CORE), and AI-driven diagnostic tools for breast cancer, glioblastoma, and thyroid nodules. Key challenges addressed include domain generalization , image harmonization , and ethical considerations in clinical AI. Incubation Award for innovative research commercialization Dr. Mazurowski has secured significant research funding from agencies including the National Institutes of Health , National Institute of Biomedical Imaging and Bioengineering , and American Roentgen Ray Society . His work spans CT segmentation , MRI analysis , and AI-based quality assessment across multiple imaging modalities.
Nozomu Togawa is a Professor at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering, specializing in Computer Science. He has held this position since 2009 and also serves as Chief Scientific Officer (CSO) of Quanmatic Inc. since 2022. With a PhD in Engineering from Waseda University (1997), his academic journey includes positions at Waseda University and the University of Kitakyushu before his current professorship. His research interests focus on integrated system design , quantum computation , and information security . Togawa has published extensively with over 368 papers and significant citation metrics (Scopus h-index: 23, Google Scholar h-index: 28). His work bridges theoretical quantum computing with practical security applications, particularly in hardware security and IoT systems. Togawa's research demonstrates a clear progression from traditional hardware security toward quantum-inspired computing solutions. His recent publications focus on Ising machines, quantum annealing, and hardware Trojan detection, showing how quantum approaches can solve complex optimization problems in security contexts. He has made significant contributions to applying quantum computing techniques to practical problems like course selection optimization, travel planning, and hardware security verification. Among his notable recognitions are the Minister of Education, Culture, Sports, Science and Technology Award for Science and Technology (2018), SCOPE Results Development Promotion Award (2022), and multiple Best Paper Awards. He serves on important committees including the Ministry of Internal Affairs and Communications Cyber Security Task Force and the Institute of Electronics, Information and Communication Engineers' VLSI Design Technology Research Committee. Togawa actively mentors students who frequently appear as co-authors on his publications. His research group produces high-impact work in quantum computing applications and hardware security, with strong industry connections through his CSO role at Quanmatic Inc. He has received substantial research funding supporting his innovative work at the intersection of quantum computing and security.
George Perry is a Professor of Anthropology at Pennsylvania State University, with research intersections in Biology, Evolutionary Medicine, and Genomics. He is affiliated with the Huck Institutes' Center for Infectious Disease Dynamics, Ecology, Molecular Cellular and Integrative Biosciences, and Bioinformatics and Genomics programs. Perry directs the Anthropological Genomics Lab , focusing on paleogenomics and evolutionary adaptation. Research areas: anthropological genomics, parasite evolution, human body size transitions, and evolutionary medicine Key collaborations: international teams in Madagascar, Europe, and Africa Leadership: Bioinformatics and Genomics Chair (2019–2023) His 2025–2022 publications span evolutionary responses to invasive species, human migration health impacts, chemosensory gene adaptation, and primate genomic diversity. Notable methodological contributions include ancient DNA recovery and comparative paleogenomics. Perry advises graduate students like Vanessa Garcia and Annette Mercedes, with grants including NIH support for Cuban health disparity studies. Scientific leadership includes tenure-line promotions (2023) and NASA Space Grant collaborations.
