CAP Data Technologies was founded in late 2014 as a spin-off from University of Jyväskylä. CAP has the unique ability to spot anomalies automatically without fingerprints in large volumes of data using machine learning algorithms.
Tuomo Sipola - Founder, CEO - PhD in information technology. Data mining and machine learning expert in various application areas such as cyber security, industrial fault monitoring, text mining and biomedical imaging. Partner at software and consulting co-operative Koodilehto. Before co-founding CAP he worked as a researcher at University of Jyväskylä. More at: LinkedIn. Contact: firstname.lastname@example.org. Phone: +358 40 753 2169.
Antti Juvonen - Founder, Data Science - PhD in Information Technology. He worked as a researcher at University of Jyväskylä before co-founding CAP. Several years of research and work experience in cyber security, intrusion detection and anomaly detection. Research interests also include data mining, machine learning, statistics, dimensionality reduction and visualization. In addition, he has studied in Japan for one year. More at LinkedIn.
Antti Tupamäki - Founder, Development - BSc in Computer science from University of Helsinki. Has programming experience from various companies including Finactu (C#, Java, Vaadin, SQL, derivatives and complex investment products), Protacon (C#, PHP, SQL) and University of Jyväskylä (Python, PHP, Laravel, Code Igniter). Professionally intrested in programming, functional programming, test-driven development and machine learning.
Markku Ranta - Founder, CFO - MSc, TechLic and MBA, long experience in IT related innovation, business development and project management, team building & management. International working experience in Finland, China, Belgium and France. 14 years at Nokia in various positions. After Nokia involved as founder in two startups and setting up a Sino-Nordic consulting team. More at LinkedIn. Contact: email@example.com. Phone: +358 50 324 6233.
Markku Ranta, Chairman of the Board
Tuomo Sipola, CEO
Risto Valtakari, senior consultant and technology professional living in Austria. 25 years in global consulting, business services and outsourcing both with Fortune 1000 and with startups.
Dr. Alexandr Seleznyov. Currently, Alexandr is a Chief Architect and Director responsible for architecture covering big data and analytics in TeliaSonera group. Before that Alexandr held numerous positions as a chief architect responsible for design and delivery of products in different areas, such as global consumer internet services (distributed large scale systems), telecommunications (traffic optimization, policy management, and analytics), health and wellbeing, etc. Alexandr’s previous work include positions of a chief security architect and different research positions related to behavioral analysis (detecting patterns and anomalies), which was also the topic of his PhD thesis. Alexandr has authored more than 20 articles and book chapters, more then ten patent applications and four granted patents in the area of telecommunications. Alexandr’s current interests include distributed computing, advanced analytics and deep learning - current (4.2016) ranking is among Top 1% of Kaggle profiles.
Vesa Laakso, VC expert and startup advisor having a strong experience in early stage venture capital investment at Aura Capital.
Prof. Pekka Neittaanmäki from University of Jyväskylä brings along his extensive scientific expertise in mathematical information processing.
Mikko Raunio has a long career in business development, M&A and JV operations at Sonera. Co-founder of the business directory company Fonecta from from Sonera’s spin-off through several international acquisitions.
Antti Juvonen, Tuomo Sipola, and Timo Hämäläinen. Online anomaly detection using dimensionality reduction techniques for HTTP log analysis. Computer Networks, 91:46-56, 2015.
Tuomo Sipola, Tapani Ristaniemi, and Amir Averbuch. Gear classification and fault detection using a diffusion map framework. Pattern Recognition Letters, 53:53-61, 2015.
Fengyu Cong, Tuomas Puoliväli, Vinoo Alluri, Tuomo Sipola, Iballa Burunat, Petri Toiviainen, Asoke K. Nandi, Elvira Brattico, and Tapani Ristaniemi. Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis. Journal of Neuroscience Methods, 223:74-84, 2014.
Paavo Nieminen, Ilkka Pölönen, and Tuomo Sipola. Research literature clustering using diffusion maps. Journal of Informetrics, 7(4):874-886, 2013.