Dana Pe'er is a Professor and Chair of the Computational and Systems Biology Program at the Sloan Kettering Institute (SKI) of Memorial Sloan Kettering Cancer Center. She is also an Investigator of the Howard Hughes Medical Institute and holds the Alan and Sandra Gerry Endowed Chair. Dr. Pe'er leads an interdisciplinary research group that combines advanced genomics approaches with machine learning to address fundamental questions in biomedical science, with particular focus on cancer biology, developmental biology, and immunology. Dr. Pe'er earned her PhD from Hebrew University in Jerusalem, Israel. Her academic journey includes a postdoctoral fellowship with George Church at Harvard Medical School. Before joining Memorial Sloan Kettering Cancer Center in 2016, she held faculty positions at Columbia University. Dr. Pe'er's research focuses on understanding cellular plasticity, the consequences of intra-tumor heterogeneity, cancer evolution and metastasis, and the mechanisms by which regulatory circuits go awry in disease. Her lab combines single-cell and spatial profiling technologies with machine learning approaches to investigate gene regulation, cellular plasticity, and cell-cell communication in the contexts of cancer, immunity, and development. They are particularly interested in how organisms develop from a single cell to generate diverse cell types, how epigenetic control rewires during development, and how cells communicate to execute multicellular responses. Analysis of Dr. Pe'er's recent publications reveals a strong focus on developing computational methods for single-cell and spatial genomics data analysis. Her work spans cancer types including pancreatic, prostate, colorectal, and breast cancer, with emphasis on tumor heterogeneity, metastasis mechanisms, and cellular plasticity. A significant portion of her research involves creating novel algorithms and tools like CellRank, REUNION, and SEACells that enable researchers to extract meaningful biological insights from complex genomic datasets. 2023 Class of 2023 Inductee - American Academy of Cancer Research (AACR) Academy 2023 Innovator Award - International Society for Computational Biology (ISCB) 2021 Fellow - International Society for Computational Biology (ISCB) Howard Hughes Medical Institute Investigator (2021) 2019 Ernst W. Bertner Memorial Award - University of Texas MD Anderson Cancer Center 2016 Lenfest Distinguished Faculty Award - Columbia University 2014 Director's Pioneer Award - National Institutes of Health 2014 Overton Prize - International Society for Computational Biology (ISCB) Dr. Pe'er is known for her dedicated mentorship approach, describing herself as "a mama bear" who cares deeply about her trainees while expecting independence, innovation, and hard work. She mentors numerous PhD students and postdocs in her lab. Her HHMI Investigator award provides approximately $9 million over seven years, enabling ambitious research directions. She also collaborates extensively with the Single-cell Analytics and Innovation Lab (SAIL) at MSK to generate new data from emerging technologies, working closely with wet-lab collaborators at MSK and beyond to apply computational methods to cutting-edge datasets across multiple disease areas. The Pe'er Lab is an interdisciplinary group of computational biologists with diverse backgrounds ranging from pure mathematics to clinical medicine. They work closely with wet-lab collaborators to apply their computational methods to cutting-edge datasets across cancer, immunology, and developmental biology. The lab is described as open, supportive, collaborative, and fun, with access to world-class facilities at the Sloan Kettering Institute. Dr. Pe'er's work continues to push the boundaries of computational biology and cancer research, with the ultimate goal of developing more effective, personalized therapies for cancer patients.
Dr. Arnab Bhattacharya is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur since December 2020. He previously served as Associate Professor (2014–2020) and Assistant Professor (2007–2014) at IIT Kanpur. Education: PhD in Computer Science (2007), University of California, Santa Barbara MS in Computer Science (2007), University of California, Santa Barbara Bachelor of Computer Science and Engineering (2001), Jadavpur University Research Focus spans Databases , Data Mining , Information Retrieval , and Artificial Intelligence . His work emphasizes graph analytics , skyline queries , probabilistic data , and knowledge graph management . Article Trends reveal expertise in graph neural networks , trajectory-aware systems , statistical significance in databases , and legal/medical data mining . His methodologies often integrate chi-square statistics , approximate indexing , and provenance tracking . Scientific Awards: IBM Faculty Research Award Yahoo! Faculty Research and Engagement Program Award Best Paper at COMAD 2011 Best Student Paper at COMAD 2010 Top-Five Student Paper at ICDM 2005 ICDM Student Travel Award sponsored by IBM Contact: Office RM 409, Department of Computer Science and Engineering, IIT Kanpur Email: arnabb@iitk.ac.in | Phone: +91-512-259-7650
Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.
Professor David Clifton is the Royal Academy of Engineering Chair of Clinical Machine Learning at the University of Oxford’s Institute of Biomedical Engineering. He leads the Computational Health Informatics (CHI) Lab, focusing on AI-driven healthcare solutions with a strong emphasis on translational research in low- and middle-income countries (LMICs). His work spans digital health technologies, medical imaging analysis, and AI ethics. Clifton holds multiple fellowships, including from the Alan Turing Institute and Fudan University. Key affiliations include co-directorship of the Oxford-CityU Centre for Cardiovascular Engineering and involvement in the Wellcome Trust’s Flagship Centre in Vietnam. His research has been commercialized through spinouts like OBS Medical and Oxehealth. Notable projects include AI tools for non-invasive vital sign monitoring and pandemic response strategies using audio-based health data. Clifton’s awards include the IEEE Early Career Award (2022) and the Vice-Chancellor’s Innovation Prize. His lab’s Suzhou branch focuses on open-source digital health research using public datasets. Current research themes include multimodal data integration, generative AI in healthcare, and equitable AI deployment across global health systems.
Dr. Gabriele Schweikert is a Senior Lecturer and Principal Investigator with a joint appointment between the Division of Computational Biology in the School of Life Sciences at University of Dundee and Cyber Valley in Tuebingen. Her research focuses on applying machine learning techniques to understand epigenetic mechanisms and molecular processes in living cells. Dr. Schweikert completed her PhD at the Max Planck Institute Tuebingen working with Schoelkopf, Weigel, and Raetsch labs on machine learning for computational gene finding. She subsequently joined Adrian Bird's lab at the Wellcome Trust Center for Cell Biology in Edinburgh, a pioneer in epigenomic research. Prior to her current position, she held prestigious Marie Curie and EMBO Fellowships at the School of Informatics, University of Edinburgh. Her research interests center on using machine learning to decode epigenetic mechanisms that determine cellular identity and function. She investigates how cells with identical DNA can differentiate into specialized cell types through epigenetic regulation, with particular focus on applications in understanding tumorigenesis where epigenetic machinery malfunctions. Her work combines high-throughput epigenomic data with advanced computational approaches to address complex biological questions. Analysis of her recent publications reveals a strong focus on epigenomic data analysis, machine learning applications in biology, and computational approaches to understanding gene regulation. Her work spans from fundamental epigenetic mechanisms to practical applications in disease research, with growing emphasis on individual-specific epigenomic analysis and explainable AI in biomedical contexts. UKRI Future Leaders Fellowship (2020, £1.6 million) Marie Curie Fellowship EMBO Fellowship Dr. Schweikert actively supervises PhD students and has received significant research funding for projects including 'Machine Learning Methods to Re-Annotate Histone Modifications,' 'Unlocking The Alternative Splicing Code,' and 'GPU-Based Machine Learning System For Fundamental Biological Research.' She is involved in multiple interdisciplinary collaborations and frequently presents her work at major conferences including ELLIS Health program retreat, Epigenetics Meetings, and RECOMB workshops. She maintains active research laboratories in both Dundee and Tuebingen, fostering international collaboration between computational biologists, machine learning experts, and experimental biologists to advance our understanding of epigenetic regulation in health and disease.
Simone Fior is a Lecturer at the Department of Environmental Systems Science , ETH Zürich , focusing on ecological genetics and plant adaptation. Their research integrates genomic, quantitative genetics, and ecological field experiments, particularly on Dianthus (Caryophyllaceae) along altitudinal and climatic gradients. Recent work explores climate-induced range shifts, local adaptation, and genomic responses to environmental changes. Professional experience includes roles at ETH Zürich since 2013 (Senior Assistant, Postdoc) and prior positions at the Edmund Mach Foundation (2009-2012) and University of Insubria (2007-2008). Education spans a PhD in Plant Biology (University of Milan, 2007) and an MSc in Natural Sciences (University of Milan, 2003). Simone co-organizes the Bioinformatics for Adaptation Genomics Winter School . Key research areas include adaptive divergence , polygenic adaptation , climate change biology , and phylogenomics . Articles emphasize genomic selection signatures, functional-structural modeling, and ecological-genetic interactions. Notable collaborations involve Jake Alexander, Alex Widmer, and interdisciplinary teams at ETH Zurich.