Tuomo Sipola, Antti Juvonen, and Joel Lehtonen. Dimensionality reduction framework for detecting anomalies from network logs. Engineering Intelligent Systems, 20(1):87-97, 2012.
Fengyu Cong, Tuomo Sipola, Tiina Huttunen-Scott, Xiaonan Xu, Tapani Ristaniemi, and Heikki Lyytinen. Hilbert-Huang versus Morlet wavelet transformation on mismatch negativity of children in uninterrupted sound paradigm. Nonlinear Biomedical Physics, 3:1, 2009.
Antti Juvonen and Timo Hämäläinen. An efficient network log anomaly detection system using random projection dimensionality reduction. New Technologies, Mobility and Security (NTMS), 2014 6th International Conference on, 2014.
Mikhail Zolotukhin, Timo Hämäläinen and Antti Juvonen. Growing hierarchical self-organizing maps and statistical distribution models for online detecion of web attacks. Web Information Systems and Technologies. Lecture Notes in Business Information Processing, Vol. 140, pp. 281–295, 2013.
Tuomo Sipola, Fengyu Cong, Tapani Ristaniemi, Vinoo Alluri, Petri Toiviainen, Elvira Brattico, and Asoke K. Nandi. Diffusion map for clustering fMRI spatial maps extracted by independent component analysis. In Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on, Southampton, United Kingdom, September 2013. IEEE.
Antti Juvonen and Tuomo Sipola. Combining conjunctive rule extraction with diffusion maps for network intrusion detection. In The Eighteenth IEEE Symposium on Computers and Communications (ISCC 2013), pages 411-416, Split, Croatia, July 2013.
Yaniv Shmueli, Tuomo Sipola, Gil Shabat, and Amir Averbuch. Using affinity perturbations to detect web traffic anomalies. In Proceedings of the 10th International Conference on Sampling Theory and Applications (SampTA), pages 444-447, Bremen, Germany, July 2013. EURASIP.
Antti Juvonen and Tuomo Sipola. Adaptive framework for network traffic classification using dimensionality reduction and clustering. In Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2012 4th International Congress on, pages 274-279, St. Petersburg, Russia, October 2012. IEEE.
Tuomo Sipola, Antti Juvonen, and Joel Lehtonen. Anomaly detection from network logs using diffusion maps. In Lazaros Iliadis and Chrisina Jayne, editors, Engineering Applications of Neural Networks, volume 363 of IFIP Advances in Information and Communication Technology, pages 172-181. Springer, 2011.
Fengyu Cong, Tuomo Sipola, Xiaonan Xu, Tiina Huttunen-Scott, Heikki Lyytinen, and Tapani Ristaniemi. Concatenated trial based Hilbert-Huang transformation on event-related potentials. In Proc. International Joint Conference on Neural Networks 2010 (IEEE World Congress on Computational Intelligence), pages 1379-1383, 2010.
Tuomo Sipola. Knowledge discovery from network logs. In Martti Lehto and Pekka Neittaanmäki, editors, Cyber Security: Analytics, Technology and Automation, volume 78 of Intelligent Systems, Control and Automation: Science and Engineering, chapter 12, pages 195-203. Springer, Berlin, Heidelberg, 2015.
Antti Juvonen and Tuomo Sipola. Anomaly detection framework using rule extraction for efficient intrusion detection. arXiv:1410.7709 [cs.NI], 2014.
Tuomo Sipola, Tapani Ristaniemi, and Amir Averbuch. Gear classification and fault detection using a diffusion map framework. Reports of the Department of Mathematical Information Technology Series B. Scientific Computing, No. B 6/2013, University of Jyväskylä, 2013.
Antti Juvonen. Intrusion Detection Applications Using Knowledge Discovery and Data Mining. Jyväskylä studies in computing 205, University of Jyväskylä, 2014.
Tuomo Sipola. Knowledge discovery using diffusion maps. Jyväskylä studies in computing 185, University of Jyväskylä, 2013.