Yuguo Chen is a Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign (UIUC), serving as Interim Department Chair and Director of the Illinois Statistics Office. He holds affiliations with the Department of Computer Science, Information Trust Institute, Coordinated Science Lab, and Illinois Informatics Institute. Chen earned his PhD in Statistics from Stanford University (2001) and a B.S. in Mathematics from the University of Science and Technology of China (1997). His research focuses on Monte Carlo methods, network data analysis, state space models, bioinformatics, and Bayesian inference. Key interests include scalable network estimation, community detection, and applications in public health, education, and computational biology. Recent work highlights include advancements in dynamic network modeling, Bayesian latent class models for cognitive diagnosis, and statistical methods for analyzing multi-layer networks. His contributions have been recognized through awards such as the American Statistical Association Fellowship (2018) and the Charles Edison Lectureship (2018). Editorial Roles: Associate Editor of Journal of the American Statistical Association , Journal of Computational and Graphical Statistics , and Journal of Algebraic Statistics . Grants & Consulting: Directs the Illinois Statistics Office, providing interdisciplinary research support. Active in collaborative projects involving healthcare, education, and computational infrastructure. Labs & Teams: Leads initiatives at the Coordinated Science Lab and Information Trust Institute, integrating statistical methods with cybersecurity and data-driven decision-making.
See Kiong Ng serves as Professor of Practice in the Department of Computer Science at the School of Computing, National University of Singapore (NUS), while concurrently holding leadership roles as Director of AI Technology at AI Singapore and Deputy Director of NUS's Institute of Data Science (IDS). His work focuses on translational data science research and developing integrated capabilities for Singapore's Smart Nation initiative through industry and public agency collaborations. His academic credentials include a B.S. in Applied Mathematics (Computer Science Track) from Carnegie Mellon University (1989), an M.S.E. in Computer & Information Science (Artificial Intelligence) from the University of Pennsylvania (1990), and a Ph.D. in Computer Science from Carnegie Mellon University (1998), supported by Singapore's National Computer Board overseas scholarship. Professor Ng's research bridges artificial intelligence with real-world applications across diverse domains. His primary interests span Data Mining, Machine Learning, Natural Language Processing, Smart Cities, and Computational Biology, with emphasis on extracting value from big data through interdisciplinary approaches. He actively pioneers applications in urban systems and bioinformatics, demonstrating data science's transformative potential beyond traditional boundaries. His publication record reveals consistent innovation in algorithm development for complex data challenges, with recent work focusing on taxonomy construction, single-cell genomics analysis, urban transportation systems, and imbalanced time series classification. These contributions demonstrate his commitment to solving practical problems through cutting-edge data science techniques. His major recognitions include: MTI Borderless Award (2014) as Green Growth Working Group project member Minister for National Development's R&D Award 2017 (Distinguished Award) for city-level analytics platform innovation A*STAR Borderless Award (2014) as Urban Systems Initiative team leader MTI Innovation Award (2013) for Strategic Technology Translation in Business Analytics Professor Ng has established significant research infrastructure including founding A*STAR's Data Analytics Department and leading the Urban Systems Initiative. His translational research model emphasizes industry partnerships and practical implementation, particularly in smart city development where he connects data science with urban planning challenges across Singapore's government agencies.
Konstantinos Kalogeropoulos is an Assistant Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark (DTU), leading research at the Cell Diversity Lab. His work bridges proteomics, computational biology, and snake venom research. Current projects: "The Proteomic Landscape during Influenza Infection" (2022-2025) Supervisor for PhD projects on protease network rewiring in psoriasis and wound exudate degradomics Research interests include: Proteomic analysis of inflammatory diseases Snake venom toxin structure prediction Extracellular matrix biomechanics De novo peptide sequencing algorithms Computational modeling of protease networks Recent article trends demonstrate his work in • Database-free proteomics (InstaNovo/InstaNexus) • Snake venom pathophysiology (V-ToCs clustering) • Inflammatory disease biomarkers (psoriasis, impaired healing) • Extracellular matrix mechanics (fibronectin tension, gut inflammation) Advising: Supervises PhD students Polhaus, C. J. M. and Haack, A. M., focusing on protease networks and wound healing.
